- Oxford Library of Psychology
- [UNTITLED]
- Oxford Library of Psychology
- About the Editor
- Contributors
- Introduction
- Overview of Traditional/Classical Statistical Approaches
- Generalized Linear Models
- Categorical Methods
- Configural Frequency Analysis
- Nonparametric Statistical Techniques
- Correspondence Analysis
- Spatial Analysis
- Analysis of Imaging Data
- Twin Studies and Behavior Genetics
- Quantitative Analysis of Genes
- Multidimensional Scaling
- Latent Variable Measurement Models
- Multilevel Regression and Multilevel Structural Equation Modeling
- Structural Equation Models
- Developments in Mediation Analysis
- Moderation
- Longitudinal Data Analysis
- Dynamical Systems and Models of Continuous Time
- Intensive Longitudinal Data
- Dynamic Factor Analysis: Modeling Person-Specific Process
- Time Series Analysis
- Analyzing Event History Data
- Clustering and Classification
- Latent Class Analysis and Finite Mixture Modeling
- Taxometrics
- Missing Data Methods
- Secondary Data Analysis
- Data Mining
- Meta-Analysis and Quantitative Research Synthesis
- Common Fallacies in Quantitative Research Methodology
- Index
Abstract and Keywords
Longitudinal data analysis is an increasingly popular approach because evaluating change is of central interest in many areas of research. Using advanced statistical techniques such as multilevel modeling (MLM) and structural equation modeling (SEM), longitudinal data analysis allows for the simultaneous evaluation of intra-individual change and interindividual differences in intra-individual change. This chapter presents an overview of both MLM and SEM approaches to evaluating change with different functional forms for continuous panel data, including linear, curvilinear, nonlinear, and spline curve models. This chapter also covers a variety of longitudinal models that take advantage of the flexibility of SEM over MLM, including autoregressive cross-lagged, latent difference, fully latent, parallel process, and second-order curve models. The chapter closes with a discussion of the advantages and disadvantages of MLM and SEM in modeling change, along with a brief review of advances in longitudinal data analysis.
Keywords: Longitudinal data analysis, multilevel modeling, structural equation modeling, repeated measures, latent curve model, change trajectory, parallel process curve model
Wei Wu, Center for Research Methods and Data Analysis and Department of Psychology, University of Kansas, Lawrence, KS
James P. Selig, Department of Psychology, University of New Mexico, Albuquerque, NM
Todd D. Little, Ph.D., is a Professor of Psychology, Director of the Quantitative training program, Director of the undergraduate Social and Behavioral Sciences Methodology minor, and a member of the Developmental training program. Since 2010, Todd has been Director of the Center for Research Methods and Data Analysis (CRMDA) at Kansas University. Little is internationally recognized for his quantitative work on various aspects of applied SEM (e.g., indicator selection, parceling, modeling developmental processes) as well as his substantive developmental research (e.g., action-control processes and motivation, coping, and self-regulation). In 2001, Little was elected to membership in the Society for Multivariate Experimental Psychology. In 2009, he was elected President of APA’s Division 5 (Evaluation, Measurement, and Statistics) and in 2010 was elected Fellow of the division. In 2012, he was elected Fellow in the Association for Psychological Science. He founded, organizes, and teaches in the internationally renowned KU “Stats Camps” each June (see crmda.KU.edu for details of the summer training programs). Little has edited five books related to methodology including The Oxford Handbook of Quantitative Methods and the Guilford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card). Little has been principal investigator or co-principal investigator on more than 15 grants and contracts, statistical consultant on more than 60 grants and he has guided the development of more than 10 different measurement tools.
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- Oxford Library of Psychology
- [UNTITLED]
- Oxford Library of Psychology
- About the Editor
- Contributors
- Introduction
- Overview of Traditional/Classical Statistical Approaches
- Generalized Linear Models
- Categorical Methods
- Configural Frequency Analysis
- Nonparametric Statistical Techniques
- Correspondence Analysis
- Spatial Analysis
- Analysis of Imaging Data
- Twin Studies and Behavior Genetics
- Quantitative Analysis of Genes
- Multidimensional Scaling
- Latent Variable Measurement Models
- Multilevel Regression and Multilevel Structural Equation Modeling
- Structural Equation Models
- Developments in Mediation Analysis
- Moderation
- Longitudinal Data Analysis
- Dynamical Systems and Models of Continuous Time
- Intensive Longitudinal Data
- Dynamic Factor Analysis: Modeling Person-Specific Process
- Time Series Analysis
- Analyzing Event History Data
- Clustering and Classification
- Latent Class Analysis and Finite Mixture Modeling
- Taxometrics
- Missing Data Methods
- Secondary Data Analysis
- Data Mining
- Meta-Analysis and Quantitative Research Synthesis
- Common Fallacies in Quantitative Research Methodology
- Index
