Information

Books on Statistics in R for behavioral data analysis

Books on Statistics in R for behavioral data analysis

Are there any statistics books geared towards teaching behavioral data analysis especially in psycholinguistics?


Just to add a few other options that I consider relevant:

-Longitudinal Data Analysis for the Behavioral Sciences Using R

-Behavioral Research Data Analysis with R

-Methods for the Behavioral, Educational, and Social Sciences: An R package (an interesting paper).


There are lots of resources out there for learning R and many of the general ones are useful for people in psychology.

For example, see the R contributed documentation: https://cran.r-project.org/other-docs.html

I really like the Quick-R site: https://www.statmethods.net/

That said, in terms of psychology-specific resources, check out:

  • Personality Project R Guide: http://personality-project.org/r/psych/

In terms of books that are psychology specific, here are a couple:

  • Learning Statistics with R: https://learningstatisticswithr.com/ This book is available for free online thanks to Danielle Navarro's use of a CC-BY-SA licence.
  • Discovering Statistics Using R. If you like Andy Field's accessible style, then you'll like this book.

General Comments

R is not overly user friendly (at first). Its error messages are at best cryptic. It is, however, very powerful and once partially mastered, easy to use. And it is free. Even more important than the cost is that it is an open source language which has become the lingua franca of statistics and data analysis. That is, as additional modules are added, it becomes even more useful. Modules included allow for multilevel (hierarchical) linear modeling, confirmatory factor analysis, etc. I believe that it is worth the time to learn how to use it. J. Baron and Y. Li's guide is very helpful. They include a one page pdf summary sheet of commands that is well worth printing out and using. A three page summary sheet of commands is available from Rpad.


The Big R-Book: From Data Science to Learning Machines and Big Data

Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science.

The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices.

  • Provides a practical guide for non-experts with a focus on business users
  • Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting
  • Uses a practical tone and integrates multiple topics in a coherent framework
  • Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R
  • Shows readers how to visualize results in static and interactive reports
  • Supplementary materials includes PDF slides based on the book&rsquos content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site

The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.


Advanced Statistics with Applications in R

Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teaching reduces to using existing packages. This book looks at what is under the hood. Many statistics issues including the recent crisis with p-value are caused by misunderstanding of statistical concepts due to poor theoretical background of practitioners and applied statisticians. This book is the product of a forty-year experience in teaching of probability and statistics and their applications for solving real-life problems.

There are more than 442 examples in the book: basically every probability or statistics concept is illustrated with an example accompanied with an R code. Many examples, such as Who said &pi? What team is better? The fall of the Roman empire, James Bond chase problem, Black Friday shopping, Free fall equation: Aristotle or Galilei, and many others are intriguing. These examples cover biostatistics, finance, physics and engineering, text and image analysis, epidemiology, spatial statistics, sociology, etc.

Advanced Statistics with Applications in R teaches students to use theory for solving real-life problems through computations: there are about 500 R codes and 100 datasets. These data can be freely downloaded from the author's website dartmouth.edu/

This book is suitable as a text for senior undergraduate students with major in statistics or data science or graduate students. Many researchers who apply statistics on the regular basis find explanation of many fundamental concepts from the theoretical perspective illustrated by concrete real-world applications.


Modern Psychometrics with R

This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method.

Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology.

Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.

Patrick Mair is Senior Lecturer in Statistics in the Department of Psychology at Harvard University. His research focuses on computational and applied statistics with special emphasis on psychometric methods, such as latent variable models and multivariate exploratory techniques. Mair holds a doctorate in Statistics and a master’s degree in Psychology from the University of Vienna. He was also a Research Fellow at the Department of Statistics at the University of California, Los Angeles.

