Statistical Analysis of Questionnaires A Unified Approach Based on R and Stata

Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. It is suitable for graduate students in applied statistics and psychometrics and practitioners in education, health, and marketing. The book covers the foundations of classical test theory (CTT), test reliability, validity, and scaling as well as item response theory (IRT) fundamentals and IRT for dichotomous and polytomous items. The authors explore the latest IRT extensions, such as IRT models with covariates, multidimensional IRT models, IRT models for hierarchical and longitudinal data, and latent class IRT models. They also describe estimation methods and diagnostics, including graphical diagnostic tools, parametric and nonparametric tests, and differential item functioning. Stata and R software codes are included for each method. To enhance comprehension, the book employs real datasets in the examples and illustrates the software outputs in detail. The datasets are available on the authors’ web page.

Preliminaries IntroductionPsychological Attributes as Latent Variables Challenges in the Measurement of Latent Constructs What Is a Questionnaire?Main Steps in Questionnaire Construction What Is Psychometric Theory? Notation Datasets Used for Examples Classical Test Theory Introduction Foundation Models Conceptual Approaches of ReliabilityReliability of Parallel and Nonparallel Tests Procedures for Estimating ReliabilityTrue Score EstimationItem Analysis ValidityTest Bias Generalizability Theory Examples Item Response Theory Models for Dichotomous Items Introduction Model Assumptions Rasch Model2PL Model 3PL Model Random-Effects Approach Summary about Model Estimation Examples Item Response Theory Models for Polytomous Items Introduction Model Assumptions Taxonomy of Models for Polytomous Responses Models for Ordinal Responses Models for Nominal Responses Examples Estimation Methods and Diagnostics Introduction Joint Maximum Likelihood MethodConditional Maximum Likelihood Method Marginal Maximum Likelihood Method Estimation of Models for Polytomous Items Graphical Diagnostic Tools Goodness-of-Fit Infit and Outfit Statistics Differential Item FunctioningExamplesAppendix Some Extensions of Traditional Item Response Theory Models Introduction Models with Covariates Models for Clustered and Longitudinal DataMultidimensional ModelsStructural Equation Modeling SettingExamples Exercises appear at the end of each chapter.

Francesco Bartolucci is a professor of statistics in the Department of Economics at the University of Perugia. Dr. Bartolucci is an associate editor of Metron and Statistical Modelling: An International Journal. His research interests include latent variable models, marginal models for categorical data, and longitudinal categorical data. He has collaborated with many researchers and published articles on these topics in top statistical journals. Silvia Bacci is an assistant professor of statistics in the Department of Economics at the University of Perugia. Her research interests include multidimensional and latent class item response theory models and extensions, estimation of item response theory models with R, latent Markov models for multivariate longitudinal data, and the application of these methods and models in educational and quality-of-life settings. She has published articles on these topics in international journals and participated in several research projects. Michela Gnaldi is an assistant professor of applied statistics in the Department of Political Sciences at the University of Perugia. She is editorial manager of the Italian Journal of Applied Statistics. Her main research interest concerns measurement in education, with particular regard to multidimensional, multilevel, and latent class item response theory models. She has published articles on these topics in international journals and participated in several projects in Italy and the United Kingdom. She actively collaborates with the "Istituto Nazionale di Valutazione del Sistema dell’Istruzione" (INVALSI).