laboratory experiments examples

Experimental Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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68 Best Chemistry Experiments: Learn About Chemical Reactions

Whether you’re a student eager to explore the wonders of chemical reactions or a teacher seeking to inspire and engage your students, we’ve compiled a curated list of the top 68 chemistry experiments so you can learn about chemical reactions.

While the theories and laws governing chemistry can sometimes feel abstract, experiments bridge the gap between these concepts and their tangible manifestations. These experiments provide hands-on experiences illuminating the intricacies of chemical reactions, molecular structures, and elemental properties.

1. Covalent Bonds

By engaging in activities that demonstrate the formation and properties of covalent bonds, students can grasp the significance of these bonds in holding atoms together and shaping the world around us.

Learn more: Covalent Bonds

2. Sulfuric Acid and Sugar Demonstration

Through this experiment, students can develop a deeper understanding of chemical properties, appreciate the power of chemical reactions, and ignite their passion for scientific exploration.

3. Make Hot Ice at Home

Making hot ice at home is a fascinating chemistry experiment that allows students to witness the captivating transformation of a liquid into a solid with a surprising twist.

4. Make a Bouncing Polymer Ball

This hands-on activity not only allows students to explore the fascinating properties of polymers but also encourages experimentation and creativity.

Learn more: Thought Co

5. Diffusion Watercolor Art

This experiment offers a wonderful opportunity for students to explore the properties of pigments, observe how they interact with water, and discover the mesmerizing patterns and textures that emerge.

Learn more: Diffusion Watercolor Art

6. Exploding Baggie

The exploding baggie experiment is a captivating and dynamic demonstration that students should engage in with caution and under the supervision of a qualified instructor.

Learn more: Exploding Baggie

7. Color Changing Chemistry Clock

This experiment not only engages students in the world of chemical kinetics but also introduces them to the concept of a chemical clock, where the color change acts as a timekeeping mechanism.

Learn more: Color Changing Chemistry Clock

8. Pipe Cleaner Crystal Trees

By adjusting the concentration of the Borax solution or experimenting with different pipe cleaner arrangements, students can customize their crystal trees and observe how it affects the growth patterns.

Learn more: Pipe Cleaner Crystal Trees

9. How To Make Ice Sculptures

Through this experiment, students gain a deeper understanding of the physical and chemical changes that occur when water freezes and melts.

Learn more: Ice Sculpture

10. How to Make Paper

Through this hands-on activity, students gain a deeper understanding of the properties of cellulose fibers and the transformative power of chemical reactions.

Learn more: How to Make Paper

11. Color Changing Chemistry

Color changing chemistry is an enchanting experiment that offers a captivating blend of science and art. Students should embark on this colorful journey to witness the mesmerizing transformations of chemicals and explore the principles of chemical reactions.

12. Gassy Banana

The gassy banana experiment is a fun and interactive way for students to explore the principles of chemical reactions and gas production.

Learn more: Gassy Banana

13. Gingerbread Man Chemistry Experiment

This hands-on activity not only introduces students to the concepts of chemical leavening and heat-induced reactions but also allows for creativity in decorating and personalizing their gingerbread creations.

Learn more: Gingerbread Man Chemistry Experiment

14. Make Amortentia Potion

While the love potion is fictional, this activity offers a chance to explore the art of potion-making and the chemistry behind it.

Learn more: How to Make Amortentia Potion

15. Strawberry DNA Extraction

This hands-on experiment offers a unique opportunity to observe DNA, the building blocks of life, up close and learn about its structure and properties.

16. Melting Snowman

The melting snowman experiment is a fun and whimsical activity that allows students to explore the principles of heat transfer and phase changes.

Learn more: Melting Snowman

17. Acid Base Cabbage Juice

The acid-base cabbage juice experiment is an engaging and colorful activity that allows students to explore the pH scale and the properties of acids and bases.

By extracting the purple pigment from red cabbage leaves and creating cabbage juice, students can use this natural indicator to identify and differentiate between acidic and basic substances.

Learn more: Acid Base Cabbage Juice

18. Magic Milk

The magic milk experiment is a mesmerizing and educational activity that allows students to explore the concepts of surface tension and chemical reactions.

By adding drops of different food colors to a dish of milk and then introducing a small amount of dish soap, students can witness a captivating display of swirling colors and patterns.

Learn more: Magic Milk

19. Melting Ice with Salt and Water

Through this hands-on activity, students can gain a deeper understanding of the science behind de-icing and how different substances can influence the physical properties of water.

Learn more: Melting Ice with Salt and Water

20. Barking Dog Chemistry Demonstration

The barking dog chemistry demonstration is an exciting and visually captivating experiment that showcases the principles of combustion and gas production.

21. How to Make Egg Geodes

Making egg geodes is a fascinating and creative chemistry experiment that students should try. By using common materials like eggshells, salt, and food coloring, students can create their own beautiful geode-like crystals.

Learn more: How to Make Egg Geodes

22. Make Sherbet

This experiment not only engages the taste buds but also introduces concepts of acidity, solubility, and the chemical reactions that occur when the sherbet comes into contact with moisture.

Learn more: Make Sherbet

23. Hatch a Baking Soda Dinosaur Egg

As the baking soda dries and hardens around the toy, it forms a “shell” resembling a dinosaur egg. To hatch the egg, students can pour vinegar onto the shell, causing a chemical reaction that produces carbon dioxide gas.

Learn more: Steam Powered Family

24. Chromatography Flowers

By analyzing the resulting patterns, students can gain insights into the different pigments present in flowers and the science behind their colors.

Learn more: Chromatography Flowers

25. Turn Juice Into Solid

Turning juice into a solid through gelification is an engaging and educational chemistry experiment that students should try. By exploring the transformation of a liquid into a solid, students can gain insights of chemical reactions and molecular interactions.

Learn more: Turn Juice into Solid

26. Bouncy Balls

Making bouncy balls allows students to explore the fascinating properties of polymers, such as their ability to stretch and rebound.

 27. Make a Lemon Battery

Creating a lemon battery is a captivating and hands-on experiment that allows students to explore the fundamentals of electricity and chemical reactions.

28. Mentos and Soda Project

The Mentos and soda project is a thrilling and explosive experiment that students should try. By dropping Mentos candies into a bottle of carbonated soda, an exciting eruption occurs.

29. Alkali Metal in Water

The reaction of alkali metals with water is a fascinating and visually captivating chemistry demonstration.

30. Rainbow Flame

The rainbow flame experiment is a captivating and visually stunning chemistry demonstration that students should explore.

31. Sugar Yeast Experiment

This experiment not only introduces students to the concept of fermentation but also allows them to witness the effects of a living organism, yeast, on the sugar substrate.

32. The Thermite Reaction

The thermite reaction is a highly energetic and visually striking chemical reaction that students can explore with caution and under proper supervision.

This experiment showcases the principles of exothermic reactions, oxidation-reduction, and the high temperatures that can be achieved through chemical reactions.

33. Polishing Pennies

Polishing pennies is a simple and enjoyable chemistry experiment that allows students to explore the concepts of oxidation and cleaning methods.

34. Elephant Toothpaste

The elephant toothpaste experiment is a thrilling and visually captivating chemistry demonstration that students should try with caution and under the guidance of a knowledgeable instructor.

35. Magic Potion

Creating a magic potion is an exciting and imaginative activity that allows students to explore their creativity while learning about the principles of chemistry.

36. Color Changing Acid-Base Experiment

Through the color changing acid-base experiment, students can gain a deeper understanding of chemical reactions and the role of pH in our daily lives.

Learn more: Color Changing Acid-Base Experiment

37. Fill up a Balloon

Filling up a balloon is a simple and enjoyable physics experiment that demonstrates the properties of air pressure. By blowing air into a balloon, you can observe how the balloon expands and becomes inflated.

38. Jello and Vinegar

The combination of Jello and vinegar is a fascinating and tasty chemistry experiment that demonstrates the effects of acid on a gelatin-based substance.

Learn more: Jello and Vinegar

39. Vinegar and Steel Wool Reaction

This experiment not only provides a visual demonstration of the oxidation process but also introduces students to the concept of corrosion and the role of acids in accelerating the process.

Learn more: Vinegar and Steel Wool Reaction

40. Dancing Rice

The dancing rice experiment is a captivating and educational demonstration that showcases the principles of density and buoyancy.

By pouring a small amount of uncooked rice into a clear container filled with water, students can witness the rice grains moving and “dancing” in the water.

Learn more: Dancing Rice

41. Soil Testing Garden Science

Soil testing is a valuable and informative experiment that allows students to assess the composition and properties of soil.

By collecting soil samples from different locations and analyzing them, students can gain insights into the nutrient content, pH level, and texture of the soil.

Learn more: Soil Testing Garden Science

42. Heat Sensitive Color Changing Slime

Creating heat-sensitive color-changing slime is a captivating and playful chemistry experiment that students should try.

Learn more: Left Brain Craft Brain

43. Experimenting with Viscosity

Experimenting with viscosity is an engaging and hands-on activity that allows students to explore the flow properties of liquids.

Viscosity refers to a liquid’s resistance to flow, and this experiment enables students to investigate how different factors affect viscosity.

Learn more: Experimenting with Viscosity

44. Rock Candy Science

Rock candy science is a delightful and educational chemistry experiment that students should try. By growing their own rock candy crystals, students can learn about crystal formation and explore the principles of solubility and saturation.

Learn more: Rock Candy Science

45. Baking Soda vs Baking Powder

Baking soda and baking powder have distinct properties that influence the leavening process in different ways.

This hands-on experiment provides a practical understanding of how these ingredients interact with acids and moisture to create carbon dioxide gas.

46. Endothermic and Exothermic Reactions Experiment

The endothermic and exothermic reactions experiment is an exciting and informative chemistry exploration that students should try.

By observing and comparing the heat changes in different reactions, students can gain a deeper understanding of energy transfer and the concepts of endothermic and exothermic processes.

Learn more: Education.com

47. Diaper Chemistry

By dissecting a diaper and examining its components, students can uncover the chemical processes that make diapers so effective at absorbing and retaining liquids.

Learn more: Diaper Chemistry

48. Candle Chemical Reaction

The “Flame out” experiment is an intriguing and educational chemistry demonstration that students should try. By exploring the effects of a chemical reaction on a burning candle, students can witness the captivating moment when the flame is extinguished.

49. Make Curds and Whey

This experiment not only introduces students to the concept of acid-base reactions but also offers an opportunity to explore the science behind cheese-making.

Learn more: Tinkerlab

50. Grow Crystals Overnight

By creating a supersaturated solution using substances like epsom salt, sugar, or borax, students can observe the fascinating process of crystal growth. This experiment allows students to explore the principles of solubility, saturation, and nucleation.

Learn more: Grow Crystals Overnight

51. Measure Electrolytes in Sports Drinks

The “Measure Electrolytes in Sports Drinks” experiment is an informative and practical chemistry activity that students should try.

By using simple tools like a multimeter or conductivity probe, students can measure the electrical conductivity of different sports drinks to determine their electrolyte content.

52. Oxygen and Fire Experiment

The oxygen and fire experiment is a captivating and educational chemistry demonstration that students should try. By observing the effects of oxygen on a controlled fire, students can witness the essential role of oxygen in supporting combustion.

53. Electrolysis Of Water

The electrolysis of water experiment is a captivating and educational chemistry demonstration that students should try.

Learn more: Electrolysis Of Water

54. Expanding Ivory Soap

The expanding Ivory Soap experiment is a fun and interactive chemistry activity that students should try. By placing a bar of Ivory soap in a microwave, students can witness the remarkable expansion of the soap as it heats up.

Learn more: Little Bins Little Hands

55. Glowing Fireworks

This experiment not only introduces students to the principles of pyrotechnics and combustion but also encourages observation, critical thinking, and an appreciation for the physics and chemistry behind.

Learn more: Glowing Fireworks

56. Colorful Polymer Chemistry

Colorful polymer chemistry is an exciting and vibrant experiment that students should try to explore polymers and colorants.

By combining different types of polymers with various colorants, such as food coloring or pigments, students can create a kaleidoscope of colors in their polymer creations.

Learn more: Colorful Polymer Chemistry

57. Sulfur Hexafluoride- Deep Voice Gas

This experiment provides a firsthand experience of how the density and composition of gases can influence sound transmission.

It encourages scientific curiosity, observation, and a sense of wonder as students witness the surprising transformation of their voices.

58. Liquid Nitrogen Ice Cream

Liquid nitrogen ice cream is a thrilling and delicious chemistry experiment that students should try. By combining cream, sugar, and flavorings with liquid nitrogen, students can create ice cream with a unique and creamy texture.

59. White Smoke Chemistry Demonstration

The White Smoke Chemistry Demonstration provides an engaging and visually captivating experience for students to explore chemical reactions and gases. By combining hydrochloric acid and ammonia solutions, students can witness the mesmerizing formation of white smoke.

60. Nitrogen Triiodide Chemistry Demonstration

The nitrogen triiodide chemistry demonstration is a remarkable and attention-grabbing experiment that students should try under the guidance of a knowledgeable instructor.

By reacting iodine crystals with concentrated ammonia, students can precipitate nitrogen triiodide (NI3), a highly sensitive compound.

61. Make a Plastic- Milk And Vinegar Reaction Experiment

Through the “Make a Plastic – Milk and Vinegar Reaction” experiment, students can gain a deeper understanding of the chemistry behind plastics, environmental sustainability, and the potential of biodegradable materials.

Learn more: Rookie Parenting

62. Eno and Water Experiment

This experiment not only introduces students to acid-base reactions but also engages their senses as they witness the visible and audible effects of the reaction.

63. The Eternal Kettle Experiment

By filling a kettle with alcohol and igniting it, students can investigate the behavior of the alcohol flame and its sustainability.

64. Coke and Chlorine Bombs

Engaging in this experiment allows students to experience the wonders of chemistry firsthand, making it an ideal choice to ignite their curiosity and passion for scientific exploration.

65. Set your Hand on Fire

This experiment showcases the fascinating nature of combustion and the science behind fire.

By carefully following proper procedures and safety guidelines, students can witness firsthand how the sanitizer’s high alcohol content interacts with an open flame, resulting in a brief but captivating display of controlled combustion.

66. Instant Ice Experiments

The Instant Ice Experiment offers an engaging and captivating opportunity for students to explore the wonders of chemistry and phase changes.

By using simple household ingredients, students can witness the fascinating phenomenon of rapid ice formation in just a matter of seconds.

67. Coke Cans in Acid and Base

Engaging in this experiment allows students to gain a deeper understanding of the chemical properties of substances and the importance of safety protocols in scientific investigations.

68. Color Changing Invisible Ink

The Color Changing Invisible Ink experiment offers an intriguing and fun opportunity for students to explore chemistry and learn about the concept of chemical reactions.

Learn more: Research Parent

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What do you think of when you hear the word "laboratory"? Do you picture people in white coats and goggles and gloves standing over a table with beakers and tubes? Well, that picture is pretty close to reality in some cases. In others, laboratory experiments, especially in psychology, focus more on observing behaviours in highly controlled settings to establish causal conclusions. Let's explore lab experiments further. 

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What is a laboratory experiment?

Why are laboratory experiments criticised for having demand characteristics?

Why are laboratory experiments criticised for having low ecological validity?

What are the advantages of laboratory experiments?

What is a field experiment?

Why are field experiments criticised for having low internal validity and reliability?

What are the advantages of a field experiment?

Why are field experiments criticised for having ethical issues?

Are lab experiments necessarily carried out in the laboratory?

What are the differences between lab and field experiments?

Lab experiments have high           validity.

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• We are going to delve into the topic of lab experiments in the context of psychology.
• We will start by looking at the lab experiment definition and how lab experiments are used in psychology.
• Moving on from this, we will look at how lab experiment examples in psychology and cognitive lab experiments may be conducted.
• And to finish off, we will also explore the strengths and weaknesses of lab experiments.

Lab Experiment Psychology Definition

You can probably guess from the name that lab experiments occur in lab settings. Although this is not always the case, they can sometimes occur in other controlled environments. The purpose of lab experiments is to identify the cause and effect of a phenomenon through experimentation.

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to accurately measure how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

In lab experiments, the IV is what the researcher predicts as the cause of a phenomenon, and the dependent variable is what the researcher predicts as the effect of a phenomenon.

Lab Experiment: P sychology

Lab experiments in psychology are used when trying to establish causal relationships between variables . For example, a researcher would use a lab experiment if they were investigating how sleep affects memory recall.

The majority of psychologists think of psychology as a form of science. Therefore, they argue that the protocol used in psychological research should resemble those used in the natural sciences. For research to be established as scientific , three essential features should be considered:

• Empiricism - the findings should be observable via the five senses.
• Reliability - if the study was replicated, similar results should be found.
• Validity - the investigation should accurately measure what it intends to.

But do lab experiments fulfil these requirements of natural sciences research? If done correctly, then yes. Lab experiments are empirical as they involve the researcher observing changes occurring in the DV. Reliability is established by using a standardised procedure in lab experiments .

A standardised procedure is a protocol that states how the experiment will be carried out. This allows the researcher to ensure the same protocol is used for each participant, increasing the study's internal reliability.

Standardised procedures are also used to help other researchers replicate the study to identify if they measure similar results.

Dissimilar results reflect low reliability.

Validity is another feature of a lab experiment considered. Lab experiments are conducted in a carefully controlled setting where the researcher has the most control compared to other experiments to prevent extraneous variables from affecting the DV .

Extraneous variables are factors other than the IV that affect the DV; as these are variables that the researcher is not interested in investigating, these reduce the validity of the research.

There are issues of validity in lab experiments, which we'll get into a bit later!

Lab Experiment Examples: Asch's Conformity Study

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question. Participants were given two pieces of paper, one depicting a 'target line' and another three, one of which resembled the 'target line' and the others of different lengths.

The participants were put in groups of eight. Unknown to the participants, the other seven were confederates (participants who were secretly part of the research team) who were instructed to give the wrong answer. If the actual participant changed their answer in response, this would be an example of conformity .

Asch controlled the location where the investigation took place, constructed a contrived scenario and even controlled the confederates who would affect the behaviour of the actual participants to measure the DV.

Some other famous examples of research that are lab experiment examples include research conducted by Milgram (the obedience study) and Loftus and Palmer's eyewitness testimony accuracy study . These researchers likely used this method because of some of their strengths , e.g., their high level of control .

Lab Experiment Examples: Cognitive Lab Experiments

Let's look at what a cognitive lab experiment may entail. Suppose a researcher is interested in investigating how sleep affects memory scores using the MMSE test. In the theoretical study , an equal number of participants were randomly allocated into two groups; sleep-deprived versus well-rested. Both groups completed the memory test after a whole night of sleep or staying awake all night.

In this research scenario , the DV can be identified as memory test scores and the IV as whether participants were sleep-deprived or well-rested.

Some examples of extraneous variables the study controlled include researchers ensuring participants did not fall asleep, the participants took the test at the same time, and participants in the well-rested group slept for the same time.

Lab Experiment Advantages and Disadvantages

It's important to consider the advantages and disadvantages of laboratory experiments . Advantages include the highly controlled setting of lab experiments, the standardised procedures and causal conclusions that can be drawn. Disadvantages include the low ecological validity of lab experiments and demand characteristics participants may present.

Strengths of Lab Experiments: Highly Controlled

Laboratory experiments are conducted in a well-controlled setting. All the variables, including extraneous and confounding variables , are rigidly controlled in the investigation. Therefore, the risk of experimental findings being affected by extraneous or confounding variables is reduced . As a result, the well-controlled design of laboratory experiments implies the research has high internal validity .

Internal validity means the study uses measures and protocols that measure exactly what it intends to, i.e. how only the changes in the IV affect the DV.

Strengths of Lab Experiments: Standardised Procedures

Laboratory experiments have standardised procedures, which means the experiments are replicable , and all participants are tested under the same conditions. T herefore, standardised procedures allow others to replicate the study to identify whether the research is reliable and that the findings are not a one-off result. As a result, the replicability of laboratory experiments allows researchers to verify the study's reliability .

Strengths of Lab Experiments: Causal Conclusions

A well-designed laboratory experiment can draw causal conclusions. Ideally, a laboratory experiment can rigidly control all the variables , including extraneous and confounding variables. Therefore, laboratory experiments provide great confidence to researchers that the IV causes any observed changes in DV.

Weaknesses of Lab Experiments

In the following, we will present the disadvantages of laboratory experiments. This discusses ecological validity and demand characteristics.

Weaknesses of Lab Experiments: Low Ecological Validity

Laboratory experiments have low ecological validity because they are conducted in an artificial study that does not reflect a real-life setting . As a result, findings generated in laboratory experiments can be difficult to generalise to real life due to the low mundane realism. Mundane realism reflects the extent to which lab experiment materials are similar to real-life events.

Weaknesses of Lab Experiments: Demand Characteristics

A disadvantage of laboratory experiments is that the research setting may lead to demand characteristics .

Demand characteristics are the cues that make participants aware of what the experimenter expects to find or how participants are expected to behave.

The participants are aware they are involved in an experiment. So, participants may have some ideas of what is expected of them in the investigation, which may influence their behaviours. As a result, the demand characteristics presented in laboratory experiments can arguably change the research outcome , reducing the findings' validity .

Lab Experiment - Key takeaways

The lab experiment definition is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affect the dependent variable (DV; variable measured).

Psychologists aim to ensure that lab experiments are scientific and must be empirical, reliable and valid.

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question.

The advantages of lab experiments are high internal validity, standardised procedures and the ability to draw causal conclusions.

The disadvantages of lab experiments are low ecological validity and demand characteristics.

Flashcards in Lab Experiment 25

A laboratory experiment is an experiment conducted in a highly controlled environment. 

The participants may be aware of the experiment’s aims and how the researcher expects them to act, which may influence their behaviours.

Laboratory experiments have low ecological validity as contrived or artificial materials are employed.

Laboratory experiments are conducted in a well-controlled setting, which implies good internal validity, standardised procedures and the ability to draw causal conclusions.

A field experiment is an experiment conducted in a natural, everyday setting. 

Field experiments are conducted in a less controlled setting which may not have standardised procedures, implying the risk of low internal validity and reliability.

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Frequently Asked Questions about Lab Experiment

What is a lab experiment?

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

What is the purpose of lab experiments?

Lab experiments investigate cause-and-effect. They aim to determine the effect of changes in the independent variable on the dependent variable. 

What is a lab experiment and field experiment?

A field experiment is an experiment conducted in a natural, everyday setting. The experimenter still controls the IV; however, extraneous and confounding variables may be difficult to control due to the natural setting.

Similar, to filed experiments researchers, can control the IV and extraneous variables. However, this takes place in an artificial setting such as a lab. 

Why would a psychologist use a laboratory experiment? 

A psychologist may use a lab experiment when trying to establish the causal relationships between variables to explain a phenomenon. 

Why is lab experience important?

Lab experience allows researchers to scientifically determine whether a hypothesis/ theory should be accepted or rejected. 

What is a lab experiment example? 

