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Social sciences --- Longitudinal studies. --- Statistical methods. --- -Longitudinal studies --- Statistical methods --- Research --- Longitudinal studies --- -Social sciences --- -Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Sciences sociales --- Mathematical statistics. --- Statistique mathématique. --- Méthodes statistiques --- Études longitudinales. --- Social sciences - Statistical methods - Research --- Social sciences - Longitudinal studies - Research --- Statistique mathématique. --- Méthodes statistiques --- Études longitudinales.

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Configural Frequency Analysis (CFA) is a method for analysis of groups of individuals in cross-classifications. Individuals belong to a type if their particular pattern of characteristics occurs more often than expected, and to an antityte if their particular pattern of characteristics occurs less often than expected. The author's original contribution is his linking of CFA to log-linear modeling and the General Linear Model, enabling the reader to relate CFA to a well-known statistical background. It is shown that CFA and log-linear modeling are methods that complement each other. Introduction to Configural Frequency Analysis covers the latest developments in CFA, and it will be easy to read even for those with only an elementary statistics course as a background.

Discriminant analysis. --- Psychometrics. --- Health Sciences --- Psychiatry & Psychology --- Analysis, Discriminant --- Classification theory (Statistics) --- Discrimination theory (Statistics) --- Multivariate analysis --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychology --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Measurement --- Scaling --- Methodology

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Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data fr

Social sciences --- Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Statistical methods.

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General Linear Model methods are the most widely used in data analysis in applied empirical research. Still, there exists no compact text that can be used in statistics courses and as a guide in data analysis. This volume fills this void by introducing the General Linear Model (GLM), whose basic concept is that an observed variable can be explained from weighted independent variables plus an additive error term that reflects imperfections of the model and measurement error. It also covers multivariate regression, analysis of variance, analysis under consideration of covariates, variable selection methods, symmetric regression, and the recently developed methods of recursive partitioning and direction dependence analysis. Each method is formally derived and embedded in the GLM, and characteristics of these methods are highlighted. Real-world data examples illustrate the application of each of these methods, and it is shown how results can be interpreted.

Linear models (Statistics) --- Social sciences --- Statistics. --- Statistical methods. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics

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This book provides developmental researchers with the basic tools for understanding how to utilize categorical variables in their data analysis. Covering the measurement of individual differences in growth rates, the measurement of stage transitions, latent class and log-linear models, chi-square, and more, the book provides a means for developmental researchers to make use of categorical data.The book covers: * Measurement and repeated observations of categorical data * Catastrophe theory * Latent class and log-linear models * Applications

Categories (Mathematics). --- Psychology --- Psychometrics. --- Statistical methods. --- Categories (Mathematics) --- -Psychometrics --- Measurement, Mental --- Measurement, Psychological --- Psychological measurement --- Psychological scaling --- Psychological statistics --- Psychometry (Psychophysics) --- Scaling, Psychological --- Psychological tests --- Scaling (Social sciences) --- Category theory (Mathematics) --- Algebra, Homological --- Algebra, Universal --- Group theory --- Logic, Symbolic and mathematical --- Topology --- Functor theory --- Behavioral sciences --- Mental philosophy --- Mind --- Science, Mental --- Human biology --- Philosophy --- Soul --- Mental health --- Statistical methods --- Measurement --- Scaling --- Methodology --- Psychometrics