“The book gives an exhaustive overview of statistical methods that may be used when analyzing results of research in psychology. Main accent is on the use of R software during analysis of data. The main goal of the book is to provide the reader with main methods used for data analysis and how those methods may be executed using software package R.” (Jonas Šiaulys, zbMath 1414.62006, 2019)


On the Importance of Learning Statistics for Psychology Students

Psychology is a very popular major for undergraduate students. Why do so many students flock to psychology programs? While the answer to this question varies by individual, reasons typically involve interest in the topic, job prospects, the appeal of certain faculty members, or the requirements for completion of the degree (Galotti, 1999). Often, students find interest in psychology because many concepts taught in introductory psychology classes are intuitively understood and directly applicable to their lives as human beings. An additional draw tends to be the avoidance of taking high-level mathematics courses. In many psychology programs, to obtain a bachelor’s degree, the only additional math course required is some form of introduction to social science statistics. However, there is quite a discrepancy between the statistics knowledge required to obtain a bachelor’s degree in psychology and what is necessary to have a career in the field of psychology. It turns out that the importance of understanding and being able to apply and interpret statistics in psychological research cannot be understated.

The public face of psychology is often represented by the therapist and on the surface, this occupation could not seem more removed from mathematics. However, this perception is quite misleading. From the development of new therapy techniques to evaluating the effectiveness of the techniques upon implementation, it is statistical analysis that provides the means by which conclusions can be drawn. While a bachelor’s degree in psychology may allow for a college graduate to obtain entry level jobs in a variety of fields (e.g. human resources, education, customer service, etc.), developing a career within the field of psychology requires a graduate degree. Further, with the exception of graduate degrees aimed at marriage and family therapy licensure, most graduate programs focus on learning to conduct, and then conducting, publishable research.

In 1990, Aiken, West, Sechrest, and Reno surveyed existing psychology programs on a number of issues to gain a sense of what was being covered in methods courses at the graduate and undergraduate levels. Generally, it was found that while analysis of variance (ANOVA) was covered at length, coverage of measurement issues and more advanced statistical methods was lacking. In revisiting this topic, Aiken, West, and Milsap (2008) found that while some improvement to the breadth of methodology courses was noted, the focus of such courses still tended to be on ANOVAs. Although some areas of psychology still rely heavily on true experiments, research in other fields of psychology often require other types of statistics beyond the use of ANOVAs. Rather, advanced techniques, such as structural equation modeling, multilevel modeling, and item response theory, are necessary to address contemporary research questions. Without the ability to apply more advanced statistical techniques, students lack the tools to conduct innovative and relevant research. Psychological research may start with a “great idea,” but this idea must be followed by a solid study design, effective data collection, and appropriate data analysis, combined with the means to analyze the data and interpret any findings. The cost of ignorance lies in the failure to optimize research design, to collect the types of data to best answer research questions, and to avoid improper analysis of data, leading to inappropriate and sometimes incorrect conclusions (Aiken et al., 1990).

As such, taking an introductory statistics course will not be sufficient in providing students with the research skills that they need. Higher level data analysis courses are necessary for success as a researcher. Most psychology programs at major universities offer courses beyond introductory statistics. Though class titles vary, a typical “advanced” statistics course will cover more complex analyses such as factorial ANOVA and multiple regression. Courses such as this provide the foundation for learning more specialized techniques that are not only more interesting, but more powerful for drawing conclusions. Many universities offer semester or quarter-length courses on structural equation modeling, where students can learn about methods like factor analysis, growth curve analysis, and multilevel modeling, which offer techniques for complex data structures and unobserved (latent) variables. Further, some courses may cover not only classical test theory, but also generalizability theory and item response theory, which constitute the future of psychological measurement. Other interesting courses may cover cluster analysis and multidimensional scaling, data mining techniques, or meta-analysis. Taking one or more of these advanced courses is extremely beneficial to undergraduates in psychology. Not only will a student learn to apply advanced techniques in his or her research, but having such courses listed on a student’s transcript gives a considerable edge when applying to graduate programs, allowing an applicant to separate oneself from the “herd.” Further, when reading contemporary research in psychology, understanding of the methods utilized engenders an enhanced ability to evaluate the implications of substantive findings.

For those who truly love the field of psychology and wish to have a career as a psychologist, statistics courses are unavoidable, but also invaluable. Fortunately, in some cases, those who believe they despise math may find themselves drawn in by the allure of techniques like structural equation modeling, which offer more eloquent ways of answering complex questions about systems of variables, rather than simple group differences. Though this might not be the case for all students, taking statistics courses even if they are particularly challenging will build the necessary skills to become stronger researchers and provide better job opportunities in the future.

Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology. American Psychologist, 62, 32-50.

Aiken, L. S., West, S. G., Sechrest, L., & Reno, R.R. (1990). Graduate training in statistics, methodology, and measurement in psychology. American Psychologist, 45, 721-734.

Galotti, K. M. (1999). Making a “major” real life decision: College students choosing an academic major. Journal of Educational Psychology, 91, 379-387.


Institution

University of California, Riverside

Current Position

Distinguished Professor of Psychology Edgar Pierce Professor of Psychology Emeritus, Harvard University

Highest Degree

Ph.D. in Psychology from University of California, Los Angeles, 1956

For more than 50 years, Robert Rosenthal has conducted research on the role of self-fulfilling prophecies in everyday life and in laboratory situations. Special interests include the effects of teacher's expectations on students' academic and physical performance, the effects of experimenters' expectations on the results of their research, and the effects of clinicians' expectations on their patients' mental and physical health.

During this time he has also been studying the role of nonverbal communication in (a) the mediation of interpersonal expectancy effects and in (b) the relationship between members of small groups. In addition, he has explored sources of artifact in behavioral research and in various quantitative procedures. In the realm of data analysis, his special interests are in experimental design and analysis, contrast analysis, and meta-analysis. His most recent books and articles are about these areas of data analysis and about the nature of nonverbal communication in teacher-student, doctor-patient, manager-employee, judge-jury, and psychotherapist-client interaction.

Professor Rosenthal is the recipient of several national awards, including election to Fellow status in the American Academy of Arts and Sciences (2009), the Gold Medal Award for Life Achievement in the Science of Psychology of the American Psychological Foundation (2003), Distinguished Scientific Award for Applications of Psychology (APA, 2002), Distinguished Scientific Contributions Award (APA Division 5, 2002), James McKeen Cattell Award (APS, 2001), Distinguished Scientist Award (SESP 1996), AAAS Prize for Behavioral Science Research (1993, with Nalini Ambady), Donald Campbell Award (SPSP, 1988), and AAAS Socio-Psychological Prize (1960, with Kermit Fode). He has also been a Guggenheim Fellow, Senior Fulbright Scholar, and a Fellow at the Center for Advanced Study in the Behavioral Sciences. Professor Rosenthal served as Co-Chair of the American Psychological Association Task Force on Statistical Inference.

Primary Interests:

  • Applied Social Psychology
  • Communication, Language
  • Interpersonal Processes
  • Nonverbal Behavior
  • Person Perception
  • Research Methods, Assessment

Note from the Network: The holder of this profile has certified having all necessary rights, licenses, and authorization to post the files listed below. Visitors are welcome to copy or use any files for noncommercial or journalistic purposes provided they credit the profile holder and cite this page as the source.


Overview

Day Date Topic
Monday January 11th Introduction
Wednesday January 13th Visualization I
Friday January 15th Visualization II
Monday January 18th No class (Martin Luther King Jr. Day)
Wednesday January 20th Data wrangling I
Friday January 22nd Data wrangling II
Monday January 25th Probability
Wednesday January 27th Simulation I
Friday January 29th Simulation II
Monday February 1st Modeling data
Wednesday February 3rd Linear model I
Friday February 5th Linear model II
Monday February 8th Linear model III
Wednesday February 10th Linear model IV
Friday February 12th Power analysis
Monday February 15th No class (Presidents’ Day)
Wednesday February 17th No class (time to work on midterm)
Thursday February 18th Midterm due
Friday February 19th Mediation and moderation
Monday February 22nd Model comparison
Wednesday February 24th Linear mixed effects models I
Thursday February 25th Project proposal due
Friday February 26th Linear mixed effects models II
Monday March 1st Linear mixed effects models III
Wednesday March 3rd Generalized linear model
Friday March 5th Bayesian data analysis I
Monday March 8th Bayesian data analysis II
Wednesday March 10th Bayesian data analysis III
Friday March 12th Guest lecture: Matthew Kay
Monday March 15th Guest lecture: Nilam Ram
Wednesday March 17th Final project presentations
Friday March 19th Final project presentations
Final project report due