The research conducted by Loftus and Palmer (accuracy of eyewitness testimony) and Milgram (obedience) used a lab experiment design. These experimental designs give the researcher high control, allowing them to control extraneous and independent variables.

Test your knowledge with multiple choice flashcards

The aim of lab experiments is to identify if observed changes in the      are caused by the      .

Is it difficult to generalise results from lab experiments to real-life settings? 

Demand characteristics lower the         of the research.

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LABORATORY EXPERIMENTS

TYPES OF EXPERIMENT

Laboratory, field experiments, natural and quasi-experiments.

 Laboratory, field, natural and quasi-experiments all investigate relationships between variables by comparing groups of scores. Still, there are also significant differences between different types of experiments, such as how respected they are.

Laboratory experiments fulfil all the criteria of an actual experiment but have problems with external validity.

Field experiments are true but don't occur in a controlled environment or have random allocation of participants.

Natural and quasi-experiments cannot prove or disprove causation with the same confidence as a lab experiment.

Natural experiments don't manipulate the IV; they observe changes in a naturally occurring IV.

Quasi-experiments don't randomly allocate participants to conditions.

DEFINITION: "A lab experiment is the 'classic' experiment with all four features of a true experiment. Its strength comes from its "lab setting" which is a controlled environment. A laboratory setting doesn't have to be a laboratory with test tubes and scientific gizmos; it could be conducted in a field. However, any experiment in a special, tightly controlled environment is a laboratory. A laboratory setting is an environment where the researcher controls everything that happens. So if you close your classroom door with a sign outside saying "DO NOT ENTER: EXPERIMENT IN PROGRESS" , you've turned your classroom into a psychology lab. Of course, there might still be a fire alarm or another interruption. However, many extraneous variables are ruled out in a lab setting. The hallmark of a lab experiment is that participants are aware of their involvement in the experiment.

In summary, the Solomon Asch study exemplifies a lab experiment where participants are aware of being part of an experiment. In contrast, the weapon focus study represents a field experiment where participants' behaviour is observed in a more naturalistic setting without their explicit awareness of being studied.

Laboratory experiments can include animals as they offer greater experimental control opportunities than research with humans.

ADVANTAGES: Lab experiments offer precise control over variables, minimizing extraneous influences and facilitating the establishment of cause-and-effect relationships, which is particularly advantageous in psychology research. For example, in a lab experiment investigating the effects of sleep deprivation on cognitive function, researchers can carefully manipulate the duration and quality of sleep participants receive, allowing for a clear understanding of how sleep affects various aspects of cognition without confounding variables from the external environment—facilitating the establishment of causal relationships. Researchers can meticulously manage all variables, enhancing the ease of replication and bolstering the reliability of study findings. Replication allows researchers to verify the results of a study. When multiple independent studies produce similar results, it increases confidence in the reliability and validity of the findings.

DISADVANTAGES :

THREATS TO THE EXTERNAL VALIDITY OF LABORATORY EXPERIMENTS:

Lab experiments may lack mundane realism, as the controlled environment may not faithfully replicate real-world conditions, potentially compromising ecological validity.

It's essential to recognize the difference between mundane realism and ecological validity in psychological research:

Mundane Realism: This term refers to the extent to which the conditions in a study resemble real-life situations. An example where mundane realism is questioned is Stanley Milgram's obedience study, as a teacher would never be asked to administer electric shocks to a student in real life, especially for minor errors. Another example is memory research, such as studies on digit span. Remembering random, nonsensical sequences of digits isn't a common real-life task.

Although researchers designing laboratory experiments try to create situations applicable to real life, they are often artificial to how the behaviour occurs. Take, for example, the Loftus study on eyewitness testimony. The participants in this experiment were shown videos of a car crash and then asked to make decisions based on what they saw. But there is a lot of difference between seeing a video of a car crash and seeing an actual car crash; the experimental version has the participants seated comfortably and with full attention intact. But seeing a real-life car crash will likely provoke a full fight/flight response and fractured attention.

Ecological Validity : This concept involves the applicability of a study's results across different environments and settings. Just because a study does not have mundane realism does not mean it can't be applied to real life; it might still have ecological validity. In the case of the digit span example, most people struggle to remember long cell phone numbers, typically exceeding the 7±2 digit span limit identified in the research. This demonstrates the ecological validity of the findings despite the lack of mundane realism in the experimental task.

Ecological validity can be challenging in lab experiments when researchers cannot manipulate the variables they want to investigate. For instance, Stanley Milgram initially sought to explore the dynamics of atrocities like those seen in the Holocaust. However, he couldn't ethically manipulate participants to engage in actual violence. Consequently, he devised the electric shock experiment as a substitute, aiming for its findings to reflect real-world behaviour. Despite the controlled lab environment, Milgram believed his study could provide insights applicable to real-life situations, showcasing ecological validity.

However, Milgram's study lacked both mundane realism and ecological validity. While one of Milgram's variations showed how obedience levels dropped when the learner and the teacher were in the same room, the case of Milgram's study lacked both mundane realism and ecological validity. While one of Milgram's variations showed how obedience levels dropped when the learner and the teacher were in the same room, it did not accurately reflect real-life dynamics. Additionally, the study's applicability to events like the Holocaust was questioned. In real-life scenarios, SS soldiers often shot their victims even when they were nearby, and their obedience to authority persisted even in dire circumstances, which was not adequately captured in Milgram's study.

In conclusion, while a study may lack mundane realism, it can still possess ecological validity and vice versa. Both concepts are integral to understanding the external validity of research, allowing us to apply findings to broader contexts, such as different cultures, locations, populations, and settings.

THREATS TO THE INTERNAL VALIDITY OF LABORATORY EXPERIMENTS: Participant awareness of being in an experiment can trigger demand characteristics, social desirability bias, and the Hawthorne effect, influencing participant behaviour and threatening internal validity.

DEMAND CHARACTERISTICS : These are cues or hints within the experimental context that suggest to participants how they are expected to behave. When participants become aware of these cues, they may alter their behaviour to align with what they believe the experimenter wants or expects rather than behaving naturally. For example, in a lab study investigating the effects of caffeine on cognitive performance, participants who know they are receiving caffeine might consciously try to perform better to confirm the hypothesis.

Demand characteristics might include the following behaviour:

Acting nervous or out of character because they feel they are being evaluated in some way because they are in an experiment.

Sometimes, if participants are in both conditions, they can guess what the experiment is about and, as a result, behave differently.

Or it could be that they guess what the researcher wants to happen in the experiment and try to please the experimenter, or vice versa!

SOCIAL DESIRABILITY BIAS bias occurs when participants respond in a way they perceive as socially acceptable or desirable rather than providing honest responses. In lab experiments, participants may modify their behaviour or responses to avoid judgment or to present themselves in a favourable light. For instance, in a lab study examining attitudes towards recycling, participants might overstate their commitment to recycling if they believe it is socially desirable, even if their behaviour differs.

THE HAWTHORNE EFFECT : This phenomenon refers to changes in behaviour that occur simply because of being observed or participating in an experiment rather than the experimental manipulation itself. When participants are aware of being studied, they may alter their behaviour, consciously or unconsciously, to conform to what they believe is expected by the experimenter. For example, workers in a factory may increase their productivity during a lab study simply because they know they are being observed, regardless of any changes in their working conditions.

INVESTIGATOR EFFECTS refer to the unintentional influence that experimenters or researchers may have on the behaviour or responses of participants in a study. This influence can occur through various means, such as subtle cues, body language, or unintentional biases conveyed by the experimenter during the experiment. For example, suppose an experimenter inadvertently expresses enthusiasm or approval when participants provide certain responses or behaviours. In that case, it may subtly encourage participants to continue or repeat those behaviours, basing the study's results. In a lab experiment investigating the effects of praise on task performance, an investigator effect may occur if the experimenter's tone of voice or facial expressions unintentionally conveys approval or encouragement to participants who receive praise, leading them to perform better on the task. Similarly, if an experimenter shows disinterest or scepticism when participants provide certain responses, it may inadvertently discourage those responses, affecting the overall outcomes of the study.

EXPERIMENTER BIAS

When the researcher’s expectations about the study affect the result, they may indirectly or unconsciously indicate how they want the results.

They may give leading behaviour or questions.

They also may be biased in assessing someone’s behaviour—seeing what they want to see as it supports their hypothesis.

ETHICS : Ethical considerations in lab experiments encompass standard principles such as participant confidentiality, informed consent, and minimizing psychological or physical harm. Unlike field experiments, ethical concerns are typically less pronounced in lab settings since participants are aware of their participation, making obtaining informed consent and managing deception less problematic.

Milgram's Obedience Study: Milgram's experiment occurred in a Yale University laboratory. The controlled environment allowed Milgram to manipulate variables systematically, such as the proximity of the authority figure and the presence of peers, to observe their effects on participant behaviour. The tightly controlled conditions facilitated the replication of the study, enhancing its reliability.

Asch Conformity Experiment: Asch's experiment was conducted in a laboratory environment where participants were seated around a table, with the experimenter and confederates also present. The controlled setting of the lab enabled Asch to manipulate the independent variable (the presence of social pressure) precisely and measure participants' responses to it. This controlled environment allowed for the systematic investigation of conformity under varying conditions.

Zimbardo's Stanford Prison Experiment: Although Zimbardo's study is often called a "field experiment," it occurred in a simulated prison environment within the confines of Stanford University's psychology department. The laboratory-like setting allowed Zimbardo to control various aspects of the experiment, such as selecting and assigning participants to roles, establishing rules and procedures within the simulated prison, and monitoring participant behaviour. While the setting resembled a real-world scenario, the experiment maintained the characteristics of a controlled laboratory study, albeit with ethical concerns regarding participant well-being.

THE EXPERIMENTAL METHOD

Field experiments.

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How the Experimental Method Works in Psychology

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The Experimental Process

Types of experiments, potential pitfalls of the experimental method.

The experimental method is a type of research procedure that involves manipulating variables to determine if there is a cause-and-effect relationship. The results obtained through the experimental method are useful but do not prove with 100% certainty that a singular cause always creates a specific effect. Instead, they show the probability that a cause will or will not lead to a particular effect.

At a Glance

While there are many different research techniques available, the experimental method allows researchers to look at cause-and-effect relationships. Using the experimental method, researchers randomly assign participants to a control or experimental group and manipulate levels of an independent variable. If changes in the independent variable lead to changes in the dependent variable, it indicates there is likely a causal relationship between them.

What Is the Experimental Method in Psychology?

The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis.

For example, researchers may want to learn how different visual patterns may impact our perception. Or they might wonder whether certain actions can improve memory . Experiments are conducted on many behavioral topics, including:

The scientific method forms the basis of the experimental method. This is a process used to determine the relationship between two variables—in this case, to explain human behavior .

Positivism is also important in the experimental method. It refers to factual knowledge that is obtained through observation, which is considered to be trustworthy.

When using the experimental method, researchers first identify and define key variables. Then they formulate a hypothesis, manipulate the variables, and collect data on the results. Unrelated or irrelevant variables are carefully controlled to minimize the potential impact on the experiment outcome.

History of the Experimental Method

The idea of using experiments to better understand human psychology began toward the end of the nineteenth century. Wilhelm Wundt established the first formal laboratory in 1879.

Wundt is often called the father of experimental psychology. He believed that experiments could help explain how psychology works, and used this approach to study consciousness .

Wundt coined the term "physiological psychology." This is a hybrid of physiology and psychology, or how the body affects the brain.

Other early contributors to the development and evolution of experimental psychology as we know it today include:

• Gustav Fechner (1801-1887), who helped develop procedures for measuring sensations according to the size of the stimulus
• Hermann von Helmholtz (1821-1894), who analyzed philosophical assumptions through research in an attempt to arrive at scientific conclusions
• Franz Brentano (1838-1917), who called for a combination of first-person and third-person research methods when studying psychology
• Georg Elias Müller (1850-1934), who performed an early experiment on attitude which involved the sensory discrimination of weights and revealed how anticipation can affect this discrimination

Key Terms to Know

To understand how the experimental method works, it is important to know some key terms.

Dependent Variable

The dependent variable is the effect that the experimenter is measuring. If a researcher was investigating how sleep influences test scores, for example, the test scores would be the dependent variable.

Independent Variable

The independent variable is the variable that the experimenter manipulates. In the previous example, the amount of sleep an individual gets would be the independent variable.

A hypothesis is a tentative statement or a guess about the possible relationship between two or more variables. In looking at how sleep influences test scores, the researcher might hypothesize that people who get more sleep will perform better on a math test the following day. The purpose of the experiment, then, is to either support or reject this hypothesis.

Operational definitions are necessary when performing an experiment. When we say that something is an independent or dependent variable, we must have a very clear and specific definition of the meaning and scope of that variable.

Extraneous Variables

Extraneous variables are other variables that may also affect the outcome of an experiment. Types of extraneous variables include participant variables, situational variables, demand characteristics, and experimenter effects. In some cases, researchers can take steps to control for extraneous variables.

Demand Characteristics

Demand characteristics are subtle hints that indicate what an experimenter is hoping to find in a psychology experiment. This can sometimes cause participants to alter their behavior, which can affect the results of the experiment.

Intervening Variables

Intervening variables are factors that can affect the relationship between two other variables. 

Confounding Variables

Confounding variables are variables that can affect the dependent variable, but that experimenters cannot control for. Confounding variables can make it difficult to determine if the effect was due to changes in the independent variable or if the confounding variable may have played a role.

Psychologists, like other scientists, use the scientific method when conducting an experiment. The scientific method is a set of procedures and principles that guide how scientists develop research questions, collect data, and come to conclusions.

The five basic steps of the experimental process are:

• Identifying a problem to study
• Devising the research protocol
• Conducting the experiment
• Analyzing the data collected
• Sharing the findings (usually in writing or via presentation)

Most psychology students are expected to use the experimental method at some point in their academic careers. Learning how to conduct an experiment is important to understanding how psychologists prove and disprove theories in this field.

There are a few different types of experiments that researchers might use when studying psychology. Each has pros and cons depending on the participants being studied, the hypothesis, and the resources available to conduct the research.

Lab Experiments

Lab experiments are common in psychology because they allow experimenters more control over the variables. These experiments can also be easier for other researchers to replicate. The drawback of this research type is that what takes place in a lab is not always what takes place in the real world.

Field Experiments

Sometimes researchers opt to conduct their experiments in the field. For example, a social psychologist interested in researching prosocial behavior might have a person pretend to faint and observe how long it takes onlookers to respond.

This type of experiment can be a great way to see behavioral responses in realistic settings. But it is more difficult for researchers to control the many variables existing in these settings that could potentially influence the experiment's results.

Quasi-Experiments

While lab experiments are known as true experiments, researchers can also utilize a quasi-experiment. Quasi-experiments are often referred to as natural experiments because the researchers do not have true control over the independent variable.

A researcher looking at personality differences and birth order, for example, is not able to manipulate the independent variable in the situation (personality traits). Participants also cannot be randomly assigned because they naturally fall into pre-existing groups based on their birth order.

So why would a researcher use a quasi-experiment? This is a good choice in situations where scientists are interested in studying phenomena in natural, real-world settings. It's also beneficial if there are limits on research funds or time.

Field experiments can be either quasi-experiments or true experiments.

Examples of the Experimental Method in Use

The experimental method can provide insight into human thoughts and behaviors, Researchers use experiments to study many aspects of psychology.

A 2019 study investigated whether splitting attention between electronic devices and classroom lectures had an effect on college students' learning abilities. It found that dividing attention between these two mediums did not affect lecture comprehension. However, it did impact long-term retention of the lecture information, which affected students' exam performance.

An experiment used participants' eye movements and electroencephalogram (EEG) data to better understand cognitive processing differences between experts and novices. It found that experts had higher power in their theta brain waves than novices, suggesting that they also had a higher cognitive load.

A study looked at whether chatting online with a computer via a chatbot changed the positive effects of emotional disclosure often received when talking with an actual human. It found that the effects were the same in both cases.

One experimental study evaluated whether exercise timing impacts information recall. It found that engaging in exercise prior to performing a memory task helped improve participants' short-term memory abilities.

Sometimes researchers use the experimental method to get a bigger-picture view of psychological behaviors and impacts. For example, one 2018 study examined several lab experiments to learn more about the impact of various environmental factors on building occupant perceptions.

A 2020 study set out to determine the role that sensation-seeking plays in political violence. This research found that sensation-seeking individuals have a higher propensity for engaging in political violence. It also found that providing access to a more peaceful, yet still exciting political group helps reduce this effect.

While the experimental method can be a valuable tool for learning more about psychology and its impacts, it also comes with a few pitfalls.

Experiments may produce artificial results, which are difficult to apply to real-world situations. Similarly, researcher bias can impact the data collected. Results may not be able to be reproduced, meaning the results have low reliability .

Since humans are unpredictable and their behavior can be subjective, it can be hard to measure responses in an experiment. In addition, political pressure may alter the results. The subjects may not be a good representation of the population, or groups used may not be comparable.

And finally, since researchers are human too, results may be degraded due to human error.

What This Means For You

Every psychological research method has its pros and cons. The experimental method can help establish cause and effect, and it's also beneficial when research funds are limited or time is of the essence.

At the same time, it's essential to be aware of this method's pitfalls, such as how biases can affect the results or the potential for low reliability. Keeping these in mind can help you review and assess research studies more accurately, giving you a better idea of whether the results can be trusted or have limitations.

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Ho A, Hancock J, Miner A. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot . J Commun . 2018;68(4):712-733. doi:10.1093/joc/jqy026

Haynes IV J, Frith E, Sng E, Loprinzi P. Experimental effects of acute exercise on episodic memory function: Considerations for the timing of exercise . Psychol Rep . 2018;122(5):1744-1754. doi:10.1177/0033294118786688

Torresin S, Pernigotto G, Cappelletti F, Gasparella A. Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works . Indoor Air . 2018;28(4):525-538. doi:10.1111/ina.12457

Schumpe BM, Belanger JJ, Moyano M, Nisa CF. The role of sensation seeking in political violence: An extension of the significance quest theory . J Personal Social Psychol . 2020;118(4):743-761. doi:10.1037/pspp0000223

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Experimental Design: The Complete Pocket Guide

Bryn Farnsworth

Our comprehensive manual on experimental design provides guidance on avoiding common mistakes and pitfalls when establishing the optimal experiment for your research.

Table of Contents

• Introduction to experimental methods

Humans are a quite curious species. We explore new grounds, improve products and services, find faster and safer ways to produce or transport goods, and we solve the mysteries of global diseases. All of these activities are guided by asking the right questions, by searching for answers in the right spots and taking appropriate decisions. Academic and commercial research have professionalized this quest for knowledge and insights into ourselves and the world surrounding us.

Every day, research institutions across the globe investigate the inner workings of our universe – from cellular levels of our synapses and neurons to macroscopic levels of planets and solar systems – by means of experimentation. Simply put: Experiments are the professional way to answer questions, identify cause and effect or determine predictors and outcomes. These insights help us understand how and why things are what they are and can ultimately be used to change the world by improving the good and overcoming the bad.

N.B. this post is an excerpt from our Experimental Design Guide. You can download your free copy below and get even more insights into the world of Experimental Design.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

• Respondent management with groups and populations
• How to set up stimulus selection and arrangement

In contrast to the early years of scientific research, modern-age experiments are not merely results of scientists randomly probing assumptions combined with the pure luck to be at the right place at the right time and observe outcomes.

Today’s scientific insights are the result of careful thinking and experimental planning, proper collecting of data, and drawing of appropriate conclusions.

Experimental Design Example

Researchers use experiments to learn something new about the world, to answer questions or to probe theoretical assumptions.

Typical examples for research questions in human cognitive-behavioral research are:

• How does sensory stimulation affect human attention? How do, for example, moving dot patterns, sounds or electrical stimulation alter our perception of the world?

• What are the changes in human physiology during information uptake? How do heart rate and galvanic skin response, for example, change as we recall correct or incorrect information?

• How does virtual reality compared to real physical environments affect human behavior? Do humans learn faster in the real world compared to VR?

• How does stress affect the interaction with other colleagues or machines in the workplace?

• How does packaging of a product affect shoppers’ frustration levels? Is the new package intuitive to open, and if not, how does it affect the behavior of the person?

• How does the new TV commercial impact on emotional expressions and brand memory? Does gender have an influence on purchase decisions after watching the ad?

• How does a website affect users’ stress levels in terms of galvanic skin response, ECG and facial expressions?

• Which intersections in town cause most frustration in bicyclists?

• What are the aspects in a presidential campaign speech that drive voters’ decisions?

As you can see, research questions can be somewhat generic. Experiments are supposed to clarify these questions in a more standardized framework. In order to do so, several steps are necessary to fine-tune the research question into a more testable form:

Step 1: Phrase a hypothesis

First, the general research question is broken down into a testable hypothesis or several hypotheses. Hypotheses are explicit statements about cause and effect and address what outcomes occur when specific factors are manipulated:

Hypotheses phrase a relationship between one or more independent variables and one or more dependent variables:

•Independent variable

The independent variable (IV) is strategically changed, or manipulated, by the experimenter. IVs are also referred to as factors.

• Dependent variable (DV)

The dependent variable (DV) is measured by the experimenter. Experiments with one DV are called univariate, experiments with two or more DV are called multivariate.

The general research question “How does stress affect the interaction with others? ” might lead to the following hypotheses about how stress (independent variable) affects interaction with others (dependent variable):

1) “Having to reply to 100 or more incoming emails per hour results in reduced verbal interaction with colleagues.”

Independent variable: Number of emails per hour Dependent variable: Number of verbal interactions with colleagues per hour

2) “Sleeping 8 hours or more per night results in increased informal sport activities with colleagues.”

Independent variable : Duration of sleep per night Dependent variable : Number of sport meetups with colleagues per week

3) “Regular physical exercise in the evening results in increased occurrences of smiles when talking to others in business meetings.”

Independent variable : Number of evening sport activities per week Dependent variable : Smile occurrences when talking with others

Hypotheses make the research question more explicit by stating an observable relationship between cause and effect. Hypotheses also determine which stimuli are used and what respondents are exposed to.

A stimulus doesn’t have to be just pictures or tones, much more constitutes a stimulus, for example, questionnaires, websites, videos, speech and conversations with others, visual and proprioceptive input while driving and much more. We will address stimuli in more detail below.

Step 2: Sample Groups

Define sample groups.

After specifying the hypothesis, you need to clarify the respondent group characteristics for your experiment. This step is necessary to exclude side effects that could alter the outcomes of your experimental data collection. Make sure that demographic characteristics such as age, gender, education level, income, marital status, occupation etc. are consistent across the respondent pool. Individual characteristics such as state of health or exposure to certain life events should be considered as they might affect experimental outcomes. For example, mothers might respond differently to a TV ad for baby toys than women without kids. Soldiers suffering from PTSD might respond differently to stress-provoking stimuli than software developers.

