The amount of data in our world has been exploding, and analyzing large data sets-so called big data-will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics.

The amount of data in our world has been exploding, and analyzing large data sets-so called big data-will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics.

The goal of the chapters in this SIOP Organizational Frontiers Series volume is to challenge researchers to break away from the rote application of traditional methodologies and to capitalize upon the wealth of data collection and analytic strategies available to them. In that spirit, many of the chapters in this book deal with methodologies that encourage organizational scientists to re-conceptualize phenomena of interest (e.g., experience sampling, catastrophe modeling), employ novel data collection strategies (e.g., data mining, Petri nets), and/or apply sophisticated analytic techniques (e.g., latent class analysis). The editors believe that these chapters provide compelling solutions for the complex problems faced by organizational researchers.

Researchers have been complaining about the lack of one single place to find information on computing effect sizes in analysis of variance (ANOVA), until now. Authors Jose M. Cortina and Hossein Nouri begin with a literature review of previous treatments of the topic (including corrections to the misleading treatments of repeated measures and ANCOVA (analysis of covariance) designs). They introduce the effect sizes, by defining the term and explaining how it is computed from summary and test statistics for the simple two independent group design. They next provide a description of methods for computing effect sizes from the results of one-way designs with more than two groups, and then extend these methods to cases in which the effects of interest are embedded within the context of two and three-way ANOVA?s. They conclude the book with an explanation of the methods for computing effect size from the results of ANCOVA designs followed by the methods for computing effect sizes from the results of repeated measures. Throughout the book, the authors offer examples with worked-out computations to illustrate each technique. Researchers who need to estimate their effect size of run a meta-analysis will find this book very useful.