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A COURSE IN RESEARCH METHODOLOGY 2018.pptx

This teaching paper is an introdcution to the field of research methodology as it enables beginners (students) to understand basic things about research, research techniques , research design and research procedure. The general aim behind this teaching paper is to facilitate the task of students to tackle this complicated field with confidence and ease.It covers a lot of courses and it can be taught to different levels of students: BA, MA and even PHd students.

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Wafae Barkani

Xochitl Ortiz

The authors felt during their several years of teaching experience that students fail to understand the books written on Research Methodology because generally they are written in technical language. Since this course is not taught before the Master’s degree, the students are not familiar with its vocabulary, methodology and course contents. The authors have made an attempt to write it in very non- technical language. It has been attempted that students who try to understand the research methodology through self-learning may also find it easy. The chapters are written with that approach. Even those students who intend to attain high level of knowledge of the research methodology in social sciences will find this book very helpful in understanding the basic concepts before they read any book on research methodology. This book is useful those students who offer the Research Methodology at Post Graduation and M.Phil. Level. This book is also very useful for Ph.D. Course Work examinations.

Anil Jharotia

Research is an important activity of any nation and societies for generating the information to its developments. Robust collection of qualitative information helps in the development of the any nations. Research & Development is an important tool for acquiring new knowledge in any field of human survival. Various type of problems and questions need to use research methodology depend on the rationale of researchers. How to use the research for finding answers of any research questions/problems.

https://www.ijrrjournal.com/IJRR_Vol.6_Issue.3_March2019/Abstract_IJRR0011.html

International Journal of Research & Review (IJRR)

Research methodology is a way to systematically solve the research problem. It may be understood as a science of studying how research is done scientifically. In it we study the various steps that are generally adopted by a researcher in studying his research problem along with the logic behind them. It is necessary for the researcher to know not only the research methods/techniques but also the methodology. Researchers not only need to know how to develop certain indices or tests, how to calculate the mean, the mode, the median or the standard deviation or chi-square, how to apply particular research techniques, but they also need to know which of these methods or techniques, are relevant and which are not, and what would they mean and indicate and why. Researchers also need to understand the assumptions underlying various techniques and they need to know the criteria by which they can decide that certain techniques and procedures will be applicable to certain problems and others will not. All this means that it is necessary for the researcher to design his methodology for his problem as the same may differ from problem to problem.

Scholarly Communication and the Publish or Perish Pressures of Academia A volume in the Advances in Knowledge Acquisition, Transfer, and Management (AKATM) Book Series

Dr. Naresh A . Babariya , Alka V. Gohel

The most important of research methodology in research study it is necessary for a researcher to design a methodology for the problem chosen and systematically solves the problem. Formulation of the research problem is to decide on a broad subject area on which has thorough knowledge and second important responsibility in research is to compare findings, it is literature review plays an extremely important role. The literature review is part of the research process and makes a valuable contribution to almost every operational step. A good research design provides information concerning with the selection of the sample population treatments and controls to be imposed and research work cannot be undertaken without sampling. Collecting the data and create data structure as organizing the data, analyzing the data help of different statistical method, summarizing the analysis, and using these results for making judgments, decisions and predictions. Keywords: Research Problem, Economical Plan, Developing Ideas, Research Strategy, Sampling Design, Theoretical Procedures, Experimental Studies, Numerical Schemes, Statistical Techniques.

Hafizi Saari

Dr. Moses Gweyi

This book is the outcome of more than four decades of experience of the author in teaching and research field. Research is a creative process and the topic of research methodology is complex and varied. The basic premise for writing this book is that research methods can be taught and learnt. The emphasis is on developing a research outlook and a frame of mind for carrying out research. The book presents current methodological techniques used in interdisciplinary research along with illustrated and worked out examples. This book is well equipped with fundamentals of research and research designs. All efforts have been made to present Research, its meaning, intention and usefulness. Focussed in designing of research programme, selection of variables, collection of data and their analysis to interpret the data are discussed extensively. Statistical tools are complemented with examples, making the complicated subject like statistics simplest usable form. The importance of software, like MS Excel, SPSS, for statistical analyses is included. Written in a simple language, it covers all aspects of management of data with details of statistical tools required for analysis in a research work. Complete with a glossary of key terms and guides to further reading, this book is an essential text for anyone coming to research for the first time and is widely relevant across the disciplines of sciences. This book is designed to introduce Masters, and doctoral students to the process of conducting scientific research in the life sciences, social sciences, education, public health, and related scientific disciplines. It conforms to the core syllabus of many universities and institutes. The target audience for this book includes those are going to start research as graduate students, junior researchers, and professors teaching courses on research methods. The book entitled “A guide to Research Methodology for Beginners” is succinct and compact by design focusing only on essential concepts rather than burden students with a voluminous text on top of their assigned readings. The book is structured into the following nine chapters. Chapter-1: What is Scientific Research? Chapter-2: Literature Review Chapter-3: How to develop a Research Questions & Hypotheses Chapter-4: Research Methods and the Research Design Chapter-5: Concept of Variables, Levels and Scales of Measurements for Data collection Chapter-6: Data Analysis, Management and Presentation Chapter-7: Tips for Writing Research Report Chapter-8: Glossary Related to Research Methodology Chapter-9: References It is a comprehensive and compact source for basic concepts in research and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. The target audience for this book includes those are going to start research as graduate students, junior researchers, and professors teaching courses on research methods.