Step 3: Assign subjects to groups

In this step, you randomly distribute subjects to the different experimental conditions. For example, for your stress in the workplace study you could create two experimental groups, where group one receives 10 emails per hour, and group two receives 100 emails per hour. You could now analyze how the two groups differ in their social interaction with others within the next 6 hours. Ideally, the assignment to experimental groups is done in a randomized fashion, such that all respondents have the same probability for ending up in the available experimental groups. There should not be any bias to assign specific respondents to one group or the other.

Step 4: Determine sampling frequency.

How often do you want to measure from respondents? Clinical trials typically measure patients’ state of health once per month over the course of several months or years. In usability studies you might ask respondents once at the end of the session several questions, either verbally or via surveys and questionnaires.

However, when you collect cognitive-behavioral data from EEG, EMG, ECG, GSR or other biosensors while respondents are doing a specific task, you are collecting tens to hundreds of data points per second – even though all of these sub-second samples might be used to compute an overall score reflecting a certain cognitive or affective state. We will address later in this guide which sensors are ideal to collect specific cognitive-behavioral metrics.

Step 5: Conduct the experiment and collect data.

In this step, you execute the experimental paradigm according to the selected methods. Make sure to observe, monitor, and report any important moments during data collection. Prior to conducting the experiment, run a pilot test to rule out any issues that might arise during data collection (stimulus was wrong length/non-randomized/not optimal, etc.)

Check out : 7 Tips & Tricks For a Smooth Lab Experience

Step 6: Pre-process data and analyze metrics.

In human cognitive-behavioral research, raw data can consist of self-reports or data from biosensors. Of course, video footage of experimental sessions such as focus groups and interviews also constitute raw data and have to be analyzed using coding schemes. Due to the wide range of statistical methods to analyze raw data and metrics, we will not address this step in the current guide. However, one crucial aspect should be mentioned here: The selection of a specific statistical method for data analysis should always be driven by the original hypothesis and the collected data.

Of course, not all experiments require the precise specification of all of these steps. Sometimes you as a researcher don’t have control of certain factors, or you are lacking access to specific respondent populations. Dependent on the amount of control that you have over the relationship between cause and effect, the following types of experiments can be distinguished:

Types of Experimental design

1. laboratory experiments.

Whenever we speak informally of experiments, lab experiments might come to mind where researchers in white lab coats observe others from behind one-side mirrors, taking minute notes on the performance and behavior of human participants executing key-press tasks in front of somewhat unpredictable machines. In fact, this is how human cognitive-behavioral research started (see the Milgram experiment ).

Gladfully, the days of sterile lab environments are long gone, and you can run your study wearing your favorite sweater. However, a core aspect still holds: Being able to control all factors and conditions that could have an effect. For example, in lab experiments you can select specific respondent groups and assign them to different experimental conditions, determine the precise timing and configuration of all stimuli, and exclude any problematic side-effects.

What you should know about laboratory experiments…

• Precise control of all external and internal factors that could affect experimental outcomes.
• Random assignment of respondents to experimental groups, ideally by means of randomization.
• Allows identification of cause-effect relationships with highest accuracy.
• Since everything is standardized, others can replicate your study, which makes your study more “credible” compared to non-standardized scenarios.

Limitations.

• Controlled experiments do not reflect the real world. Respondents might not respond naturally because the lab doesn’t reflect the natural environment. In technical terms, lab experiments are lacking ecological validity.
• Observer effects might change respondents’ behavior. An experimenter sitting right next to a respondent or observing them via webcam might bias experimental outcomes (read up on the Hawthorne Effect ).

2. Field experiments

In contrast to lab experiments, field experiments are done in the natural surroundings of respondents. While the experimenter manipulates the “cause”-aspect, there’s no control of what else could potentially affect the effects and outcomes (such as the Hofling’s Hospital Experiment based on Milgram‘s work).

Quite often, engineers also conduct field tests of prototypes of soft- and hardware to validate earlier lab tests and to obtain broader feedback from respondents in real life.

What you should know about field experiments…

>>  strengths..

• Field experiments reflect real-life scenarios more than lab experiments. They have higher ecological validity
• When experiments are covert and respondents don’t feel observed, the observed behavior is much closer to real life compared to lab settings.

>> Limitations.

• No control over external factors that could potentially affect outcomes. The outcomes are therefore much more varied. More respondents are therefore needed to compensate the variation.
• Difficult to replicate by others.
• Limited ability to obtain informed consent from respondents.

3. Natural experiments.

Natural experiments are pure observation studies in the sense that the experimenter doesn’t have any control. Respondent groups are observed as-is and not strategically assigned to different experimental conditions.

You might want to compare existing iPhone and Android users, people living close to Chernobyl and people living somewhere else, or patients suffering from cancer and healthy populations. In this case, the groups that you’d like to compare already exist by nature – you don’t have to create them.

What you should know about natural experiments…

• Behavior in natural experiments more likely reflects real life.
• Ideal in situations where it would be ethically unacceptable to manipulate the group assignment (e.g., expose respondents to radiation).
• More expensive and time-consuming than lab experiments.
• No control over any factors implies that replication by others is almost impossible.

How can I measure human behavior?

Laboratory, field and natural experiments all have one aspect in common: Insights are accomplished empirically. “Empirical” means that research questions and hypotheses are not answered by mere reflection or thought experiments.

Instead of leaning back in a chair and pondering over the potential outcomes of a thought experiment, researchers in human cognitive-behavioral science accomplish their work by means of active observation and probing of the environment in order to identify the underlying processes as well as the ultimate “driving forces” of human behavior.

Within the last decades, researchers have developed intricate experimental techniques and procedures that have found their way also into commercial testing of emotional, cognitive and attentional effects of new products and services, or how personality traits and problem-solving strategies have an impact on brand likeability and consumer preferences.

Two ways to study Human Behavior

Qualitative studies on human behavior.

Qualitative studies gather observational insights. Examples include the investigation of diary entries, open questionnaires, unstructured interviews or observations. Because nothing is counted or quantified and every observation is described as-is, qualitative data is also referred to as descriptive.

In qualitative field studies or usability studies, for example, researchers directly observe how respondents are using the technology, allowing them to directly ask questions, probe on behavior or potentially even adjust the experimental protocol to incorporate the individual’s behavior. The focus of qualitative studies is primarily on understanding how respondents see the world and why they react in a specific way.

What you should know about qualitative studies…

•  Ideal to answer “why” and “how to fix a problem?” questions.
• Focus on individual experience of the respondent.
• Small respondent samples required.
• Knowledge gained in the specific study might not be transferrable to other groups.
• Data collection might take longer per respondent.
• Risk that results are affected by researcher’s biases and preferences.

Typical use cases.

•  UX, web and software usability tests (description of user journeys).
• Open-ended interviews and surveys on biographical events.
• Focus groups with / without experimenter present.

Check out: How to Deliver better UX with Emotion Detection 

Quantitative studies

Quantitative studies by contrast, quantitative studies characterize the systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques. In other words, quantitative studies use numbers to describe and characterize human behavior.

Examples for quantitative techniques include structured surveys and interviews, observations with dedicated coding schemes (e.g., counting the number of cigarettes smoked within a day), or physiological measurements from EEG, EMG, ECG, GSR and other sensors producing numerical output. Whenever researchers are using quantitative methods, they translate behavioral observations into countable numbers and statistical outputs. All of this is done to guarantee maximum experimental control.

What you should know about quantitative studies…

• Ideal for answering “how many” and “how much” questions.
• Useful to analyze large respondent groups, focus on entire populations.
• High amount of standardization requires less time than qualitative studies.
• Provides numerical values that can be analyzed statistically.
• Experimenter might miss out phenomena because the measurement tool is too narrow.
• Contextual factors are often ignored or missing.
• Studies are expensive and time-consuming.
• Behavioral observation using coding schemes (e.g., on facial expressions or action occurrences within a certain time frame)
• Structured interviews and surveys containing single- or multiple-choice questions as well as scales.
• Physiological measurements of bodily processes (EEG, EMG, GSR etc.)

Psychology Research with iMotions

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Check out: Qualitative vs Quantitative Research 

Which numbers could human cognitive-behavioral research potentially use to describe our complex inner workings, our intelligence, personality traits or skill levels? What are measurable indicators of a person being a shopaholic, for example?

Indicators that can be counted might be the average time spent in department stores during a week, the cumulative amount of money laid out for certain lifestyle products, or the number of shoe boxes filling up the closet under the stairs (have a look at our reading tip on measurement and the assignment of numbers or events).

The basic principle is that hidden factors of our personality can be made visible (and therefore measurable) by breaking them into feasible and tangible, graspable and observable units which can be counted numerically. This “making visible” of latent constructs of our personality and identity is referred to as operationalization.

While some measures are more suitable to capture an underlying latent characteristic, others might fail. So the question is, what actually constitutes an appropriate measure?

Measurements to avoid bias

This is generally described with respect to the following criteria:

Objectivity

Objectivity is the most general requirement and reflects the fact that measures should come to the same result no matter who is using them. Also, they should generate the same outcomes independent of the outside influences. For example, a multiple-choice personality questionnaire or survey is objective if it returns the same score irrelevant of whether the participant responds verbally or in written form. Further, the result should be independent of the knowledge or attitude of the experimenter, so that the results are purely driven by the performance of the respondent.

Reliability

A measure is said to have high reliability if it returns the same value under consistent conditions. There are several sub-categories of reliability. For example, “retest reliability” describes the stability of a measure over time, “inter-rater reliability” reflects the amount to which different experimenters give consistent estimates of the same behavior, while “split-half reliability” breaks a test into two and examines to what extent the two halves generate identical results.

This is the final and most crucial criterion. It reflects the extent to which a measure collects what it is supposed to collect. Imagine an experiment where body size is collected to measure its relationship with happiness. Obviously, the measure is both objective and reliable (body size measures are quite consistent irrespective of the person taking the measurement) but it is truly a poor measure with respect to its construct validity (i.e., its capability to truly capture the underlying variable) for happiness.

Once you have identified measures that fulfill objectivity, reliability and validity criteria at the same time, you are on the right track to generate experimental outcomes that will push beyond the frontiers of our existing knowledge.

Respondent Management

While Iceland has research programs where experiments are applied to the entire nation, other countries and situations do not allow testing everybody. Of course, it would grant maximum insights into your research question, but due to time and resource constraints studies and experiments are generally carried out on respondent groups rather than entire populations.

The most challenging part is to find respondents that truly represent the larger target population allowing you to generalize, or infer, from your study group findings to the population. You might have heard the phrase “representative sample” before. This describes respondent groups where each and every member of the population has an equal chance of being selected for your experiment. Populations don’t necessarily have to be entire countries – the term simply reflects “all people that share certain characteristics” (height, weight, BMI, hemoglobin levels, experience, income, nationality etc.) which are considered relevant for your experiment.

Exemplary populations are:

• Female academics between 30 and 40 years in the US with an average annual income of $50k
• Software developers with more than 5 years of experience in C#
• Patients suffering from secondary progressive Multiple Sclerosis
• After-work shoppers of any age and gender
• Danish mothers up to 50 years
• People wearing glasses

A sample now can be a group of 100 Multiple Sclerosis patients, or 20 dog owners. Finding “representative samples” is not that easy as there is some bias in almost all studies. Samples can be found as following:

Non-random respondent sampling

Non-random sampling can be done during initial pre-screening phases, where generalization is not important. In that case, the experimental outcomes only apply to the tested respondent group. Sampling is done as following:

• Volunteers . You ask people on the street, and whoever agrees to participate is tested.
• Snowball sample . One case identifies others of his kind (e.g., HSE shoppers).
• Convenience sample . You test your co-workers and colleagues or other readily available groups.
• Quota sample . At-will selection of a fixed number from several groups (e.g., 30 male and 30 female respondents).

Random respondent sampling .

Random sampling is actually giving everyone in the population the same chance of being included in your experiment. The benefit of being able to conclude from your research findings obtained from several respondents to the general public comes, however, with high demands on time and resources. The following random sampling strategies exist:

Simple random sampling

In random samples chances for everyone are identical to being included in your test. This means that you had to identify, for example, every female academic between 30 and 40 years in the US with an average annual income of $50k, or every dog owner. Subsequently, you draw random samples and only contact those. Random sampling disallows any selection bias based on volunteering or cooperation.

Systematic sampling

Instead of a completely random selection, you systematically select every nth person from an existing list, for example ordered by respondent age, disease duration, membership, distance etc.

Multistage sampling

Sampling can be done in multiple steps. For example, to find representative students for testing, you can first draw a random selection of counties, then proceed with random drawing of cities, schools, and classes. Finally, you randomly draw students for observation and recording.

Cluster sampling

Particularly for self-reports, studies are carried out on large and geographically dispersed populations. In order to obtain the required number of respondents for testing, clusters may be identified and randomly drawn. Subsuequently, all members of the drawn samples are tested. For example, clustering might be done using households – in this case, all household members are tested, reducing the time and resources for testing massively.

Which sampling method you use is generally determined by feasibility in terms of time and resources. It might often be difficult to obtain truly random samples, particularly in field research. You can find more details on suggested procedures for representative sampling in Banerjee and colleagues (2007; 2010).

How many respondents do I need?

Sampling strategies are closely linked to the sample size of your experiment. If you would like to do a single case study, of course only one respondent is needed. In this case, however, you cannot generalize any findings to the larger population. On the other hand, sampling from the entire population is not possible. The question is, how many respondents are suitable for your experiment? What is the ideal sample size?

Martinez and colleagues (2014) as well as Niles (2011) provide recommendations. Without delving too deep into statistics, the main message is about this: Always collect as many respondents as necessary. For quantitative usability testing 20 respondents might be sufficient, but more respondents should be tested whenever the expected effects are smaller, for example, if there’s only subtle differences between the different stimulus conditions.

This is why academic researchers run studies with dozens to hundreds or thousands of respondents. With more respondents, you reduce the ambiguity of individual variation that could have affected experimental outcomes.Top of Page

The amount of security about your findings is typically expressed with respect to confidence, which is roughly expressed with the following formula:

N is the sample size. As you can see, higher respondent samples cause confidence to become smaller (which is the desired outcome). In other words, testing more people gives you more accurate results.

For example, if you tested the preference for a new product with 10 out of 10,000 respondents, then the confidence is at 32%. If 7 out of 10 respondents (70%) liked the new product, the actual proportion in the population could be as low as 48% (70-32) and as high as 100% (70+32, you can’t be above 100). With a variation from 48% to 100%, your test might not be that helpful.

If you increase the sample size to 100 respondents out of 10,000, the confidence is at 10%. With 70 out of 100 respondents liking the product, the actual value in the population is somewhere between 60% and 80%. You’re getting much closer!

If you would like to further reduce the confidence to 5%, you have to test at least 500 randomly-selected respondents. The bottom line is, you have to test lots of respondents before being able to get to conclusions. For more information visit the Creative Research Systems website , where you can find a more exact formula as well as a sample size calculator tool.

Cross-sectional vs. longitudinal designs

Experimental design and the way your study is carried out depends on the nature of your research question. If you’re interested in how a new TV advertisement is perceived by the general public in terms of attention, cognition and affect, there’s several ways to design your study. Do you want to compare cognitive-behavioral outcomes of the ad among different populations of low and high-income households at the same point in time? Or, do you want to measure the TV ad effects in a single population (say, male high-income shoppers with specific demographic characteristics) over an extended period of time? The former approach is generally referred to as cross-sectional design. The latter is called longitudinal design. The two can further be combined (mixed design)

Cross-sectional design

In cross-sectional studies two or more groups are compared at a single point in time. Similar to taking a snapshot, every respondent is invited and tested just once. In our example, you would show the new TV ad to respondents from low- and high-income households. You would not, however, invite them and show them the TV ad again a week later.

Other examples of cross-sectional studies are:

• Gaming. Compare effects of video games on emotional responsiveness of healthy children and children suffering from ADHS.
• Web testing. Compare website usability evaluation of young, middle-aged and senior shoppers.
• Psychology. Compare evaluation of parenting style of mothers and fathers.

The primary benefit of a cross-sectional experimental design is that it allows you to compare many different variables at the same time. You could, for example, investigate the impact of age, gender, experience or educational levels on respondents’ cognitive-emotional evaluation of the TV ad with little or no additional cost. The only thing you have to do is collect the data (for example, by means of interviews or surveys).

Longitudinal design

In a longitudinal study you conduct several observations of the same respondent group over time, lasting from hours to days, months and many years. By doing this, you establish a sequence of events and minimize the noise that could potentially affect each of the single measurements. In other words, you simply make the outcomes more robust against potential side effects.

For example, you could show a TV ad several times to your group of interest (male high-income shoppers) and see how their preference for the ad changes over time.

Other examples for longitudinal designs are:

• Media / package testing. Two or more media trailers or packages are shown in sequence to a group of respondents who evaluate how much they like each of the presented items.
• Food and flavor testing. Respondents are exposed to two or more flavors presented in sequence and asked for their feedback.
• UI and UX testing. Respondents navigate two or more websites and are interviewed with respect to usability questions.
• Psychology and Training. A group of respondents attending a professional training session answers a questionnaire on emotional well-being before, during and after training.
• Physiology. You monitor EEG, GSR, EMG, facial expressions, etc. while respondents are exposed to pictures, sounds or video stimuli.

The primary benefit of longitudinal designs is that you obtain a time-course of values within one group of respondents. Even if you only obtain cognitive-affective test scores before and after the experimental intervention, you are more likely to understand the impact of the intervention on already existing levels of attention, cognition or affect. Therefore, longitudinal studies are more likely to suggest cause-and-effect relationships than cross-sectional studies.

Mixed design

Mixed designs combine the best of two worlds as they allow you to collect longitudinal data across several groups. Strictly spoken, whenever you collect physiological data (like EEG, GSR, EMG, ECG, facial expressions, etc.) from several respondent groups in order to compare different populations, you have a mixed study design. The data itself is longitudinal (several samples over time), while the group comparison has cross-sectional aspects.

Typical examples for mixed designs are:

• Product / media testing. Two or more versions of a product or service are compared with respect to cognitive-behavioral outcomes of two or more groups (e.g., novices and experts, male and female, young and old).
• A-B testing. Two versions of a website or app are compared with respect to cognitive-behavioral outcomes of two or more groups.

Mixed design experiments are ideal for collecting time-courses across several groups of interest, allowing you to investigate the driving forces of human behavior in more detail than cross-sectional or longitudinal designs alone.

Ultimately, which design you choose is driven primarily by your research question. Of course, you can run a cross-sectional study first to get an idea of the potential factors affecting outcomes, and then do a more fine-grained longitudinal study to investigate cause and effect in more detail.

In the next section we will explain in more detail how stimuli should be arranged and which sensors are relevant.

Selecting and arranging stimuli

Experiments in human cognitive-behavior research typically involve some kind of stimulation used to evoke a reaction from respondents. The two most crucial stimulus-related questions are: Which stimuli do I need? In which sequence shall I present the stimuli?

Types of stimuli

Stimuli come in a range of modalities including audio, visual, haptic, olfactory etc. Multimodal stimuli combine several modalities. The following stimuli are used in academic and commercial research studies on human behavior:

• Images / pictures
• Software interfaces
• Devices (car interieur, aircraft cockpit, milkshake machine etc.)
• Communication with others via phone, web or face-to-face
• Complex scenes (VR, real environments)
• Sound (sine waves, complex sound, spoken language, music)
• Olfaction (flavors, smells)
• Haptic stimuli (object exploration by touch, pressure plates, vibrating sensors, haptic robots)
• Questionnaires and surveys (web- or software-based, paper and pencil)

Stimulus sequence

Stimuli are generally presented to respondents in a specific sequence. What are typical sequences used in human cognitive-behavioral research?

Fixed stimulus sequence

Fixed sequences are necessary whenever randomized sequences do not make sense or cannot be employed. For example, when combining a website test with a website-related interview it doesn’t make sense to ask website-related questions first and then tell the respondent to actually use the website.

Here, the only meaningful sequence is to do the website exploration first and the questionnaire second. When it comes to comparing different versions of a stimulus, for example, websites A and B, fixed sequences can also be used.

Random stimulus sequence

As you have learned before, presenting stimuli in the same sequence to all respondents bears the risk of sequential effects. Respondents might rate the first stimulus always higher because they are still motivated, engaged and curious.

After two long hours at the lab, exhaustion might take over, so ratings might be low even if the tested product or service exceeds all previous expectations. This can be avoided by presenting stimuli in random order.

Counterbalanced sequence

To avoid the issues of complete randomization, counterbalanced designs try to achieve an even distribution of conditions across the stimulus slots of the experiment. In the example below, two stimulus conditions A and B are counterbalanced across six respondents, so that three respondents are exposed to stimulus A first, and the other three respondents are exposed to stimulus B first.

Block design

Sometimes it doesn’t make sense to randomize the entire stimulus list as there might be some internal logic and sequence. Let’s assume you would like to evaluate respondents’ behavior when unpacking several food packages.

For each package, there’s a fixed evaluation protocol where (a) the package is unveiled and (b) respondents are asked to describe their associations verbally. Then, (c) they should pick up the package and open it and (d) describe their experience. This sequence from step (a) to (d) can also be characterized as an experimental “block”, which is supposed to be repeated for all tested packages.

While the package presentation sequence is randomized, the content of each of the blocks stays the same.

Repeated design

EEG and other physiological recordings sometimes require repeated presentations of the same stimulus. This is necessary because the stimulus-driven changes in brain activity are much smaller compared to the ongoing activity. Presenting the same stimulus several times makes sure that enough data is present to get to valid conclusions.

However, stimulus repetition can also be done for eye tracking studies. In this case, the randomization procedures listed above apply as well.

You might be interested in the number of repetitions necessary to get to results. Unfortunately, this cannot be answered globally, as it depends on several factors such as magnitude of the expected effect/difference between two conditions, stimulus modality, physiological effect of interest, and other factors that take impact on experimental outcomes.

Also, there are strong statistical considerations which are beyond the scope of this general introduction.

Modalities and sensors

Whenever you design experiments for human cognitive-behavior research, you certainly want to consider which biosensors you collect data from. Human behavior is a complex interplay of a variety of different processes, ranging from completely unconscious modulations of emotional reactions to decision-making based on conscious thoughts and cognition. In fact, each of our emotional and cognitive responses is driven by factors such as arousal, workload, and environmental conditions that impact our well-being in that very moment.

All of these aspects of human behavior can be captured by self-reports (via interviews or surveys), specific devices (such as eye trackers, EEG systems, GSR and ECG sensors ) or camera-based facial expression analysis.

TV ads, video games, movies, websites, devices as well as social interaction partners in private life and in the workplace – we could process none of these without our vision. The human brain is fine-tuned for visual input and controlling eye movements. Therefore, it makes immediate sense to collect information on gaze position and pupil dilation from eye tracking. If you present visual stimuli on screen, you should always collect eye tracking data to be absolutely sure where respondents are directing their gaze to and how this is affecting cognitive processing. Second, monitoring pupil dilation can give valuable insights into arousal and stress levels of a respondent. As pupil dilation is an autonomic process, it cannot be controlled consciously. Eye tracking recordings allow you to monitor both respondents’ engagement and motivation as well as arousal levels during the encounter with emotional or cognitively challenging stimuli.

Galvanic skin response (GSR) or electrodermal activity (EDA) reflects the amount of sweat secretion from sweat glands in our skin. Increased sweating results in higher skin conductivity. When exposed to emotional content, we sweat emotionally. GSR recordings in conjunction with EEG are extremely powerful as skin conductance is controlled subconsciously, that is, by deeper and older brain structures than the cognitive processes that are monitored by EEG. Therefore, adding GSR offers tremendous insights into the unfiltered, unbiased emotional arousal of a respondent.

Facial Expression Analysis

With facial expression analysis you can assess if respondents are truly expressing their positive attitude in observable behavior. Facial expression analysis is a non-intrusive method to assess head position and orientation (so you always know where your respondents are positioned relative to the stimulus), expressions (such as lifting of the eyebrows or opening of the mouth) and global facial expressions of basic emotions (joy, anger, surprise etc.) using a webcam placed in front of the respondent. Facial data is extremely helpful to monitor engagement, frustration or drowsiness.

(facial) EMG

Electromyographic sensors monitor the electric energy generated by body movements. EMG sensors can be used to monitor muscular responses of the face, hands or fingers in response to any type of stimulus material. Even subtle activation patterns associated with consciously controlled hand/finger movements (startle reflex) can be assessed with EMG. Collecting synchronized EMG data is relevant for anyone interested in how movements of the eyes and limbs are prepared and executed, but also how movements are prevented and actions are inhibited.

Monitoring heart activity with ECG electrodes attached to the chest or optical heart rate sensors attached to finger tips allows you to track respondents’ physical state, their anxiety and stress levels (arousal), and how changes in physiological state relate to their actions and decisions. Tracking respondents’ physical exhaustion with ECG sensors can provide helpful insights into cognitive-affective processes under bodily straining activity.

Electroencephalography (EEG) is a neuroimaging technique measuring electrical activity generated by the brain from the scalp surface using portable sensors and amplifier systems. It undoubtedly is your means of choice when it comes to assess brain activity associated with perception, cognitive behavior, and emotional processes. EEG reveals substantial insights into sub-second brain dynamics of engagement, motivation, frustration, cognitive workload, and further metrics associated with stimulus processing, action preparation, and execution. Simply put: EEG impressively tells which parts of the brain are active while we perform a task or are exposed to certain stimulus material.

Self-reports

Any experiment should contain self-reported data collection stages, for example at the beginning of the session, during data collection , and at the very end. Gathering demographic data (gender, age, socio-economical status, etc.) helps describing the respondent group in more detail. Also, self-reported data from interviews and surveys helps tremendously to gain insights into the subjective world of the respondents – their self-perceived levels of attention, motivation and engagement – beyond quantitative values reported by biosensors. Of course, survey results can be utilized to segment your respondents into specific groups for analysis (e.g., young vs. old; male vs. female; novice vs. experienced users).

Experimental design done right with iMotions

Properly designed experiments allow you deep insights into attention, cognition and emotional processing of your desired target population when confronted with physical objects or stimuli. Experimental research has come up with dedicated recommendations on how to prevent experimenter or segmentation bias – randomization strategies for respondent and stimulus selection are an excellent starting point.

Before you get started designing your next human cognitive-behavioral experiment, you certainly want to think about how to arrange stimuli, how to select respondents and which biosensors to use in order to gain maximum insights.

What if there was a multimodal software solution that allows for loading and arranging any type of stimuli, for example, in fixed or randomized sequences, while recording data from EEG, eye tracking, facial expression analysis and other biosensors (such as GSR, ECG, EMG) without having to manually piece everything together?

The iMotions Platform

The iMotions Platform is one easy-to-use software solution for study design, multi-sensor calibration, data collection, and analysis.

Out of the box, iMotions supports over 50 leading biosensors including facial expression analysis, GSR, eye tracking, EEG, ECG, and EMG, as well as surveys for multimodal human behavior research.

• Banerjee, Chaudhury, et al. (2007). Statistics without tears – inputs for sample size calculations. Indian Psychiatry Journal, 16, 150–152.
• Banerjee & Chaudhury (2010). Statistics without tears: Populations and samples. Industrial Psychiatry Journal, 19(1), 60–65.
• Creative Research Systems (2003). Sample Size Calculator. Retrieved from https://www.surveysystem.com/sscalc.htm on 2016-08-06.
• Cooper, Camic et al. (2012). APA handbook of research methods in psychology, Vol 1: Foundations, planning, measures, and psychometrics.
• Cooper, Camic et al. (2012). APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological.
• Farrington (1991). Longitudinal research strategies: advantages, problems, and prospects. Journal of the American Academy of Child and Adolescent Psychiatry, 30(3), 369–374.
• Hofling et al. (1966). An experimental study of nurse-physician relationships“. Journal of Nervous and Mental Disease, 143, pp. 171-180.
• McLeod (2007). The Milgram Experiment. Retrieved from www.simplypsychology.org/milgram.html on 2016-07-31.
• Martinez-Mesa, Gonzalez-Chica et al. (2014). Sample size: How many participants do need in my research? Anais Brasileiros de Dermatologia, 89(4), 609–615.
• Monahan & Fisher (2010). Benefits of observer effects: Lessons from the field Qualitative Research, 10(1), pp. 357-376.
• Niles (2014). Sample size: How many survey participants do I need ? Retrieved from https://www.sciencebuddies.org/science-fair-projects/project_ideas/Soc_participants.shtml on 2016-08-06
• Ryan (2006). Modern Experimental Design (2nd edition). New York: Wiley Interscience.

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them.

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive.
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure.

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Experimental Research vs. Alternatives and When to Use Them

1. experimental research vs causal comparative.

Experimental research enables you to control variables and identify how the independent variable affects the dependent variable. Causal-comparative find out the cause-and-effect relationship between the variables by comparing already existing groups that are affected differently by the independent variable.

For example, in an experiment to see how K-12 education affects children and teenager development. An experimental research would split the children into groups, some would get formal K-12 education, while others won’t. This is not ethically right because every child has the right to education. So, what we do instead would be to compare already existing groups of children who are getting formal education with those who due to some circumstances can not.

Pros and Cons of Experimental vs Causal-Comparative Research

• Causal-Comparative:   Strengths:  More realistic than experiments, can be conducted in real-world settings.  Weaknesses:  Establishing causality can be weaker due to the lack of manipulation.

2. Experimental Research vs Correlational Research

When experimenting, you are trying to establish a cause-and-effect relationship between different variables. For example, you are trying to establish the effect of heat on water, the temperature keeps changing (independent variable) and you see how it affects the water (dependent variable).

For correlational research, you are not necessarily interested in the why or the cause-and-effect relationship between the variables, you are focusing on the relationship. Using the same water and temperature example, you are only interested in the fact that they change, you are not investigating which of the variables or other variables causes them to change.

Pros and Cons of Experimental vs Correlational Research

3. experimental research vs descriptive research.

With experimental research, you alter the independent variable to see how it affects the dependent variable, but with descriptive research you are simply studying the characteristics of the variable you are studying.

So, in an experiment to see how blown glass reacts to temperature, experimental research would keep altering the temperature to varying levels of high and low to see how it affects the dependent variable (glass). But descriptive research would investigate the glass properties.

Pros and Cons of Experimental vs Descriptive Research

4. experimental research vs action research.

Experimental research tests for causal relationships by focusing on one independent variable vs the dependent variable and keeps other variables constant. So, you are testing hypotheses and using the information from the research to contribute to knowledge.

However, with action research, you are using a real-world setting which means you are not controlling variables. You are also performing the research to solve actual problems and improve already established practices.

For example, if you are testing for how long commutes affect workers’ productivity. With experimental research, you would vary the length of commute to see how the time affects work. But with action research, you would account for other factors such as weather, commute route, nutrition, etc. Also, experimental research helps know the relationship between commute time and productivity, while action research helps you look for ways to improve productivity

Pros and Cons of Experimental vs Action Research

Conclusion  .

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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Experimental Psychology: 10 Examples & Definition

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Experimental psychology refers to studying psychological phenomena using scientific methods. Originally, the primary scientific method involved manipulating one variable and observing systematic changes in another variable.

Today, psychologists utilize several types of scientific methodologies.

Experimental psychology examines a wide range of psychological phenomena, including: memory, sensation and perception, cognitive processes, motivation, emotion, developmental processes, in addition to the neurophysiological concomitants of each of these subjects.

Studies are conducted on both animal and human participants, and must comply with stringent requirements and controls regarding the ethical treatment of both.

Definition of Experimental Psychology

Experimental psychology is a branch of psychology that utilizes scientific methods to investigate the mind and behavior.

It involves the systematic and controlled study of human and animal behavior through observation and experimentation .

Experimental psychologists design and conduct experiments to understand cognitive processes, perception, learning, memory, emotion, and many other aspects of psychology. They often manipulate variables ( independent variables ) to see how this affects behavior or mental processes (dependent variables).

The findings from experimental psychology research are often used to better understand human behavior and can be applied in a range of contexts, such as education, health, business, and more.

Experimental Psychology Examples

1. The Puzzle Box Studies (Thorndike, 1898) Placing different cats in a box that can only be escaped by pulling a cord, and then taking detailed notes on how long it took for them to escape allowed Edward Thorndike to derive the Law of Effect: actions followed by positive consequences are more likely to occur again, and actions followed by negative consequences are less likely to occur again (Thorndike, 1898).

2. Reinforcement Schedules (Skinner, 1956) By placing rats in a Skinner Box and changing when and how often the rats are rewarded for pressing a lever, it is possible to identify how each schedule results in different behavior patterns (Skinner, 1956). This led to a wide range of theoretical ideas around how rewards and consequences can shape the behaviors of both animals and humans.

3. Observational Learning (Bandura, 1980) Some children watch a video of an adult punching and kicking a Bobo doll. Other children watch a video in which the adult plays nicely with the doll. By carefully observing the children’s behavior later when in a room with a Bobo doll, researchers can determine if television violence affects children’s behavior (Bandura, 1980).

4. The Fallibility of Memory (Loftus & Palmer, 1974) A group of participants watch the same video of two cars having an accident. Two weeks later, some are asked to estimate the rate of speed the cars were going when they “smashed” into each other. Some participants are asked to estimate the rate of speed the cars were going when they “bumped” into each other. Changing the phrasing of the question changes the memory of the eyewitness.

5. Intrinsic Motivation in the Classroom (Dweck, 1990) To investigate the role of autonomy on intrinsic motivation, half of the students are told they are “free to choose” which tasks to complete. The other half of the students are told they “must choose” some of the tasks. Researchers then carefully observe how long the students engage in the tasks and later ask them some questions about if they enjoyed doing the tasks or not.

6. Systematic Desensitization (Wolpe, 1958) A clinical psychologist carefully documents his treatment of a patient’s social phobia with progressive relaxation. At first, the patient is trained to monitor, tense, and relax various muscle groups while viewing photos of parties. Weeks later, they approach a stranger to ask for directions, initiate a conversation on a crowded bus, and attend a small social gathering. The therapist’s notes are transcribed into a scientific report and published in a peer-reviewed journal.

7. Study of Remembering (Bartlett, 1932) Bartlett’s work is a seminal study in the field of memory, where he used the concept of “schema” to describe an organized pattern of thought or behavior. He conducted a series of experiments using folk tales to show that memory recall is influenced by cultural schemas and personal experiences.

8. Study of Obedience (Milgram, 1963) This famous study explored the conflict between obedience to authority and personal conscience. Milgram found that a majority of participants were willing to administer what they believed were harmful electric shocks to a stranger when instructed by an authority figure, highlighting the power of authority and situational factors in driving behavior.

9. Pavlov’s Dog Study (Pavlov, 1927) Ivan Pavlov, a Russian physiologist, conducted a series of experiments that became a cornerstone in the field of experimental psychology. Pavlov noticed that dogs would salivate when they saw food. He then began to ring a bell each time he presented the food to the dogs. After a while, the dogs began to salivate merely at the sound of the bell. This experiment demonstrated the principle of “classical conditioning.”

10, Piaget’s Stages of Development (Piaget, 1958) Jean Piaget proposed a theory of cognitive development in children that consists of four distinct stages: the sensorimotor stage (birth to 2 years), where children learn about the world through their senses and motor activities, through to the the formal operational stage (12 years and beyond), where abstract reasoning and hypothetical thinking develop. Piaget’s theory is an example of experimental psychology as it was developed through systematic observation and experimentation on children’s problem-solving behaviors .

Types of Research Methodologies in Experimental Psychology 

Researchers utilize several different types of research methodologies since the early days of Wundt (1832-1920).

1. The Experiment

The experiment involves the researcher manipulating the level of one variable, called the Independent Variable (IV), and then observing changes in another variable, called the Dependent Variable (DV).

The researcher is interested in determining if the IV causes changes in the DV. For example, does television violence make children more aggressive?

So, some children in the study, called research participants, will watch a show with TV violence, called the treatment group. Others will watch a show with no TV violence, called the control group.

So, there are two levels of the IV: violence and no violence. Next, children will be observed to see if they act more aggressively. This is the DV.

If TV violence makes children more aggressive, then the children that watched the violent show will me more aggressive than the children that watched the non-violent show.

A key requirement of the experiment is random assignment . Each research participant is assigned to one of the two groups in a way that makes it a completely random process. This means that each group will have a mix of children: different personality types, diverse family backgrounds, and range of intelligence levels.

2. The Longitudinal Study

A longitudinal study involves selecting a sample of participants and then following them for years, or decades, periodically collecting data on the variables of interest.

For example, a researcher might be interested in determining if parenting style affects academic performance of children. Parenting style is called the predictor variable , and academic performance is called the outcome variable .

Researchers will begin by randomly selecting a group of children to be in the study. Then, they will identify the type of parenting practices used when the children are 4 and 5 years old.

A few years later, perhaps when the children are 8 and 9, the researchers will collect data on their grades. This process can be repeated over the next 10 years, including through college.

If parenting style has an effect on academic performance, then the researchers will see a connection between the predictor variable and outcome variable.

Children raised with parenting style X will have higher grades than children raised with parenting style Y.

3. The Case Study

The case study is an in-depth study of one individual. This is a research methodology often used early in the examination of a psychological phenomenon or therapeutic treatment.

For example, in the early days of treating phobias, a clinical psychologist may try teaching one of their patients how to relax every time they see the object that creates so much fear and anxiety, such as a large spider.

The therapist would take very detailed notes on how the teaching process was implemented and the reactions of the patient. When the treatment had been completed, those notes would be written in a scientific form and submitted for publication in a scientific journal for other therapists to learn from.

There are several other types of methodologies available which vary different aspects of the three described above. The researcher will select a methodology that is most appropriate to the phenomenon they want to examine.

They also must take into account various practical considerations such as how much time and resources are needed to complete the study. Conducting research always costs money.

People and equipment are needed to carry-out every study, so researchers often try to obtain funding from their university or a government agency. 

Origins and Key Developments in Experimental Psychology

Wilhelm Maximilian Wundt (1832-1920) is considered one of the fathers of modern psychology. He was a physiologist and philosopher and helped establish psychology as a distinct discipline (Khaleefa, 1999).  

In 1879 he established the world’s first psychology research lab at the University of Leipzig. This is considered a key milestone for establishing psychology as a scientific discipline. In addition to being the first person to use the term “psychologist,” to describe himself, he also founded the discipline’s first scientific journal Philosphische Studien in 1883.

Another notable figure in the development of experimental psychology is Ernest Weber . Trained as a physician, Weber studied sensation and perception and created the first quantitative law in psychology.

The equation denotes how judgments of sensory differences are relative to previous levels of sensation, referred to as the just-noticeable difference (jnd). This is known today as Weber’s Law (Hergenhahn, 2009).    

Gustav Fechner , one of Weber’s students, published the first book on experimental psychology in 1860, titled Elemente der Psychophysik. His worked centered on the measurement of psychophysical facets of sensation and perception, with many of his methods still in use today.    

The first American textbook on experimental psychology was Elements of Physiological Psychology, published in 1887 by George Trumball Ladd .

Ladd also established a psychology lab at Yale University, while Stanley Hall and Charles Sanders continued Wundt’s work at a lab at Johns Hopkins University.

In the late 1800s, Charles Pierce’s contribution to experimental psychology is especially noteworthy because he invented the concept of random assignment (Stigler, 1992; Dehue, 1997).

Go Deeper: 15 Random Assignment Examples

This procedure ensures that each participant has an equal chance of being placed in any of the experimental groups (e.g., treatment or control group). This eliminates the influence of confounding factors related to inherent characteristics of the participants.

Random assignment is a fundamental criterion for a study to be considered a valid experiment.

From there, experimental psychology flourished in the 20th century as a science and transformed into an approach utilized in cognitive psychology, developmental psychology, and social psychology .

Today, the term experimental psychology refers to the study of a wide range of phenomena and involves methodologies not limited to the manipulation of variables.

The Scientific Process and Experimental Psychology

The one thing that makes psychology a science and distinguishes it from its roots in philosophy is the reliance upon the scientific process to answer questions. This makes psychology a science was the main goal of its earliest founders such as Wilhelm Wundt.

There are numerous steps in the scientific process, outlined in the graphic below.

1. Observation

First, the scientist observes an interesting phenomenon that sparks a question. For example, are the memories of eyewitnesses really reliable, or are they subject to bias or unintentional manipulation?

2. Hypothesize

Next, this question is converted into a testable hypothesis. For instance: the words used to question a witness can influence what they think they remember.

3. Devise a Study

Then the researcher(s) select a methodology that will allow them to test that hypothesis. In this case, the researchers choose the experiment, which will involve randomly assigning some participants to different conditions.

In one condition, participants are asked a question that implies a certain memory (treatment group), while other participants are asked a question which is phrased neutrally and does not imply a certain memory (control group).

The researchers then write a proposal that describes in detail the procedures they want to use, how participants will be selected, and the safeguards they will employ to ensure the rights of the participants.

That proposal is submitted to an Institutional Review Board (IRB). The IRB is comprised of a panel of researchers, community representatives, and other professionals that are responsible for reviewing all studies involving human participants.

4. Conduct the Study

If the IRB accepts the proposal, then the researchers may begin collecting data. After the data has been collected, it is analyzed using a software program such as SPSS.

Those analyses will either support or reject the hypothesis. That is, either the participants’ memories were affected by the wording of the question, or not.

5. Publish the study

Finally, the researchers write a paper detailing their procedures and results of the statistical analyses. That paper is then submitted to a scientific journal.

The lead editor of that journal will then send copies of the paper to 3-5 experts in that subject. Each of those experts will read the paper and basically try to find as many things wrong with it as possible. Because they are experts, they are very good at this task.

After reading those critiques, most likely, the editor will send the paper back to the researchers and require that they respond to the criticisms, collect more data, or reject the paper outright.

In some cases, the study was so well-done that the criticisms were minimal and the editor accepts the paper. It then gets published in the scientific journal several months later.

That entire process can easily take 2 years, usually more. But, the findings of that study went through a very rigorous process. This means that we can have substantial confidence that the conclusions of the study are valid.

Experimental psychology refers to utilizing a scientific process to investigate psychological phenomenon.

There are a variety of methods employed today. They are used to study a wide range of subjects, including memory, cognitive processes, emotions and the neurophysiological basis of each.

The history of psychology as a science began in the 1800s primarily in Germany. As interest grew, the field expanded to the United States where several influential research labs were established.

As more methodologies were developed, the field of psychology as a science evolved into a prolific scientific discipline that has provided invaluable insights into human behavior.

Bartlett, F. C., & Bartlett, F. C. (1995).  Remembering: A study in experimental and social psychology . Cambridge university press.

Dehue, T. (1997). Deception, efficiency, and random groups: Psychology and the gradual origination of the random group design. Isis , 88 (4), 653-673.

Ebbinghaus, H. (2013). Memory: A contribution to experimental psychology.  Annals of neurosciences ,  20 (4), 155.

Hergenhahn, B. R. (2009). An introduction to the history of psychology. Belmont. CA: Wadsworth Cengage Learning .

Khaleefa, O. (1999). Who is the founder of psychophysics and experimental psychology? American Journal of Islam and Society , 16 (2), 1-26.

Loftus, E. F., & Palmer, J. C. (1974).  Reconstruction of auto-mobile destruction : An example of the interaction between language and memory.  Journal of Verbal Learning and Verbal behavior , 13, 585-589.

Pavlov, I.P. (1927). Conditioned reflexes . Dover, New York.

Piaget, J. (1959).  The language and thought of the child  (Vol. 5). Psychology Press.

Piaget, J., Fraisse, P., & Reuchlin, M. (2014). Experimental psychology its scope and method: Volume I (Psychology Revivals): History and method . Psychology Press.

Skinner, B. F. (1956). A case history in scientlfic method. American Psychologist, 11 , 221-233

Stigler, S. M. (1992). A historical view of statistical concepts in psychology and educational research. American Journal of Education , 101 (1), 60-70.

Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. Psychological Review Monograph Supplement 2 .

Wolpe, J. (1958). Psychotherapy by reciprocal inhibition. Stanford, CA: Stanford University Press.

Appendix: Images reproduced as Text

Definition: Experimental psychology is a branch of psychology that focuses on conducting systematic and controlled experiments to study human behavior and cognition.

Overview: Experimental psychology aims to gather empirical evidence and explore cause-and-effect relationships between variables. Experimental psychologists utilize various research methods, including laboratory experiments, surveys, and observations, to investigate topics such as perception, memory, learning, motivation, and social behavior .

Example: The Pavlov’s Dog experimental psychology experiment used scientific methods to develop a theory about how learning and association occur in animals. The same concepts were subsequently used in the study of humans, wherein psychology-based ideas about learning were developed. Pavlov’s use of the empirical evidence was foundational to the study’s success.

Experimental Psychology Milestones:

1890: William James publishes “The Principles of Psychology”, a foundational text in the field of psychology.

1896: Lightner Witmer opens the first psychological clinic at the University of Pennsylvania, marking the beginning of clinical psychology.

1913: John B. Watson publishes “Psychology as the Behaviorist Views It”, marking the beginning of Behaviorism.

1920: Hermann Rorschach introduces the Rorschach inkblot test.

1938: B.F. Skinner introduces the concept of operant conditioning .

1967: Ulric Neisser publishes “Cognitive Psychology” , marking the beginning of the cognitive revolution.

1980: The third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) is published, introducing a new classification system for mental disorders.

The Scientific Process

• Observe an interesting phenomenon
• Formulate testable hypothesis
• Select methodology and design study
• Submit research proposal to IRB
• Collect and analyzed data; write paper
• Submit paper for critical reviews

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19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

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10 Cool Chemistry Experiments

ThoughtCo / Hilary Allison

• Projects & Experiments
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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
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Chemistry is king when it comes to making science cool. There are many interesting and fun projects to try, but these 10 chemistry experiments might be the coolest.

