experimental research design according to experts

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

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.

Prevent plagiarism. Run a free check.

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.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bevans, R. (2023, June 21). Guide to Experimental Design | Overview, 5 steps & Examples. Scribbr. Retrieved June 28, 2024, from https://www.scribbr.com/methodology/experimental-design/

Is this article helpful?

Rebecca Bevans

Other students also liked, random assignment in experiments | introduction & examples, quasi-experimental design | definition, types & examples, how to write a lab report, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Athl Train
• v.45(1); Jan-Feb 2010

Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand.

To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs.

Description:

The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style . At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary.

Advantages:

Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.

Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.

A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.

Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).

The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.

WHAT IS A STUDY (OR EXPERIMENTAL OR RESEARCH) DESIGN?

The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.

A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.

Table. Elements of a “Methods” Section

The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.

The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.

Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.

The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.

The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.

STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS

Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:

The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6

Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)

The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.

Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.

For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.

MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS

With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.

As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.

CONCLUSIONS

The goal of scientific writing, or any writing, for that matter, is to communicate information. Including 2 design statements or subsections in scientific papers—one to explain how the data were collected and another to explain how they were statistically analyzed—will improve the clarity of communication and bring praise from readers. To summarize:

• Purge from your thoughts and vocabulary the idea that experimental design and statistical design are synonymous.
• Study or experimental design plays a much broader role than simply defining and directing the statistical analysis of an experiment.
• A properly written study design serves as a road map to the “Methods” section of an experiment and, therefore, improves communication with the reader.
• Study design should include a description of the type of design used, each factor (and each level) involved in the experiment, and the time at which each measurement was made.
• Clarify when the variables involved in data collection and data analysis are different, such as when data analysis involves only a subset of a collected variable or a resultant variable from the mathematical manipulation of 2 or more collected variables.

Experimental design: Guide, steps, examples

Last updated

27 April 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

Experimental research design is a scientific framework that allows you to manipulate one or more variables while controlling the test environment. 

When testing a theory or new product, it can be helpful to have a certain level of control and manipulate variables to discover different outcomes. You can use these experiments to determine cause and effect or study variable associations. 

This guide explores the types of experimental design, the steps in designing an experiment, and the advantages and limitations of experimental design. 

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• What is experimental research design?

You can determine the relationship between each of the variables by: 

Manipulating one or more independent variables (i.e., stimuli or treatments)

Applying the changes to one or more dependent variables (i.e., test groups or outcomes)

With the ability to analyze the relationship between variables and using measurable data, you can increase the accuracy of the result. 

What is a good experimental design?

A good experimental design requires: 

Significant planning to ensure control over the testing environment

Sound experimental treatments

Properly assigning subjects to treatment groups

Without proper planning, unexpected external variables can alter an experiment's outcome. 

To meet your research goals, your experimental design should include these characteristics:

Provide unbiased estimates of inputs and associated uncertainties

Enable the researcher to detect differences caused by independent variables

Include a plan for analysis and reporting of the results

Provide easily interpretable results with specific conclusions

What's the difference between experimental and quasi-experimental design?

The major difference between experimental and quasi-experimental design is the random assignment of subjects to groups. 

A true experiment relies on certain controls. Typically, the researcher designs the treatment and randomly assigns subjects to control and treatment groups. 

However, these conditions are unethical or impossible to achieve in some situations.

When it's unethical or impractical to assign participants randomly, that’s when a quasi-experimental design comes in. 

This design allows researchers to conduct a similar experiment by assigning subjects to groups based on non-random criteria. 

Another type of quasi-experimental design might occur when the researcher doesn't have control over the treatment but studies pre-existing groups after they receive different treatments.

When can a researcher conduct experimental research?

Various settings and professions can use experimental research to gather information and observe behavior in controlled settings. 

Basically, a researcher can conduct experimental research any time they want to test a theory with variable and dependent controls. 

Experimental research is an option when the project includes an independent variable and a desire to understand the relationship between cause and effect. 

• The importance of experimental research design

Experimental research enables researchers to conduct studies that provide specific, definitive answers to questions and hypotheses. 

Researchers can test Independent variables in controlled settings to:

Test the effectiveness of a new medication

Design better products for consumers

Answer questions about human health and behavior

Developing a quality research plan means a researcher can accurately answer vital research questions with minimal error. As a result, definitive conclusions can influence the future of the independent variable. 

Types of experimental research designs

There are three main types of experimental research design. The research type you use will depend on the criteria of your experiment, your research budget, and environmental limitations. 

Pre-experimental research design

A pre-experimental research study is a basic observational study that monitors independent variables’ effects. 

During research, you observe one or more groups after applying a treatment to test whether the treatment causes any change. 

The three subtypes of pre-experimental research design are:

One-shot case study research design

This research method introduces a single test group to a single stimulus to study the results at the end of the application. 

After researchers presume the stimulus or treatment has caused changes, they gather results to determine how it affects the test subjects. 

One-group pretest-posttest design

This method uses a single test group but includes a pretest study as a benchmark. The researcher applies a test before and after the group’s exposure to a specific stimulus. 

Static group comparison design

This method includes two or more groups, enabling the researcher to use one group as a control. They apply a stimulus to one group and leave the other group static. 

A posttest study compares the results among groups. 

True experimental research design

A true experiment is the most common research method. It involves statistical analysis to prove or disprove a specific hypothesis . 

Under completely experimental conditions, researchers expose participants in two or more randomized groups to different stimuli. 

Random selection removes any potential for bias, providing more reliable results. 

These are the three main sub-groups of true experimental research design:

Posttest-only control group design

This structure requires the researcher to divide participants into two random groups. One group receives no stimuli and acts as a control while the other group experiences stimuli.

Researchers perform a test at the end of the experiment to observe the stimuli exposure results.

Pretest-posttest control group design

This test also requires two groups. It includes a pretest as a benchmark before introducing the stimulus. 

The pretest introduces multiple ways to test subjects. For instance, if the control group also experiences a change, it reveals that taking the test twice changes the results.

Solomon four-group design

This structure divides subjects into two groups, with two as control groups. Researchers assign the first control group a posttest only and the second control group a pretest and a posttest. 

The two variable groups mirror the control groups, but researchers expose them to stimuli. The ability to differentiate between groups in multiple ways provides researchers with more testing approaches for data-based conclusions. 

Quasi-experimental research design

Although closely related to a true experiment, quasi-experimental research design differs in approach and scope. 

Quasi-experimental research design doesn’t have randomly selected participants. Researchers typically divide the groups in this research by pre-existing differences. 

Quasi-experimental research is more common in educational studies, nursing, or other research projects where it's not ethical or practical to use randomized subject groups.

• 5 steps for designing an experiment

Experimental research requires a clearly defined plan to outline the research parameters and expected goals. 

Here are five key steps in designing a successful experiment:

Step 1: Define variables and their relationship

Your experiment should begin with a question: What are you hoping to learn through your experiment? 

The relationship between variables in your study will determine your answer.

Define the independent variable (the intended stimuli) and the dependent variable (the expected effect of the stimuli). After identifying these groups, consider how you might control them in your experiment. 

Could natural variations affect your research? If so, your experiment should include a pretest and posttest. 

Step 2: Develop a specific, testable hypothesis

With a firm understanding of the system you intend to study, you can write a specific, testable hypothesis. 

What is the expected outcome of your study? 

Develop a prediction about how the independent variable will affect the dependent variable. 

How will the stimuli in your experiment affect your test subjects? 

Your hypothesis should provide a prediction of the answer to your research question . 

Step 3: Design experimental treatments to manipulate your independent variable

Depending on your experiment, your variable may be a fixed stimulus (like a medical treatment) or a variable stimulus (like a period during which an activity occurs). 

Determine which type of stimulus meets your experiment’s needs and how widely or finely to vary your stimuli. 

Step 4: Assign subjects to groups

When you have a clear idea of how to carry out your experiment, you can determine how to assemble test groups for an accurate study. 

When choosing your study groups, consider: 

The size of your experiment

Whether you can select groups randomly

Your target audience for the outcome of the study

You should be able to create groups with an equal number of subjects and include subjects that match your target audience. Remember, you should assign one group as a control and use one or more groups to study the effects of variables. 

Step 5: Plan how to measure your dependent variable

This step determines how you'll collect data to determine the study's outcome. You should seek reliable and valid measurements that minimize research bias or error. 

You can measure some data with scientific tools, while you’ll need to operationalize other forms to turn them into measurable observations.

• Advantages of experimental research

Experimental research is an integral part of our world. It allows researchers to conduct experiments that answer specific questions. 

While researchers use many methods to conduct different experiments, experimental research offers these distinct benefits:

Researchers can determine cause and effect by manipulating variables.

It gives researchers a high level of control.

Researchers can test multiple variables within a single experiment.

All industries and fields of knowledge can use it. 

Researchers can duplicate results to promote the validity of the study .

Replicating natural settings rapidly means immediate research.

Researchers can combine it with other research methods.

It provides specific conclusions about the validity of a product, theory, or idea.

• Disadvantages (or limitations) of experimental research

Unfortunately, no research type yields ideal conditions or perfect results. 

While experimental research might be the right choice for some studies, certain conditions could render experiments useless or even dangerous. 

Before conducting experimental research, consider these disadvantages and limitations:

Required professional qualification

Only competent professionals with an academic degree and specific training are qualified to conduct rigorous experimental research. This ensures results are unbiased and valid. 

Limited scope

Experimental research may not capture the complexity of some phenomena, such as social interactions or cultural norms. These are difficult to control in a laboratory setting.

Resource-intensive

Experimental research can be expensive, time-consuming, and require significant resources, such as specialized equipment or trained personnel.

Limited generalizability

The controlled nature means the research findings may not fully apply to real-world situations or people outside the experimental setting.

Practical or ethical concerns

Some experiments may involve manipulating variables that could harm participants or violate ethical guidelines . 

Researchers must ensure their experiments do not cause harm or discomfort to participants. 

Sometimes, recruiting a sample of people to randomly assign may be difficult. 

• Experimental research design example

Experiments across all industries and research realms provide scientists, developers, and other researchers with definitive answers. These experiments can solve problems, create inventions, and heal illnesses. 

Product design testing is an excellent example of experimental research. 

A company in the product development phase creates multiple prototypes for testing. With a randomized selection, researchers introduce each test group to a different prototype. 

When groups experience different product designs , the company can assess which option most appeals to potential customers. 

Experimental research design provides researchers with a controlled environment to conduct experiments that evaluate cause and effect. 

Using the five steps to develop a research plan ensures you anticipate and eliminate external variables while answering life’s crucial questions.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 6 February 2023

Last updated: 6 October 2023

Last updated: 5 February 2023

Last updated: 16 April 2023

Last updated: 7 March 2023

Last updated: 9 March 2023

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

• Types of experimental

Log in or sign up

Get started for free

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

Share this:

Reader Interactions

March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

Comments and Questions Cancel reply

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

good and valuable

Very very good

Good presentation.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Publishing Research
• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

• Industry News
• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

Achieving Research Excellence: Checklist for good research practices

Academia is built on the foundation of trustworthy and high-quality research, supported by the pillars…

• Promoting Research

Plain Language Summary — Communicating your research to bridge the academic-lay gap

Science can be complex, but does that mean it should not be accessible to the…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

What would be most effective in reducing research misconduct?

