Statistics for Social and Behavioral Sciences – serie

Basics of Item Response Theory Using R

Kernel Method of Test Equating

Marginal Models

For Dependent, Clustered, and Longitudinal Categorical Data

Bayesian Evaluation of Informative Hypotheses

Linear Models for Optimal Test Design

Multivariate and Mixture Distribution Rasch Models

Extensions and Applications

Introduction to Variance Estimation

Explanatory Item Response Models

A Generalized Linear and Nonlinear Approach

Linking and Aligning Scores and Scales

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Elements of Adaptive Testing

Multidimensional Item Response Theory

Prove It with Figures

Empirical Methods in Law and Litigation

Generalizability Theory

Statistical Models for Test Equating, Scaling, and Linking

Ordinal Data Modeling

Practical Considerations in Computer-Based Testing

Public Policy and Statistics

Case Studies from RAND

Relative Distribution Methods in the Social Sciences

Statistical Science in the Courtroom

Statistics for Lawyers

Statistics for Social and Behavioral Science

Statistics for Lawyers

Statistics for Social and Behavioral Science

Bayesian Item Response Modeling

Theory and Applications

Marginal Models

For Dependent, Clustered, and Longitudinal Categorical Data

Bayesian Evaluation of Informative Hypotheses

Linear Models for Optimal Test Design

Multivariate and Mixture Distribution Rasch Models

Extensions and Applications

Introduction to Variance Estimation

Explanatory Item Response Models

A Generalized Linear and Nonlinear Approach