Performance of Second-Order Latent Growth Model Under Partial Longitudinal Measurement Invariance
A Comparison of Two Scaling Approaches
Bibliographic Data
| ID | 21641943 |
|---|---|
| Authors | Min-Jeong Jeon (0000-0001-6206-5182, Ewha Womans University), Su-Young Kim (0000-0002-1195-6126, Ewha Womans University, corresponding author) |
| Year | 2021 |
| Volume | 28 |
| Issue | 2 |
| Pages | 261-277 |
| Publication date | 2021-03-04 |
| Peer Reviewed | Yes |
| 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 | 10.1080/10705511.2020.1783270 |
| OpenAlex | W3047312027 |
| Language | EN |
| Citations received | 5 |
| References cited | 53 |
Second-order latent growth models (SLGMs) have recently been highlighted over the traditional first-order latent growth model. Although SLGMs can show several intuitive strengths, the model has remained less understood due to the issue of scaling-related misspecification. As one source of model misspecification, scaling could influence the estimation of SLGM. Since the impact could differ depending on which scaling method is employed, selecting a scaling method becomes crucial for the practical use of SLGM. The present study investigated and compared the impact of two different scaling methods for the estimation of SLGM under various partial measurement invariance situations. The results of comprehensive Monte Carlo simulations do not support a single superior scaling method under all generated partial MI conditions. In this regard, the careful and strategic selection of a scaling method in the context of SLGM is required, as discussed in the final section of the study
Econometrics · Model selection · Monte Carlo method · Physics · Scale invariance · Scaling · Statistical physics · Statistics · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Applied Mathematics
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| Unique citing works | 5 |
|---|---|
| Citations per year | 1,67 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |