• Introduction
• General Textbooks
• Other Resources
• Journals
• Statistical Programs
• History and Trends
• Measurement Issues
• Hypothesis Testing
• Longitudinal Processes
• Qualitative Methods
• Structural Equation Modeling
• Data Cleaning
• Multiple Regression
• Multilevel Analyses
• Factor Analysis (Exploratory and Confirmatory)
• Meta-analysis
• Miscellaneous

# Data Analytic MethodsbyRonald S. LandisLAST REVIEWED: 19 October 2016LAST MODIFIED: 28 January 2013DOI: 10.1093/obo/9780199846740-0065

## Introduction

Data analytic methods cover the myriad techniques used to make sense of the information collected through research. Organizational scientists use a variety of analytic methods to clean/screen data, describe characteristics of the data, test hypotheses, and draw inferences. Broadly speaking, data analytic methods also include topics of measurement (e.g., reliability, validity) and statistical significance testing (e.g., null hypothesis significance testing [NHST], Bayesian approaches). Frequently, data analytic methods are discussed in conjunction with research design because of the fundamental relations between the two.

## General Textbooks

Some textbooks summarize commonly used data analytic methods and are frequently adopted in courses (e.g., those taken relatively early in graduate training) that present students with a broad overview of this material. For example, Cohen, et al. 2003 and Howell 2010 provide treatment of topics relevant to fundamental statistical concepts and analytic techniques, including probability, null hypothesis significance testing, power, effect size, descriptive statistics, analysis of variance (ANOVA), and simple correlation and regression. Guion 1998 presents similar topics in the applied context of personnel selection. Hair, et al. 2006; Stevens 2009; and Tabachnick and Fidell 2007 are used in courses that emphasize more advanced, multivariate statistical techniques, such as factor analysis, multivariate analysis of variance (MANOVA), logistic regression, discriminant analysis, and cluster analysis.

• Cohen, Jacob, Patricia Cohen, Stephen G. West, and Leona S. Aiken. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3d ed. Mahwah, NJ: Lawrence Erlbaum, 2003.

This textbook provides a comprehensive explanation of the use of both basic and advanced applications of correlation and regression. Not only is the book easy to read, there are numerous examples and included data sets that facilitate learning the fundamental concepts.

• Guion, Robert M. Assessment, Measurement, and Prediction for Personnel Decisions. Mahwah, NJ: Lawrence Erlbaum, 1998.

Although this text is targeted toward the development, evaluation, and implementation of assessment tools (i.e., selection tests), the presentation serves as an excellent foundation for developing a strong understanding of critical data analytic techniques related to reliability, validity, and applications of multiple regression.

• Hair, Joseph F., Jr., William C. Black, Barry J. Babin, Rolph E. Anderson, and Ronald L. Tatham. Multivariate Data Analysis. 6th ed. Upper Saddle River, NJ: Pearson, 2006.

A general textbook that provides detailed discussions of a broad array of multivariate data analytic techniques. The presentation is technically detailed and also written with an eye toward application of techniques (e.g., presentation of common “rules of thumb” regarding interpretation as well as other decision rules).

• Howell, David C. Statistical Methods for Psychology. 7th ed. Belmont, CA: Cengage Wadsworth, 2010.

This text is a thorough presentation of core univariate and simple multivariate data analytic techniques with an emphasis on descriptive statistics, hypothesis testing, power, t-tests, ANOVA, and correlation/regression. Although this text is written primarily for psychologists, topics and examples are of high relevance for organizational researchers and students.

• Stevens, James P. Applied Multivariate Statistics for the Social Sciences. 5th ed. New York: Routledge, 2009.

A thorough and well-written presentation of primary multivariate techniques that are likely to be used by organizational researchers. In addition to a focus on conceptual understanding of material, this text also includes exercises at the end of each chapter that readers can use to “test” their understanding of the presented analytic techniques and concepts.

• Tabachnick, Barbara G., and Linda S. Fidell. Using Multivariate Statistics. 5th ed. Boston: Pearson, 2007.

Comprehensive presentation of the primary multivariate techniques used by organizational researchers. In addition to technical discussions of topics such as data cleaning, MANOVA, exploratory factor analysis, discriminant analysis, and logistic regression, syntax and output files for two commonly used statistical software programs and examples of communicating results are provided.