Whether you want to witness color transformations with copper and nitric acid or create a foam spectacle with hydrogen peroxide and potassium iodide, there's something here to spark curiosity in everyone. There's even a famous chemical reaction that will emit blue light and a characteristic barking or woofing sound.

Copper and Nitric Acid

When you place a piece of copper in nitric acid , the Cu 2+ ions and nitrate ions coordinate to color the solution green and then brownish-green. If you dilute the solution, water displaces nitrate ions around the copper, and the solution changes to blue.

Hydrogen Peroxide with Potassium Iodide

Affectionately known as elephant toothpaste , the chemical reaction between peroxide and potassium iodide shoots out a column of foam. If you add food coloring, you can customize the "toothpaste" for holiday-colored themes.

Any Alkali Metal in Water

Any of the alkali metals will react vigorously in water . How vigorously? Sodium burns bright yellow. Potassium burns violet. Lithium burns red. Cesium explodes. Experiment by moving down the alkali metals group of the periodic table. 

Thermite Reaction

The thermite reaction essentially shows what would happen if iron rusted instantly, rather than over time. In other words, it's making metal burn. If the conditions are right, just about any metal will burn. However, the reaction usually is performed by reacting iron oxide with aluminum:

Fe 2 O 3  + 2Al → 2Fe + Al 2 O 3  + heat and light

If you want a truly stunning display, try placing the mixture inside a block of dry ice and then lighting the mixture.

Coloring Fire

 SEAN GLADWELL / Getty Images

When ions are heated in a flame, electrons become excited and then drop to a lower energy state, emitting photons. The energy of the photons is characteristic of the chemical and corresponds to specific flame colors . It's the basis for the flame test in analytical chemistry , plus it's fun to experiment with different chemicals to see what colors they produce in a fire.

Make Polymer Bouncy Balls

Who doesn't enjoy playing with bouncy balls ? The chemical reaction used to make the balls makes a terrific experiment because you can alter the properties of the balls by changing the ratio of the ingredients.

Make a Lichtenberg Figure

A Lichtenberg figure or "electrical tree" is a record of the path taken by electrons during an electrostatic discharge. It's basically frozen lightning. There are several ways you can make an electrical tree.

Experiment with 'Hot Ice'

Hot ice is a name given to sodium acetate, a chemical you can make by reacting vinegar and baking soda. A solution of sodium acetate can be supercooled​ so that it will crystallize on command. Heat is evolved when the crystals form, so although it resembles water ice, it's hot.

Barking Dog Experiment

The Barking Dog is the name given to a chemiluminescent reaction involving the exothermic combination of either nitrous oxide or nitrogen monoxide with carbon disulfide. The reaction proceeds down a tube, emitting blue light and a characteristic "woof" sound.

Another version of the demonstration involves coating the inside of a clear jug with alcohol and igniting the vapor. The  flame front proceeds down the ​bottle , which also barks.

Dehydration of Sugar

When you react sugar with sulfuric acid , the sugar is violently dehydrated. The result is a growing column of carbon black, heat, and the overwhelming odor of burnt caramel.

Easy Science Experiments

Want something less extravagant but still fun? These easy science experiments are doable with items you likely already have at home—from creating invisible ink with baking soda to making homemade ice cream in a plastic bag.

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Field experiments, explained

Editor’s note: This is part of a series called “The Day Tomorrow Began,” which explores the history of breakthroughs at UChicago.  Learn more here.

A field experiment is a research method that uses some controlled elements of traditional lab experiments, but takes place in natural, real-world settings. This type of experiment can help scientists explore questions like: Why do people vote the way they do? Why do schools fail? Why are certain people hired less often or paid less money?

University of Chicago economists were early pioneers in the modern use of field experiments and conducted innovative research that impacts our everyday lives—from policymaking to marketing to farming and agriculture.  

Jump to a section:

What is a field experiment, why do a field experiment, what are examples of field experiments, when did field experiments become popular in modern economics, what are criticisms of field experiments.

Field experiments bridge the highly controlled lab environment and the messy real world. Social scientists have taken inspiration from traditional medical or physical science lab experiments. In a typical drug trial, for instance, participants are randomly assigned into two groups. The control group gets the placebo—a pill that has no effect. The treatment group will receive the new pill. The scientist can then compare the outcomes for each group.

A field experiment works similarly, just in the setting of real life.

It can be difficult to understand why a person chooses to buy one product over another or how effective a policy is when dozens of variables affect the choices we make each day. “That type of thinking, for centuries, caused economists to believe you can't do field experimentation in economics because the market is really messy,” said Prof. John List, a UChicago economist who has used field experiments to study everything from how people use  Uber and  Lyft to  how to close the achievement gap in Chicago-area schools . “There are a lot of things that are simultaneously moving.”

The key to cleaning up the mess is randomization —or assigning participants randomly to either the control group or the treatment group. “The beauty of randomization is that each group has the same amount of bad stuff, or noise or dirt,” List said. “That gets differenced out if you have large enough samples.”

Though lab experiments are still common in the social sciences, field experiments are now often used by psychologists, sociologists and political scientists. They’ve also become an essential tool in the economist’s toolbox.  

Some issues are too big and too complex to study in a lab or on paper—that’s where field experiments come in.

In a laboratory setting, a researcher wants to control as many variables as possible. These experiments are excellent for testing new medications or measuring brain functions, but they aren’t always great for answering complex questions about attitudes or behavior.

Labs are highly artificial with relatively small sample sizes—it’s difficult to know if results will still apply in the real world. Also, people are aware they are being observed in a lab, which can alter their behavior. This phenomenon, sometimes called the Hawthorne effect, can affect results.

Traditional economics often uses theories or existing data to analyze problems. But, when a researcher wants to study if a policy will be effective or not, field experiments are a useful way to look at how results may play out in real life.

In 2019, UChicago economist Michael Kremer (then at Harvard) was awarded the Nobel Prize alongside Abhijit Banerjee and Esther Duflo of MIT for their groundbreaking work using field experiments to help reduce poverty . In the 1990s and 2000s, Kremer conducted several randomized controlled trials in Kenyan schools testing potential interventions to improve student performance. 

In the 1990s, Kremer worked alongside an NGO to figure out if buying students new textbooks made a difference in academic performance. Half the schools got new textbooks; the other half didn’t. The results were unexpected—textbooks had no impact.

“Things we think are common sense, sometimes they turn out to be right, sometimes they turn out to be wrong,” said Kremer on an episode of  the Big Brains podcast. “And things that we thought would have minimal impact or no impact turn out to have a big impact.”

In the early 2000s, Kremer returned to Kenya to study a school-based deworming program. He and a colleague found that providing deworming pills to all students reduced absenteeism by more than 25%. After the study, the program was scaled nationwide by the Kenyan government. From there it was picked up by multiple Indian states—and then by the Indian national government.

“Experiments are a way to get at causal impact, but they’re also much more than that,” Kremer said in  his Nobel Prize lecture . “They give the researcher a richer sense of context, promote broader collaboration and address specific practical problems.”    

Among many other things, field experiments can be used to:

Study bias and discrimination

A 2004 study published by UChicago economists Marianne Bertrand and Sendhil Mullainathan (then at MIT) examined racial discrimination in the labor market. They sent over 5,000 resumes to real job ads in Chicago and Boston. The resumes were exactly the same in all ways but one—the name at the top. Half the resumes bore white-sounding names like Emily Walsh or Greg Baker. The other half sported African American names like Lakisha Washington or Jamal Jones. The study found that applications with white-sounding names were 50% more likely to receive a callback.

Examine voting behavior

Political scientist Harold Gosnell , PhD 1922, pioneered the use of field experiments to examine voting behavior while at UChicago in the 1920s and ‘30s. In his study “Getting out the vote,” Gosnell sorted 6,000 Chicagoans across 12 districts into groups. One group received voter registration info for the 1924 presidential election and the control group did not. Voter registration jumped substantially among those who received the informational notices. Not only did the study prove that get-out-the-vote mailings could have a substantial effect on voter turnout, but also that field experiments were an effective tool in political science.

Test ways to reduce crime and shape public policy

Researchers at UChicago’s  Crime Lab use field experiments to gather data on crime as well as policies and programs meant to reduce it. For example, Crime Lab director and economist Jens Ludwig co-authored a  2015 study on the effectiveness of the school mentoring program  Becoming a Man . Developed by the non-profit Youth Guidance, Becoming a Man focuses on guiding male students between 7th and 12th grade to help boost school engagement and reduce arrests. In two field experiments, the Crime Lab found that while students participated in the program, total arrests were reduced by 28–35%, violent-crime arrests went down by 45–50% and graduation rates increased by 12–19%.

The earliest field experiments took place—literally—in fields. Starting in the 1800s, European farmers began experimenting with fertilizers to see how they affected crop yields. In the 1920s, two statisticians, Jerzy Neyman and Ronald Fisher, were tasked with assisting with these agricultural experiments. They are credited with identifying randomization as a key element of the method—making sure each plot had the same chance of being treated as the next.

The earliest large-scale field experiments in the U.S. took place in the late 1960s to help evaluate various government programs. Typically, these experiments were used to test minor changes to things like electricity pricing or unemployment programs.

Though field experiments were used in some capacity throughout the 20th century, this method didn’t truly gain popularity in economics until the 2000s. Kremer and List were early pioneers and first began experimenting with the method in the 1990s.

In 2004, List co-authored  a seminal paper defining field experiments and arguing for the importance of the method. In 2008,  he and UChicago economist Steven Levitt published another study tracing the history of field experiments and their impact on economics.

In the past few decades, the use of field experiments has exploded. Today, economists often work alongside NGOs or nonprofit organizations to study the efficacy of programs or policies. They also partner with companies to test products and understand how people use services.  

There are several  ethical discussions happening among scholars as field experiments grow in popularity. Chief among them is the issue of informed consent. All studies that involve human test subjects must be approved by an institutional review board (IRB) to ensure that people are protected.

However, participants in field experiments often don’t know they are in an experiment. While an experiment may be given the stamp of approval in the research community, some argue that taking away peoples’ ability to opt out is inherently unethical. Others advocate for stricter review processes as field experiments continue to evolve.

According to List, another major issue in field experiments is the issue of scale . Many experiments only test small groups—say, dozens to hundreds of people. This may mean the results are not applicable to broader situations. For example, if a scientist runs an experiment at one school and finds their method works there, does that mean it will also work for an entire city? Or an entire country?

List believes that in addition to testing option A and option B, researchers need a third option that accounts for the limitations that come with a larger scale. “Option C is what I call critical scale features. I want you to bring in all of the warts, all of the constraints, whether they're regulatory constraints, or constraints by law,” List said. “Option C is like your reality test, or what I call policy-based evidence.”

This problem isn’t unique to field experiments, but List believes tackling the issue of scale is the next major frontier for a new generation of economists.

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70 Best High School Science Fair Projects in Every Subject

Fire up the Bunsen burners!

The cool thing about high school science fair projects is that kids are old enough to tackle some pretty amazing concepts. Some science experiments for high school are just advanced versions of simpler projects they did when they were younger, with detailed calculations or fewer instructions. Other projects involve fire, chemicals, or other materials they couldn’t use before.

Note: Some of these projects were written as classroom labs but can be adapted to become science fair projects too. Just consider variables that you can change up, like materials or other parameters. That changes a classroom activity into a true scientific method experiment!

To make it easier to find the right high school science fair project idea for you, we’ve rated all the projects by difficulty and the materials needed:

Difficulty:

• Easy: Low or no-prep experiments you can do pretty much anytime
• Medium: These take a little more setup or a longer time to complete
• Advanced: Experiments like these take a fairly big commitment of time or effort
• Basic: Simple items you probably already have around the house
• Medium: Items that you might not already have but are easy to get your hands on
• Advanced: These require specialized or more expensive supplies to complete
• Biology and Life Sciences High School Science Fair Projects

Chemistry High School Science Fair Projects

Physics high school science fair projects, engineering high school stem fair projects, biology and life science high school science fair projects.

Explore the living world with these biology science project ideas, learning more about plants, animals, the environment, and much more.

Extract DNA from an onion

Difficulty: Medium / Materials: Medium

You don’t need a lot of supplies to perform this experiment, but it’s impressive nonetheless. Turn this into a science fair project by trying it with other fruits and vegetables too.

Re-create Mendel’s pea plant experiment

Difficulty: Medium / Materials: Medium ADVERTISEMENT

Gregor Mendel’s pea plant experiments were some of the first to explore inherited traits and genetics. Try your own cross-pollination experiments with fast-growing plants like peas or beans.

Make plants move with light

By this age, kids know that many plants move toward sunlight, a process known as phototropism. So high school science fair projects on this topic need to introduce variables into the process, like covering seedling parts with different materials to see the effects.

Test the 5-second rule

We’d all like to know the answer to this one: Is it really safe to eat food you’ve dropped on the floor? Design and conduct an experiment to find out (although we think we might already know the answer).

Find out if color affects taste

Just how interlinked are all our senses? Does the sight of food affect how it tastes? Find out with a fun food science fair project like this one!

See the effects of antibiotics on bacteria

Difficulty: Medium / Materials: Advanced

Bacteria can be divided into two groups: gram-positive and gram-negative. In this experiment, students first determine the two groups, then try the effects of various antibiotics on them. You can get a gram stain kit , bacillus cereus and rhodospirillum rubrum cultures, and antibiotic discs from Home Science Tools.

Learn more: Antibiotics Project at Home Science Tools

Witness the carbon cycle in action

Experiment with the effects of light on the carbon cycle. Make this science fair project even more interesting by adding some small aquatic animals like snails or fish into the mix.

Learn more: Carbon Cycle at Science Lessons That Rock

Look for cell mitosis in an onion

Cell mitosis (division) is actually easy to see in action when you look at onion root tips under a microscope. Students will be amazed to see science theory become science reality right before their eyes. Adapt this lab into a high school science fair project by applying the process to other organisms too.

Test the effects of disinfectants

Grow bacteria in a petri dish along with paper disks soaked in various antiseptics and disinfectants. You’ll be able to see which ones effectively inhibit bacteria growth.

Learn more: Effectiveness of Antiseptics and Disinfectants at Amy Brown Science

Pit hydroponics against soil

Growing vegetables without soil (hydroponics) is a popular trend, allowing people to garden just about anywhere.

More Life Sciences and Biology Science Fair Projects for High School

Use these questions and ideas to design your own experiment:

• Explore ways to prevent soil erosion.
• What are the most accurate methods of predicting various weather patterns?
• Try out various fertilization methods to find the best and safest way to increase crop yield.
• What’s the best way to prevent mold growth on food for long-term storage?
• Does exposure to smoke or other air pollutants affect plant growth?
• Compare the chemical and/or bacterial content of various water sources (bottled, tap, spring, well water, etc.).
• Explore ways to clean up after an oil spill on land or water.
• Conduct a wildlife field survey in a given area and compare it to results from previous surveys.
• Find a new use for plastic bottles or bags to keep them out of landfills.
• Devise a way to desalinate seawater and make it safe to drink.

Bunsen burners, beakers and test tubes, and the possibility of (controlled) explosions? No wonder chemistry is such a popular topic for high school science fair projects!

Break apart covalent bonds

Break the covalent bond of H 2 O into H and O with this simple experiment. You only need simple supplies for this one. Turn it into a science fair project by changing up the variables—does the temperature of the water matter? What happens if you try this with other liquids?

Learn more: Covalent Bonds at Teaching Without Chairs

Measure the calories in various foods

Are the calorie counts on your favorite snacks accurate? Build your own calorimeter and find out! This kit from Home Science Tools has all the supplies you’ll need.

Detect latent fingerprints

Forensic science is engrossing and can lead to important career opportunities too. Explore the chemistry needed to detect latent (invisible) fingerprints, just like they do for crime scenes!

Learn more: Fingerprints Project at Hub Pages

Use Alka-Seltzer to explore reaction rate

Difficulty: Easy / Materials: Easy

Tweak this basic concept to create a variety of high school chemistry science fair projects. Change the temperature, surface area, pressure, and more to see how reaction rates change.

Determine whether sports drinks provide more electrolytes than OJ

Are those pricey sports drinks really worth it? Try this experiment to find out. You’ll need some special equipment for this one; buy a complete kit at Home Science Tools .

Turn flames into a rainbow

You’ll need to get your hands on a few different chemicals for this experiment, but the wow factor will make it worth the effort! Make it a science project by seeing if different materials, air temperature, or other factors change the results.

Discover the size of a mole

The mole is a key concept in chemistry, so it’s important to ensure students really understand it. This experiment uses simple materials like salt and chalk to make an abstract concept more concrete. Make it a project by applying the same procedure to a variety of substances, or determining whether outside variables have an effect on the results.

Learn more: How Big Is a Mole? at Amy Brown Science

Cook up candy to learn mole and molecule calculations

This edible experiment lets students make their own peppermint hard candy while they calculate mass, moles, molecules, and formula weights. Tweak the formulas to create different types of candy and make this into a sweet science fair project!

Learn more: Candy Chemistry at Dunigan Science on TpT

Make soap to understand saponification

Take a closer look at an everyday item: soap! Use oils and other ingredients to make your own soap, learning about esters and saponification. Tinker with the formula to find one that fits a particular set of parameters.

Learn more: Saponification at Chemistry Solutions on TpT

Uncover the secrets of evaporation

Explore the factors that affect evaporation, then come up with ways to slow them down or speed them up for a simple science fair project.

Learn more: Evaporation at Science Projects

More Chemistry Science Fair Projects for High School

These questions and ideas can spark ideas for a unique experiment:

• Compare the properties of sugar and artificial sweeteners.
• Explore the impact of temperature, concentration, and seeding on crystal growth.
• Test various antacids on the market to find the most effective product.
• What is the optimum temperature for yeast production when baking bread from scratch?
• Compare the vitamin C content of various fruits and vegetables.
• How does temperature affect enzyme-catalyzed reactions?
• Investigate the effects of pH on an acid-base chemical reaction.
• Devise a new natural way to test pH levels (such as cabbage leaves).
• What’s the best way to slow down metal oxidation (the form of rust)?
• How do changes in ingredients and method affect the results of a baking recipe?

When you think of physics science projects for high school, the first thing that comes to mind is probably the classic build-a-bridge. But there are plenty of other ways for teens to get hands-on with physics concepts. Here are some to try.

Remove the air in a DIY vacuum chamber

You can use a vacuum chamber to do lots of cool high school science fair projects, but a ready-made one can be expensive. Try this project to make your own with basic supplies.

Learn more: Vacuum Chamber at Instructables

Put together a mini Tesla coil

Looking for a simple but showy high school science fair project? Build your own mini Tesla coil and wow the crowd!

Boil water in a paper cup

Logic tells us we shouldn’t set a paper cup over a heat source, right? Yet it’s actually possible to boil water in a paper cup without burning the cup up! Learn about heat transfer and thermal conductivity with this experiment. Go deeper by trying other liquids like honey to see what happens.

Build a better light bulb

Emulate Edison and build your own simple light bulb. You can turn this into a science fair project by experimenting with different types of materials for filaments.

Measure the speed of light—with your microwave

Grab an egg and head to your microwave for this surprisingly simple experiment. By measuring the distance between cooked portions of egg whites, you’ll be able to calculate the wavelength of the microwaves in your oven and, in turn, the speed of light.

Generate a Lichtenberg figure

See electricity in action when you generate and capture a Lichtenberg figure with polyethylene sheets, wood, or even acrylic and toner. Change the electrical intensity and materials to see what types of patterns you can create.

Learn more: Lichtenberg Figure at Science Notes

Explore the power of friction with sticky note pads

Difficulty: Medium / Materials: Basic

Ever try to pull a piece of paper out of the middle of a big stack? It’s harder than you think it would be! That’s due to the power of friction. In this experiment, students interleave the sheets of two sticky note pads, then measure how much weight it takes to pull them apart. The results are astonishing!

Build a cloud chamber to prove background radiation

Ready to dip your toe into particle physics? Learn about background radiation and build a cloud chamber to prove the existence of muons.

Measure the effect of temperature on resistance

This is a popular and classic science fair experiment in physics. You’ll need a few specialized supplies, but they’re pretty easy to find.

Learn more: Temperature and Resistance at Science Project

Launch the best bottle rocket

A basic bottle rocket is pretty easy to build, but it opens the door to lots of different science fair projects. Design a powerful launcher, alter the rocket so it flies higher or farther, or use only recycled materials for your flyer.

More Physics Science Fair Projects for High School

Design your own experiment in response to these questions and prompts.

• Determine the most efficient solar panel design and placement.
• What’s the best way to eliminate friction between two objects?
• Explore the best methods of insulating an object against heat loss.
• What effect does temperature have on batteries when stored for long periods of time?
• Test the effects of magnets or electromagnetic fields on plants or other living organisms.
• Determine the best angle and speed of a bat swing in baseball.
• What’s the best way to soundproof an area or reduce noise produced by an item?
• Explore methods for reducing air resistance in automotive design.
• Use the concepts of torque and rotation to perfect a golf swing.
• Compare the strength and durability of various building materials.

Many schools are changing up their science fairs to STEM fairs, to encourage students with an interest in engineering to participate. Many great engineering science fair projects start with a STEM challenge, like those shown here. Use these ideas to spark a full-blown project to build something new and amazing!

Construct a model maglev train

Maglev trains may just be the future of mass transportation. Build a model at home, and explore ways to implement the technology on a wider basis.

Learn more: Maglev Model Train at Supermagnete

Design a more efficient wind turbine

Wind energy is renewable, making it a good solution for the fossil fuel problem. For a smart science fair project, experiment to find the most efficient wind turbine design for a given situation.

Re-create Da Vinci’s flying machine

Da Vinci sketched several models of “flying machines” and hoped to soar through the sky. Do some research into his models and try to reconstruct one of your own.

Learn more: Da Vinci Flying Machine at Student Savvy

Design a heart-rate monitor

Smartwatches are ubiquitous these days, so pretty much anyone can wear a heart-rate monitor on their wrist. But do they work any better than one you can build yourself? Get the specialized items you need like the Arduino LilyPad Board on Amazon.

Race 3D printed cars

3D printers are a marvel of the modern era, and budding engineers should definitely learn to use them. Use Tinkercad or a similar program to design and print race cars that can support a defined weight, then see which can roll the fastest! (No 3D printer in your STEM lab? Check the local library. Many of them have 3D printers available for patrons to use.)

Learn more: 3D Printed Cars at Instructables

Grow veggies in a hydroponic garden

Hydroponics is the gardening wave of the future, making it easy to grow plants anywhere with minimal soil required. For a science fair STEM engineering challenge, design and construct your own hydroponic garden capable of growing vegetables to feed a family. This model is just one possible option.

Learn more: Hydroponics at Instructables

Grab items with a mechanical claw

Delve into robotics with this engineering project. This kit includes all the materials you need, with complete video instructions. Once you’ve built the basic structure, tinker around with the design to improve its strength, accuracy, or other traits.

