Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences – serie

Modern Applied Regressions

Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan

Handbook of Item Response Theory

Volume 1: Models

Handbook of Item Response Theory

Volume 2: Statistical Tools

Handbook of Item Response Theory

Volume 3: Applications

Regression Analysis in R

A Comprehensive View for the Social Sciences

Big Data and Social Science

Data Science Methods and Tools for Research and Practice

Introduction to Bayesian Data Analysis for Cognitive Science

Introduction to Bayesian Data Analysis for Cognitive Science

Statistical Test Theory for the Behavioral Sciences

Applied Regularization Methods for the Social Sciences

Measurement Models for Psychological Attributes

Classical Test Theory, Factor Analysis, Item Response Theory, and Latent Class Models

Introduction to R for Social Scientists

A Tidy Programming Approach

Introduction to R for Social Scientists

A Tidy Programming Approach

Computational Aspects of Psychometric Methods

With R

Computational Aspects of Psychometric Methods

With R

Big Data and Social Science

Data Science Methods and Tools for Research and Practice

Bayesian Demographic Estimation and Forecasting

Age-Period-Cohort Models

Approaches and Analyses with Aggregate Data

Analyzing Spatial Models of Choice and Judgment

Multivariate Analysis for the Behavioral Sciences, Second Edition

Crime Mapping and Spatial Data Analysis using R

Adaptive Survey Design

Bayesian Psychometric Modeling

Ordered Regression Models

Parallel, Partial, and Non-Parallel Alternatives

Generalized Structured Component Analysis

A Component-Based Approach to Structural Equation Modeling

Linear Regression Models

Applications in R

Linear Regression Models

Applications in R

Understanding Elections through Statistics

Polling, Prediction, and Testing

Interviewer Effects from a Total Survey Error Perspective