Free statistics e-books for download

This post will eventually grow to hold a wide list of books on statistics (e-books, pdf books and so on) that are available for free download. But for now we’ll start off with just one several books:

    • The Elements of Statistical Learning written by Trevor Hastie, Robert Tibshirani and Jerome Friedman. you can legally download a copy of the book in pdf format from the authors website! Direct download (First discovered on the “one R tip a day” blog) – a wikibook. Download link by Michael Lavine. The book is organized into seven chapters: “Probability,” “Modes of Inference,” “Regression,” “More Probability,” “Special Distributions,” “More Models,” and “Mathematical Statistics.” and makes extensive use of R. Here is a favoring review the book received in JASA. 328 pages. Download link (approx. 40 mbyte) by Sanjoy Mahajan. Download link by Miller and Haden. an introductory textbook describing statistical analysis with analysis of variance (ANOVA, including repeated-measures and mixed designs), simple and multiple regression, and analysis of covariance. 274 pages. Download link(p.s: this book makes no reference to R. see here for R tutorials and functions for performing repeated measures anova) by Barbara Illowsky and Susan Dean. This textbook is intended for introductory statistics courses. 627 pages. R is not used in this book. Download link
    • Using R for Introductory Statistics by John Verzani Publisher: Chapman & Hall/CRC 2004 ISBN/ASIN: 1584884509 ISBN-13: 9781584884507 Number of pages: 114 Description: The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R. Download link
    • R Graphics (Three chapters only) by Paul Murrell ISBN: 9781584884866 ISBN 10: 158488486X Publication Date: July 29, 2005 Number of Pages: 328 Description: Chapter 1: An Introduction to R Graphics Chapter 4: Trellis Graphics: The Lattice Package Chapter 5: The Grid Graphics Model Download link (see scripts and images here)
    • Using RDownload link
    • R introDownload link
    • Psychometric Theory with Applications in R by William Revelle (a work in progress) Download link
    • A great long list of R related texts, for free download, can be found here.
    • Using Graphs Instead of Tableswebsite link (This web page accompanies the article “Using Graphs Instead of Tables in Political Science”, by Jonathan Kastellec and Eduardo Leoni, which appears in the December 2007 issue of Perspectives on Politics. It contains complete replication code for all the graphs that appear in the text)
    • IPSUR: Introduction to Probability and Statistics Using R by G. Jay Kerns, is FREE (in the GNU sense of the word) and comes with a plugin for Rcmdr. 412 pages. Download link(first discovered through the Revolution blog)
    • Multivariate Statistics with R by Paul J. Hewson. 189 pages. Download link(first discovered through open text book blog)
    • R Programming – a wikibook. (no PDF version is available as of yet) – direct PDF link
    • Modeling and Solving Linear Programming with R – free (pdf) download link

    Several of these books were discovered through a CrossValidated discussion:

    Know of any more e-books freely available for download? Please write to us about them in the comments.


    Free Web Books for Learning R

    Many Statistical Horizons seminars now use R as their primary computational platform for examples and exercises. Those seminars include:

    There are lots of good reasons to learn R, even if it won’t be your main statistical package. One excellent way to do that is to take our remote seminar, Statistics with R. But what if you want to learn just enough R to feel competent and comfortable in one of our R-based seminars? Although the web is full of resources for learning R, finding the right one isn’t easy.

    Good news! We’ve done the work for you. After scouring the web, we have identified three online books that we think do a terrific job of giving you the knowledge and skills you need to participate in our R seminars. And like R itself, they’re absolutely free.

    YaRrr! The Pirate’s Guide to R

    This accessible (and playful!) guide is oriented to behavioral scientists and will get you analyzing data right away. Working through chapters 2-4, 9, 13, 15 will prepare you for most of what you will encounter in a Statistical Horizons R course. Don’t be put off by the pirate theme. This book is packed with useful information that’s quick and easy to digest.

    Click here to read YaRrr! The Pirate’s Guide to R.

