Structural Factor Analysis Experiments with Incomplete Data
Abstract
This article presents some benefits and limitations of structural equation models for multivariate experiments with incomplete data. Examples from studies of latent variable path models of cognitive performances illustrate analyses with four different kinds of incomplete data: (a) latent variables, (b) omitted variables, (c) randomly missing data, and (d) non- randomly missing data. Power based cost-benefit analyses for experimental design and planning are also presented. These incomplete data approaches are closely related to models used in classical experimental design, interbattery measurement analysis, longitudinal analyses, and behavioral genetic analyses. These structural equation methods for old experimental design problems indicate some new opportunities for future multivariate research.
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The covariates helped accommodate longitudinal selectivity under the assumption that incomplete data were missing at random.
“These factors thus helped to accommodate longitudinal selectivity under the assumption that incomplete data were missing at random (Little & Rubin, 1987; McArdle, 1994).”
From Cohort differences in cognitive aging and terminal decline in the Seattle Longitudinal Study. (2011) · cites this paper as McArdle, 1994
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- Authors
- McArdle, J. J.
- Affiliations
- University of Virginia (US)
- Corresponding author
- McArdle, John J.
- Venue
- Multivariate Behavioral Research
- Publisher
- Informa UK Limited
- Published
- 1994-10
- Volume
- 29
- Issue
- 4
- Pages
- 409-454
- ISSN
- 0027-3171 (ISSN-L)
- 1532-7906
- OpenAlex
- W2088671310
- Cited by
- 198
- References
- 3
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 6 · 2023: 5 · 2022: 4 · 2021: 1 · 2020: 5 · 2019: 7 · 2018: 9 · 2017: 5 · 2016: 9 · 2015: 9 · 2014: 7 · 2013: 8 · 2012: 8
- FWCI
- 2.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Multivariate Behavioral Research (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- structural equation modeling, missing data, latent variables, omitted variables, power analysis, cost-benefit analysis, experimental design, cognitive performance, longitudinal analysis, behavioral genetics
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