In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.

• Systematic review
• Open access
• Published: 24 June 2024

A systematic review of experimentally tested implementation strategies across health and human service settings: evidence from 2010-2022

• Laura Ellen Ashcraft   ORCID: orcid.org/0000-0001-9957-0617 1 , 2 ,
• David E. Goodrich 3 , 4 , 5 ,
• Joachim Hero 6 ,
• Angela Phares 3 ,
• Rachel L. Bachrach 7 , 8 ,
• Deirdre A. Quinn 3 , 4 ,
• Nabeel Qureshi 6 ,
• Natalie C. Ernecoff 6 ,
• Lisa G. Lederer 5 ,
• Leslie Page Scheunemann 9 , 10 ,
• Shari S. Rogal 3 , 11   na1 &
• Matthew J. Chinman 3 , 4 , 6   na1  

Implementation Science volume  19 , Article number:  43 ( 2024 ) Cite this article

1159 Accesses

18 Altmetric

Metrics details

Studies of implementation strategies range in rigor, design, and evaluated outcomes, presenting interpretation challenges for practitioners and researchers. This systematic review aimed to describe the body of research evidence testing implementation strategies across diverse settings and domains, using the Expert Recommendations for Implementing Change (ERIC) taxonomy to classify strategies and the Reach Effectiveness Adoption Implementation and Maintenance (RE-AIM) framework to classify outcomes.

We conducted a systematic review of studies examining implementation strategies from 2010-2022 and registered with PROSPERO (CRD42021235592). We searched databases using terms “implementation strategy”, “intervention”, “bundle”, “support”, and their variants. We also solicited study recommendations from implementation science experts and mined existing systematic reviews. We included studies that quantitatively assessed the impact of at least one implementation strategy to improve health or health care using an outcome that could be mapped to the five evaluation dimensions of RE-AIM. Only studies meeting prespecified methodologic standards were included. We described the characteristics of studies and frequency of implementation strategy use across study arms. We also examined common strategy pairings and cooccurrence with significant outcomes.

Our search resulted in 16,605 studies; 129 met inclusion criteria. Studies tested an average of 6.73 strategies (0-20 range). The most assessed outcomes were Effectiveness ( n =82; 64%) and Implementation ( n =73; 56%). The implementation strategies most frequently occurring in the experimental arm were Distribute Educational Materials ( n =99), Conduct Educational Meetings ( n =96), Audit and Provide Feedback ( n =76), and External Facilitation ( n =59). These strategies were often used in combination. Nineteen implementation strategies were frequently tested and associated with significantly improved outcomes. However, many strategies were not tested sufficiently to draw conclusions.

This review of 129 methodologically rigorous studies built upon prior implementation science data syntheses to identify implementation strategies that had been experimentally tested and summarized their impact on outcomes across diverse outcomes and clinical settings. We present recommendations for improving future similar efforts.

Peer Review reports

Contributions to the literature

While many implementation strategies exist, it has been challenging to compare their effectiveness across a wide range of trial designs and practice settings

This systematic review provides a transdisciplinary evaluation of implementation strategies across population, practice setting, and evidence-based interventions using a standardized taxonomy of strategies and outcomes.

Educational strategies were employed ubiquitously; nineteen other commonly used implementation strategies, including External Facilitation and Audit and Provide Feedback, were associated with positive outcomes in these experimental trials.

This review offers guidance for scholars and practitioners alike in selecting implementation strategies and suggests a roadmap for future evidence generation.

Implementation strategies are “methods or techniques used to enhance the adoption, implementation, and sustainment of evidence-based practices or programs” (EBPs) [ 1 ]. In 2015, the Expert Recommendations for Implementing Change (ERIC) study organized a panel of implementation scientists to compile a standardized set of implementation strategy terms and definitions [ 2 , 3 , 4 ]. These 73 strategies were then organized into nine “clusters” [ 5 ]. The ERIC taxonomy has been widely adopted and further refined [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. However, much of the evidence for individual or groups of ERIC strategies remains narrowly focused. Prior systematic reviews and meta-analyses have assessed strategy effectiveness, but have generally focused on a specific strategy, (e.g., Audit and Provide Feedback) [ 14 , 15 , 16 ], subpopulation, disease (e.g., individuals living with dementia) [ 16 ], outcome [ 15 ], service setting (e.g., primary care clinics) [ 17 , 18 , 19 ] or geography [ 20 ]. Given that these strategies are intended to have broad applicability, there remains a need to understand how well implementation strategies work across EBPs and settings and the extent to which implementation knowledge is generalizable.

There are challenges in assessing the evidence of implementation strategies across many EBPs, populations, and settings. Heterogeneity in population characteristics, study designs, methods, and outcomes have made it difficult to quantitatively compare which strategies work and under which conditions [ 21 ]. Moreover, there remains significant variability in how researchers operationalize, apply, and report strategies (individually or in combination) and outcomes [ 21 , 22 ]. Still, synthesizing data related to using individual strategies would help researchers replicate findings and better understand possible mediating factors including the cost, timing, and delivery by specific types of health providers or key partners [ 23 , 24 , 25 ]. Such an evidence base would also aid practitioners with implementation planning such as when and how to deploy a strategy for optimal impact.

Building upon previous efforts, we therefore conducted a systematic review to evaluate the level of evidence supporting the ERIC implementation strategies across a broad array of health and human service settings and outcomes, as organized by the evaluation framework, RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) [ 26 , 27 , 28 ]. A secondary aim of this work was to identify patterns in scientific reporting of strategy use that could not only inform reporting standards for strategies but also the methods employed in future. The current study was guided by the following research questions Footnote 1 :

What implementation strategies have been most commonly and rigorously tested in health and human service settings?

Which implementation strategies were commonly paired?

What is the evidence supporting commonly tested implementation strategies?

We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) model [ 29 , 30 , 31 ] to develop and report on the methods for this systematic review (Additional File 1). This study was considered to be non-human subjects research by the RAND institutional review board.

Registration

The protocol was registered with PROSPERO (PROSPERO 2021 CRD42021235592).

Eligibility criteria

This review sought to synthesize evidence for implementation strategies from research studies conducted across a wide range of health-related settings and populations. Inclusion criteria required studies to: 1) available in English; 2) published between January 1, 2010 and September 20, 2022; 3) based on experimental research (excluded protocols, commentaries, conference abstracts, or proposed frameworks); 4) set in a health or human service context (described below); 5) tested at least one quantitative outcome that could be mapped to the RE-AIM evaluation framework [ 26 , 27 , 28 ]; and 6) evaluated the impact of an implementation strategy that could be classified using the ERIC taxonomy [ 2 , 32 ]. We defined health and human service setting broadly, including inpatient and outpatient healthcare settings, specialty clinics, mental health treatment centers, long-term care facilities, group homes, correctional facilities, child welfare or youth services, aging services, and schools, and required that the focus be on a health outcome. We excluded hybrid type I trials that primarily focused on establishing EBP effectiveness, qualitative studies, studies that described implementation barriers and facilitators without assessing implementation strategy impact on an outcome, and studies not meeting standardized rigor criteria defined below.

Information sources

Our three-pronged search strategy included searching academic databases (i.e., CINAHL, PubMed, and Web of Science for replicability and transparency), seeking recommendations from expert implementation scientists, and assessing existing, relevant systematic reviews and meta-analyses.

Search strategy

Search terms included “implementation strateg*” OR “implementation intervention*” OR “implementation bundl*” OR “implementation support*.” The search, conducted on September 20, 2022, was limited to English language and publication between 2010 and 2022, similar to other recent implementation science reviews [ 22 ]. This timeframe was selected to coincide with the advent of Implementation Science and when the term “implementation strategy” became conventionally used [ 2 , 4 , 33 ]. A full search strategy can be found in Additional File 2.

Title and abstract screening process

Each study’s title and abstract were read by two reviewers, who dichotomously scored studies on each of the six eligibility criteria described above as yes=1 or no=0, resulting in a score ranging from 1 to 6. Abstracts receiving a six from both reviewers were included in the full text review. Those with only one score of six were adjudicated by a senior member of the team (MJC, SSR, DEG). The study team held weekly meetings to troubleshoot and resolve any ongoing issues noted through the abstract screening process.

Full text screening

During the full text screening process, we reviewed, in pairs, each article that had progressed through abstract screening. Conflicts between reviewers were adjudicated by a senior member of the team for a final inclusion decision (MJC, SSR, DEG).

Review of study rigor

After reviewing published rigor screening tools [ 34 , 35 , 36 ], we developed an assessment of study rigor that was appropriate for the broad range of reviewed implementation studies. Reviewers evaluated studies on the following: 1) presence of a concurrent comparison or control group (=2 for traditional randomized controlled trial or stepped wedge cluster randomized trial and =1 for pseudo-randomized and other studies with concurrent control); 2) EBP standardization by protocol or manual (=1 if present); 3) EBP fidelity tracking (=1 if present); 4) implementation strategy standardization by operational description, standard training, or manual (=1 if present); 5) length of follow-up from full implementation of intervention (=2 for twelve months or longer, =1 for six to eleven months, or =0 for less than six months); and 6) number of sites (=1 for more than one site). Rigor scores ranged from 0 to 8, with 8 indicating the most rigorous. Articles were included if they 1) included a concurrent control group, 2) had an experimental design, and 3) received a score of 7 or 8 from two independent reviewers.

Outside expert consultation

We contacted 37 global implementation science experts who were recognized by our study team as leaders in the field or who were commonly represented among first or senior authors in the included abstracts. We asked each expert for recommendations of publications meeting study inclusion criteria (i.e., quantitatively evaluating the effectiveness of an implementation strategy). Recommendations were recorded and compared to the full abstract list.

Systematic reviews

Eighty-four systematic reviews were identified through the initial search strategy (See Additional File 3). Systematic reviews that examined the effectiveness of implementation strategies were reviewed in pairs for studies that were not found through our initial literature search.

Data abstraction and coding

Data from the full text review were abstracted in pairs, with conflicts resolved by senior team members (DEG, MJC) using a standard Qualtrics abstraction form. The form captured the setting, number of sites and participants studied, evidence-based practice/program of focus, outcomes assessed (based on RE-AIM), strategies used in each study arm, whether the study took place in the U.S. or outside of the U.S., and the findings (i.e., was there significant improvement in the outcome(s)?). We coded implementation strategies used in the Control and Experimental Arms. We defined the Control Arm as receiving the lowest number of strategies (which could mean zero strategies or care as usual) and the Experimental Arm as the most intensive arm (i.e., receiving the highest number of strategies). When studies included multiple Experimental Arms, the Experimental Arm with the least intensive implementation strategy(ies) was classified as “Control” and the Experimental Arm with the most intensive implementation strategy(ies) was classified as the “Experimental” Arm.