    Modern Dive

    This online book provides a balanced introduction to R with a strong emphasis on data wrangling and visualization. After going through the first two parts, you would be ready for any of our R courses.

    R for Data Science

    This is the gold standard for developing R programming, data management, and visualization skills. This book has many short chapters. Even just going through chapters 2-6 would give you a basic familiarity with R.


    Overview

    Day Date Topic
    Monday January 11th Introduction
    Wednesday January 13th Visualization I
    Friday January 15th Visualization II
    Monday January 18th No class (Martin Luther King Jr. Day)
    Wednesday January 20th Data wrangling I
    Friday January 22nd Data wrangling II
    Monday January 25th Probability
    Wednesday January 27th Simulation I
    Friday January 29th Simulation II
    Monday February 1st Modeling data
    Wednesday February 3rd Linear model I
    Friday February 5th Linear model II
    Monday February 8th Linear model III
    Wednesday February 10th Linear model IV
    Friday February 12th Power analysis
    Monday February 15th No class (Presidents’ Day)
    Wednesday February 17th No class (time to work on midterm)
    Thursday February 18th Midterm due
    Friday February 19th Mediation and moderation
    Monday February 22nd Model comparison
    Wednesday February 24th Linear mixed effects models I
    Thursday February 25th Project proposal due
    Friday February 26th Linear mixed effects models II
    Monday March 1st Linear mixed effects models III
    Wednesday March 3rd Generalized linear model
    Friday March 5th Bayesian data analysis I
    Monday March 8th Bayesian data analysis II
    Wednesday March 10th Bayesian data analysis III
    Friday March 12th Guest lecture: Matthew Kay
    Monday March 15th Guest lecture: Nilam Ram
    Wednesday March 17th Final project presentations
    Friday March 19th Final project presentations
    Final project report due

    Institution

    University of California, Riverside

    Current Position

    Distinguished Professor of Psychology Edgar Pierce Professor of Psychology Emeritus, Harvard University

    Highest Degree

    Ph.D. in Psychology from University of California, Los Angeles, 1956

    For more than 50 years, Robert Rosenthal has conducted research on the role of self-fulfilling prophecies in everyday life and in laboratory situations. Special interests include the effects of teacher's expectations on students' academic and physical performance, the effects of experimenters' expectations on the results of their research, and the effects of clinicians' expectations on their patients' mental and physical health.

    During this time he has also been studying the role of nonverbal communication in (a) the mediation of interpersonal expectancy effects and in (b) the relationship between members of small groups. In addition, he has explored sources of artifact in behavioral research and in various quantitative procedures. In the realm of data analysis, his special interests are in experimental design and analysis, contrast analysis, and meta-analysis. His most recent books and articles are about these areas of data analysis and about the nature of nonverbal communication in teacher-student, doctor-patient, manager-employee, judge-jury, and psychotherapist-client interaction.

    Professor Rosenthal is the recipient of several national awards, including election to Fellow status in the American Academy of Arts and Sciences (2009), the Gold Medal Award for Life Achievement in the Science of Psychology of the American Psychological Foundation (2003), Distinguished Scientific Award for Applications of Psychology (APA, 2002), Distinguished Scientific Contributions Award (APA Division 5, 2002), James McKeen Cattell Award (APS, 2001), Distinguished Scientist Award (SESP 1996), AAAS Prize for Behavioral Science Research (1993, with Nalini Ambady), Donald Campbell Award (SPSP, 1988), and AAAS Socio-Psychological Prize (1960, with Kermit Fode). He has also been a Guggenheim Fellow, Senior Fulbright Scholar, and a Fellow at the Center for Advanced Study in the Behavioral Sciences. Professor Rosenthal served as Co-Chair of the American Psychological Association Task Force on Statistical Inference.

    Primary Interests:

    • Applied Social Psychology
    • Communication, Language
    • Interpersonal Processes
    • Nonverbal Behavior
    • Person Perception
    • Research Methods, Assessment

    Note from the Network: The holder of this profile has certified having all necessary rights, licenses, and authorization to post the files listed below. Visitors are welcome to copy or use any files for noncommercial or journalistic purposes provided they credit the profile holder and cite this page as the source.


