Robustness of Latent Profile Analysis to Measurement Noninvariance between Profiles

Autor/inn/en	Wang, Yan; Kim, Eunsook; Yi, Zhiyao
Titel	Robustness of Latent Profile Analysis to Measurement Noninvariance between Profiles
Quelle	In: Educational and Psychological Measurement, 82 (2022) 1, S.5-28 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wang, Yan) ORCID (Kim, Eunsook)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164421997896
Schlagwörter	Robustness (Statistics); Profiles; Statistical Analysis; Classification; Scores + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Charakterisierung; Profilanalyse; Statistische Analyse; Classification system; Klassifikation; Klassifikationssystem
Abstract	Latent profile analysis (LPA) identifies heterogeneous subgroups based on continuous indicators that represent different dimensions. It is a common practice to measure each dimension using items, create composite or factor scores for each dimension, and use these scores as indicators of profiles in LPA. In this case, measurement models for dimensions are not included and potential noninvariance across latent profiles is not modeled in LPA. This simulation study examined the robustness of LPA in terms of class enumeration and parameter recovery when the noninvariance was unmodeled by using composite or factor scores as profile indicators. Results showed that correct class enumeration rates of LPA were relatively high with small degree of noninvariance, large class separation, large sample size, and equal proportions. Severe bias in profile indicator mean difference was observed with intercept and loading noninvariance, respectively. Implications for applied researchers are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01