Preface

Welcome to Advanced Statistical Analysis in Behavioral Science, a comprehensive guide designed for researchers, graduate students, and practitioners who want to master sophisticated statistical techniques commonly used in psychology, education, health sciences, and related fields.

About This Book

This book provides detailed, hands-on tutorials for five essential advanced statistical methods:

Multilevel/Hierarchical Linear Modeling - Analyzing nested data structures
Structural Equation Modeling - Testing complex theoretical models
Survival/Time-to-Event Analysis - Modeling time until events occur
Factor Analysis & Scale Development - Developing and validating measurement instruments
Mixed-Effects Models for Repeated Measures - Analyzing longitudinal and clustered data

Each chapter includes:

Conceptual foundations with clear explanations of when and why to use each method
Real-world applications from various behavioral science domains
Step-by-step tutorials with simulated data that mirrors realistic research scenarios
Comprehensive code examples using R and modern statistical packages
Interpretation guidance for understanding and communicating results
APA-style reporting templates for manuscript preparation
Diagnostic procedures for validating model assumptions

Intended Audience

This book is designed for:

Graduate students in psychology, education, health sciences, and related fields
Researchers who need to apply advanced statistical methods in their work
Statistical consultants seeking comprehensive reference materials
Practitioners who want to understand and critique advanced statistical analyses

Prerequisites

Readers should have:

Solid foundation in basic statistics (t-tests, ANOVA, correlation, regression)
Familiarity with R programming language
Understanding of research design principles
Basic knowledge of statistical inference

How to Use This Book

Each chapter is self-contained and can be read independently, though concepts build upon each other. The book is structured to be both a learning resource and a reference guide:

For Learning: Work through chapters sequentially, running all code examples
For Reference: Use individual chapters as needed for specific analyses
For Teaching: Adapt examples and exercises for classroom use

Software and Packages

All analyses use R with the following key packages:

General: tidyverse, ggplot2, dplyr
Multilevel Models: lme4, nlme, lmerTest
Structural Equation Models: lavaan, semPlot, semTools
Survival Analysis: survival, survminer, flexsurv
Factor Analysis: psych, GPArotation, nFactors
Mixed-Effects Models: lme4, lmerTest, performance

Getting Help

Code examples are provided in full and tested
Common errors and solutions are discussed
Additional resources are listed at the end of each chapter
Online materials are available at the book’s GitHub repository

Acknowledgments

This book builds upon decades of methodological development by statisticians and researchers in behavioral science. We acknowledge the contributions of the R community and package developers who have made these advanced methods accessible to researchers worldwide.

“The best thing about being a statistician is that you get to play in everyone’s backyard.” - John Tukey

Let’s begin exploring the powerful world of advanced statistical analysis!