- 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
Taxometric methods were developed to ascertain which behavioral traits—particularly psychiatric syndromes—comprise discrete latent classes. Although individual differences in most forms of psychopathology are likely distributed along continua, knowing which are typologal may have significant implications for diagnostic precision, treatment development, early identification of latent vulnerability to psychopathology, and improved understanding of etiology. In practice, however, distinguishing types from continua has proved difficult because most behavioral measures include several sources of error, which obscures true scores. Furthermore, conflicting results have often been reported by different research groups studying the same or similar traits. This has led some authors to question the utility of taxometrics. In this chapter, I (1) outline the conceptual bases of taxometrics, (2) provide descriptions of several taxometric procedures, (3) discuss strategies for selecting valid indicator variables, (4) provide brief example analyses, and (5) discuss common pitfalls of the taxometric approach. Despite certain limitations, careful attention to the types of variables subjected to taxometric analysis can produce valid and replicable results.
Keywords: Taxometrics, classification, MAMBAC, MAXCOV, MAXEIG, L-Mode
Theodore P. Beauchaine, Department of Psychology, The Ohio State University
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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