Learn more: Hydraulic Claw at KiwiCo

Construct a crystal radio

Return to the good old days and build a radio from scratch. This makes a cool science fair project if you experiment with different types of materials for the antenna. It takes some specialized equipment, but fortunately, Home Science Tools has an all-in-one kit for this project.

Learn more: Crystal Radio at Scitoys.com

Build a burglar alarm

The challenge? Set up a system to alert you when someone has broken into your house or classroom. This can take any form students can dream up, and you can customize this STEM high school science experiment for multiple skill levels. Keep it simple with an alarm that makes a sound that can be heard from a specified distance. Or kick it up a notch and require the alarm system to send a notification to a cell phone, like the project at the link.

Learn more: Intruder Alarm at Instructables

Walk across a plastic bottle bridge

Balsa wood bridges are OK, but this plastic bottle bridge is really impressive! In fact, students can build all sorts of structures using the concept detailed at the link. It’s the ultimate upcycled STEM challenge!

Learn more: TrussFab Structures at Instructables

Looking for more science content? Check out the Best Science Websites for Middle and High School .

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Lab Examples

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AI Lamp Building Project

The discussion of AI and its implications for teaching and learning is pervasive. The future and present are intricately connected to how we leverage AI to enhance our daily interactions. AI involves teaching computers to gather information, analyze it, and make informed decisions. This lab introduces students to the fundamentals of AI, engineering design, and coding by guiding them through the process of building their own AI-powered lamp using an Arduino board.

Making My Own Medicine (Protein Synthesis)

Our capacity to build proteins in our body is an important component of our health. We make protein to serve important purposes in our lives. This lesson examines two very important protein synthesis processes. First, our bodies make Enzymes to help the chemical activities in our bodies. Enzymes speed up chemical reactions and lower the activation energy on chemical processes. Lactase in the enzyme we produce that allows us to process Lactose sugar in milk. This is an important enzyme, but many people have DNA allele patterns that do not allow them to make this protein. Second, antibodies are proteins that fight disease. Both vaccines and our natural response to being exposed to the virus rely on our bodies making proteins to make our own medicine.

A Tornado In a Bottle

Air can be among the most powerful substances in existence. The powerful results of air movements can lead to hurricanes and tornados. The question is how does a hurricane work. The mixture of circular rapid air movements and a voice of space in between allows the air movement to be enhance and powerful by reducing resistance. This lesson plan and laboratory will help provide students a model for how tornados function.

Square Bubbles – Surface Tension

Who doesn’t love bubbles! The things with bubbles is that they offer a quick and easy way to view how electrostatic forces impact small interactions. In the bubbles we see, there is an interesting effect, where the maximum distance of the surface tension is a globe. However, have you ever seen bubbles in different shapes. This lesson explores how making square bubbles might be an option.

Smoke Rings – Air Vortex Movements

A vortex ring is a circular shaped ring of spinning gasses that move together as a unit. A vortex ring can happen in liquid or gasses, but are rarely seen because they happen inside of liquids or gases. When a vortex ring happens inside of suspended particles—as in the smoke rings which are often produced by smoke they can be seen. Visible vortex rings can also be formed by the firing of certain artillery, in mushroom clouds, and in microbursts.[1][2]

A vortex ring usually tends to move in a direction that is perpendicular to the plane of the ring and such that the inner edge of the ring moves faster forward than the outer edge. Within a stationary body of fluid, a vortex ring can travel for relatively long distance, carrying the spinning fluid with it.

Sling Shot Rockets

Slingshot physics involves the use of stored elastic energy to shoot a something at a high speed. This elastic energy comes from rubber bands which are specially made for slingshots. This energy is provided initially by the muscle energy of the slingshot operator. One of the goals of a slingshot is to fire the projectile at the greatest speed possible. To do this two basic physics conditions must be satisfied.

The Science of Making Slime

Admit it, slime is simply awesome! Kids will make slime at home in their spare time, but what it the science of this uber relaxing materials. This lesson prepares your students to understand how substances engage in the formation of Polymers. The discussions of polymers can start at slime and explore environmental justice. Enjoy this engaging interpretation of slime.

Genetics Through Reebops

Genetics plays an important role in our life. How often have you wondered why someone’s brother or sister looks dramatically different from them? Our genes operate by a set of rules that we should talk about more often. Each parent has genes that split in half, scramble and then replicate. Even after that there are environmental factors that cause the genes to work. This lab uses simply marshmallows to teach this idea.

Make Makey – Introducing Engineering Through Circuts

Circuits are central to how we interact with the world You need a closed path, or closed circuit, to get electric current to flow. If there’s a break anywhere in the path where electricity travels, you have an open circuit, and the current stops flowing — and the metal atoms in the wire quickly settle down to a peaceful, electrically neutral existence. This lesson teaches this concept in a simple and engaging way.

A closed circuit allows current to flow, but an open circuit leaves electrons stranded. Picture a gallon of water flowing through an open pipe. The water will flow for a short time but then stop when all the water exits the pipe. If you pump water through a closed pipe system, the water will continue to flow as long as you keep forcing it to move.

The Science of Ice Cream

To make ice cream, the ingredients—typically milk (or half and half), sugar and vanilla extract—need to be cooled down. One way to do this is by using salt. If you live in a cold climate, you may have seen trucks spreading salt and sand on the streets in the wintertime to prevent roads from getting slick after snow or ice. Why is this? The salt lowers the temperature at which water freezes, so with salt ice will melt even when the temperature is below the normal freezing point of water. This is an easy way to teach phase change.

Air Powered Hover Crafts

All of our most widely used modes of transportation rely on Friction to move. Airplanes, Cars, Boats, Bikes, and Skateboards all rely on generating friction against something. In the case of the Airplane, it is the friction between the air and the airplane jets. For the Car, Bike, and Skateboards it is the friction between the tires and the ground. If the tires have a good grip (another word for friction) cars, bikes, and skateboards can travel. So what would happen if a care or skateboard did not have a good grip?

A Cloud In a Bottle

This lesson is a great way to teach young people about gas laws and the water cycle. Using a small bottle and an air pump you can create the air pressure differential that you need to cause water droplets to move from their gas form to the liquid form of a cloud. This simple lab will teach your students to understand the states of water during the water cycle and how air pressure influence that change.

Home Projector

Students create their very own projector in this lesson to study optics.

Goo Polymer

Polymers are interesting substances that can teach students about material science. In this lesson, students create and explore the attributes of polymers.

Floating Ping Pong Ball

Students explore Bernoulli’s Principle in relation to atmospheric pressure and volume in this lesson.

Fidget Spinners

Inertia and centripetal force are hard topics for students to learn. Through this lesson, students will explore these topics in relation to changing designs of Fidget Spinners with different weights (mass).

Fan Propelled Car

By making cars that are propelled by a fan, students in this lesson learn about motion, force, and circuits. This lesson also leverages engineering design skills for students to iteratively think about how some designs work ‘better’ than others.

Egg in a Bottle

By exploring the influence pressure has on a closed system, students in this lesson will gain a better understanding of air pressure.

Digestive System

Students in this lesson study a modelled process of the digestive system. With this primer, students can go on to study more nuanced processes that happen in the body.

Cloud in a Bottle

By conducting an observation-based exploration of the effects that pressure has on condensation, students in this lesson gain a better understanding of the relationship between pressure and the phase change from gas to liquid.

Cartesian Diver

By observing changes in density, students in this lesson gain a more complex understanding of air pressure and density.

Can Crushing

By making observations about the impact temperature has on heated gases, students in this lesson are provided with a phenomenon-based learning experience to gain a more complex understanding of gases in relation to temperature and volume.

Boo Bubbles

Sublimation is a rare, yet powerful, phase change. In this lesson, students explore this phenomenon and gain first-hand evidence to discuss and analyze for a more comprehensive view of this phase change.

Air Pressure Rockets

One of the challenges of teaching science involves getting students to see the value of micro level phenomenon. “Air” is among the things that is most challenging to teach. Air pressure impacts us everyday, but can be hard to understand because it is largely invisible. This lesson uses the building and launching of air pressure powered rockets as a means to give students an understanding of how air pressure impacts our world.

The Flint Water Crisis – The Water Cycle

The basic concept of the water cycle can be one that is hard for students to connect to larger sociocultural issues. In helping students set a sense of how the water cycle matters to their lives, this lesson uses the issues of The Flint Water Cycle to help students understand how the water cycle is a vital component in providing clean water for everyone. This lesson includes slides, lesson plans, and handouts to be used for instruction. All of the lessons are available in downloadable and accessible in MS Word and Powerpoint formats that you can adjust.

Sports Science – The Physics of Landing (Newton’s 2nd Law)

Sports and dance provide a wealth of opportunities to learn science. This introductory physics  lesson explores the physics of landing. Many young people experience traumatic injuries that are the result of landing from a jump. The impact of their bodies hitting the ground after accelerating from a height magnifies the weight of their body onto…

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Types of Experiment: Overview

Last updated 6 Sept 2022

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Different types of methods are used in research, which loosely fall into 1 of 2 categories.

Experimental (Laboratory, Field & Natural) & N on experimental ( correlations, observations, interviews, questionnaires and case studies).

All the three types of experiments have characteristics in common. They all have:

• an independent variable (I.V.) which is manipulated or a naturally occurring variable
• a dependent variable (D.V.) which is measured
• there will be at least two conditions in which participants produce data.

Note – natural and quasi experiments are often used synonymously but are not strictly the same, as with quasi experiments participants cannot be randomly assigned, so rather than there being a condition there is a condition.

Laboratory Experiments

These are conducted under controlled conditions, in which the researcher deliberately changes something (I.V.) to see the effect of this on something else (D.V.).

Control – lab experiments have a high degree of control over the environment & other extraneous variables which means that the researcher can accurately assess the effects of the I.V, so it has higher internal validity.

Replicable – due to the researcher’s high levels of control, research procedures can be repeated so that the reliability of results can be checked.

Limitations

Lacks ecological validity – due to the involvement of the researcher in manipulating and controlling variables, findings cannot be easily generalised to other (real life) settings, resulting in poor external validity.

Field Experiments

These are carried out in a natural setting, in which the researcher manipulates something (I.V.) to see the effect of this on something else (D.V.).

Validity – field experiments have some degree of control but also are conducted in a natural environment, so can be seen to have reasonable internal and external validity.

Less control than lab experiments and therefore extraneous variables are more likely to distort findings and so internal validity is likely to be lower.

Natural / Quasi Experiments

These are typically carried out in a natural setting, in which the researcher measures the effect of something which is to see the effect of this on something else (D.V.). Note that in this case there is no deliberate manipulation of a variable; this already naturally changing, which means the research is merely measuring the effect of something that is already happening.

High ecological validity – due to the lack of involvement of the researcher; variables are naturally occurring so findings can be easily generalised to other (real life) settings, resulting in high external validity.

Lack of control – natural experiments have no control over the environment & other extraneous variables which means that the researcher cannot always accurately assess the effects of the I.V, so it has low internal validity.

Not replicable – due to the researcher’s lack of control, research procedures cannot be repeated so that the reliability of results cannot be checked.

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• Natural Experiment
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130 Laboratory Apparatus And Their Uses (With Pictures)

A laboratory is a special room or place that is equipped to facilitate scientific experiments, observations and for teaching science. Laboratory apparatus refers to the various tools, equipment, and instruments used in scientific research, experimentation, and analysis within a laboratory setting. These tools are essential for conducting experiments, measuring and analyzing data, and ensuring the accuracy and reliability of scientific results.

Some of the laboratory apparatus are used as a source of heat, for safety, for making observations and for measurement of variables such as voltage, temperature, volume, time and mass.

There are apparatus that are used in general laboratory experiments while others serve specific in experiments. They are also made from materials that are resistant to chemical reactions and corrosion. Common materials include glass, stainless steel, and various types of plastics.

It is important to note that most of the apparatus that are used as containers or reaction vessels are made of transparent glass or plastic and may come in different sizes. Let us talk about Laboratory apparatus in three categories: Basic Apparatus, Safety Apparatus , General Apparatus and Specialized Apparatus

Here is a list of 130 laboratory apparatus / Equipment

General equipment/apparatus that are found in almost all laboratories:

• Alcohol burner
• Bunsen burner
• Burette clamp
• Buchner funnel
• Balance scale
• Conical or titration flask
• Crucible tong
• china dish (Evaporating Dish)
• Crucible with cover
• Clay Triangles
• Dry-cell battery
• Dissecting set
• Erlenmeyer flask
• Flat bottomed flask
• Filter paper
• Friability tester
• Glass funnel
• Glass tubing
• Litmus paper
• Measuring cylinders

Mortar and pestle

• Measuring flasks
• No of weights
• Petri dishes
• Rubber stopper
• Reagent bottle
• Rubber tubing
• Stirring rod
• Separatory funnel
• Stethoscope
• Speedometer
• Test tube rack
• Tripod stand
• Test tube holder
• Test tube stand
• Test tube brush
• Tuning fork

Thermometer

• Wash bottle
• Watch glass

Others Laboratory Apparatus or Equipment

• Analytical balance
• Atomic absorption spectrometer
• BOD incubator
• Chromatography column
• Cryogenic freezer
• Colorimeter
• Conductivity meter
• Dewar flask
• Distillation apparatus
• Electrophoresis chamber
• Flame photometer
• Gas chromatograph
• Geiger-Muller counter
• Inoculating loop
• Inverted microscope
• Kjeldahl apparatus
• Laboratory oven
• Laboratory refrigerator
• Laser spectrometer
• Magnetic stirrer
• Mass spectrometer

Microcentrifuge

• NMR spectrometer
• Orbital shaker
• Oscilloscope
• Particle counter
• PCR machine (Polymerase Chain Reaction)
• Peristaltic pump
• pH electrode
• Pipette filler
• Polarimeter
• Refractometer
• Rotary evaporator

Spectrophotometer

• Syringe filter

Ultracentrifuge

• UV-Vis spectrophotometer
• Vortex mixer
• X-ray diffraction machine
• YSI meter (for measuring dissolved oxygen)
• Gas syringe
• Melting point apparatus
• Infrared spectrometer
• Particle size analyzer
• Bacterial incubator
• Thermal cycler (PCR machine)
• Gas manifold
• Conductivity cell
• Reflux condenser
• Freeze dryer
• Inert gas chamber
• Ultrasonic cleaner
• Atomic force microscope (AFM)
• Gas generator
• Digital pH meter
• Atomic emission spectrometer
• Magnetic balance
• Tensiometer
• Ultraviolet lamp
• Inoculation needle
• Rotary shaker
• Autotitrator
• Freeze-thaw chamber
• Gel documentation system
• Pipette tips
• Rotary vane pump
• Vacuum desiccator
• Gas chromatography-mass spectrometry (GC-MS)
• High-performance liquid chromatography (HPLC) system
• Inverted fluorescence microscope

Basic Laboratory Apparatus

Bunsen Burner

This is a piece of apparatus that is used as a safe source of heat in laboratories using a single gas flame. A Bunsen has an inlet that is usually connected to an external source of laboratory gas by rubber tubing. Its flame is used not only for heating, but for combustion and sterilizing objects too.

This is an apparatus that is used to give finer details of small objects that would otherwise not be seen by the naked eye or a hand lens. It does so by magnifying objects up to thousands times their original size. There exist two main variants of a microscope namely; a light microscope and an electron microscope

Weighing Balances

These are used to weigh the mass of substances in a laboratory. There are different types of weigh balances such as beam balance, spring balance, top pan balances and electronic balances.

Watches and clocks

These are apparatus for measuring time. Stop watches and stop clocks are the most commonly used for accurately measuring time during experiments.

When it comes to measuring the voltage between any two points, nothing does the job better than a voltmeter. It is normally connected in parallel with a device so as to measure its voltage.

Beakers serve a wide range of purposes. Calibrated beakers are used to measure approximate volumes of liquids, holding both liquids and solids and heating them when necessary. In addition to that, beakers may be used for stirring and mixing different substances in a laboratory.

Volumetric Flask

Volumetric flasks come in handy when fairly accurate and precise volumes of liquids are required. They can as well be used for dilution when preparing standard solutions.

This is an apparatus that is used for adding fairly accurate volumes of liquids up to nearly 0.01ml especially during titrations. It is fitted with an adjustable stopcock that regulates the amount of liquid that is released at a time.

A pipette (sometimes spelled pipet ) is a laboratory tool commonly used in chemistry, biology and medicine to transport a measured volume of liquid, often as a media dispenser. Pipettes come in several designs for various purposes with differing levels of accuracy and precision, from single piece glass pipettes to more complex adjustable or electronic pipettes.

A thermometer is used to measure the degree of hotness or coldness of a substance. They come in different types such as maximum and minimum thermometer, clinical thermometer and general purpose thermometers.

Flat-bottomed Flask

It is used for general laboratory experiments. A flat-bottomed flask can be used to collect, measure and hold liquids. They may as well be used for heating substances and mixing solutions in a laboratory.

Filter Funnel

Filter funnels are used for delivering different amounts of liquids carefully into holding apparatus. It can also be used together with a filter paper to separate finer solid substances from liquids. They vary in sizes and material from which they are built from depending on the purpose for which they are needed.

A desiccator is a sealable storage unit used for drying or keeping moisture sensitive substances free from moisture. There are two main types of desiccators that are made from polycarbonate or polypropylene material. These are; vacuum desiccators and non-vacuum desiccators.

Reagent Bottle

Reagent bottle or media bottle refers to containers used for storing and sampling both liquid and solid bench reagents in a variety of laboratory experiments. Most reagent bottles are made of glass or plastic.

A spatula is a broad, flat, hand-held blade apparatus that is used for spreading, mixing and scooping solid substances. The do come in various shapes and sizes.

Dropping funnel

This is an apparatus that is used to add controlled amounts of liquids into reaction vessels more so when the reaction is expected to be too vigorous if large amounts of the reagent are used at a go.

These apparatus are used to prepare solid reagents into a paste or powder by grinding, crushing or pounding them. They are mostly made of metal, wood, nonporous marble and granite material.

Test-tube is a tubular apparatus that is used for general laboratory experiments. They may be used to hold and compare chemical substances. In addition to that, test-tubes can be used to mix liquid substances and heating small chemical samples.

This is a heat resistant apparatus used when heating solid substances under high temperatures. It is commonly made of porcelain as it is resistant to heat when strongly heating solid substances.

Safety Apparatus

It is essential for any laboratory to have a wide range of safety equipment at its disposal. They are intended to keep laboratory users and their working environment safe from injuries, corrosive chemicals, poisonous fumes or accidental fires while carrying out experiments. The list of protective gear ranges from:

Safety Goggles

• Purpose : Safety goggles provide eye protection by shielding the eyes from chemical splashes, flying debris, and hazardous fumes or liquids.
• Usage : They are essential in laboratories, workshops, and industrial environments where eye hazards are present. Goggles should fit snugly to prevent entry of harmful substances.

Disposable Coveralls and Aprons

• Purpose : Disposable coveralls and aprons are protective garments that shield the body and clothing from chemical spills, contaminants, or biohazards.
• Usage : Workers wear these items to prevent exposure to hazardous substances, ensuring both personal safety and contamination control.

Disposable Latex Gloves

• Purpose : Disposable latex gloves are worn to protect the hands from contact with chemicals, biological materials, and contaminants.
• Usage : These gloves are common in laboratories, healthcare settings, and industries where hand protection is essential. They reduce the risk of skin contact and contamination.

Plastic Bags

• Purpose : Plastic bags are used for containing and disposing of hazardous waste materials, contaminated items, or biohazards.
• Usage : In laboratories and medical facilities, plastic bags are crucial for safe disposal of waste materials and maintaining cleanliness.
• Purpose : Gas masks protect the respiratory system by filtering out harmful gases, fumes, and particulates from the air.
• Usage : Gas masks are used in environments where there is a risk of exposure to toxic or hazardous airborne substances, such as during chemical spills or in industrial settings.

Fire Blanket or Extinguisher

• Purpose : Fire blankets and extinguishers are used to suppress fires in emergency situations.
• Usage : In the event of a small fire, fire blankets can be used to smother flames. Fire extinguishers are designed to spray fire-suppressing agents to extinguish fires safely.

First Aid Kits

• Purpose : First aid kits contain essential medical supplies and equipment to provide immediate medical assistance in case of injuries or accidents.
• Usage : First aid kits are located in workplaces, laboratories, and public areas to address injuries, burns, cuts, and other medical emergencies.

Plumbed Eyewash Units

• Purpose : Plumbed eyewash units provide a continuous flow of water to rinse and flush the eyes in case of chemical exposure.
• Usage : Eyewash stations are installed in laboratories and workplaces where hazardous chemicals are handled, ensuring prompt eye irrigation in case of accidents.

Flammable Safe

• Purpose : A flammable safe is designed to store flammable liquids and materials safely, preventing ignition or explosions.
• Usage : These safes are essential for fire safety in laboratories, where flammable substances are often used or stored.

Chemical Spill Kits

• Purpose : Chemical spill kits contain materials and equipment for responding to chemical spills, containing and neutralizing the spill, and protecting personnel.
• Usage : In laboratory environments, chemical spill kits are crucial to mitigate the effects of accidental chemical spills, preventing harm and environmental damage.

Plastic Dust Pan and Scoop

• Purpose : Plastic dust pans and scoops are used to collect and safely dispose of solid chemical spills, dust, or debris.
• Usage : They are essential tools for cleaning up laboratory or industrial workspaces, ensuring the safe removal of potentially hazardous materials.

General Laboratory Apparatus

• Purpose : Microscopes are used to magnify and visualize objects or specimens that are too small to be seen with the naked eye. They are essential tools in fields such as biology, microbiology, and materials science.
• Components : A typical microscope consists of an eyepiece, objective lenses with varying magnification powers, a stage for holding the sample, and a light source for illumination.
• Usage : Researchers place a sample on the stage, adjust the focus using the fine and coarse adjustment knobs, and select the appropriate objective lens for the desired magnification.
• Purpose : Bunsen burners are used for heating, sterilizing, and flame-related experiments in the laboratory. They provide a consistent open flame.
• Components : A Bunsen burner has a gas inlet, an adjustable air vent, and a flame nozzle.
• Usage : The flame intensity and type (oxidizing or reducing) can be adjusted by controlling the air mixture. Bunsen burners are commonly used in chemistry for tasks like heating solutions and sterilizing equipment.
• Purpose : Beakers are used for holding, mixing, and heating liquids. They come in various sizes and are a staple in laboratories for general-purpose tasks.
• Features : Beakers typically have volume markings, a spout for pouring, and a flat bottom.
• Usage : Beakers are versatile containers, but they are not designed for precise measurements. They are often used for mixing solutions, conducting simple reactions, or as a vessel for holding liquids during experiments.

