Multiple Regression and Beyond

Timothy Z. Keith is Professor Emeritus at the University of Texas, Austin. Before retiring he was director of the graduate school psychology program (now school and clinical child psychology) in the Department of Educational Psychology. His research focused on the nature and measurement of intelligence, including the validity of tests of intelligence and the theories from which they are drawn.Matthew R. Reynolds is Professor of Educational Psychology at the University of Kansas. His research focuses on the measurement and structure of human cognitive abilities and on sex differences in cognitive abilities and academic achievement.Jacqueline M. Caemmerer is an Assistant Professor in the Department of Educational Psychology (school psychology graduate programs) at the University of Connecticut. Her research interests are in psychological assessment and validity issues. She is interested in better understanding what frequently used tests measure, their predictive validity, and developmental and cultural considerations of tests.

• PrefaceNotes for the Fourth EditionAcknowledgmentsPart I: Multiple RegressionChapter 1: Simple bivariate regressionChapter 2: Multiple regression: IntroductionChapter 3: Multiple regression: More detailChapter 4: Three and more independent variables and related issuesChapter 5: Three Types of multiple regressionChapter 6: Analysis of categorical variablesChapter 7: Regression with categorical and continuous variablesChapter 8: Testing for interactions and curves with continuous variablesChapter 9: Mediation, moderation, common cause, and suppressionChapter 10: Multiple regression: Summary, assumptions, diagnostics, power, and problemsChapter 11: Related methods: Quantile regression, logistic regression and multilevel modelingPart II: Beyond Multiple Regression: Structural Equation ModelingChapter 12: Path modeling: Structural equation modeling with measured variablesChapter 13: Path analysis: Assumptions and dangersChapter 14: Analyzing path models using SEM programsChapter 15: Error: The scourge of researchChapter 16: Confirmatory factor analysis IChapter 17: Putting it all together: Introduction to latent variable SEMInformation Classification: GeneralChapter 18: Latent variable models II: Single indicators, correlated errors, multigroup models, panel models, dangers & assumptionsChapter 19: Latent means in SEMChapter 20: Confirmatory factor analysis II: Invariance and latent meansChapter 21: Latent growth modelsChapter 22: Latent variable interactions and multilevel modeling in SEMChapter 23: Summary: Path analysis, CFA, SEM, mean structures, and latent growth modelsAppendicesAppendix A: Data files and statistical program notesAppendices B: Review of basic statistics conceptsAppendix C: Partial and semipartial correlationAppendix D: Symbols used in this bookAppendix E: Useful formulaeReferenceAuthor indexSubject index