Implementation strategies were classified using standard definitions (MJC, SSR, DEG), based on minor modifications to the ERIC taxonomy [ 2 , 3 , 4 ]. Modifications resulted in 70 named strategies and were made to decrease redundancy and improve clarity. These modifications were based on input from experts, cognitive interview data, and team consensus [ 37 ] (See Additional File 4). Outcomes were then coded into RE-AIM outcome domains following best practices as recommended by framework experts [ 26 , 27 , 28 ]. We coded the RE-AIM domain of Effectiveness as either an assessment of the effectiveness of the EBP or the implementation strategy. We did not assess implementation strategy fidelity or effects on health disparities as these are recently adopted reporting standards [ 27 , 28 ] and not yet widely implemented in current publications. Further, we did not include implementation costs as an outcome because reporting guidelines have not been standardized [ 38 , 39 ].

Assessment and minimization of bias

Assessment and minimization of bias is an important component of high-quality systematic reviews. The Cochrane Collaboration guidance for conducting high-quality systematic reviews recommends including a specific assessment of bias for individual studies by assessing the domains of randomization, deviations of intended intervention, missing data, measurement of the outcome, and selection of the reported results (e.g., following a pre-specified analysis plan) [ 40 , 41 ]. One way we addressed bias was by consolidating multiple publications from the same study into a single finding (i.e., N =1), so-as to avoid inflating estimates due to multiple publications on different aspects of a single trial. We also included high-quality studies only, as described above. However, it was not feasible to consistently apply an assessment of bias tool due to implementation science’s broad scope and the heterogeneity of study design, context, outcomes, and variable measurement, etc. For example, most implementation studies reviewed had many outcomes across the RE-AIM framework, with no one outcome designated as primary, precluding assignment of a single score across studies.

We used descriptive statistics to present the distribution of health or healthcare area, settings, outcomes, and the median number of included patients and sites per study, overall and by country (classified as U.S. vs. non-U.S.). Implementation strategies were described individually, using descriptive statistics to summarize the frequency of strategy use “overall” (in any study arm), and the mean number of strategies reported in the Control and Experimental Arms. We additionally described the strategies that were only in the experimental (and not control) arm, defining these as strategies that were “tested” and may have accounted for differences in outcomes between arms.

We described frequencies of pair-wise combinations of implementation strategies in the Experimental Arm. To assess the strength of the evidence supporting implementation strategies that were used in the Experimental Arm, study outcomes were categorized by RE-AIM and coded based on whether the association between use of the strategies resulted in a significantly positive effect (yes=1; no=0). We then created an indicator variable if at least one RE-AIM outcome in the study was significantly positive (yes=1; no=0). We plotted strategies on a graph with quadrants based on the combination of median number of studies in which a strategy appears and the median percent of studies in which a strategy was associated with at least one positive RE-AIM outcome. The upper right quadrant—higher number of studies overall and higher percent of studies with a significant RE-AIM outcome—represents a superior level of evidence. For implementation strategies in the upper right quadrant, we describe each RE-AIM outcome and the proportion of studies which have a significant outcome.

Search results

We identified 14,646 articles through the initial literature search, 17 articles through expert recommendation (three of which were not included in the initial search), and 1,942 articles through reviewing prior systematic reviews (Fig. 1 ). After removing duplicates, 9,399 articles were included in the initial abstract screening. Of those, 48% ( n =4,075) abstracts were reviewed in pairs for inclusion. Articles with a score of five or six were reviewed a second time ( n =2,859). One quarter of abstracts that scored lower than five were reviewed for a second time at random. We screened the full text of 1,426 articles in pairs. Common reasons for exclusion were 1) study rigor, including no clear delineation between the EBP and implementation strategy, 2) not testing an implementation strategy, and 3) article type that did not meet inclusion criteria (e.g., commentary, protocol, etc.). Six hundred seventeen articles were reviewed for study rigor with 385 excluded for reasons related to study design and rigor, and 86 removed for other reasons (e.g., not a research article). Among the three additional expert-recommended articles, one met inclusion criteria and was added to the analysis. The final number of studies abstracted was 129 representing 143 publications.

Expanded PRISMA Flow Diagram

The expanded PRISMA flow diagram provides a description of each step in the review and abstraction process for the systematic review

Descriptive results

Of 129 included studies (Table 1 ; see also Additional File 5 for Summary of Included Studies), 103 (79%) were conducted in a healthcare setting. EBP health care setting varied and included primary care ( n =46; 36%), specialty care ( n =27; 21%), mental health ( n =11; 9%), and public health ( n =30; 23%), with 64 studies (50%) occurring in an outpatient health care setting. Studies included a median of 29 sites and 1,419 target population (e.g., patients or students). The number of strategies varied widely across studies, with Control Arms averaging approximately two strategies (Range = 0-20, including studies with no strategy in the comparison group) and Experimental Arms averaging eight strategies (Range = 1-21). Non-US studies ( n =73) included more sites and target population on average, with an overall median of 32 sites and 1,531 patients assessed in each study.

Organized by RE-AIM, the most evaluated outcomes were Effectiveness ( n = 82, 64%) and Implementation ( n = 73, 56%); followed by Maintenance ( n =40; 31%), Adoption ( n =33; 26%), and Reach ( n =31; 24%). Most studies ( n = 98, 76%) reported at least one significantly positive outcome. Adoption and Implementation outcomes showed positive change in three-quarters of studies ( n =78), while Reach ( n =18; 58%), Effectiveness ( n =44; 54%), and Maintenance ( n =23; 58%) outcomes evidenced positive change in approximately half of studies.

The following describes the results for each research question.

Table 2 shows the frequency of studies within which an implementation strategy was used in the Control Arm, Experimental Arm(s), and tested strategies (those used exclusively in the Experimental Arm) grouped by strategy type, as specified by previous ERIC reports [ 2 , 6 ].

Control arm

In about half the studies (53%; n =69), the Control Arms were “active controls” that included at least one strategy, with an average of 1.64 (and up to 20) strategies reported in control arms. The two most common strategies used in Control Arms were: Distribute Educational Materials ( n =52) and Conduct Educational Meetings ( n =30).

Experimental arm

Experimental conditions included an average of 8.33 implementation strategies per study (Range = 1-21). Figure 2 shows a heat map of the strategies that were used in the Experimental Arms in each study. The most common strategies in the Experimental Arm were Distribute Educational Materials ( n =99), Conduct Educational Meetings ( n =96), Audit and Provide Feedback ( n =76), and External Facilitation ( n =59).

Implementation strategies used in the Experimental Arm of included studies. Explore more here: https://public.tableau.com/views/Figure2_16947070561090/Figure2?:language=en-US&:display_count=n&:origin=viz_share_link

Tested strategies

The average number of implementation strategies that were included in the Experimental Arm only (and not in the Control Arm) was 6.73 (Range = 0-20). Footnote 2 Overall, the top 10% of tested strategies included Conduct Educational Meetings ( n =68), Audit and Provide Feedback ( n =63), External Facilitation ( n =54), Distribute Educational Materials ( n =49), Tailor Strategies ( n =41), Assess for Readiness and Identify Barriers and Facilitators ( n =38) and Organize Clinician Implementation Team Meetings ( n =37). Few studies tested a single strategy ( n =9). These strategies included, Audit and Provide Feedback, Conduct Educational Meetings, Conduct Ongoing Training, Create a Learning Collaborative, External Facilitation ( n =2), Facilitate Relay of Clinical Data To Providers, Prepare Patients/Consumers to be Active Participants, and Use Other Payment Schemes. Three implementation strategies were included in the Control or Experimental Arms but were not Tested including, Use Mass Media, Stage Implementation Scale Up, and Fund and Contract for the Clinical Innovation.

Table 3  shows the five most used strategies in Experimental Arms with their top ten most frequent pairings, excluding Distribute Educational Materials and Conduct Educational Meetings, as these strategies were included in almost all Experimental and half of Control Arms. The five most used strategies in the Experimental Arm included Audit and Provide Feedback ( n =76), External Facilitation ( n =59), Tailor Strategies ( n =43), Assess for Readiness and Identify Barriers and Facilitators ( n =43), and Organize Implementation Teams ( n =42).

Strategies frequently paired with these five strategies included two educational strategies: Distribute Educational Materials and Conduct Educational Meetings. Other commonly paired strategies included Develop a Formal Implementation Blueprint, Promote Adaptability, Conduct Ongoing Training, Purposefully Reexamine the Implementation, and Develop and Implement Tools for Quality Monitoring.

We classified the strength of evidence for each strategy by evaluating both the number of studies in which each strategy appeared in the Experimental Arm and the percentage of times there was at least one significantly positive RE-AIM outcome. Using these factors, Fig. 3 shows the number of studies in which individual strategies were evaluated (on the y axis) compared to the percentage of times that studies including those strategies had at least one positive outcome (on the x axis). Due to the non-normal distribution of both factors, we used the median (rather than the mean) to create four quadrants. Strategies in the lower left quadrant were tested in fewer than the median number of studies (8.5) and were less frequently associated with a significant RE-AIM outcome (75%). The upper right quadrant included strategies that occurred in more than the median number of studies (8.5) and had more than the median percent of studies with a significant RE-AIM outcome (75%); thus those 19 strategies were viewed as having stronger evidence. Of those 19 implementation strategies, Conduct Educational Meetings, Distribute Educational Materials, External Facilitation, and Audit and Provide Feedback continued to occur frequently, appearing in 59-99 studies.

Experimental Arm Implementation Strategies with significant RE-AIM outcome. Explore more here: https://public.tableau.com/views/Figure3_16947017936500/Figure3?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

Figure 4 graphically illustrates the proportion of significant outcomes for each RE-AIM outcome for the 19 commonly used and evidence-based implementation strategies in the upper right quadrant. These findings again show the widespread use of Conduct Educational Meetings and Distribute Educational Materials. Implementation and Effectiveness outcomes were assessed most frequently, with Implementation being the mostly commonly reported significantly positive outcome.

RE-AIM outcomes for the 19 Top-Right Quadrant Implementation Strategies . The y-axis is the number of studies and the x-axis is a stacked bar chart for each RE-AIM outcome with R=Reach, E=Effectiveness, A=Adoption, I=Implementation, M=Maintenance. Blue denotes at least one significant RE-AIM outcome; Light blue denotes studies which used the given implementation strategy and did not have a significant RE-AIM . Explore more here: https://public.tableau.com/views/Figure4_16947017112150/Figure4?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

This systematic review identified 129 experimental studies examining the effectiveness of implementation strategies across a broad range of health and human service studies. Overall, we found that evidence is lacking for most ERIC implementation strategies, that most studies employed combinations of strategies, and that implementation outcomes, categorized by RE-AIM dimensions, have not been universally defined or applied. Accordingly, other researchers have described the need for universal outcomes definitions and descriptions across implementation research studies [ 28 , 42 ]. Our findings have important implications not only for the current state of the field but also for creating guidance to help investigators determine which strategies and in what context to examine.

