Parceling to Lower Indicator-to-Sample-Size Ratio? Instead Try Correcting Your SEM Chi-Square for High p and Modest N to Obtain a Nominal Type I Error Rate Without Parcel-Allocation Variability
Bibliographic Data
| ID | 21641803 |
|---|---|
| Authors | Sonya K Sterba (Vanderbilt University, corresponding author), Amy Liang (Vanderbilt University) |
| Year | 2026 |
| Pages | 1-14 |
| Publication date | 2026-03-09 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2026.2620394 |
| OpenAlex | W7134838183 |
| Language | EN |
| References cited | 58 |
Parceling is routinely recommended to SEM researchers with many items (high p) and modest N in order to “lower indicator-to-sample-size ratio.” Here we show that this directive is a roundabout response to address chi-square distortion at high p and modest N, which in turn leads to egregiously inflated Type I error for the item-level model’s chi-square test of absolute fit. We explain, demonstrate, and provide software to implement two more-direct strategies to address this chi-square distortion. These strategies involve fitting an item-level model and either (i) empirically correcting the chi-square critical value or (ii) employing a high-p and modest-N adjustment to the chi-square statistic itself (and other chi-square based fit indices). Both strategies are shown to control Type I error more consistently than parceling and, critically, avoid across-item-to-parcel-allocation variability in Type I error rates. Both item-level model strategies can also legitimately improve fit of a correct item-level model by addressing high-p, modest-N distortion of chi-square—without inducing parcel-allocation variability in model fit
Population · Statistical analysis · Type I and type II errors · Electron and X-Ray Spectroscopy Techniques · Meta-analysis and systematic reviews · SAS software applications and methods
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| Highly cited | No |
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