Biases of Parameter Estimates in Misspecified Structural Equation Models
Bibliographic Data
| ID | 6169723 |
|---|---|
| Authors | Stanislav Kolenikov (0000-0003-2977-7326, University of Missouri, corresponding author) |
| Year | 2011 |
| Volume | 41 |
| Issue | 1 |
| Pages | 119-157 |
| Publication date | 2011-06-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methodology (JOURNAL) |
| Journal identifiers | ISSN: 0081-1750 • E-ISSN: 1467-9531 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1111/j.1467-9531.2011.01236.x |
| OpenAlex | W2138641327 |
| Language | EN |
| Citations received | 9 |
| References cited | 28 |
We deal with the problem of quantifying the degree to which parameter estimates in a structural equation model can be biased when structural relationships were not specified correctly by the researcher. We propose a framework to relate moment residuals to biases of parameter estimates and the overall noncentrality of the model. For each parameter in the model, an impact of either particular moment residual or the overall model noncentrality can be evaluated, although the latter tends to give error bounds that are rather conservative. We provide illustrative analytical and empirical examples to demonstrate the steps in application of the proposed procedures. The first example is a mildly misspecified model with causal indicators mistaken to be effect indicators. The resulting biases can be approximated very accurately by accounting for the effect of a single misfitted residual moment. The second example is a grossly misspecified model in which a mediating latent variable was erroneously omitted. In this case, the misspecification spreads to all entries of the covariance matrix, and measures based on overall noncentrality give a good indication of the magnitudes of plausible biases. The third example is an empirical study that uses Holzinger-Swineford factor analysis data that shows how the procedures can be used in practice
Algorithm · Contrast (vision · Covariance · Covariance matrix · Econometrics · Latent variable · Mathematical analysis · Moment (physics · Residual · Statistics · Structural equation modeling · Variable (mathematics · Applied Mathematics · Computer Science · Mathematics · Psychometric Methodologies and Testing · Sensory Analysis and Statistical Methods
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| Unique citing works | 9 |
|---|---|
| Citations per year | 0,69 |
| Citation span | 2013 - 2023 (11) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 9 |