The four most evaluated strategies were Distribute Educational Materials, Conduct Educational Meetings, External Facilitation, and Audit and Provide Feedback. Conducting Educational Meetings and Distributing Educational Materials were surprisingly the most common. This may reflect the fact that education strategies are generally considered to be “necessary but not sufficient” for successful implementation [ 43 , 44 ]. Because education is often embedded in interventions, it is critical to define the boundary between the innovation and the implementation strategies used to support the innovation. Further specification as to when these strategies are EBP core components or implementation strategies (e.g., booster trainings or remediation) is needed [ 45 , 46 ].

We identified 19 implementation strategies that were tested in at least 8 studies (more than the median) and were associated with positive results at least 75% of the time. These strategies can be further categorized as being used in early or pre-implementation versus later in implementation. Preparatory activities or pre-implementation, strategies that had strong evidence included educational activities (Meetings, Materials, Outreach visits, Train for Leadership, Use Train the Trainer Strategies) and site diagnostic activities (Assess for Readiness, Identify Barriers and Facilitators, Conduct Local Needs Assessment, Identify and Prepare Champions, and Assess and Redesign Workflows). Strategies that target the implementation phase include those that provide coaching and support (External and Internal Facilitation), involve additional key partners (Intervene with Patients to Enhance Uptake and Adherence), and engage in quality improvement activities (Audit and Provide Feedback, Facilitate the Relay of Clinical Data to Providers, Purposefully Reexamine the Implementation, Conduct Cyclical Small Tests of Change, Develop and Implement Tools for Quality Monitoring).

There were many ERIC strategies that were not represented in the reviewed studies, specifically the financial and policy strategies. Ten strategies were not used in any studies, including: Alter Patient/Consumer Fees, Change Liability Laws, Change Service Sites, Develop Disincentives, Develop Resource Sharing Agreements, Identify Early Adopters, Make Billing Easier, Start a Dissemination Organization, Use Capitated Payments, and Use Data Experts. One of the limitations of this investigation was that not all individual strategies or combinations were investigated. Reasons for the absence of these strategies in our review may include challenges with testing certain strategies experimentally (e.g., changing liability laws), limitations in our search terms, and the relative paucity of implementation strategy trials compared to clinical trials. Many “untested” strategies require large-scale structural changes with leadership support (see [ 47 ] for policy experiment example). Recent preliminary work has assessed the feasibility of applying policy strategies and described the challenges with doing so [ 48 , 49 , 50 ]. While not impossible in large systems like VA (for example: the randomized evaluation of the VA Stratification Tool for Opioid Risk Management) the large size, structure, and organizational imperative makes these initiatives challenging to experimentally evaluate. Likewise, the absence of these ten strategies may have been the result of our inclusion criteria, which required an experimental design. Thus, creative study designs may be needed to test high-level policy or financial strategies experimentally.

Some strategies that were likely under-represented in our search strategy included electronic medical record reminders and clinical decision support tools and systems. These are often considered “interventions” when used by clinical trialists and may not be indexed as studies involving ‘implementation strategies’ (these tools have been reviewed elsewhere [ 51 , 52 , 53 ]). Thus, strategies that are also considered interventions in the literature (e.g., education interventions) were not sought or captured. Our findings do not imply that these strategies are ineffective, rather that more study is needed. Consistent with prior investigations [ 54 ], few studies meeting inclusion criteria tested financial strategies. Accordingly, there are increasing calls to track and monitor the effects of financial strategies within implementation science to understand their effectiveness in practice [ 55 , 56 ]. However, experts have noted that the study of financial strategies can be a challenge given that they are typically implemented at the system-level and necessitate research designs for studying policy-effects (e.g., quasi-experimental methods, systems-science modeling methods) [ 57 ]. Yet, there have been some recent efforts to use financial strategies to support EBPs that appear promising [ 58 ] and could be a model for the field moving forward.

The relationship between the number of strategies used and improved outcomes has been described inconsistently in the literature. While some studies have found improved outcomes with a bundle of strategies that were uniquely combined or a standardized package of strategies (e.g., Replicating Effective Programs [ 59 , 60 ] and Getting To Outcomes [ 61 , 62 ]), others have found that “more is not always better” [ 63 , 64 , 65 ]. For example, Rogal and colleagues documented that VA hospitals implementing a new evidence-based hepatitis C treatment chose >20 strategies, when multiple years of data linking strategies to outcomes showed that 1-3 specific strategies would have yielded the same outcome [ 39 ]. Considering that most studies employed multiple or multifaceted strategies, it seems that there is a benefit of using a targeted bundle of strategies that are purposefully aligns with site/clinic/population norms, rather than simply adding more strategies [ 66 ].

It is difficult to assess the effectiveness of any one implementation strategy in bundles where multiple strategies are used simultaneously. Even a ‘single’ strategy like External Facilitation is, in actuality, a bundle of narrowly constructed strategies (e.g., Conduct Educational Meetings, Identify and Prepare Champions, and Develop a Formal Implementation Blueprint). Thus, studying External Facilitation does not allow for a test of the individual strategies that comprise it, potentially masking the effectiveness of any individual strategy. While we cannot easily disaggregate the effects of multifaceted strategies, doing so may not yield meaningful results. Because strategies often synergize, disaggregated results could either underestimate the true impact of individual strategies or conversely, actually undermine their effectiveness (i.e., when their effectiveness comes from their combination with other strategies). The complexity of health and human service settings, imperative to improve public health outcomes, and engagement with community partners often requires the use of multiple strategies simultaneously. Therefore, the need to improve real-world implementation may outweigh the theoretical need to identify individual strategy effectiveness. In situations where it would be useful to isolate the impact of single strategies, we suggest that the same methods for documenting and analyzing the critical components (or core functions) of complex interventions [ 67 , 68 , 69 , 70 ] may help to identify core components of multifaceted implementation strategies [ 71 , 72 , 73 , 74 ].

In addition, to truly assess the impacts of strategies on outcomes, it may be necessary to track fidelity to implementation strategies (not just the EBPs they support). While this can be challenging, without some degree of tracking and fidelity checks, one cannot determine whether a strategy’s apparent failure to work was because it 1) was ineffective or 2) was not applied well. To facilitate this tracking there are pragmatic tools to support researchers. For example, the Longitudinal Implementation Strategy Tracking System (LISTS) offers a pragmatic and feasible means to assess fidelity to and adaptations of strategies [ 75 ].

Implications for implementation science: four recommendations

Based on our findings, we offer four recommended “best practices” for implementation studies.

Prespecify strategies using standard nomenclature. This study reaffirmed the need to apply not only a standard naming convention (e.g., ERIC) but also a standard reporting of for implementation strategies. While reporting systems like those by Proctor [ 1 ] or Pinnock [ 75 ] would optimize learning across studies, few manuscripts specify strategies as recommended [ 76 , 77 ]. Pre-specification allows planners and evaluators to assess the feasibility and acceptability of strategies with partners and community members [ 24 , 78 , 79 ] and allows evaluators and implementers to monitor and measure the fidelity, dose, and adaptations to strategies delivered over the course of implementation [ 27 ]. In turn, these data can be used to assess the costs, analyze their effectiveness [ 38 , 80 , 81 ], and ensure more accurate reporting [ 82 , 83 , 84 , 85 ]. This specification should include, among other data, the intensity, stage of implementation, and justification for the selection. Information regarding why strategies were selected for specific settings would further the field and be of great use to practitioners. [ 63 , 65 , 69 , 79 , 86 ].

Ensure that standards for measuring and reporting implementation outcomes are consistently applied and account for the complexity of implementation studies. Part of improving standardized reporting must include clearly defining outcomes and linking each outcome to particular implementation strategies. It was challenging in the present review to disentangle the impact of the intervention(s) (i.e., the EBP) versus the impact of the implementation strategy(ies) for each RE-AIM dimension. For example, often fidelity to the EBP was reported but not for the implementation strategies. Similarly, Reach and Adoption of the intervention would be reported for the Experimental Arm but not for the Control Arm, prohibiting statistical comparisons of strategies on the relative impact of the EBP between study arms. Moreover, there were many studies evaluating numerous outcomes, risking data dredging. Further, the significant heterogeneity in the ways in which implementation outcomes are operationalized and reported is a substantial barrier to conducting large-scale meta-analytic approaches to synthesizing evidence for implementation strategies [ 67 ]. The field could look to others in the social and health sciences for examples in how to test, validate, and promote a common set of outcome measures to aid in bringing consistency across studies and real-world practice (e.g., the NIH-funded Patient-Reported Outcomes Measurement Information System [PROMIS], https://www.healthmeasures.net/explore-measurement-systems/promis ).

Develop infrastructure to learn cross-study lessons in implementation science. Data repositories, like those developed by NCI for rare diseases, U.S. HIV Implementation Science Coordination Initiative [ 87 ], and the Behavior Change Technique Ontology [ 88 ], could allow implementation scientists to report their findings in a more standardized manner, which would promote ease of communication and contextualization of findings across studies. For example, the HIV Implementation Science Coordination Initiative requested all implementation projects use common frameworks, developed user friendly databases to enable practitioners to match strategies to determinants, and developed a dashboard of studies that assessed implementation determinants [ 89 , 90 , 91 , 92 , 93 , 94 ].

Develop and apply methods to rigorously study common strategies and bundles. These findings support prior recommendations for improved empirical rigor in implementation studies [ 46 , 95 ]. Many studies were excluded from our review based on not meeting methodological rigor standards. Understanding the effectiveness of discrete strategies deployed alone or in combination requires reliable and low burden tracking methods to collect information about strategy use and outcomes. For example, frameworks like the Implementation Replication Framework [ 96 ] could help interpret findings across studies using the same strategy bundle. Other tracking approaches may leverage technology (e.g., cell phones, tablets, EMR templates) [ 78 , 97 ] or find novel, pragmatic approaches to collect recommended strategy specifications over time (e.g.., dose, deliverer, and mechanism) [ 1 , 9 , 27 , 98 , 99 ]. Rigorous reporting standards could inform more robust analyses and conclusions (e.g., moving toward the goal of understanding causality, microcosting efforts) [ 24 , 38 , 100 , 101 ]. Such detailed tracking is also required to understand how site-level factors moderate implementation strategy effects [ 102 ]. In some cases, adaptive trial designs like sequential multiple assignment randomized trials (SMARTs) and just-in-time adaptive interventions (JITAIs) can be helpful for planning strategy escalation.

Limitations

Despite the strengths of this review, there were certain notable limitations. For one, we only included experimental studies, omitting many informative observational investigations that cover the range of implementation strategies. Second, our study period was centered on the creation of the journal Implementation Science and not on the standardization and operationalization of implementation strategies in the publication of the ERIC taxonomy (which came later). This, in conjunction with latency in reporting study results and funding cycles, means that the employed taxonomy was not applied in earlier studies. To address this limitation, we retroactively mapped strategies to ERIC, but it is possible that some studies were missed. Additionally, indexing approaches used by academic databases may have missed relevant studies. We addressed this particular concern by reviewing other systematic reviews of implementation strategies and soliciting recommendations from global implementation science experts.