Erlenmeyer Flask

• Purpose : Erlenmeyer flasks are conical-shaped containers with narrow necks. They are used for mixing, heating, and storing liquids, particularly when you need to prevent splashes and evaporation.
• Features : Erlenmeyer flasks have volume markings and can be fitted with stoppers or caps.
• Usage : They are commonly used for titration, as reaction vessels for chemical reactions, or as containers for cultures in microbiology.
• Purpose : Test tubes are small, cylindrical containers used for holding, heating, or mixing small quantities of liquids or solids.
• Features : They come in various sizes, and some have screw caps or stoppers.
• Usage : Test tubes are versatile and widely used in chemical and biological experiments, such as holding reagents, conducting small-scale reactions, or culturing microorganisms.

Graduated Cylinder

• Purpose : Graduated cylinders are used to accurately measure the volume of liquids. They have volume markings for precise measurements.
• Features : They have a narrow, graduated scale and a spout for pouring.
• Usage : Graduated cylinders are essential for preparing solutions with precise volumes and measuring liquids accurately.
• Purpose : Pipettes are used for precise measurement and transfer of small volumes of liquid. They come in various types, including micropipettes for ultra-precise measurements.
• Features : Pipettes have a calibrated scale for volume selection, and some are disposable while others are reusable and require calibration.
• Usage : Pipettes are commonly used in biology, chemistry, and analytical chemistry for tasks like transferring samples, making dilutions, and preparing standards.
• Purpose : Burets are used for precise titrations in analytical chemistry. They allow for controlled dispensing of a titrant into a solution.
• Features : Burets are long, graduated tubes with a stopcock at the bottom for controlling the flow of liquid.
• Usage : Burets are essential in titration experiments where the volume of titrant needed to reach a specific endpoint is critical.

Florence Flask

• Purpose : Florence flasks are used for boiling and heating liquids. They have a round bottom that allows for even heating.
• Features : They typically have a long neck and are often used with a rubber stopper or glass tubing for attaching other equipment.
• Usage : Florence flasks are commonly used in distillation setups and refluxing reactions.
• Purpose : Volumetric flasks are used for preparing solutions with precise volumes. They come in various sizes and are designed to hold a specific volume when filled to the calibration mark.
• Features : Volumetric flasks have a long neck with a single calibration mark on the neck.
• Usage : They are crucial for preparing accurate and known concentrations of solutions, such as standards used in chemical analysis.
• Purpose : Funnels are used for transferring liquids or fine-grained substances from one container to another. They help avoid spills and maintain accuracy.
• Features : Funnels have a wide, tapered opening at the top and a narrow spout at the bottom.
• Usage : Funnels are essential for tasks like filtering solutions, adding reagents to containers, and filling smaller vessels without spillage.
• Purpose : Crucibles are heat-resistant containers used for heating substances to high temperatures. They are typically made of porcelain or ceramic materials.
• Features : They have a small, cylindrical shape and come with lids.
• Usage : Crucibles are commonly used for processes such as heating samples to dryness, ashing organic materials, and performing high-temperature reactions.
• Purpose : Tongs are used for safely handling hot glassware and objects in the laboratory.
• Features : They have long, pincer-like arms with insulated handles.
• Usage : Tongs are essential for gripping and moving hot crucibles, beakers, flasks, and other equipment without direct contact.

Evaporating Dish

• Purpose : Evaporating dishes are shallow, flat-bottomed containers used for evaporating solvents from solutions.
• Features : They are typically made of porcelain or borosilicate glass and are resistant to high temperatures.
• Usage : Evaporating dishes are used to concentrate solutions by gently heating them to drive off the solvent, leaving behind the solute.
• Purpose : Desiccators are sealed containers used to store substances in a dry environment, protecting them from moisture.
• Features : They have an airtight seal and often contain a drying agent like silica gel or calcium chloride.
• Usage : Desiccators are used for storing moisture-sensitive materials, such as hygroscopic chemicals or humidity-sensitive samples.
• Purpose : Centrifuges are used for separating components of a liquid or mixture based on density by spinning them at high speeds.
• Features : They have a rotor that holds sample tubes and can generate centrifugal forces.
• Usage : Centrifuges are used in various fields, including biology, chemistry, and clinical laboratories, for tasks like separating cells, proteins, and particles from liquids.
• Purpose : A hot plate is an electric heating device used to heat glassware or other containers, usually with a flat, heated surface.
• Usage : Hot plates are commonly used for tasks such as boiling water, heating solutions, or conducting reactions that require controlled and consistent temperature.

Magnetic Stirrer

• Purpose : Magnetic stirrers use a rotating magnetic field to create a vortex in a liquid, which stirs or mixes the contents of a container without the need for a physical stirring rod.
• Usage : They are used for even and continuous mixing of solutions, particularly in chemistry and biology experiments.
• Purpose : A pH meter measures the acidity or alkalinity (pH) of a solution. It provides a numerical pH value based on the concentration of hydrogen ions in the solution.
• Usage : pH meters are vital in various fields, including chemistry, biology, and environmental science, for accurately determining pH levels in solutions.
• Purpose : A spectrophotometer measures the absorption or transmission of light by a substance across a range of wavelengths. It is used for quantitative analysis of substances in a solution.
• Usage : Spectrophotometers are essential for applications like quantifying the concentration of a solute, identifying compounds, and studying chemical reactions.
• Purpose : Autoclaves are pressurized and high-temperature chambers used to sterilize equipment, media, and samples in a laboratory.
• Usage : Autoclaves are crucial for maintaining sterile conditions in microbiology, biotechnology, and medical laboratories.
• Purpose : Incubators provide a controlled environment with regulated temperature and humidity for the growth of microorganisms or the incubation of biological samples.
• Usage : They are essential for cell culture, microbial culturing, and other biological research applications.

Refrigerator/Freezer

• Purpose : Laboratory refrigerators and freezers are used to store temperature-sensitive reagents, samples, and biological materials at controlled temperatures.
• Usage : They are crucial for preserving the integrity and stability of materials, such as enzymes, vaccines, and DNA.
• Purpose : A microcentrifuge is a high-speed centrifuge designed to spin small volumes of liquid at very high speeds, separating components based on density.
• Usage : They are used for tasks such as pelleting cells or particles, separating DNA, and isolating proteins.

Gel Electrophoresis Apparatus

• Purpose : Gel electrophoresis apparatus is used to separate and analyze DNA, RNA, or proteins based on their size and charge.
• Usage : It is a fundamental tool in molecular biology for tasks like DNA fingerprinting, DNA fragment separation, and protein analysis.

PCR Machine (Polymerase Chain Reaction)

• Purpose : A PCR machine amplifies specific DNA sequences through repeated cycles of heating and cooling.
• Usage : PCR machines are vital in molecular biology for DNA amplification, genetic testing, and DNA sequencing.

Spectrofluorometer

• Purpose : A spectrofluorometer measures the fluorescence emission spectra of substances when excited by light of a specific wavelength.
• Components : It typically includes a light source, monochromator, sample holder, and photodetector.
• Usage : Spectrofluorometers are used to study the fluorescence properties of compounds, such as fluorescent dyes, proteins, and biomolecules, in chemical and biological research. They are crucial for characterizing fluorescent materials and quantifying their concentrations.

Distillation Apparatus :

• Purpose : Distillation apparatus is used to separate components of a liquid mixture based on their different boiling points.
• Components : It comprises a boiling flask, distillation head, condenser, receiver flask, and a heat source.
• Usage : Distillation is a common technique for purifying or separating liquids in chemistry, including the production of distilled water or the isolation of pure chemicals.

Condenser :

• Purpose : A condenser cools and condenses vaporized substances back into a liquid state, typically in distillation setups.
• Components : It includes a coiled or straight glass tube through which cooling water circulates.
• Usage : Condensers are essential components in distillation and reflux processes, allowing the collection of purified liquids.
• Purpose : A spatula is a small, flat utensil used for transferring solid chemicals or powders.
• Materials : Spatulas are typically made of stainless steel, plastic, or glass.
• Usage : Spatulas are commonly used to weigh or transfer small quantities of solids in chemistry and analytical work. They come in various shapes and sizes to suit different applications.

Pipette Bulb :

• Purpose : A pipette bulb is a rubber bulb that attaches to a pipette for creating suction and facilitating liquid transfer.
• Usage : Pipette bulbs are used to draw liquids into pipettes accurately. They provide a manual means of controlling the volume of liquid aspirated and dispensed.

Buchner Funnel :

• Purpose : A Buchner funnel is used in vacuum filtration to separate solids from liquids. It contains a perforated plate and a vacuum source to pull liquid through.
• Components : It includes a funnel with a flat, porous base and a conical flask or vacuum flask below it.
• Usage : Buchner funnels are commonly used for isolating precipitates or collecting solid residues from liquid suspensions. Vacuum filtration speeds up the process.

Mortar and Pestle :

• Purpose : A mortar and pestle are tools used for grinding, crushing, and mixing solid materials into fine powders or pastes.
• Materials : Mortars are typically made of ceramic, glass, or stone, while the pestle is a heavy rod.
• Usage : They are widely used in chemistry and biology for tasks such as sample preparation, grinding chemicals, or creating homogenous mixtures.

Stirring Rod :

• Purpose : A stirring rod is a long, thin glass or plastic rod used for manually stirring liquids or suspensions.
• Usage : Stirring rods are commonly used for mixing solutions, ensuring homogeneity in reactions, and transferring small quantities of liquid.

Thermometer :

• Purpose : A thermometer measures temperature. Laboratory thermometers are designed for accuracy and precision.
• Types : There are various types of thermometers, including mercury-in-glass, digital, and infrared.
• Usage : Thermometers are used in various applications, from monitoring reaction temperatures to maintaining controlled conditions in incubators and ovens.

Melting Point Apparatus

• Purpose : A melting point apparatus is used to determine the melting point of a solid substance, which is a characteristic property.
• Components : It includes a heating block, sample holder, and a magnifying lens.
• Usage : It is employed in chemistry for identifying and verifying the purity of organic compounds by comparing their melting points to known standards.
• Purpose : A Petri dish is a shallow, flat, cylindrical container with a lid, used for culturing and observing microorganisms and small specimens.
• Materials : Petri dishes are typically made of glass or clear plastic.
• Usage : Petri dishes are widely used in microbiology for bacterial and fungal cultures and in various biological experiments, including bacterial plate counts and tissue culture.

Separatory Funnel

• Purpose : A separatory funnel is used to separate immiscible liquids or liquids with different densities.
• Components : It has a conical shape with a stopcock at the bottom for controlled liquid drainage.
• Usage : Separatory funnels are commonly used in chemistry for processes like liquid-liquid extraction, purification, and phase separations.

Gas Burette

• Purpose : A gas burette is a graduated glass tube used to measure the volume of gases in chemical experiments.
• Usage : It is employed in experiments where precise gas volume measurements are necessary, such as in gas collection or stoichiometry experiments.

Hemocytometer

• Purpose : A hemocytometer is a special counting chamber used for manually counting blood cells and other small particles under a microscope.
• Components : It consists of a thick glass slide with a grid etched on it and a coverslip.
• Usage : Hemocytometers are essential in clinical laboratories and research for accurate cell counting in applications like blood cell analysis and cell culture.

Vortex Mixer :

• Purpose : A vortex mixer is a high-speed mixer that creates a vortex in a liquid sample to mix its contents.
• Components : It has a motorized base with a rubber cup or platform for holding sample tubes.
• Usage : Vortex mixers are used to quickly and thoroughly mix liquids, suspensions, and small samples in test tubes or microcentrifuge tubes.

Ultrasonic Cleaner

• Purpose : An ultrasonic cleaner uses high-frequency sound waves to remove contaminants from objects immersed in a liquid.
• Components : It consists of a tank filled with cleaning solution, ultrasonic transducers, and a timer.
• Usage : Ultrasonic cleaners are commonly used to clean laboratory glassware, small parts, and delicate instruments, ensuring thorough cleaning without manual scrubbing.

TLC Plate (Thin-Layer Chromatography Plate)

• Purpose : TLC is a chromatography technique used to separate and analyze mixtures. A TLC plate is a flat, thin sheet coated with a stationary phase for this purpose.
• Components : The plate is typically made of glass or plastic with a thin layer of absorbent material (such as silica gel) as the stationary phase.
• Usage : Researchers spot or apply a sample mixture at the base of the plate, which is then placed in a solvent chamber. As the solvent rises through capillary action, it carries the components of the mixture, allowing for separation based on their interactions with the stationary phase.

Rotary Evaporator

• Purpose : A rotary evaporator is used for the gentle and efficient removal of solvents from liquid mixtures, typically in chemical synthesis or sample preparation.
• Components : It consists of a rotating flask, a water bath or heating bath, a vacuum system, and a condenser.
• Usage : The sample is placed in the rotating flask and heated under vacuum. The reduced pressure lowers the boiling point of the solvent, facilitating its removal. The condenser then collects the vapor, which condenses back into a liquid.
• Purpose : A viscometer measures the viscosity of a fluid, which is a measure of its resistance to flow.
• Types : There are various types of viscometers, including capillary viscometers, rotational viscometers, and falling ball viscometers.
• Usage : Viscometers are used in industries like pharmaceuticals, food, and oil to determine fluid properties and quality control. They are also employed in research to study the flow behavior of fluids.
• Purpose : A hydrometer is an instrument used to measure the specific gravity (density) of a liquid.
• Components : It typically consists of a graduated glass tube with a weighted bulb at the bottom.
• Usage : Hydrometers are commonly used in various applications, such as in breweries to measure the alcohol content of beer, in laboratories for density measurements, and in the petroleum industry for testing fuel quality.

Microtome :

• Purpose : A microtome is a precision instrument used to cut thin slices (sections) of biological or material samples for microscopy or analysis.
• Types : There are different types of microtomes, including rotary microtomes, cryostats, and ultramicrotomes.
• Usage : Microtomes are vital in histology, biology, and material science for preparing samples for examination under microscopes or other analytical instruments.

Autotitrator (Automatic Titrator)

• Purpose : An autotitrator is an automated titration instrument used for precise and efficient chemical analysis, especially in determining the concentration of analytes in a solution.
• Components : It consists of a burette, a titration vessel, a pH electrode, and automated control systems.
• Usage : Autotitrators perform titrations accurately and with reduced human error. They are widely used in analytical chemistry, quality control, and environmental monitoring.

Gas Syringe

• Purpose : A gas syringe is a device used to measure and transfer known volumes of gases in laboratory experiments.
• Components : It typically consists of a cylindrical glass tube with a plunger or piston.
• Usage : Gas syringes are used in experiments where precise gas volumes are required, such as in gas collection, gas stoichiometry, and determining gas properties like molar mass or density.

Specialized Laboratory Apparatus/Equipment

Nuclear Magnetic Resonance (NMR) Spectrometer :

• Purpose : An NMR spectrometer is used for the analysis of organic compounds’ structure and properties. It measures the nuclear magnetic resonance of atomic nuclei.
• Components : It consists of a powerful magnet, radiofrequency (RF) transmitter and receiver, and a sample holder.
• Usage : Researchers place a sample in the magnet, which aligns the nuclei with the magnetic field. RF pulses are applied, and the resulting signals provide information about the chemical environment and connectivity of atoms in the sample.

Scanning Electron Microscope (SEM)

• Purpose : SEM produces high-resolution images of the surface of specimens using focused electron beams.
• Components : It includes an electron source, electromagnetic lenses, a sample chamber, and detectors for secondary electrons and backscattered electrons.
• Usage : The electron beam scans the sample’s surface, and signals from interactions with the beam create detailed images, revealing surface topography and composition.

Gas Chromatography-Mass Spectrometry (GC-MS)

• Purpose : GC-MS combines gas chromatography with mass spectrometry to identify and quantify chemical compounds in a mixture.
• Components : It has a gas chromatograph to separate compounds and a mass spectrometer to analyze their masses.
• Usage : The mixture is vaporized and separated in the chromatograph. The separated compounds are then ionized in the mass spectrometer and identified by their mass-to-charge ratios.

High-Performance Liquid Chromatograph (HPLC)

• Purpose : HPLC separates and quantifies compounds in a liquid mixture based on their interactions with a stationary phase.
• Components : It includes a pump, injector, column, detector, and data system.
• Usage : Liquid samples are pumped through a column filled with stationary phase. Different compounds interact differently, leading to separation. The detector records signals that are used for quantification.

UV-Visible Spectrophotometer

• Purpose : This instrument measures the absorption of ultraviolet and visible light by a sample, often for quantitative analysis.
• Components : It has a light source, monochromator, sample holder, and detector.
• Usage : A beam of light passes through the sample, and the detector measures how much light is absorbed. This data can be used to determine the concentration of an absorbing substance.

Flame Photometer

• Purpose : Flame photometers are used to measure the concentration of specific elements in a sample by analyzing the color of the flame produced when the elements are atomized.
• Components : It consists of a flame, nebulizer, burner, and a system for detecting emitted light.
• Usage : A sample is introduced into the flame, and the characteristic colors produced are compared to known standards to determine the element’s concentration.

Mass Spectrometer

• Purpose : Mass spectrometers determine the molecular composition of a sample by measuring the mass-to-charge ratio of ions.
• Components : They include an ionization source, mass analyzer, and detector.
• Usage : Samples are ionized, and the resulting ions are separated based on their mass-to-charge ratio. The detector records these ions, providing information about the sample’s composition.

Atomic Force Microscope (AFM)

• Purpose : AFMs allow for imaging and manipulating materials at the nanoscale by scanning a sharp tip across the surface.
• Components : AFMs have a cantilever with a sharp tip and a detector for measuring tip-sample interactions.
• Usage : The tip is brought close to the sample’s surface, and interactions between the tip and sample are measured, producing high-resolution topographical images.

Differential Scanning Calorimeter (DSC)

• Purpose : DSC measures changes in heat flow associated with phase transitions and chemical reactions in materials.
• Components : It consists of a sample holder, reference cell, and heating element.
• Usage : The sample and a reference are heated or cooled simultaneously, and the heat flow difference between them is recorded. This provides information about thermal properties and transitions.

Gas Density Meter

• Purpose : Gas density meters determine the density of gases under varying conditions of temperature and pressure.
• Components : They typically involve a sensor that measures the speed of sound in the gas.
• Usage : By measuring the speed of sound, these meters can calculate the density of gases, which is important in various industrial and research applications.

Circular Dichroism Spectrometer (CD)

• Purpose : CD spectrometers analyze the optical activity of chiral molecules to determine their secondary structure.
• Components : They include a light source, sample holder, and detectors for measuring differences in left and right circularly polarized light.
• Usage : CD spectroscopy is widely used in chemistry and biochemistry to study the conformation of biomolecules like proteins and nucleic acids.
• Purpose : Ultracentrifuges separate particles in suspensions based on size and density using high centrifugal forces.
• Components : They have a rotor, sample tubes, and a powerful motor for high-speed spinning.
• Usage : Ultracentrifugation is essential for tasks like separating macromolecules, organelles, or colloidal particles in biological and biochemical research.

Sonication Bath

• Purpose : Sonication baths use high-frequency sound waves to disrupt and disperse particles in liquids for sample preparation.
• Components : They consist of a bath filled with liquid and a sonication probe or transducer.
• Usage : Sonication is employed for tasks like cell disruption, homogenization, and degassing of solutions.

Raman Spectrometer

• Purpose : Raman spectrometers measure the scattering of monochromatic light by molecules to identify and characterize chemical compounds.
• Components : They include a laser source, spectrometer, and a detector for Raman scattering.
• Usage : Raman spectroscopy is used for chemical analysis, materials characterization, and identifying molecular structures.

Atomic Emission Spectrometer

• Purpose : Atomic emission spectrometers analyze the emission of light by excited atoms to determine elemental composition in samples.
• Components : They include a sample introduction system, excitation source (flame or plasma), and a detector.
• Usage : This instrument is widely used in elemental analysis, such as in environmental monitoring and metal analysis.

Microplate Reader

• Purpose : Microplate readers read absorbance, fluorescence, or luminescence in microplate wells for high-throughput screening and assays.
• Components : They have multiple detectors and can accommodate microplates with multiple sample wells.
• Usage : Microplate readers are essential in molecular biology, biochemistry, and drug discovery for rapid analysis of numerous samples.

Chromatography Data System (CDS)

• Purpose : A Chromatography Data System is software used to control and analyze data from chromatography instruments.
• Components : It includes data acquisition, processing, and reporting capabilities.
• Usage : CDS is crucial for managing and interpreting data generated from chromatography experiments, ensuring accurate and reliable results.

Cryo-Electron Microscope

• Purpose : Cryo-EM uses extremely low temperatures to study the structure of biological macromolecules and large assemblies.
• Components : It includes a specialized electron microscope and a cryogenic sample stage.
• Usage : Cryo-EM is revolutionizing structural biology by enabling the visualization of complex structures at near-atomic resolution.

Potentiostat-Galvanostat

• Purpose : A potentiostat-galvanostat is used to control and measure electrochemical reactions, often in corrosion studies and battery research.
• Components : It has three electrodes (working, reference, and counter electrodes) and a control unit.
• Usage : It’s employed in a wide range of electrochemical experiments, including corrosion rate determination and battery testing.

Laser Ablation-Inductively Coupled Plasma-Mass Spectrometer (LA-ICP-MS) :

• Purpose : LA-ICP-MS analyzes solid samples by vaporizing them with a laser and measuring the elemental composition with ICP-MS.
• Components : It involves a laser ablation system coupled to an ICP-MS instrument.
• Usage : LA-ICP-MS is used for spatially-resolved elemental analysis in various fields, including geology, environmental science, and materials research.

Further References

• Laboratory Apparatus : https://owlcation.com/stem/A-Chemistry-Guide-List-of-Common-Laboratory-Equipment-Names-and-Uses
• Lab Equipments : https://www.google.com/amp/s/www.cnlabglassware.com/more-than-20-common-laboratory-apparatus-their-uses.html%3famp=1?espv=1

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• Bunsen Burner Parts: Operation, Uses And Flames
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Summer holiday science: turn your home into a lab with these three easy experiments

Associate Professor in Biology, University of Limerick

Disclosure statement

Audrey O'Grady receives funding from Science Foundation Ireland. She is affiliated with Department of Biological Sciences, University of Limerick.

University of Limerick provides funding as a member of The Conversation UK.

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Many people think science is difficult and needs special equipment, but that’s not true.

Science can be explored at home using everyday materials. Everyone, especially children, naturally ask questions about the world around them, and science offers a structured way to find answers.

Misconceptions about the difficulty of science often stem from a lack of exposure to its fun and engaging side. Science can be as simple as observing nature, mixing ingredients or exploring the properties of objects. It’s not just for experts in white coats, but for everyone.

Don’t take my word for it. Below are three experiments that can be done at home with children who are primary school age and older.

Extract DNA from bananas

DNA is all the genetic information inside cells. Every living thing has DNA, including bananas.

Did you know you can extract DNA from banana cells?

What you need: ¼ ripe banana, Ziploc bag, salt, water, washing-up liquid, rubbing alcohol (from a pharmacy), coffee filter paper, stirrer.

What you do:

Place a pinch of salt into about 20ml of water in a cup.