Yuanita Damayanti

Khamis S Moh'd

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Education Standards

Radford university.

Learning Domain: Social Work

Standard: Basic Research Methodology

Lesson 10: Sampling in Qualitative Research

Lesson 11: qualitative measurement & rigor, lesson 12: qualitative design & data gathering, lesson 1: introduction to research, lesson 2: getting started with your research project, lesson 3: critical information literacy, lesson 4: paradigm, theory, and causality, lesson 5: research questions, lesson 6: ethics, lesson 7: measurement in quantitative research, lesson 8: sampling in quantitative research, lesson 9: quantitative research designs, powerpoint slides: sowk 621.01: research i: basic research methodology.

The twelve lessons for SOWK 621.01: Research I: Basic Research Methodology as previously taught by Dr. Matthew DeCarlo at Radford University. Dr. DeCarlo and his team developed a complete package of materials that includes a textbook, ancillary materials, and a student workbook as part of a VIVA Open Course Grant.

The PowerPoint slides associated with the twelve lessons of the course, SOWK 621.01: Research I: Basic Research Methodology, as previously taught by Dr. Matthew DeCarlo at Radford University. 

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Introduction to Data Analysis

Apr 06, 2019

2.59k likes | 6.06k Views

Introduction to Data Analysis. Data Measurement Measurement of the data is the first step in the process that ultimately guides the final analysis. Consideration of sampling, controls, errors (random and systematic) and the required precision all influence the final analysis.

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• central limit theorem
• normal distributions
• determination
• univariate analysis skewed distributions
• column chart
• time period

Presentation Transcript

Introduction to Data Analysis • Data Measurement • Measurement of the data is the first step in the process that ultimately guides the final analysis. • Consideration of sampling, controls, errors (random and systematic) and the required precision all influence the final analysis. • Validation: Instruments and methods used to measure the data must be validated for accuracy. • Precision and accuracy…Determination of error • Social vs. Physical Sciences

Introduction to Data Analysis • Types of data • Univariate/Multivariate • Univariate: When we use one variable to describe a person, place, or thing. • Multivariate: When we use two or more variables to measure a person, place or thing. Variables may or may not be dependent on each other. • Cross-sectional data/Time-ordered data (business, social sciences) • Cross-Sectional: Measurements taken at one time period • Time-Ordered: Measurements taken over time in chronological sequence. • The type of data will dictate (in part) the appropriate data-analysis method.

Introduction to Data Analysis • Measurement Scales • Nominal or Categorical Scale • Classification of people, places, or things into categories (e.g. age ranges, colors, etc.). • Classifications must be mutually exclusive (every element should belong to one category with no ambiguity). • Weakest of the four scales. No category is greater than or less (better or worse) than the others. They are just different. • Ordinal or Ranking Scale • Classification of people, places, or things into a ranking such that the data is arranged into a meaningful order (e.g. poor, fair, good, excellent). • Qualitative classification only

Introduction to Data Analysis • Measurement Scales (business, social sciences) • Interval Scale • Data classified by ranking. • Quantitative classification (time, temperature, etc). • Zero point of scale is arbitrary (differences are meaningful). • Ratio Scale • Data classified as the ratio of two numbers. • Quantitative classification (height, weight, distance, etc). • Zero point of scale is real (data can be added, subtracted, multiplied, and divided).