Another potential limitation comes from the ERIC taxonomy itself—i.e., strategy listings like ERIC are only useful when they are widely adopted and used in conjunction with guidelines for specifying and reporting strategies [ 1 ] in protocol and outcome papers. Although the ERIC paper has been widely cited (over three thousand times, accessed about 186 thousand times), it is still not universally applied, making tracking the impact of specific strategies more difficult. However, our experience with this review seemed to suggest that ERIC’s use was increasing over time. Also, some have commented that ERIC strategies can be unclear and are missing key domains. Thus, researchers are making definitions clearer for lay users [ 37 , 103 ], increasing the number of discrete strategies for specific domains like HIV treatment, acknowledging strategies for new functions (e.g., de-implementation [ 104 ], local capacity building), accounting for phases of implementation (dissemination, sustainment [ 13 ], scale-up), addressing settings [ 12 , 20 ], actors roles in the process, and making mechanisms of change to select strategies more user-friendly through searchable databases [ 9 , 10 , 54 , 73 , 104 , 105 , 106 ]. In sum, we found the utility of the ERIC taxonomy to outweigh any of the taxonomy’s current limitations.

As with all reviews, the search terms influenced our findings. As such, the broad terms for implementation strategies (e.g., “evidence-based interventions”[ 7 ] or “behavior change techniques” [ 107 ]) may have led to inadvertent omissions of studies of specific strategies. For example, the search terms may not have captured tests of policies, financial strategies, community health promotion initiatives, or electronic medical record reminders, due to differences in terminology used in corresponding subfields of research (e.g., health economics, business, health information technology, and health policy). To manage this, we asked experts to inform us about any studies that they would include and cross-checked their lists with what was identified through our search terms, which yielded very few additional studies. We included standard coding using the ERIC taxonomy, which was a strength, but future work should consider including the additional strategies that have been recommended to augment ERIC, around sustainment [ 13 , 79 , 106 , 108 ], community and public health research [ 12 , 109 , 110 , 111 ], consumer or service user engagement [ 112 ], de-implementation [ 104 , 113 , 114 , 115 , 116 , 117 ] and related terms [ 118 ].

We were unable to assess the bias of studies due to non-standard reporting across the papers and the heterogeneity of study designs, measurement of implementation strategies and outcomes, and analytic approaches. This could have resulted in over- or underestimating the results of our synthesis. We addressed this limitation by being cautious in our reporting of findings, specifically in identifying “effective” implementation strategies. Further, we were not able to gather primary data to evaluate effect sizes across studies in order to systematically evaluate bias, which would be fruitful for future study.

Conclusions

This novel review of 129 studies summarized the body of evidence supporting the use of ERIC-defined implementation strategies to improve health or healthcare. We identified commonly occurring implementation strategies, frequently used bundles, and the strategies with the highest degree of supportive evidence, while simultaneously identifying gaps in the literature. Additionally, we identified several key areas for future growth and operationalization across the field of implementation science with the goal of improved reporting and assessment of implementation strategies and related outcomes.

Availability and materials

All data for this study are included in this published article and its supplementary information files.

We modestly revised the following research questions from our PROSPERO registration after reading the articles and better understanding the nature of the literature: 1) What is the available evidence regarding the effectiveness of implementation strategies in supporting the uptake and sustainment of evidence intended to improve health and healthcare outcomes? 2) What are the current gaps in the literature (i.e., implementation strategies that do not have sufficient evidence of effectiveness) that require further exploration?

Tested strategies are those which exist in the Experimental Arm but not in the Control Arm. Comparative effectiveness or time staggered trials may not have any unique strategies in the Experimental Arm and therefore in our analysis would have no Tested Strategies.

Abbreviations

Centers for Disease Control

Cumulated Index to Nursing and Allied Health Literature

Dissemination and Implementation

Evidence-based practices or programs

Expert Recommendations for Implementing Change

Multiphase Optimization Strategy

National Cancer Institute

National Institutes of Health

The Pittsburgh Dissemination and Implementation Science Collaborative

Sequential Multiple Assignment Randomized Trial

United States

Department of Veterans Affairs

Proctor EK, Powell BJ, McMillen JC. Implementation strategies: recommendations for specifying and reporting. Implement Sci. 2013;8:139.

Article   PubMed   PubMed Central   Google Scholar  

Powell BJ, Waltz TJ, Chinman MJ, Damschroder LJ, Smith JL, Matthieu MM, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implement Sci. 2015;10:21.

Waltz TJ, Powell BJ, Chinman MJ, Smith JL, Matthieu MM, Proctor EK, et al. Expert recommendations for implementing change (ERIC): protocol for a mixed methods study. Implement Sci IS. 2014;9:39.

Article   PubMed   Google Scholar  

Powell BJ, McMillen JC, Proctor EK, Carpenter CR, Griffey RT, Bunger AC, et al. A Compilation of Strategies for Implementing Clinical Innovations in Health and Mental Health. Med Care Res Rev. 2012;69:123–57.

Waltz TJ, Powell BJ, Matthieu MM, Damschroder LJ, Chinman MJ, Smith JL, et al. Use of concept mapping to characterize relationships among implementation strategies and assess their feasibility and importance: results from the Expert Recommendations for Implementing Change (ERIC) study. Implement Sci. 2015;10:109.

Perry CK, Damschroder LJ, Hemler JR, Woodson TT, Ono SS, Cohen DJ. Specifying and comparing implementation strategies across seven large implementation interventions: a practical application of theory. Implement Sci. 2019;14(1):32.

Community Preventive Services Task Force. Community Preventive Services Task Force: All Active Findings June 2023 [Internet]. 2023 [cited 2023 Aug 7]. Available from: https://www.thecommunityguide.org/media/pdf/CPSTF-All-Findings-508.pdf

Solberg LI, Kuzel A, Parchman ML, Shelley DR, Dickinson WP, Walunas TL, et al. A Taxonomy for External Support for Practice Transformation. J Am Board Fam Med JABFM. 2021;34:32–9.

Leeman J, Birken SA, Powell BJ, Rohweder C, Shea CM. Beyond “implementation strategies”: classifying the full range of strategies used in implementation science and practice. Implement Sci. 2017;12:1–9.

Article   Google Scholar  

Leeman J, Calancie L, Hartman MA, Escoffery CT, Herrmann AK, Tague LE, et al. What strategies are used to build practitioners’ capacity to implement community-based interventions and are they effective?: a systematic review. Implement Sci. 2015;10:1–15.

Nathan N, Shelton RC, Laur CV, Hailemariam M, Hall A. Editorial: Sustaining the implementation of evidence-based interventions in clinical and community settings. Front Health Serv. 2023;3:1176023.

Balis LE, Houghtaling B, Harden SM. Using implementation strategies in community settings: an introduction to the Expert Recommendations for Implementing Change (ERIC) compilation and future directions. Transl Behav Med. 2022;12:965–78.

Nathan N, Powell BJ, Shelton RC, Laur CV, Wolfenden L, Hailemariam M, et al. Do the Expert Recommendations for Implementing Change (ERIC) strategies adequately address sustainment? Front Health Serv. 2022;2:905909.

Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, et al. Audit and feedback effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012;6:CD000259.

Google Scholar  

Moore L, Guertin JR, Tardif P-A, Ivers NM, Hoch J, Conombo B, et al. Economic evaluations of audit and feedback interventions: a systematic review. BMJ Qual Saf. 2022;31:754–67.

Sykes MJ, McAnuff J, Kolehmainen N. When is audit and feedback effective in dementia care? A systematic review. Int J Nurs Stud. 2018;79:27–35.

Barnes C, McCrabb S, Stacey F, Nathan N, Yoong SL, Grady A, et al. Improving implementation of school-based healthy eating and physical activity policies, practices, and programs: a systematic review. Transl Behav Med. 2021;11:1365–410.

Tomasone JR, Kauffeldt KD, Chaudhary R, Brouwers MC. Effectiveness of guideline dissemination and implementation strategies on health care professionals’ behaviour and patient outcomes in the cancer care context: a systematic review. Implement Sci. 2020;15:1–18.

Seda V, Moles RJ, Carter SR, Schneider CR. Assessing the comparative effectiveness of implementation strategies for professional services to community pharmacy: A systematic review. Res Soc Adm Pharm. 2022;18:3469–83.

Lovero KL, Kemp CG, Wagenaar BH, Giusto A, Greene MC, Powell BJ, et al. Application of the Expert Recommendations for Implementing Change (ERIC) compilation of strategies to health intervention implementation in low- and middle-income countries: a systematic review. Implement Sci. 2023;18:56.

Chapman A, Rankin NM, Jongebloed H, Yoong SL, White V, Livingston PM, et al. Overcoming challenges in conducting systematic reviews in implementation science: a methods commentary. Syst Rev. 2023;12:1–6.

Article   CAS   Google Scholar  

Proctor EK, Bunger AC, Lengnick-Hall R, Gerke DR, Martin JK, Phillips RJ, et al. Ten years of implementation outcomes research: a scoping review. Implement Sci. 2023;18:1–19.

Michaud TL, Pereira E, Porter G, Golden C, Hill J, Kim J, et al. Scoping review of costs of implementation strategies in community, public health and healthcare settings. BMJ Open. 2022;12:e060785.

Sohn H, Tucker A, Ferguson O, Gomes I, Dowdy D. Costing the implementation of public health interventions in resource-limited settings: a conceptual framework. Implement Sci. 2020;15:1–8.

Peek C, Glasgow RE, Stange KC, Klesges LM, Purcell EP, Kessler RS. The 5 R’s: an emerging bold standard for conducting relevant research in a changing world. Ann Fam Med. 2014;12:447–55.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999;89:1322–7.

Shelton RC, Chambers DA, Glasgow RE. An Extension of RE-AIM to Enhance Sustainability: Addressing Dynamic Context and Promoting Health Equity Over Time. Front Public Health. 2020;8:134.

Holtrop JS, Estabrooks PA, Gaglio B, Harden SM, Kessler RS, King DK, et al. Understanding and applying the RE-AIM framework: Clarifications and resources. J Clin Transl Sci. 2021;5:e126.

Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1.

Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;349:g7647.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ [Internet]. 2021;372. Available from: https://www.bmj.com/content/372/bmj.n71

Rabin BA, Brownson RC, Haire-Joshu D, Kreuter MW, Weaver NL. A Glossary for Dissemination and Implementation Research in Health. J Public Health Manag Pract. 2008;14:117–23.

Eccles MP, Mittman BS. Welcome to Implementation Science. Implement Sci. 2006;1:1.

Article   PubMed Central   Google Scholar  

Miller WR, Wilbourne PL. Mesa Grande: a methodological analysis of clinical trials of treatments for alcohol use disorders. Addict Abingdon Engl. 2002;97:265–77.

Miller WR, Brown JM, Simpson TL, Handmaker NS, Bien TH, Luckie LF, et al. What works? A methodological analysis of the alcohol treatment outcome literature. Handb Alcohol Treat Approaches Eff Altern 2nd Ed. Needham Heights, MA, US: Allyn & Bacon; 1995:12–44.

Wells S, Tamir O, Gray J, Naidoo D, Bekhit M, Goldmann D. Are quality improvement collaboratives effective? A systematic review BMJ Qual Saf. 2018;27:226–40.

