Using "n"-Level Structural Equation Models for Causal Modeling in Fully Nested, Partially Nested, and Cross-Classified Randomized Controlled Trials

Autor/inn/en	Petscher, Yaacov; Schatschneider, Christopher
Titel	Using "n"-Level Structural Equation Models for Causal Modeling in Fully Nested, Partially Nested, and Cross-Classified Randomized Controlled Trials
Quelle	(2019), (43 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
DOI	10.1177/0013164419840071
Schlagwörter	Structural Equation Models; Causal Models; Randomized Controlled Trials; Hierarchical Linear Modeling; Students; Educational Research + Suchen Sie Ihr Suchwort? Kausalanalyse; Student; Schüler; Schülerin; Studentin; Bildungsforschung; Pädagogische Forschung
Abstract	Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Further, in many cases only some students may be nested within a unit while other students may not. Such instances of partial nesting requires a more flexible framework for estimating treatment effects so that the model coefficients are correctly estimate. Although several recommendations have been offered to the field on handling partially nested data, few are comprehensive in their treatment of manifest and latent variables in the context of partial nesting, full nesting, and cross-classification. The present study introduces "n"-level SEM (Mehta, 2013a) as a flexible measurement and analytic framework for the estimation of treatment effects for complex data structures that frequently present in randomized controlled trials. In this tutorial, we explore how the notation of "n"-level SEM allows for parsimonious model specification whether data are observed or latent and in the presence of partial nested or cross-classified designs. By using the xxm package in R, the advantage of using "n"-level SEM framework is demonstrated through five examples for single outcome manifest variables, as in the traditional multilevel model, as well as latent applications as in multilevel SEM. [This paper was published in "Educational and Psychological Measurement" v79 p1075-1102 2019 (EJ1227460).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01