Multilevel Latent State-Trait Models with Experience Sampling Data: An Illustrative Case of Examining Situational Engagement

Autor/inn/en	Vongkulluksn, Vanessa W.; Xie, Kui
Titel	Multilevel Latent State-Trait Models with Experience Sampling Data: An Illustrative Case of Examining Situational Engagement
Quelle	In: Open Education Studies, 4 (2022) 1, S.252-272 (21 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1515/edu-2022-0016
Schlagwörter	Learning Processes; Sampling; Hierarchical Linear Modeling; Experience; Psychological Patterns; Prediction; Regression (Statistics); Undergraduate Students + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Erfahrung; Vorhersage; Regression; Regressionsanalyse
Abstract	Learning processes often occur at a situational level. Changes in learning context have implications on how students are motivated or are able to cognitively process information. To study such situational phenomena, Experience Sampling Method (ESM) can help assess psychological variables in the moment and in context. However, data collected via ESM is voluminous and imbalanced. Special types of statistical modeling are needed to handle this unique data structure in order to maximize its potential for scientific discovery. The purpose of this paper is to illustrate how Latent State-Trait modeling used within a multilevel framework can help model complex data as derived by ESM. A study of situational engagement is presented as an illustrative case. We describe methodological considerations which facilitated the following analyses: (1) "Decomposition of trait-level and state-level engagement"; (2) "Group differences in variance decomposition"; and (3) "Predicting state component of engagement." Discussions include the relative advantages and disadvantages of ESM and multilevel Latent State-Trait modeling in facilitating situational psychological research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01