    Free Web Books for Learning R

    Many Statistical Horizons seminars now use R as their primary computational platform for examples and exercises. Those seminars include:

    There are lots of good reasons to learn R, even if it won’t be your main statistical package. One excellent way to do that is to take our remote seminar, Statistics with R. But what if you want to learn just enough R to feel competent and comfortable in one of our R-based seminars? Although the web is full of resources for learning R, finding the right one isn’t easy.

    Good news! We’ve done the work for you. After scouring the web, we have identified three online books that we think do a terrific job of giving you the knowledge and skills you need to participate in our R seminars. And like R itself, they’re absolutely free.

    YaRrr! The Pirate’s Guide to R

    This accessible (and playful!) guide is oriented to behavioral scientists and will get you analyzing data right away. Working through chapters 2-4, 9, 13, 15 will prepare you for most of what you will encounter in a Statistical Horizons R course. Don’t be put off by the pirate theme. This book is packed with useful information that’s quick and easy to digest.

    Click here to read YaRrr! The Pirate’s Guide to R.

    Modern Dive

    This online book provides a balanced introduction to R with a strong emphasis on data wrangling and visualization. After going through the first two parts, you would be ready for any of our R courses.

    R for Data Science

    This is the gold standard for developing R programming, data management, and visualization skills. This book has many short chapters. Even just going through chapters 2-6 would give you a basic familiarity with R.


    On the Importance of Learning Statistics for Psychology Students

    Psychology is a very popular major for undergraduate students. Why do so many students flock to psychology programs? While the answer to this question varies by individual, reasons typically involve interest in the topic, job prospects, the appeal of certain faculty members, or the requirements for completion of the degree (Galotti, 1999). Often, students find interest in psychology because many concepts taught in introductory psychology classes are intuitively understood and directly applicable to their lives as human beings. An additional draw tends to be the avoidance of taking high-level mathematics courses. In many psychology programs, to obtain a bachelor’s degree, the only additional math course required is some form of introduction to social science statistics. However, there is quite a discrepancy between the statistics knowledge required to obtain a bachelor’s degree in psychology and what is necessary to have a career in the field of psychology. It turns out that the importance of understanding and being able to apply and interpret statistics in psychological research cannot be understated.

    The public face of psychology is often represented by the therapist and on the surface, this occupation could not seem more removed from mathematics. However, this perception is quite misleading. From the development of new therapy techniques to evaluating the effectiveness of the techniques upon implementation, it is statistical analysis that provides the means by which conclusions can be drawn. While a bachelor’s degree in psychology may allow for a college graduate to obtain entry level jobs in a variety of fields (e.g. human resources, education, customer service, etc.), developing a career within the field of psychology requires a graduate degree. Further, with the exception of graduate degrees aimed at marriage and family therapy licensure, most graduate programs focus on learning to conduct, and then conducting, publishable research.

    In 1990, Aiken, West, Sechrest, and Reno surveyed existing psychology programs on a number of issues to gain a sense of what was being covered in methods courses at the graduate and undergraduate levels. Generally, it was found that while analysis of variance (ANOVA) was covered at length, coverage of measurement issues and more advanced statistical methods was lacking. In revisiting this topic, Aiken, West, and Milsap (2008) found that while some improvement to the breadth of methodology courses was noted, the focus of such courses still tended to be on ANOVAs. Although some areas of psychology still rely heavily on true experiments, research in other fields of psychology often require other types of statistics beyond the use of ANOVAs. Rather, advanced techniques, such as structural equation modeling, multilevel modeling, and item response theory, are necessary to address contemporary research questions. Without the ability to apply more advanced statistical techniques, students lack the tools to conduct innovative and relevant research. Psychological research may start with a “great idea,” but this idea must be followed by a solid study design, effective data collection, and appropriate data analysis, combined with the means to analyze the data and interpret any findings. The cost of ignorance lies in the failure to optimize research design, to collect the types of data to best answer research questions, and to avoid improper analysis of data, leading to inappropriate and sometimes incorrect conclusions (Aiken et al., 1990).

