Statistical Analysis of Questionnaires A Unified Approach Based on R and Stata Chapman & Hall/CRC Interdisciplinary Statistics Series
Auteurs : Bartolucci Francesco, Bacci Silvia, Gnaldi Michela
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. Classical Test Theory. Item Response Theory Models for Dichotomous Items. Item Response Theory Models for Polytomous Items. Estimation Methods and Diagnostics. Some Extensions of Traditional Item Response Theory Models.
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).
Date de parution : 01-2023
15.6x23.4 cm
Date de parution : 07-2015
15.6x23.4 cm
Thème de Statistical Analysis of Questionnaires :
Mots-clés :
IRT Model; Rasch Model; testing and measurement; Latent Trait; classical test theory; Item Parameters; item response theory; 2PL Model; multidimensional IRT models; Polytomous Items; latent class IRT models; Conditional Response Probabilities; test validity and bias; Latent Trait Level; analyzing data collected by questionnaires; Roc Curve; IRT analyses; MML Method; Item Y4; Stata and R for Statistical Analysis; Observed Scores; NoDIF NoDIF NoDIF; DIF Detection; Polytomous IRT Model; Person Item Map; INVALSI Tests; Test Information Curve; Rasch Type Models; Uniform DIF; True Score; Random Intercepts; Infit Statistics; HADS Data; Van Der Ark