Complex Sample Data in Structural Equation Modeling
Abstract
Large-scale surveys using complex sample designs are frequently carried out by government agencies. The statistical analysis technology available for such data is, however, limited in scope. This study investigates and further develops statistical methods that could be used in software for the analysis of data collected under complex sample designs. First, it identifies several recent methodological lines of inquiry which taken together provide a powerful and general statistical basis for a complex sample, structural equation modeling analysis. Second, it extends some of this research to new situations of interest. A Monte Carlo study that empirically evaluates these techniques on simulated data comparable to those in largescale complex surveys demonstrates that they work well in practice. Due to the generality of the approaches, the methods cover not only continuous normal variables but also continuous nonnormal variables and dichotomous variables. Two methods designed to take into account the complex sample structure were
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The design effect for science identity (1.77) and research self-efficacy (1.57) was smaller than 2, so multilevel modeling was not necessary.
“The design effect for the two outcomes of interest was smaller than 2 (science identity 1.77; research self-efficacy 1.57). Thus, multilevel modeling (individuals nested within cohorts) is not necessary (Satorra and Muthen, 1995; Maas and Hox, 2005).”
From Outcomes and Processes in the Meyerhoff Scholars Program: STEM PhD Completion, Sense of Community, Perceived Program Benefit, Science Identity, and Research Self-Efficacy (2016) · cites this paper as Satorra and Muthen, 1995
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- Authors
- Muthen, B. O., & Satorra, A.
- ORCID
- Satorra, Albert
- Venue
- Sociological Methodology
- Publisher
- JSTOR
- Published
- 1995
- Volume
- 25
- Pages
- 267
- ISSN
- 0081-1750 (ISSN-L)
- OpenAlex
- W1988361795
- Cited by
- 1,586
- References
- 2
- Citations by year
- 2026: 76 · 2025: 82 · 2024: 64 · 2023: 88 · 2022: 84 · 2021: 94 · 2020: 110 · 2019: 108 · 2018: 98 · 2017: 90 · 2016: 79 · 2015: 83 · 2014: 77 · 2013: 80 · 2012: 68
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Sociological Methodology (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- complex sampling designs, structural equation modeling, survey data analysis, survey weights, binary variables, Monte Carlo simulation, large-scale surveys
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