    As such, taking an introductory statistics course will not be sufficient in providing students with the research skills that they need. Higher level data analysis courses are necessary for success as a researcher. Most psychology programs at major universities offer courses beyond introductory statistics. Though class titles vary, a typical “advanced” statistics course will cover more complex analyses such as factorial ANOVA and multiple regression. Courses such as this provide the foundation for learning more specialized techniques that are not only more interesting, but more powerful for drawing conclusions. Many universities offer semester or quarter-length courses on structural equation modeling, where students can learn about methods like factor analysis, growth curve analysis, and multilevel modeling, which offer techniques for complex data structures and unobserved (latent) variables. Further, some courses may cover not only classical test theory, but also generalizability theory and item response theory, which constitute the future of psychological measurement. Other interesting courses may cover cluster analysis and multidimensional scaling, data mining techniques, or meta-analysis. Taking one or more of these advanced courses is extremely beneficial to undergraduates in psychology. Not only will a student learn to apply advanced techniques in his or her research, but having such courses listed on a student’s transcript gives a considerable edge when applying to graduate programs, allowing an applicant to separate oneself from the “herd.” Further, when reading contemporary research in psychology, understanding of the methods utilized engenders an enhanced ability to evaluate the implications of substantive findings.

    For those who truly love the field of psychology and wish to have a career as a psychologist, statistics courses are unavoidable, but also invaluable. Fortunately, in some cases, those who believe they despise math may find themselves drawn in by the allure of techniques like structural equation modeling, which offer more eloquent ways of answering complex questions about systems of variables, rather than simple group differences. Though this might not be the case for all students, taking statistics courses even if they are particularly challenging will build the necessary skills to become stronger researchers and provide better job opportunities in the future.

    Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology. American Psychologist, 62, 32-50.

    Aiken, L. S., West, S. G., Sechrest, L., & Reno, R.R. (1990). Graduate training in statistics, methodology, and measurement in psychology. American Psychologist, 45, 721-734.

    Galotti, K. M. (1999). Making a “major” real life decision: College students choosing an academic major. Journal of Educational Psychology, 91, 379-387.


    The Big R-Book: From Data Science to Learning Machines and Big Data

    Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science.

    The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices.

    • Provides a practical guide for non-experts with a focus on business users
    • Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting
    • Uses a practical tone and integrates multiple topics in a coherent framework
    • Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R
    • Shows readers how to visualize results in static and interactive reports
    • Supplementary materials includes PDF slides based on the book&rsquos content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site

    The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.


    Modern Psychometrics with R

    This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method.

    Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology.

    Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.

    Patrick Mair is Senior Lecturer in Statistics in the Department of Psychology at Harvard University. His research focuses on computational and applied statistics with special emphasis on psychometric methods, such as latent variable models and multivariate exploratory techniques. Mair holds a doctorate in Statistics and a master’s degree in Psychology from the University of Vienna. He was also a Research Fellow at the Department of Statistics at the University of California, Los Angeles.

    “The book gives an exhaustive overview of statistical methods that may be used when analyzing results of research in psychology. Main accent is on the use of R software during analysis of data. The main goal of the book is to provide the reader with main methods used for data analysis and how those methods may be executed using software package R.” (Jonas Šiaulys, zbMath 1414.62006, 2019)


    Advanced Statistics with Applications in R

    Advanced Statistics with Applications in R fills the gap between several excellent theoretical statistics textbooks and many applied statistics books where teaching reduces to using existing packages. This book looks at what is under the hood. Many statistics issues including the recent crisis with p-value are caused by misunderstanding of statistical concepts due to poor theoretical background of practitioners and applied statisticians. This book is the product of a forty-year experience in teaching of probability and statistics and their applications for solving real-life problems.

    There are more than 442 examples in the book: basically every probability or statistics concept is illustrated with an example accompanied with an R code. Many examples, such as Who said &pi? What team is better? The fall of the Roman empire, James Bond chase problem, Black Friday shopping, Free fall equation: Aristotle or Galilei, and many others are intriguing. These examples cover biostatistics, finance, physics and engineering, text and image analysis, epidemiology, spatial statistics, sociology, etc.

