In defense of the RMSEA-P for latent variable model evaluation: A failed reproducibility study of Lance et al. (2016)

Management researchers now have the option of using the path model fit index RMSEA-P as they evaluate their latent variable models to overcome limitations of global fit indices (CFI, RMSEA, SRMR). Two simulation studies of the effectiveness of the RMSEA-P reached different conclusions about its merit, one favorable and the other very unfavorable ( Lance et al., 2016 ). The present research is a constructive reproduction of Lance et al. with analytical improvements. We failed to reproduce their key negative findings that correctly specified models were retained in less than 50% of cases, and incorrectly specified models were retained in over 50% of cases. Instead, using our improved analysis techniques, we found that correct models were identified in 92% of cases and incorrect models were retained in less than 10% of cases. Our results also show the RMSEA-P works best when used with a Traditional Model Selection Strategy.

Authors

Institutions

Publication Details

Journal
Journal of Management Scientific Reports
Published
2026-01-19
DOI
https://doi.org/10.1177/27550311251404933
Primary Topic
Psychometric Methodologies and Testing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

In defense of the RMSEA-P for latent variable model evaluation: A failed reproducibility study of Lance et al. (2016)

Larry J. Williams, Christopher M. Castille, Steven Culpepper
Journal of Management Scientific Reports
Psychometric Methodologies and Testing
article

In defense of the RMSEA-P for latent variable model evaluation: A failed reproducibility study of Lance et al. (2016)

Larry J. Williams, Christopher M. Castille, Steven Culpepper
article en

Abstract

Management researchers now have the option of using the path model fit index RMSEA-P as they evaluate their latent variable models to overcome limitations of global fit indices (CFI, RMSEA, SRMR). Two simulation studies of the effectiveness of the RMSEA-P reached different conclusions about its merit, one favorable and the other very unfavorable ( Lance et al., 2016 ). The present research is a constructive reproduction of Lance et al. with analytical improvements. We failed to reproduce their key negative findings that correctly specified models were retained in less than 50% of cases, and incorrectly specified models were retained in over 50% of cases. Instead, using our improved analysis techniques, we found that correct models were identified in 92% of cases and incorrect models were retained in less than 10% of cases. Our results also show the RMSEA-P works best when used with a Traditional Model Selection Strategy.

Journal of Management Scientific ReportsVol. 4(1)
Texas Tech University (US), University of Illinois Urbana-Champaign (US), Nichols College (US), Barber-Nichols (United States) (US), Nicholls State University (US)
Gender equality
Openalex Percentile: Top 86%
Psychometric Methodologies and Testing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

In defense of the RMSEA-P for latent variable model evaluation: A failed reproducibility study of Lance et al. (2016) — Larry J. Williams, Christopher M. Castille, et al. · Journal of Management Scientific Reports (2026) | TGRS Research Map | TGRS