Yakovchenko V, Chinman MJ, Lamorte C, Powell BJ, Waltz TJ, Merante M, et al. Refining Expert Recommendations for Implementing Change (ERIC) strategy surveys using cognitive interviews with frontline providers. Implement Sci Commun. 2023;4:1–14.

Wagner TH, Yoon J, Jacobs JC, So A, Kilbourne AM, Yu W, et al. Estimating costs of an implementation intervention. Med Decis Making. 2020;40:959–67.

Gold HT, McDermott C, Hoomans T, Wagner TH. Cost data in implementation science: categories and approaches to costing. Implement Sci. 2022;17:11.

Boutron I, Page MJ, Higgins JP, Altman DG, Lundh A, Hróbjartsson A. Considering bias and conflicts of interest among the included studies. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane Handbook for Systematic Reviews of Interventions. 2019. https://doi.org/10.1002/9781119536604.ch7 . 

Higgins JP, Savović J, Page MJ, Elbers RG, Sterne J. Assessing risk of bias in a randomized trial. Cochrane Handb Syst Rev Interv. 2019;6:205–28.

Reilly KL, Kennedy S, Porter G, Estabrooks P. Comparing, Contrasting, and Integrating Dissemination and Implementation Outcomes Included in the RE-AIM and Implementation Outcomes Frameworks. Front Public Health [Internet]. 2020 [cited 2024 Apr 24];8. Available from: https://www.frontiersin.org/journals/public-health/articles/ https://doi.org/10.3389/fpubh.2020.00430/full

Grimshaw JM, Thomas RE, MacLennan G, Fraser C, Ramsay CR, Vale L, et al. Effectiveness and efficiency of guideline dissemination and implementation strategies. Health Technol Assess Winch Engl. 2004;8:iii–iv 1-72.

CAS   Google Scholar  

Beidas RS, Kendall PC. Training Therapists in Evidence-Based Practice: A Critical Review of Studies From a Systems-Contextual Perspective. Clin Psychol Publ Div Clin Psychol Am Psychol Assoc. 2010;17:1–30.

Powell BJ, Beidas RS, Lewis CC, Aarons GA, McMillen JC, Proctor EK, et al. Methods to Improve the Selection and Tailoring of Implementation Strategies. J Behav Health Serv Res. 2017;44:177–94.

Powell BJ, Fernandez ME, Williams NJ, Aarons GA, Beidas RS, Lewis CC, et al. Enhancing the Impact of Implementation Strategies in Healthcare: A Research Agenda. Front Public Health [Internet]. 2019 [cited 2021 Mar 31];7. Available from: https://www.frontiersin.org/articles/ https://doi.org/10.3389/fpubh.2019.00003/full

Frakt AB, Prentice JC, Pizer SD, Elwy AR, Garrido MM, Kilbourne AM, et al. Overcoming Challenges to Evidence-Based Policy Development in a Large. Integrated Delivery System Health Serv Res. 2018;53:4789–807.

PubMed   Google Scholar  

Crable EL, Lengnick-Hall R, Stadnick NA, Moullin JC, Aarons GA. Where is “policy” in dissemination and implementation science? Recommendations to advance theories, models, and frameworks: EPIS as a case example. Implement Sci. 2022;17:80.

Crable EL, Grogan CM, Purtle J, Roesch SC, Aarons GA. Tailoring dissemination strategies to increase evidence-informed policymaking for opioid use disorder treatment: study protocol. Implement Sci Commun. 2023;4:16.

Bond GR. Evidence-based policy strategies: A typology. Clin Psychol Sci Pract. 2018;25:e12267.

Loo TS, Davis RB, Lipsitz LA, Irish J, Bates CK, Agarwal K, et al. Electronic Medical Record Reminders and Panel Management to Improve Primary Care of Elderly Patients. Arch Intern Med. 2011;171:1552–8.

Shojania KG, Jennings A, Mayhew A, Ramsay C, Eccles M, Grimshaw J. Effect of point-of-care computer reminders on physician behaviour: a systematic review. CMAJ Can Med Assoc J. 2010;182:E216-25.

Sequist TD, Gandhi TK, Karson AS, Fiskio JM, Bugbee D, Sperling M, et al. A Randomized Trial of Electronic Clinical Reminders to Improve Quality of Care for Diabetes and Coronary Artery Disease. J Am Med Inform Assoc JAMIA. 2005;12:431–7.

Dopp AR, Kerns SEU, Panattoni L, Ringel JS, Eisenberg D, Powell BJ, et al. Translating economic evaluations into financing strategies for implementing evidence-based practices. Implement Sci. 2021;16:1–12.

Dopp AR, Hunter SB, Godley MD, Pham C, Han B, Smart R, et al. Comparing two federal financing strategies on penetration and sustainment of the adolescent community reinforcement approach for substance use disorders: protocol for a mixed-method study. Implement Sci Commun. 2022;3:51.

Proctor EK, Toker E, Tabak R, McKay VR, Hooley C, Evanoff B. Market viability: a neglected concept in implementation science. Implement Sci. 2021;16:98.

Dopp AR, Narcisse M-R, Mundey P, Silovsky JF, Smith AB, Mandell D, et al. A scoping review of strategies for financing the implementation of evidence-based practices in behavioral health systems: State of the literature and future directions. Implement Res Pract. 2020;1:2633489520939980.

PubMed   PubMed Central   Google Scholar  

Dopp AR, Kerns SEU, Panattoni L, Ringel JS, Eisenberg D, Powell BJ, et al. Translating economic evaluations into financing strategies for implementing evidence-based practices. Implement Sci IS. 2021;16:66.

Kilbourne AM, Neumann MS, Pincus HA, Bauer MS, Stall R. Implementing evidence-based interventions in health care:application of the replicating effective programs framework. Implement Sci. 2007;2:42–51.

Kegeles SM, Rebchook GM, Hays RB, Terry MA, O’Donnell L, Leonard NR, et al. From science to application: the development of an intervention package. AIDS Educ Prev Off Publ Int Soc AIDS Educ. 2000;12:62–74.

Wandersman A, Imm P, Chinman M, Kaftarian S. Getting to outcomes: a results-based approach to accountability. Eval Program Plann. 2000;23:389–95.

Wandersman A, Chien VH, Katz J. Toward an evidence-based system for innovation support for implementing innovations with quality: Tools, training, technical assistance, and quality assurance/quality improvement. Am J Community Psychol. 2012;50:445–59.

Rogal SS, Yakovchenko V, Waltz TJ, Powell BJ, Kirchner JE, Proctor EK, et al. The association between implementation strategy use and the uptake of hepatitis C treatment in a national sample. Implement Sci. 2017;12:1–13.

Smith SN, Almirall D, Prenovost K, Liebrecht C, Kyle J, Eisenberg D, et al. Change in patient outcomes after augmenting a low-level implementation strategy in community practices that are slow to adopt a collaborative chronic care model: a cluster randomized implementation trial. Med Care. 2019;57:503.

Rogal SS, Yakovchenko V, Waltz TJ, Powell BJ, Gonzalez R, Park A, et al. Longitudinal assessment of the association between implementation strategy use and the uptake of hepatitis C treatment: Year 2. Implement Sci. 2019;14:1–12.

Harvey G, Kitson A. Translating evidence into healthcare policy and practice: Single versus multi-faceted implementation strategies – is there a simple answer to a complex question? Int J Health Policy Manag. 2015;4:123–6.

Engell T, Stadnick NA, Aarons GA, Barnett ML. Common Elements Approaches to Implementation Research and Practice: Methods and Integration with Intervention Science. Glob Implement Res Appl. 2023;3:1–15.

Michie S, Fixsen D, Grimshaw JM, Eccles MP. Specifying and reporting complex behaviour change interventions: the need for a scientific method. Implement Sci IS. 2009;4:40.

Smith JD, Li DH, Rafferty MR. The Implementation Research Logic Model: a method for planning, executing, reporting, and synthesizing implementation projects. Implement Sci IS. 2020;15:84.

Perez Jolles M, Lengnick-Hall R, Mittman BS. Core Functions and Forms of Complex Health Interventions: a Patient-Centered Medical Home Illustration. JGIM J Gen Intern Med. 2019;34:1032–8.

Schroeck FR, Ould Ismail AA, Haggstrom DA, Sanchez SL, Walker DR, Zubkoff L. Data-driven approach to implementation mapping for the selection of implementation strategies: a case example for risk-aligned bladder cancer surveillance. Implement Sci IS. 2022;17:58.

Frank HE, Kemp J, Benito KG, Freeman JB. Precision Implementation: An Approach to Mechanism Testing in Implementation Research. Adm Policy Ment Health. 2022;49:1084–94.

Lewis CC, Klasnja P, Lyon AR, Powell BJ, Lengnick-Hall R, Buchanan G, et al. The mechanics of implementation strategies and measures: advancing the study of implementation mechanisms. Implement Sci Commun. 2022;3:114.

Geng EH, Baumann AA, Powell BJ. Mechanism mapping to advance research on implementation strategies. PLoS Med. 2022;19:e1003918.

Pinnock H, Barwick M, Carpenter CR, Eldridge S, Grandes G, Griffiths CJ, et al. Standards for Reporting Implementation Studies (StaRI) Statement. BMJ. 2017;356:i6795.

Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for Implementation Research: Conceptual Distinctions, Measurement Challenges, and Research Agenda. Adm Policy Ment Health Ment Health Serv Res. 2011;38:65–76.

Hooley C, Amano T, Markovitz L, Yaeger L, Proctor E. Assessing implementation strategy reporting in the mental health literature: a narrative review. Adm Policy Ment Health Ment Health Serv Res. 2020;47:19–35.

Proctor E, Ramsey AT, Saldana L, Maddox TM, Chambers DA, Brownson RC. FAST: a framework to assess speed of translation of health innovations to practice and policy. Glob Implement Res Appl. 2022;2:107–19.

Cullen L, Hanrahan K, Edmonds SW, Reisinger HS, Wagner M. Iowa Implementation for Sustainability Framework. Implement Sci IS. 2022;17:1.

Saldana L, Ritzwoller DP, Campbell M, Block EP. Using economic evaluations in implementation science to increase transparency in costs and outcomes for organizational decision-makers. Implement Sci Commun. 2022;3:40.

Eisman AB, Kilbourne AM, Dopp AR, Saldana L, Eisenberg D. Economic evaluation in implementation science: making the business case for implementation strategies. Psychiatry Res. 2020;283:112433.

Akiba CF, Powell BJ, Pence BW, Nguyen MX, Golin C, Go V. The case for prioritizing implementation strategy fidelity measurement: benefits and challenges. Transl Behav Med. 2022;12:335–42.

Akiba CF, Powell BJ, Pence BW, Muessig K, Golin CE, Go V. “We start where we are”: a qualitative study of barriers and pragmatic solutions to the assessment and reporting of implementation strategy fidelity. Implement Sci Commun. 2022;3:117.

Rudd BN, Davis M, Doupnik S, Ordorica C, Marcus SC, Beidas RS. Implementation strategies used and reported in brief suicide prevention intervention studies. JAMA Psychiatry. 2022;79:829–31.

