Abstract
This article argues that ideal longitudinal research is characterized by the seamless integration of three elements: (a) a well-articulated theoretical model of change observed using (b) a temporal design that affords a clear and detailed view of the process, with the resulting data analyzed by means of (c) a statistical model that is an operationalization of the theoretical model. Two general varieties of theoretical models are considered: models in which the time-related change of primary interest is continuous, and those in which it is characterized by movement between discrete states. In addition, two general types of temporal designs are considered: the longitudinal panel design and the intensive longitudinal design. For each general category of theoretical models, some of the analytic possibilities available for longitudinal panel designs and for intensive longitudinal designs are discussed. The article concludes with brief discussions of two issues particularly relevant to longitudinal research—missing data and measurement—and a few words about exploratory research.
Keywords
Affiliated Institutions
Related Publications
Analysis of Longitudinal Data
1. Introduction 2. Design considerations 3. Exploring longitudinal data 4. General linear models 5. Parametric models for covariance structure 6. Analysis of variance methods 7....
New developments in latent variable panel analyses of longitudinal data
We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number...
Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling.
R. M. Baron and D. A. Kenny (1986; see record 1987-13085-001) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data....
Random Allocation Designs I: On General Classes of Estimation Methods
Certain linear estimation procedures for randomized experimental designs are evaluated relative to the criteria of bias, variance and mean square error. For the designs consider...
Random-Effects Models for Longitudinal Data
Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure ...
Publication Info
- Year
- 2005
- Type
- review
- Volume
- 57
- Issue
- 1
- Pages
- 505-528
- Citations
- 542
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1146/annurev.psych.57.102904.190146