Univariate Analysis/Descriptive Statistics • Descriptive Statistics • The Range • Min/Max • Average • Median • Mode • Variance • Standard Deviation • Histograms and Normal Distributions

Distributions Descriptive statistics are easier to interpret when graphically illustrated. However, charting each data element can lead to very busy and confusing charts that do not help interpret the data. Grouping the data elements into categories and charting the frequency within these categories yields a graphical illustration of how the data is distributed throughout its range. Univariate Analysis/Histograms

Univariate Analysis/Histograms With just a few columns this chart is difficult to interpret. It tells you very little about the data set. Even finding the Min and Max can be difficult. The data can be presented such that more statistical parameters can be estimated from the chart (average, standard deviation).

Univariate Analysis/Histograms • Frequency Table • The first step is to decide on the categories and group the data appropriately. (45, 49, 50, 53, 60, 62, 63, 65, 66, 67, 69, 71, 73, 74, 74, 78, 81, 85, 87, 100)

Univariate Analysis/Histograms • Histogram • A histogram is simply a column chart of the frequency table.

Histogram Univariate Analysis/Histograms Average (68.6) and Median (68) Mode (74) -1SD +1SD

Univariate Analysis/Normal Distributions • Distributions that can be described mathematically as Gaussian are also called Normal • The Bell curve • Symmetrical • Mean ≈ Median Mean, Median, Mode

Univariate Analysis/Skewed Distributions • When data are skewed, the mean and SD can be misleading • Skewness sk= 3(mean-median)/SD If sk>|1| then distribution is non-symetrical • Negatively skewed • Mean<Median • Sk is negative • Positively Skewed • Mean>Median • Sk is positive

Central Limit Theorem • Regardless of the shape of a distribution, the distribution of the sample mean based on samples of size N approaches a normal curve as N increases. • N must be less than the entire sample N=10

Univariate Analysis/Descriptive Statistics • The Range • Difference between minimum and maximum values in a data set • Larger range usually (but not always) indicates a large spread or deviation in the values of the data set. (73, 66, 69, 67, 49, 60, 81, 71, 78, 62, 53, 87, 74, 65, 74, 50, 85, 45, 63, 100)

Univariate Analysis/Descriptive Statistics • The Average (Mean) • Sum of all values divided by the number of values in the data set. • One measure of central location in the data set. Average = Average=(73+66+69+67+49+60+81+71+78+62+53+87+74+65+74+50+85+45+63+100)/20 = 68.6 Excel function: AVERAGE()

The data may or may not be symmetrical around its average value 0 2.5 7.5 10 4.8 0 2.5 7.5 10 4.8 Univariate Analysis/Descriptive Statistics

The Median The middle value in a sorted data set. Half the values are greater and half are less than the median. Another measure of central location in the data set. (45, 49, 50, 53, 60, 62, 63, 65, 66, 67, 69, 71, 73, 74, 74, 78, 81, 85, 87, 100) Median: 68 (1, 2, 4, 7, 8, 9, 9) Excel function: MEDIAN() Univariate Analysis/Descriptive Statistics

The Median May or may not be close to the mean. Combination of mean and median are used to define the skewness of a distribution. 0 2.5 7.5 10 6.25 Univariate Analysis/Descriptive Statistics

The Mode Most frequently occurring value. Another measure of central location in the data set. (45, 49, 50, 53, 60, 62, 63, 65, 66, 67, 69, 71, 73, 74, 74, 78, 81, 85, 87, 100) Mode: 74 Generally not all that meaningful unless a larger percentage of the values are the same number. Univariate Analysis/Descriptive Statistics

Univariate Analysis/Descriptive Statistics • Variance • One measure of dispersion (deviation from the mean) of a data set. The larger the variance, the greater is the average deviation of each datum from the average value. Variance = Average value of the data set Variance = [(45 – 68.6)2 + (49 – 68.6)2 + (50 – 68.6)2 + (53 – 68.6)2 + …]/20 = 181 Excel Functions: VARP(), VAR()

Standard Deviation Square root of the variance. Can be thought of as the average deviation from the mean of a data set. The magnitude of the number is more in line with the values in the data set. Univariate Analysis/Descriptive Statistics Standard Deviation = ([(45 – 68.6)2 + (49 – 68.6)2 + (50 – 68.6)2 + (53 – 68.6)2 + …]/20)1/2 = 13.5 Excel Functions: STDEVP(), STDEV()

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Introduction to RHESSI Data Analysis

Introduction to RHESSI Data Analysis. Documentation Data Products Access to Data SSW Analysis Modes RHESSI GUI Combining GUI and Command Line RHESSI Objects Details and Links for these topics. Documentation RHESSI Data and Software Center Web Page

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Methods & Tools of Data Collection

Published by Laureen Evans Modified over 8 years ago

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