Painter JT, Raciborski RA, Matthieu MM, Oliver CM, Adkins DA, Garner KK. Engaging stakeholders to retrospectively discern implementation strategies to support program evaluation: Proposed method and case study. Eval Program Plann. 2024;103:102398.

Bunger AC, Powell BJ, Robertson HA, MacDowell H, Birken SA, Shea C. Tracking implementation strategies: a description of a practical approach and early findings. Health Res Policy Syst. 2017;15:1–12.

Mustanski B, Smith JD, Keiser B, Li DH, Benbow N. Supporting the growth of domestic HIV implementation research in the united states through coordination, consultation, and collaboration: how we got here and where we are headed. JAIDS J Acquir Immune Defic Syndr. 2022;90:S1-8.

Marques MM, Wright AJ, Corker E, Johnston M, West R, Hastings J, et al. The Behaviour Change Technique Ontology: Transforming the Behaviour Change Technique Taxonomy v1. Wellcome Open Res. 2023;8:308.

Merle JL, Li D, Keiser B, Zamantakis A, Queiroz A, Gallo CG, et al. Categorising implementation determinants and strategies within the US HIV implementation literature: a systematic review protocol. BMJ Open. 2023;13:e070216.

Glenshaw MT, Gaist P, Wilson A, Cregg RC, Holtz TH, Goodenow MM. Role of NIH in the Ending the HIV Epidemic in the US Initiative: Research Improving Practice. J Acquir Immune Defic Syndr. 1999;2022(90):S9-16.

Purcell DW, Namkung Lee A, Dempsey A, Gordon C. Enhanced Federal Collaborations in Implementation Science and Research of HIV Prevention and Treatment. J Acquir Immune Defic Syndr. 1999;2022(90):S17-22.

Queiroz A, Mongrella M, Keiser B, Li DH, Benbow N, Mustanski B. Profile of the Portfolio of NIH-Funded HIV Implementation Research Projects to Inform Ending the HIV Epidemic Strategies. J Acquir Immune Defic Syndr. 1999;2022(90):S23-31.

Zamantakis A, Li DH, Benbow N, Smith JD, Mustanski B. Determinants of Pre-exposure Prophylaxis (PrEP) Implementation in Transgender Populations: A Qualitative Scoping Review. AIDS Behav. 2023;27:1600–18.

Li DH, Benbow N, Keiser B, Mongrella M, Ortiz K, Villamar J, et al. Determinants of Implementation for HIV Pre-exposure Prophylaxis Based on an Updated Consolidated Framework for Implementation Research: A Systematic Review. J Acquir Immune Defic Syndr. 1999;2022(90):S235-46.

Chambers DA, Emmons KM. Navigating the field of implementation science towards maturity: challenges and opportunities. Implement Sci. 2024;19:26, s13012-024-01352–0.

Chinman M, Acosta J, Ebener P, Shearer A. “What we have here, is a failure to [replicate]”: Ways to solve a replication crisis in implementation science. Prev Sci. 2022;23:739–50.

Chambers DA, Glasgow RE, Stange KC. The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change. Implement Sci. 2013;8:117.

Lengnick-Hall R, Gerke DR, Proctor EK, Bunger AC, Phillips RJ, Martin JK, et al. Six practical recommendations for improved implementation outcomes reporting. Implement Sci. 2022;17:16.

Miller CJ, Barnett ML, Baumann AA, Gutner CA, Wiltsey-Stirman S. The FRAME-IS: a framework for documenting modifications to implementation strategies in healthcare. Implement Sci IS. 2021;16:36.

Xu X, Lazar CM, Ruger JP. Micro-costing in health and medicine: a critical appraisal. Health Econ Rev. 2021;11:1.

Barnett ML, Dopp AR, Klein C, Ettner SL, Powell BJ, Saldana L. Collaborating with health economists to advance implementation science: a qualitative study. Implement Sci Commun. 2020;1:82.

Lengnick-Hall R, Williams NJ, Ehrhart MG, Willging CE, Bunger AC, Beidas RS, et al. Eight characteristics of rigorous multilevel implementation research: a step-by-step guide. Implement Sci. 2023;18:52.

Riley-Gibson E, Hall A, Shoesmith A, Wolfenden L, Shelton RC, Doherty E, et al. A systematic review to determine the effect of strategies to sustain chronic disease prevention interventions in clinical and community settings: study protocol. Res Sq [Internet]. 2023 [cited 2024 Apr 19]; Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312971/

Ingvarsson S, Hasson H, von Thiele Schwarz U, Nilsen P, Powell BJ, Lindberg C, et al. Strategies for de-implementation of low-value care—a scoping review. Implement Sci IS. 2022;17:73.

Lewis CC, Powell BJ, Brewer SK, Nguyen AM, Schriger SH, Vejnoska SF, et al. Advancing mechanisms of implementation to accelerate sustainable evidence-based practice integration: protocol for generating a research agenda. BMJ Open. 2021;11:e053474.

Hailemariam M, Bustos T, Montgomery B, Barajas R, Evans LB, Drahota A. Evidence-based intervention sustainability strategies: a systematic review. Implement Sci. 2019;14:N.PAG-N.PAG.

Michie S, Atkins L, West R. The behaviour change wheel. Guide Des Interv 1st Ed G B Silverback Publ. 2014;1003:1010.

Birken SA, Haines ER, Hwang S, Chambers DA, Bunger AC, Nilsen P. Advancing understanding and identifying strategies for sustaining evidence-based practices: a review of reviews. Implement Sci IS. 2020;15:88.

Metz A, Jensen T, Farley A, Boaz A, Bartley L, Villodas M. Building trusting relationships to support implementation: A proposed theoretical model. Front Health Serv. 2022;2:894599.

Rabin BA, Cain KL, Watson P, Oswald W, Laurent LC, Meadows AR, et al. Scaling and sustaining COVID-19 vaccination through meaningful community engagement and care coordination for underserved communities: hybrid type 3 effectiveness-implementation sequential multiple assignment randomized trial. Implement Sci IS. 2023;18:28.

Gyamfi J, Iwelunmor J, Patel S, Irazola V, Aifah A, Rakhra A, et al. Implementation outcomes and strategies for delivering evidence-based hypertension interventions in lower-middle-income countries: Evidence from a multi-country consortium for hypertension control. PLOS ONE. 2023;18:e0286204.

Woodward EN, Ball IA, Willging C, Singh RS, Scanlon C, Cluck D, et al. Increasing consumer engagement: tools to engage service users in quality improvement or implementation efforts. Front Health Serv. 2023;3:1124290.

Norton WE, Chambers DA. Unpacking the complexities of de-implementing inappropriate health interventions. Implement Sci IS. 2020;15:2.

Norton WE, McCaskill-Stevens W, Chambers DA, Stella PJ, Brawley OW, Kramer BS. DeImplementing Ineffective and Low-Value Clinical Practices: Research and Practice Opportunities in Community Oncology Settings. JNCI Cancer Spectr. 2021;5:pkab020.

McKay VR, Proctor EK, Morshed AB, Brownson RC, Prusaczyk B. Letting Go: Conceptualizing Intervention De-implementation in Public Health and Social Service Settings. Am J Community Psychol. 2018;62:189–202.

Patey AM, Grimshaw JM, Francis JJ. Changing behaviour, ‘more or less’: do implementation and de-implementation interventions include different behaviour change techniques? Implement Sci IS. 2021;16:20.

Rodriguez Weno E, Allen P, Mazzucca S, Farah Saliba L, Padek M, Moreland-Russell S, et al. Approaches for Ending Ineffective Programs: Strategies From State Public Health Practitioners. Front Public Health. 2021;9:727005.

Gnjidic D, Elshaug AG. De-adoption and its 43 related terms: harmonizing low-value care terminology. BMC Med. 2015;13:273.

Download references

Acknowledgements

The authors would like to acknowledge the early contributions of the Pittsburgh Dissemination and Implementation Science Collaborative (Pitt DISC). LEA would like to thank Dr. Billie Davis for analytical support. The authors would like to acknowledge the implementation science experts who recommended articles for our review, including Greg Aarons, Mark Bauer, Rinad Beidas, Geoffrey Curran, Laura Damschroder, Rani Elwy, Amy Kilbourne, JoAnn Kirchner, Jennifer Leeman, Cara Lewis, Dennis Li, Aaron Lyon, Gila Neta, and Borsika Rabin.

Dr. Rogal’s time was funded in part by a University of Pittsburgh K award (K23-DA048182) and by a VA Health Services Research and Development grant (PEC 19-207). Drs. Bachrach and Quinn were supported by VA HSR Career Development Awards (CDA 20-057, PI: Bachrach; CDA 20-224, PI: Quinn). Dr. Scheunemann’s time was funded by the US Agency for Healthcare Research and Quality (K08HS027210). Drs. Hero, Chinman, Goodrich, Ernecoff, and Mr. Qureshi were funded by the Patient-Centered Outcomes Research Institute (PCORI) AOSEPP2 Task Order 12 to conduct a landscape review of US studies on the effectiveness of implementation strategies with results reported here ( https://www.pcori.org/sites/default/files/PCORI-Implementation-Strategies-for-Evidence-Based-Practice-in-Health-and-Health-Care-A-Review-of-the-Evidence-Full-Report.pdf and https://www.pcori.org/sites/default/files/PCORI-Implementation-Strategies-for-Evidence-Based-Practice-in-Health-and-Health-Care-Brief-Report-Summary.pdf ). Dr. Ashcraft and Ms. Phares were funded by the Center for Health Equity Research and Promotion, (CIN 13-405). The funders had no involvement in this study.

Author information

Shari S. Rogal and Matthew J. Chinman are co-senior authors.

Authors and Affiliations

Center for Health Equity Research and Promotion, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA

Laura Ellen Ashcraft

Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA

David E. Goodrich, Angela Phares, Deirdre A. Quinn, Shari S. Rogal & Matthew J. Chinman

Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

David E. Goodrich, Deirdre A. Quinn & Matthew J. Chinman

Clinical & Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA

David E. Goodrich & Lisa G. Lederer

RAND Corporation, Pittsburgh, PA, USA

Joachim Hero, Nabeel Qureshi, Natalie C. Ernecoff & Matthew J. Chinman

Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA

Rachel L. Bachrach

Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA

Division of Geriatric Medicine, University of Pittsburgh, Department of Medicine, Pittsburgh, PA, USA

Leslie Page Scheunemann

Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Pittsburgh, Department of Medicine, Pittsburgh, PA, USA

Departments of Medicine and Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

Shari S. Rogal

You can also search for this author in PubMed   Google Scholar

Contributions

LEA, SSR, and MJC conceptualized the study. LEA, SSR, MJC, and JOH developed the study design. LEA and JOH acquired the data. LEA, DEG, AP, RLB, DAQ, LGL, LPS, SSR, NQ, and MJC conducted the abstract, full text review, and rigor assessment. LEA, DEG, JOH, AP, RLB, DAQ, NQ, NCE, SSR, and MJC conducted the data abstraction. DEG, SSR, and MJC adjudicated conflicts. LEA and SSR analyzed the data. LEA, SSR, JOH, and MJC interpreted the data. LEA, SSR, and MJC drafted the work. All authors substantially revised the work. All authors approved the submitted version and agreed to be personally accountable for their contributions and the integrity of the work.