Add the salty water to the Ziploc bag with a quarter of a banana and mash the banana up with the salty water inside the bag, using your hands. Mashing the banana separates out the banana cells. The salty water helps clump the DNA together.

Once the banana is mashed up well, pour the banana and salty water into a coffee filter (you can lay the filter in the cup you used to make the salty water). Filtering removes the big clumps of banana cells.

Once a few ml have filtered out, add a drop of washing-up liquid and swirl gently. Washing-up liquid breaks down the fats in the cell membranes which makes the DNA separate from the other parts of the cell.

Slowly add some rubbing alcohol (about 10ml) to the filtered solution. DNA is insoluble in alcohol, therefore the DNA will clump together away from the alcohol and float, making it easy to see.

DNA will start to precipitate out looking slightly cloudy and stringy. What you’re seeing is thousands of DNA strands – the strands are too small to be seen even with a normal microscope. Scientists use powerful equipment to see individual strands.

Learn how plants ‘drink’ water

What you need: celery stalks (with their leaves), glass or clear cup, water, food dye, camera.

• Fill the glass ¾ full with water and add 10 drops of food dye.
• Place a celery stalk into the glass of coloured water. Take a photograph of the celery.
• For two to three days, photograph the celery at the same time every day. Make sure you take a photograph at the very start of the experiment.

What happens and why?

All plants, such as celery, have vertical tubes that act like a transport system. These narrow tubes draw up water using a phenomenon known as capillarity.

Imagine you have a thin straw and you dip it into a glass of water. Have you ever noticed how the water climbs up the straw a little bit, even though you didn’t suck on it? This is because of capillarity.

In plants, capillarity helps move water from the roots to the leaves. Plants have tiny tubes inside them, like thin straws, called capillaries. The water sticks to the sides of these tubes and climbs up. In your experiment, you will see the food dye in the water make its way to the leaves.

Build a balloon-powered racecar

What you need: tape, scissors, two skewers, cardboard, four bottle caps, one straw, one balloon.

• Cut the cardboard to about 10cm long and 5cm wide. This will form the base of your car.
• Make holes in the centre of four bottle caps. These are your wheels.
• To make the axles insert the wooden skewers through the holes in the cap. You will need to cut the skewers to fit the width of the cardboard base, but leave room for the wheels.
• Secure the wheels to the skewers with tape.
• Attach the axles to the underside of the car base with tape, ensuring the wheels can spin freely.
• Insert a straw into the opening of a balloon and secure it with tape, ensuring there are no air leaks.
• Attach the other end of the straw to the top of the car base, positioning it so the balloon can inflate and deflate towards the back of the car. Secure the straw with tape.
• Inflate the balloon through the straw, pinch the straw to hold the air, place the car on a flat surface, then release the straw.

The inflated balloon stores potential energy when blown up. When the air is released, Newton’s third law of motion kicks into gear: for every action, there is an equal and opposite reaction.

As the air rushes out of the balloon (action), it pushes the car in the opposite direction (reaction). The escaping air propels the car forward, making it move across the surface.

• Science experiments

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The Art of the Unexpected: A Guide to Situational Irony

• The Speaker Lab
• August 23, 2024

Table of Contents

If you think back to your high school days, you may recall having learned about situational irony in your English Language Arts class. This literary device occurs when what we anticipate should occur is completely flipped on its head, and a contrasting outcome unfolds instead. From literature to everyday mishaps, situational irony adds a splash of unexpectedness that can make a story truly unforgettable.

Understanding Situational Irony

Situational irony thrives on the contrast between expectation and reality. It’s like carefully setting the stage for a grand finale, only to have the curtain fall prematurely, leaving the audience bewildered.

How to Spot Situational Irony

Look for instances where the outcome directly contradicts the expected result, often humorously or tragically. Consider these key components:

• Established Expectations: The audience is led to anticipate a specific outcome based on clues, setups, or common knowledge.
• An Unexpected Turn of Events: Instead of following the predicted path, a sharp turn leads to a result that throws everyone for a loop.
• A Sense of Surprise or Irony: The discrepancy between what was expected and what occurs creates a sense of surprise, amusement, or even disbelief.

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Situational Irony vs. Other Ironies

While various types of irony exist, it’s easy to get them confused. In addition to situational irony, there’s also:

• Dramatic Irony: Imagine watching a horror movie where you know the killer is lurking behind the door, but the oblivious character walks right in. That’s dramatic irony —the audience possesses knowledge that the characters lack, increasing suspense.
• Verbal Irony: Ever groaned “Wonderful weather” during a downpour? If so, you’ve utilized verbal irony . In other words, you’ve said one thing while implying the opposite. Verbal irony is a playground for sarcasm and wit.

Think of these ironies as tools in a storyteller’s toolkit. Situational irony surprises us with outcomes. Dramatic irony keeps us on the edge of our seats by providing suspense. Verbal irony uses words to express a different meaning.

Now that we’ve clarified these differences, let’s look at how you, as a writer, can use situational irony in your narratives .

Mastering Situational Irony as a Writer

Ready to infuse your narratives with unexpected outcomes? These tricks are all about setting the stage and then yanking the rug out from under your readers’ expectations. Here’s how to do it.

1. Foreshadowing

Imagine this: you introduce a character who’s constantly reminding everyone to wear their seatbelt. Later in your story, this character, in a rush, forgets to buckle their seatbelt. As their car collides with another, the reader experiences the gut punch of irony, especially if an earlier scene showed them buckling up in calmer circumstances.

Remember, effective foreshadowing is like a subtle whisper—enough to pique interest but not so overt that it reveals the twist prematurely.

2. Establish Norms

Define the rules, expectations, and logic of your world or setting. Readers anticipate these norms will govern how situations play out. However, strategically subverting those established norms later will make that unexpected twist all the more jarring.

A simple example of this would be Aesop’s fable, “The Tortoise and the Hare.” If you were reading this story for the first time, you would obviously expect the speedy hare to win. However, because the hare becomes overconfident, the determined tortoise wins, subverting the reader’s expectations.

3. Create Contrasting Situations

A firehouse burning down is a classic example of situational irony because we least expect a place filled with firefighters to fall victim to the very danger they’re trained to combat. These kinds of contradictions highlight the unexpected.

4. Unreliable Narrators

Consider employing an unreliable narrator, leading your audience down one path through deception, only to deliver a plot twist that reveals their skewed perspective. The impact deepens when the audience sees the truth, revealing the gap between reality and the narrator’s perception.

Why Situational Irony is More Than a Plot Device

Situational irony does more than surprise. It provokes introspection, humor, and curiosity. So, the next time you craft a narrative or observe a peculiar turn of events in real life, remember situational irony. This literary device has a way of adding spice to our experiences, making us laugh, ponder, and marvel at the absurdities and unexpected twists that come our way.

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FAQs on Situational Irony

What is situational irony and examples.

Situational irony occurs when the expected outcome is flipped, creating a surprising contrast. For example, imagine a world-renowned marriage counsellor announcing their divorce—it throws you off guard because their profession centers around fixing relationships.

What are three irony examples?

Here are three examples of situational irony from everyday life:

• A fire station burns down—ironic because it’s meant to combat fires.
• An anti-technology group establishes a website—contradictory because they use the very tool they seemingly oppose.
• The “expert” makes a fool of themselves—think of a financial guru making fun of an investment strategy, only to lose everything using that same method later.

Situational irony, whether subtle or overt, injects a healthy dose of surprise, humor, or tragedy into stories and real life. It keeps us on our toes and makes us more aware of the gap between expectations and reality. The more adept you are at spotting these ironic turns in your own writing and the world around you, the better equipped you’ll be to engage with them fully.

• Last Updated: August 16, 2024

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What do you think of when you hear the word "laboratory"? Do you picture people in white coats and goggles and gloves standing over a table with beakers and tubes? Well, that picture is pretty close to reality in some cases. In others, laboratory experiments, especially in psychology, focus more on observing behaviours in highly controlled settings to establish causal conclusions. Let's explore lab experiments further. 

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• We are going to delve into the topic of lab experiments in the context of psychology.
• We will start by looking at the lab experiment definition and how lab experiments are used in psychology.
• Moving on from this, we will look at how lab experiment examples in psychology and cognitive lab experiments may be conducted.
• And to finish off, we will also explore the strengths and weaknesses of lab experiments.

Lab Experiment Psychology Definition

You can probably guess from the name that lab experiments occur in lab settings. Although this is not always the case, they can sometimes occur in other controlled environments. The purpose of lab experiments is to identify the cause and effect of a phenomenon through experimentation.

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to accurately measure how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

In lab experiments, the IV is what the researcher predicts as the cause of a phenomenon, and the dependent variable is what the researcher predicts as the effect of a phenomenon.

Lab Experiment: P sychology

Lab experiments in psychology are used when trying to establish causal relationships between variables . For example, a researcher would use a lab experiment if they were investigating how sleep affects memory recall.

The majority of psychologists think of psychology as a form of science. Therefore, they argue that the protocol used in psychological research should resemble those used in the natural sciences. For research to be established as scientific , three essential features should be considered:

• Empiricism - the findings should be observable via the five senses.
• Reliability - if the study was replicated, similar results should be found.
• Validity - the investigation should accurately measure what it intends to.

But do lab experiments fulfil these requirements of natural sciences research? If done correctly, then yes. Lab experiments are empirical as they involve the researcher observing changes occurring in the DV. Reliability is established by using a standardised procedure in lab experiments .

A standardised procedure is a protocol that states how the experiment will be carried out. This allows the researcher to ensure the same protocol is used for each participant, increasing the study's internal reliability.

Standardised procedures are also used to help other researchers replicate the study to identify if they measure similar results.

Dissimilar results reflect low reliability.

Validity is another feature of a lab experiment considered. Lab experiments are conducted in a carefully controlled setting where the researcher has the most control compared to other experiments to prevent extraneous variables from affecting the DV .

Extraneous variables are factors other than the IV that affect the DV; as these are variables that the researcher is not interested in investigating, these reduce the validity of the research.

There are issues of validity in lab experiments, which we'll get into a bit later!

Lab Experiment Examples: Asch's Conformity Study

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question. Participants were given two pieces of paper, one depicting a 'target line' and another three, one of which resembled the 'target line' and the others of different lengths.

The participants were put in groups of eight. Unknown to the participants, the other seven were confederates (participants who were secretly part of the research team) who were instructed to give the wrong answer. If the actual participant changed their answer in response, this would be an example of conformity .

Asch controlled the location where the investigation took place, constructed a contrived scenario and even controlled the confederates who would affect the behaviour of the actual participants to measure the DV.

Some other famous examples of research that are lab experiment examples include research conducted by Milgram (the obedience study) and Loftus and Palmer's eyewitness testimony accuracy study . These researchers likely used this method because of some of their strengths , e.g., their high level of control .

Lab Experiment Examples: Cognitive Lab Experiments

Let's look at what a cognitive lab experiment may entail. Suppose a researcher is interested in investigating how sleep affects memory scores using the MMSE test. In the theoretical study , an equal number of participants were randomly allocated into two groups; sleep-deprived versus well-rested. Both groups completed the memory test after a whole night of sleep or staying awake all night.

In this research scenario , the DV can be identified as memory test scores and the IV as whether participants were sleep-deprived or well-rested.

Some examples of extraneous variables the study controlled include researchers ensuring participants did not fall asleep, the participants took the test at the same time, and participants in the well-rested group slept for the same time.

Lab Experiment Advantages and Disadvantages

It's important to consider the advantages and disadvantages of laboratory experiments . Advantages include the highly controlled setting of lab experiments, the standardised procedures and causal conclusions that can be drawn. Disadvantages include the low ecological validity of lab experiments and demand characteristics participants may present.

Strengths of Lab Experiments: Highly Controlled

Laboratory experiments are conducted in a well-controlled setting. All the variables, including extraneous and confounding variables , are rigidly controlled in the investigation. Therefore, the risk of experimental findings being affected by extraneous or confounding variables is reduced . As a result, the well-controlled design of laboratory experiments implies the research has high internal validity .

Internal validity means the study uses measures and protocols that measure exactly what it intends to, i.e. how only the changes in the IV affect the DV.

Strengths of Lab Experiments: Standardised Procedures

Laboratory experiments have standardised procedures, which means the experiments are replicable , and all participants are tested under the same conditions. T herefore, standardised procedures allow others to replicate the study to identify whether the research is reliable and that the findings are not a one-off result. As a result, the replicability of laboratory experiments allows researchers to verify the study's reliability .

Strengths of Lab Experiments: Causal Conclusions

A well-designed laboratory experiment can draw causal conclusions. Ideally, a laboratory experiment can rigidly control all the variables , including extraneous and confounding variables. Therefore, laboratory experiments provide great confidence to researchers that the IV causes any observed changes in DV.

Weaknesses of Lab Experiments

In the following, we will present the disadvantages of laboratory experiments. This discusses ecological validity and demand characteristics.

Weaknesses of Lab Experiments: Low Ecological Validity

Laboratory experiments have low ecological validity because they are conducted in an artificial study that does not reflect a real-life setting . As a result, findings generated in laboratory experiments can be difficult to generalise to real life due to the low mundane realism. Mundane realism reflects the extent to which lab experiment materials are similar to real-life events.

Weaknesses of Lab Experiments: Demand Characteristics

A disadvantage of laboratory experiments is that the research setting may lead to demand characteristics .

Demand characteristics are the cues that make participants aware of what the experimenter expects to find or how participants are expected to behave.

The participants are aware they are involved in an experiment. So, participants may have some ideas of what is expected of them in the investigation, which may influence their behaviours. As a result, the demand characteristics presented in laboratory experiments can arguably change the research outcome , reducing the findings' validity .

Lab Experiment - Key takeaways

The lab experiment definition is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affect the dependent variable (DV; variable measured).

Psychologists aim to ensure that lab experiments are scientific and must be empirical, reliable and valid.

The Asch (1951) conformity study is an example of a lab experiment. The investigation aimed to identify if the presence and influence of others would pressure participants to change their response to a straightforward question.

The advantages of lab experiments are high internal validity, standardised procedures and the ability to draw causal conclusions.

The disadvantages of lab experiments are low ecological validity and demand characteristics.

Flashcards in Lab Experiment 25

A laboratory experiment is an experiment conducted in a highly controlled environment. 

The participants may be aware of the experiment’s aims and how the researcher expects them to act, which may influence their behaviours.

Laboratory experiments have low ecological validity as contrived or artificial materials are employed.

Laboratory experiments are conducted in a well-controlled setting, which implies good internal validity, standardised procedures and the ability to draw causal conclusions.

A field experiment is an experiment conducted in a natural, everyday setting. 

Field experiments are conducted in a less controlled setting which may not have standardised procedures, implying the risk of low internal validity and reliability.

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Frequently Asked Questions about Lab Experiment

What is a lab experiment?

A lab experiment is an experiment that uses a carefully controlled setting and standardised procedure to establish how changes in the independent variable (IV; variable that changes) affects the dependent variable (DV; variable measured).

What is the purpose of lab experiments?

Lab experiments investigate cause-and-effect. They aim to determine the effect of changes in the independent variable on the dependent variable. 

What is a lab experiment and field experiment?

A field experiment is an experiment conducted in a natural, everyday setting. The experimenter still controls the IV; however, extraneous and confounding variables may be difficult to control due to the natural setting.

Similar, to filed experiments researchers, can control the IV and extraneous variables. However, this takes place in an artificial setting such as a lab. 

Why would a psychologist use a laboratory experiment? 

A psychologist may use a lab experiment when trying to establish the causal relationships between variables to explain a phenomenon. 

Why is lab experience important?

Lab experience allows researchers to scientifically determine whether a hypothesis/ theory should be accepted or rejected. 

What is a lab experiment example? 

The research conducted by Loftus and Palmer (accuracy of eyewitness testimony) and Milgram (obedience) used a lab experiment design. These experimental designs give the researcher high control, allowing them to control extraneous and independent variables.

Test your knowledge with multiple choice flashcards

The aim of lab experiments is to identify if observed changes in the      are caused by the      .

Is it difficult to generalise results from lab experiments to real-life settings? 

Demand characteristics lower the         of the research.

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Introduction to Arduino

Prelab due 9/11/22 at 11:00 am, writeup due 9/18/22 at 11:00 am.

This lab introduces you to the Arduino platform and to working with basic circuits. By the end of this lab, you will have been introduced to the Arduino IDE and some basic Arduino programming. You will also have used a circuit diagram to wire up some circuits with LEDs, resistors, and push buttons.

Lab 1 Rubric

• Arduino API
• Arduino Help

Included in your kits:

• Arduino MKR 1000 and micro-USB cable
• 3 LEDs (any colors)
• 5 resistors (to be determined by you during the lab)
• 2 push buttons
• Jumpers/wires
• None necessary

Install or open the Arduino IDE:

Install the Arduino IDE .

When the IDE is installed, open it. Under the Board Manager menu, make sure Board reads Arduino MKR1000 . If not, use the menu to select it. You might have to use the Boards Manager link in the menu to install the Arduino SAMD Boards core. More information on the board manager is here .

Load your first program onto the Arduino:

Under the File > Examples menu in your IDE, open 01. Basics > Blink . This opens an example Sketch (Arduino program) called Blink . Take a moment to read through the code, including the documentation at the top, with your partners.

Connect your Arduino to a USB drive on your computer using a micro USB cable. You can keep the Arduino pins in the foam it came with for this step ( some students have found that they needed to take the Arduino out of the foam )

Use the upload button to upload the Sketch to the board. Instructions on how to use the Arduino IDE to upload are here .

Troubleshooting : if you get an error about a device not found on a port, try selecting a different port in the Tools > Port menu. If you continue to have connectivity support, please try the steps on this page

Observe the on-board LED (near the 5V pin) blinking, with the LED toggling between ON and OFF every 1 second.

Edit the sketch to make the LED blink twice as slowly. Upload the code and verify that the LED is blinking slower.

Edit the sketch to make the LED gradually change from blinking slowly, to blinking quickly, and back:

Reference the Arduino Language Reference on Structure for the syntax of for loops, if statements, etc.

Observe that the loop() function of the sketch will repeat forever. Instead of making a for loop inside this function, use global variable(s) that you initialize in the setup() function and manipulate in the loop() function, such that you only call digitalWrite(...) twice during each iteration of the loop() function.

The delay between toggling the LED on and off should change from 2000 ms to 100 ms and back to 2000 ms in increments of 100ms. Concretely, the LED should be on for 2000 ms, then off for 2000 ms, then on for 1900 ms, then off for 1900ms, then on for 1800 ms, then off for 1800 ms, and so on, counting down to 100 ms, and then counting back up to 2000 ms.

Upload, observe, and debug your code. When you are confident that it works, get checked off by a TA.

Run the same code using an external LED, by connecting your first circuit on the breadboard:

You will connect a physical LED to the MKR1000. Study the pin labels on your Arduino. Some have special roles, such as GND (ground pin), VIN (input voltage, if you were, for example, supplying battery power), and TX and TR (for serial communication). There are also 7 analog pins ( A0 - A6 ) and 8 pins just for digital I/O (simply labeled 0 - 7 ). Because an LED requires a digital (on/off) signal, we will be using one of the digital I/O pins, namely 4.

Before wiring up your circuit, it is good practice to connect the ground rails to ground. From the prelab, you learned how a breadboard is connected internally. Thus, to connect both ground rails to the ground of the Arduino, you should connect the GND pin to one of the rails, and connect the rails to each other.

When plugging the Arduino into the breadboard, make sure the two rows of Arduino pins are separated by the middle channel of the breadboard. Also make sure that the Arduino is seated firmly in the breadboard (you might have to apply some pressure to the Arduino. To avoid damage, it is good practice to press down on the black plastic pin headers rather than the metal).

Now, you can ground any circuit by connecting it to any hole on either the top or bottom ground rail!

Disconnect your arduino from power (unplug the cable) and wire up the circuit. Refer to the prelab for a reminder on how the circuit diagram corresponds to the physical circuit. Use the same resistor that you computed in the prelab.

Before powering up the circuit, go through the circuit checklist .

In your code, create a constant global variable for your LED pin, with value 4. Change all appearances of LED_BUILTIN in your code to this variable.

Connect your Arduino to the computer and upload your code, verifying that the LED you added lights up instead of the on-board LED.

Get checked off by a TA

Practice wiring up a button:

Image credit: Brian Carbonette on Arduino Project Hub

The orientation in which the button is plugged in to the circuit matters. Notice that, on your button, there are two sides that do not have legs attached, and two sides that each have two legs attached. The sides with the legs should sit on either side of the DIP support (the breadboard’s “ditch”).

Wire up your circuit. Make sure the button is connected to VCC, not 5V!!! Do not use the same resistor as in the breadboard graphic above, but instead use the resistor color codes as a reference to find the 10kΩ resistor.

Open the serial monitor by pressing the button at the top of the screen. Some students have had to open the monitor before loading the code, and some students have had to do it after.

Study and run the following skeleton code:

Observe the output change as you press and release the button

Now, implement a binary counter using this circuit:

The resistors connected to each of the LEDs have a resistance of 1 kΩ. The resistors connected to each of the buttons have a resistance of 10 kΩ.

Remember that crossing lines are only physically connected when there is a solid dot at the junction of the lines. Otherwise, interpret the wires as crossing over each other without being connected.

Each of the 3 LEDs represents a binary digit. The most significant digit is connected to pin 3. As an example, for the LEDs connected to pins 3-5, refer to the LEDs as L2 , L1 , and L0 , respectively. If L1 and L0 are on but L2 is off, this displays 011 and represents 3 in binary. Wire up your circuit and use the checklist to check it.

The push button on pin 7 is to decrement the counter, and the push button on pin 6 is to increment the counter. If the decrement button is pushed, the LED binary counter should decrement by 1. If the decrement button is pushed when the counter is displaying a 0 (all LEDs off, representing binary 000 ), nothing should happen. Similarly, if the increment button is pushed, the LED binary counter should increment by 1, and if it is pushed when the counter is displaying 7 (all LEDs on, representing binary 111 ), nothing should happen. Assume that only one button is pushed at a time. Also assume that the counter starts at 0.

Start a new sketch (using Examples > 01. Basics > Bare Minimum gives you skeleton code to start with) and implement the functionality described above. Remember to use pinMode(...) to define pins as inputs or outputs. The input from the buttons can be ready using digitalRead(...) .

Upload your sketch to the Arduino and verify if it works. If it does, congrats! Get checked off by the TA, and you are done with the in-class portion of the lab. Otherwise, spend at least 15 minutes debugging with your partner before asking a TA for help. We suggest using Serial.println(…) and Tools > Serial Monitor as in Step 6 to print debug information.

Hint: your first instinct may be to poll digitalRead(...) for a high signal, making the numbers increment rapidly as long as the button is pushed, rather than checking for the edge where it changes from 0 to 1. How do you detect this change?

Turn in your work:

Save your binary counter sketch as lab01_bincount . Upload this to the “Lab 1 Code” assignment on Gradescope (include all partner(s) on the submission).

INDIVIDUALLY, complete the Lab 1 writeup assignment on Gradescope.