A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data

Autor/inn/en	Yamashita, Takashi; Smith, Thomas J.; Cummins, Phyllis A.
Titel	A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data
Quelle	(2020), (30 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Learning Analytics; Computer Software; Syntax; Adults; Competence; Structural Equation Models; Sampling; Prediction; Learning Motivation; Program for the International Assessment of Adult Competencies (PIAAC) + Suchen Sie Ihr Suchwort? Kompetenz; Vorhersage; Motivation for studies; Lernmotivation
Abstract	Background: Several statistical applications including Mplus, STATA, and R are available to conduct analyses such as structural equation modeling and multi-level modeling using large-scale assessment data that employ complex sampling and assessment designs and that provide associated information such as sampling weights, replicate weights, and plausible values to facilitate these analyses. However, to date, little guidance is available for applied researchers in Education. In order to promote the use of large-scale assessment data in education and expand the scope of analytic capabilities among applied researchers, this study provides step-by-step guidance, and practical examples of syntax and data analysis using Mplus. Methods: Concise overview and key unique aspects of large-scale assessment data from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC) are described. Using commonly-used statistical software including SAS and R, a simple macro program and syntax are developed to streamline the data preparation process. Then, two examples of structural equation models are demonstrated using Mplus. Results: With the practical guidance and resources provided in this study, education researchers can efficiently prepare and analyze large-scale assessment data such as PIAAC and similar dataset using Mplus. Some methodological limitations are also highlighted. Conclusions: This study summarized the key aspects of large-scale assessment data from PIAAC, and provided practical guidance and tools including a macro program and syntax to conduct advanced statistical analysis in Mplus. The suggested data preparation and analytic approaches can be immediately applicable with existing large-scale assessment data, although further refinement could be carried out in future research. [This paper was published in "Journal of Educational and Behavioral Statistics" (EJ1300359).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01