Corresponding author

Correspondence to Laura Ellen Ashcraft .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

The manuscript does not contain any individual person’s data.

Competing interests

Additional information, publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., supplementary material 2., supplementary material 3., supplementary material 4., supplementary material 5., supplementary material 6., supplementary material 7., supplementary material 8., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Ashcraft, L.E., Goodrich, D.E., Hero, J. et al. A systematic review of experimentally tested implementation strategies across health and human service settings: evidence from 2010-2022. Implementation Sci 19 , 43 (2024). https://doi.org/10.1186/s13012-024-01369-5

Download citation

Received : 09 November 2023

Accepted : 27 May 2024

Published : 24 June 2024

DOI : https://doi.org/10.1186/s13012-024-01369-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Implementation strategy
• Health-related outcomes

Implementation Science

ISSN: 1748-5908

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• Search Menu

Sign in through your institution

• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Urban Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Literature
• Classical Reception
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Epigraphy
• Greek and Roman Law
• Greek and Roman Archaeology
• Greek and Roman Papyrology
• Late Antiquity
• Religion in the Ancient World
• Social History
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Agriculture
• History of Education
• History of Emotions
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Acquisition
• Language Variation
• Language Families
• Language Evolution
• Language Reference
• Lexicography
• Linguistic Theories
• Linguistic Typology
• Linguistic Anthropology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies (Modernism)
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Religion
• Music and Culture
• Music and Media
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Science
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Politics
• Law and Society
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Oncology
• Medical Toxicology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Clinical Neuroscience
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Ethics
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Security
• Computer Games
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Neuroscience
• Cognitive Psychology
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Strategy
• Business History
• Business Ethics
• Business and Government
• Business and Technology
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic Systems
• Economic Methodology
• Economic History
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• Ethnic Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Political Theory
• Political Behaviour
• Political Economy
• Political Institutions
• Politics and Law
• Politics of Development
• Public Administration
• Public Policy
• Qualitative Political Methodology
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

• < Previous chapter
• Next chapter >

2 Pre-Experimental Research Designs

• Published: February 2012
• Cite Icon Cite
• Permissions Icon Permissions

The simplest of the group research designs involve the assessment of the functioning of a single group of persons who receive social work services. These methods are called pre-experimental designs. Tightly controlled studies done in laboratory or special treatment settings are known as efficacy studies, and are used to demonstrate if a given treatment can produce positive results under ideal conditions. Outcome studies done with more clinically representative clients and therapists, in real world agency settings, are known as effectiveness studies. Ideally the latter are conducted after the former, under conditions of increasing complexity, so as to determine treatments that work well in real-world contexts. Among the pre-experimental designs are the one group posttreatment-only study and the one group pretest-posttest design. Various ways in which these designs can be strengthened are presented, along with descriptions of published articles illustrating their use in social work and other human service settings. The limitations of these designs are also discussed, as is a review of the major threats to internal validity that can inhibit causal inferences.

Personal account

• Sign in with email/username & password
• Get email alerts
• Save searches
• Purchase content
• Activate your purchase/trial code
• Add your ORCID iD

Institutional access

Sign in with a library card.

• Sign in with username/password
• Recommend to your librarian
• Institutional account management
• Get help with access

Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:

IP based access

Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.

Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.

• Click Sign in through your institution.
• Select your institution from the list provided, which will take you to your institution's website to sign in.
• When on the institution site, please use the credentials provided by your institution. Do not use an Oxford Academic personal account.
• Following successful sign in, you will be returned to Oxford Academic.

If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.

Enter your library card number to sign in. If you cannot sign in, please contact your librarian.

Society Members

Society member access to a journal is achieved in one of the following ways:

Sign in through society site

Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:

• Click Sign in through society site.
• When on the society site, please use the credentials provided by that society. Do not use an Oxford Academic personal account.

If you do not have a society account or have forgotten your username or password, please contact your society.

Sign in using a personal account

Some societies use Oxford Academic personal accounts to provide access to their members. See below.

A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.

Some societies use Oxford Academic personal accounts to provide access to their members.

Viewing your signed in accounts

Click the account icon in the top right to:

• View your signed in personal account and access account management features.
• View the institutional accounts that are providing access.

Signed in but can't access content

Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.

For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.

Our books are available by subscription or purchase to libraries and institutions.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Rights and permissions
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Experimental Research

• First Online: 25 February 2021

Cite this chapter

• C. George Thomas 2  

4611 Accesses

Experiments are part of the scientific method that helps to decide the fate of two or more competing hypotheses or explanations on a phenomenon. The term ‘experiment’ arises from Latin, Experiri, which means, ‘to try’. The knowledge accrues from experiments differs from other types of knowledge in that it is always shaped upon observation or experience. In other words, experiments generate empirical knowledge. In fact, the emphasis on experimentation in the sixteenth and seventeenth centuries for establishing causal relationships for various phenomena happening in nature heralded the resurgence of modern science from its roots in ancient philosophy spearheaded by great Greek philosophers such as Aristotle.

The strongest arguments prove nothing so long as the conclusions are not verified by experience. Experimental science is the queen of sciences and the goal of all speculation . Roger Bacon (1214–1294)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

Best, J.W. and Kahn, J.V. 1993. Research in Education (7th Ed., Indian Reprint, 2004). Prentice–Hall of India, New Delhi, 435p.

Google Scholar  

Campbell, D. and Stanley, J. 1963. Experimental and quasi-experimental designs for research. In: Gage, N.L., Handbook of Research on Teaching. Rand McNally, Chicago, pp. 171–247.

Chandel, S.R.S. 1991. A Handbook of Agricultural Statistics. Achal Prakashan Mandir, Kanpur, 560p.

Cox, D.R. 1958. Planning of Experiments. John Wiley & Sons, New York, 308p.

Fathalla, M.F. and Fathalla, M.M.F. 2004. A Practical Guide for Health Researchers. WHO Regional Publications Eastern Mediterranean Series 30. World Health Organization Regional Office for the Eastern Mediterranean, Cairo, 232p.

Fowkes, F.G.R., and Fulton, P.M. 1991. Critical appraisal of published research: Introductory guidelines. Br. Med. J. 302: 1136–1140.

Gall, M.D., Borg, W.R., and Gall, J.P. 1996. Education Research: An Introduction (6th Ed.). Longman, New York, 788p.

Gomez, K.A. 1972. Techniques for Field Experiments with Rice. International Rice Research Institute, Manila, Philippines, 46p.

Gomez, K.A. and Gomez, A.A. 1984. Statistical Procedures for Agricultural Research (2nd Ed.). John Wiley & Sons, New York, 680p.

Hill, A.B. 1971. Principles of Medical Statistics (9th Ed.). Oxford University Press, New York, 390p.

Holmes, D., Moody, P., and Dine, D. 2010. Research Methods for the Bioscience (2nd Ed.). Oxford University Press, Oxford, 457p.

Kerlinger, F.N. 1986. Foundations of Behavioural Research (3rd Ed.). Holt, Rinehart and Winston, USA. 667p.

Kirk, R.E. 2012. Experimental Design: Procedures for the Behavioural Sciences (4th Ed.). Sage Publications, 1072p.

Kothari, C.R. 2004. Research Methodology: Methods and Techniques (2nd Ed.). New Age International, New Delhi, 401p.

Kumar, R. 2011. Research Methodology: A Step-by step Guide for Beginners (3rd Ed.). Sage Publications India, New Delhi, 415p.

Leedy, P.D. and Ormrod, J.L. 2010. Practical Research: Planning and Design (9th Ed.), Pearson Education, New Jersey, 360p.

Marder, M.P. 2011. Research Methods for Science. Cambridge University Press, 227p.

Panse, V.G. and Sukhatme, P.V. 1985. Statistical Methods for Agricultural Workers (4th Ed., revised: Sukhatme, P.V. and Amble, V. N.). ICAR, New Delhi, 359p.

Ross, S.M. and Morrison, G.R. 2004. Experimental research methods. In: Jonassen, D.H. (ed.), Handbook of Research for Educational Communications and Technology (2nd Ed.). Lawrence Erlbaum Associates, New Jersey, pp. 10211043.

Snedecor, G.W. and Cochran, W.G. 1980. Statistical Methods (7th Ed.). Iowa State University Press, Ames, Iowa, 507p.

Download references

Author information

Authors and affiliations.

Kerala Agricultural University, Thrissur, Kerala, India

C. George Thomas

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to C. George Thomas .

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s)

About this chapter

Thomas, C.G. (2021). Experimental Research. In: Research Methodology and Scientific Writing . Springer, Cham. https://doi.org/10.1007/978-3-030-64865-7_5

Download citation

DOI : https://doi.org/10.1007/978-3-030-64865-7_5

Published : 25 February 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-64864-0

Online ISBN : 978-3-030-64865-7

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Research Methodologies Guide

• Action Research
• Bibliometrics
• Case Studies
• Content Analysis
• Digital Scholarship This link opens in a new window
• Documentary
• Ethnography
• Focus Groups
• Grounded Theory
• Life Histories/Autobiographies
• Longitudinal
• Participant Observation
• Qualitative Research (General)

Quasi-Experimental Design

• Usability Studies

Quasi-Experimental Design is a unique research methodology because it is characterized by what is lacks. For example, Abraham & MacDonald (2011) state:

" Quasi-experimental research is similar to experimental research in that there is manipulation of an independent variable. It differs from experimental research because either there is no control group, no random selection, no random assignment, and/or no active manipulation. "

This type of research is often performed in cases where a control group cannot be created or random selection cannot be performed. This is often the case in certain medical and psychological studies. 

For more information on quasi-experimental design, review the resources below: 

Where to Start

Below are listed a few tools and online guides that can help you start your Quasi-experimental research. These include free online resources and resources available only through ISU Library.

• Quasi-Experimental Research Designs by Bruce A. Thyer This pocket guide describes the logic, design, and conduct of the range of quasi-experimental designs, encompassing pre-experiments, quasi-experiments making use of a control or comparison group, and time-series designs. An introductory chapter describes the valuable role these types of studies have played in social work, from the 1930s to the present. Subsequent chapters delve into each design type's major features, the kinds of questions it is capable of answering, and its strengths and limitations.
• Experimental and Quasi-Experimental Designs for Research by Donald T. Campbell; Julian C. Stanley. Call Number: Q175 C152e Written 1967 but still used heavily today, this book examines research designs for experimental and quasi-experimental research, with examples and judgments about each design's validity.

Online Resources

• Quasi-Experimental Design From the Web Center for Social Research Methods, this is a very good overview of quasi-experimental design.
• Experimental and Quasi-Experimental Research From Colorado State University.
• Quasi-experimental design--Wikipedia, the free encyclopedia Wikipedia can be a useful place to start your research- check the citations at the bottom of the article for more information.
• << Previous: Qualitative Research (General)
• Next: Sampling >>
• Last Updated: Dec 19, 2023 2:12 PM
• URL: https://instr.iastate.libguides.com/researchmethods