Advanced Multitrait-Multimethod Analyses for the Behavioral and Social Sciences
- Available for pre-order. Item will ship after July 20, 2021
This book summarizes a range of new analytic tools for multitrait-multimethod (MTMM) data. Providing an expository yet accessible approach to cutting-edge developments for MTMM analysis, a selection of quantitative researchers reveal their recent contributions to the field including non-technical summaries and empirical examples.
The contributions inform quantitative social scientists of some of the most cutting-edge developments for MTMM analysis. A range of developments have emerged over the past decade for MTMM analyses, and this book presents these novel additions to the quantitative community as a cohesive narrative. Second, the book makes these recent MTMM contributions accessible to applied researchers (most MTMM innovations are presented in less approachable journals for applied researchers) by providing non-technical summaries and empirical examples.
This book will serve as a stepping stone for applied researchers seeking to adopt MTMM analysis into their program of research, and will be relevant to researchers, both within a professional and academic context, across the social and behavioral sciences.
Table of Contents
- An Introduction to Advanced Multitrait-Multimethod Analysis
- Multitrait-Multimethod Matrix: Method in the Madness
- Restricted Correlated Trait - Correlated Method Models for Analyzing Multitrait - Multimethod Data
- Musing on Alternate Confirmatory Factor Models for Multitrait - Multimethod Data
- Rank Deficiencies in a Reduced Information Latent Variable Model
- Calculating the Probability of Accurate Model Selection for Multitrait - Multimethod Structural Equation Models
- Construction of Informative Priors for the Application of CFA - MTMM Models in Small Samples: A Model - Free Approach
- Leveraging Component-Based Methods to Improve MTMM Analysis: Exploration and Outlier Detection
- Analysing Multitrait-Multimethod Data with Exploratory Multivariate Analysis…The French Way: A Multiple Factor Analysis Perspective
Jonathan Lee Helm
David A. Kenny
Michael Eid, Jana Holtmann, and Tobias Koch
Keith F. Widaman
Jonathan Lee Helm
Tobias Koch, Jana Holtmann, and Johannes Heekerens
Dr. Jonathan Helm is an Assistant Professor of quantitative psychology at San Diego State University, California, USA. In general, Dr. Helm develops and refines statistical models that measure psychological constructs, and analyze longitudinal data.