Modeling Intraindividual Variability With Repeated Measures Data Methods and Applications Volume in the Multivariate Application Series 1st Edition by Moskowitz, Hershberger ISBN 1135673208 9781135673208

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ISBN 10: 1135673208
ISBN 13: 9781135673208
Author: Moskowitz, Hershberger

This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable. It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a “user-friendly” style such that even the “novice” data analyst can easily apply the techniques. Each chapter features: a minimum discussion of mathematical detail; an empirical example applying the technique; and a discussion of the software related to that technique. Content highlights include analysis of mixed, multi-level, structural equation, and categorical data models. It is ideal for researchers, professionals, and students working with repeated measures data from the social and behavioral sciences, business, or biological sciences.

Modeling Intraindividual Variability With Repeated Measures Data Methods and Applications Volume in the Multivariate Application Series 1st Table of contents:

1 Traditional Methods for Estimating Multilevel Models

Standard ANOVA Analysis for Balanced Datas

Multilevel Models

Multilevel Data Structure

Example Data Set

Most Basic Approach to Multilevel Modeling: Ordinary Least Squares

Multilevel Model Equations

Computer Applications of Multilevel Models with OLS Estimation

Complications in Estimation with Unbalanced Data

Multilevel Estimation Methods that Weight the Second-Step Regressions

Multilevel Modeling with Weighted Least Squares

Multilevel Modeling with Maximum Likelihood

Estimation of WLS Using Standard Computer Programs

Comparison between Methods

Between and Within Slopes

Scale Invariance

Estimation of Variances and Covariances

Statistical Efficiency

Generality

Summary

Acknowledgments

References

2 Alternative Covariance Structures for Polynomial Models of Individual Growth and Change

Hierarchical Model and its Implications for Variation and Covariation Over Time

Data

Simple Model

Results

Implied Assumptions Concerning Variation and Covariation over Time

The Generalized Multivariate Linear Model as a Hierarchical Model

Reformulation as a Two-Level Model

Placing Restrictions on the Model

The Unrestricted Model as a “Standard” HLM Model

Sensitivity of Inferences to Alternative Covariance Specifications

On the “Generality” of the Unrestricted Model

Robust Standard Errors

Incorporating Clustering of Persons within Social Settings

Three-Level HLM Model

Reformulation as a Hierarchical Multivariate Model with Incomplete Data

Discussion

Homogeneity Versus Heterogeneity of Dispersion

Normal Versus Non-normal Models

Clustering of Participants

Final Remarks

Acknowledgements

Technical Appendix

References

3 Structural Equation Modeling of Repeated Measures Data: Latent Curve Analysis

The Development of Antisocial Behavior in Children

Sample and Measures

The Structural Equation Modeling Framework

The Autoregressive Crosslagged Model

Latent Curve Analysis

Latent Curve Modeling of the Proposed Research Questions

Conclusions

References

4 Multilevel Modeling of Longitudinal and Functional Data

Multilevel Data

Special Features of Longitudinal Data

The Missing Data or Attrition Problem

Registration Problem

Serial Correlation Problem

Resolution of Longitudinal Data

Multilevel Models

Notation

Upper Model Level

Lower Model Level

Variance Components

Effects as Fixed or Determined by Further Covariates

Multilevel Modeling Objectives

Special Features of Longitudinal Models and Functional Data

Within-Subject or Curve Modeling

Variance Components

Conclusions

References

5 Analysis of Repeated Measures Designs with Linear Mixed Models

The Mixed Model

A Simple Example

Estimation and Statistical Inference

Issues in Applying Mixed Models to Longitudinal Data

Random-Effects Components of the Mixed Model

Between-Subjects Random Effects (G Matrix)

Within-Subjects Random Effects Matrix

Fixed-Effects Component of the Mixed Model

Implementing Mixed Models using SAS PROC MIXED

PROC MIXED

Class

Model

Random

Repeated

A Four-Step Modeling Strategy for Building and Refining Mixed Models

Example Mixed Model Analysis

Step 1

Step 2

Step 3

Conclusion

References

6 Fitting Individual Growth Models Using SAS PROC MIXED

Introduction

Creating a Person-Period Data Set

Fitting a Basic Individual Growth Model to Data

Unconditional Means Model

Unconditional Linear Growth Model

Adding Person-Level Covariates to the Individual Growth Model

Exploring the Structure of the Variance Covariance Matrix Within Persons

A Brief Comment about Examining Assumptions

Conclusion

Acknowledgments

References

7 Multilevel Modeling of Longitudinal and Functional Data

Full Information Maximum Likelihood (FIML)

Limited Information Multilevel Latent Growth Modeling (MLGM)

Hierarchical Linear Modeling (HLM)

Hierarchical Nature of Adolescent Alcohol Use

Method

Research Participants

Measures

Statistical Modeling Approaches

Full Information Maximum Likelihood

Limited Information Multilevel Latent Growth Modeling Analysis

Hierarchical Linear Modeling

Results

Full Information Maximum Likelihood

Limited Information Multilevel Latent Growth Modeling Analysis

Hierarchical Linear Modeling

Discussion

Full Information Maximum Likelihood Factor-of-Curves LGM

Limited Information Multilevel Latent Growth Modeling Analysis

Hierarchical Linear Modeling

Summary

Appendix A

Appendix B

Appendix C

Acknowledgments

References

8 Times Series Regressions

Time Series Models

Linear Stationary Models

Time Series Models for Nonstationary Data

A Strategy for Building Time Series Models for Observed Data

Model Identification

Model Estimation

Model Diagnostic Checking

Seasonal Time Series Models

Nonlinear Transformation of the Original Time Series Data

Example

Regression Models with Time Series Errors

Example

References

9 Dynamic Factor Analysis Models for Representing Process in Multivariate Time-Series

Linear Structural Equation Models for Time-Series

Basic P-Technique Factor Analysis Model

Direct Autoregressive Factor Score Model

White Noise Factor Score Model (WNFS)

Differences of the Two Models

Fitting the WNFS and DAFS Models to Data

Estimating the Model Parameters

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