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 Introduction Psychological 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 Reliability Reliability of Parallel and Nonparallel Tests Procedures for Estimating Reliability True Score Estimation Item Analysis Validity Test Bias Generalizability Theory Examples Item Response Theory Models for Dichotomous Items Introduction Model Assumptions Rasch Model 2PL 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 Method Conditional 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 Functioning Examples Appendix Some Extensions of Traditional Item Response Theory Models Introduction Models with Covariates Models for Clustered and Longitudinal Data Multidimensional Models Structural Equation Modeling Setting Examples Exercises appear at the end of each chapter.