    Advanced Statistics with Applications in R teaches students to use theory for solving real-life problems through computations: there are about 500 R codes and 100 datasets. These data can be freely downloaded from the author's website dartmouth.edu/

    This book is suitable as a text for senior undergraduate students with major in statistics or data science or graduate students. Many researchers who apply statistics on the regular basis find explanation of many fundamental concepts from the theoretical perspective illustrated by concrete real-world applications.


    General Comments

    R is not overly user friendly (at first). Its error messages are at best cryptic. It is, however, very powerful and once partially mastered, easy to use. And it is free. Even more important than the cost is that it is an open source language which has become the lingua franca of statistics and data analysis. That is, as additional modules are added, it becomes even more useful. Modules included allow for multilevel (hierarchical) linear modeling, confirmatory factor analysis, etc. I believe that it is worth the time to learn how to use it. J. Baron and Y. Li's guide is very helpful. They include a one page pdf summary sheet of commands that is well worth printing out and using. A three page summary sheet of commands is available from Rpad.


    Free statistics e-books for download

    This post will eventually grow to hold a wide list of books on statistics (e-books, pdf books and so on) that are available for free download. But for now we’ll start off with just one several books:

      • The Elements of Statistical Learning written by Trevor Hastie, Robert Tibshirani and Jerome Friedman. you can legally download a copy of the book in pdf format from the authors website! Direct download (First discovered on the “one R tip a day” blog) – a wikibook. Download link by Michael Lavine. The book is organized into seven chapters: “Probability,” “Modes of Inference,” “Regression,” “More Probability,” “Special Distributions,” “More Models,” and “Mathematical Statistics.” and makes extensive use of R. Here is a favoring review the book received in JASA. 328 pages. Download link (approx. 40 mbyte) by Sanjoy Mahajan. Download link by Miller and Haden. an introductory textbook describing statistical analysis with analysis of variance (ANOVA, including repeated-measures and mixed designs), simple and multiple regression, and analysis of covariance. 274 pages. Download link(p.s: this book makes no reference to R. see here for R tutorials and functions for performing repeated measures anova) by Barbara Illowsky and Susan Dean. This textbook is intended for introductory statistics courses. 627 pages. R is not used in this book. Download link
      • Using R for Introductory Statistics by John Verzani Publisher: Chapman & Hall/CRC 2004 ISBN/ASIN: 1584884509 ISBN-13: 9781584884507 Number of pages: 114 Description: The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models. This text lays the foundation for further study and development in statistics using R. Download link
      • R Graphics (Three chapters only) by Paul Murrell ISBN: 9781584884866 ISBN 10: 158488486X Publication Date: July 29, 2005 Number of Pages: 328 Description: Chapter 1: An Introduction to R Graphics Chapter 4: Trellis Graphics: The Lattice Package Chapter 5: The Grid Graphics Model Download link (see scripts and images here)
      • Using RDownload link
      • R introDownload link
      • Psychometric Theory with Applications in R by William Revelle (a work in progress) Download link
      • A great long list of R related texts, for free download, can be found here.
      • Using Graphs Instead of Tableswebsite link (This web page accompanies the article “Using Graphs Instead of Tables in Political Science”, by Jonathan Kastellec and Eduardo Leoni, which appears in the December 2007 issue of Perspectives on Politics. It contains complete replication code for all the graphs that appear in the text)
      • IPSUR: Introduction to Probability and Statistics Using R by G. Jay Kerns, is FREE (in the GNU sense of the word) and comes with a plugin for Rcmdr. 412 pages. Download link(first discovered through the Revolution blog)
      • Multivariate Statistics with R by Paul J. Hewson. 189 pages. Download link(first discovered through open text book blog)
      • R Programming – a wikibook. (no PDF version is available as of yet) – direct PDF link
      • Modeling and Solving Linear Programming with R – free (pdf) download link

      Several of these books were discovered through a CrossValidated discussion:

      Know of any more e-books freely available for download? Please write to us about them in the comments.


      Watch the video: Statistics for Data Analysis Using R (January 2022).