Assessing Measurement Invariance across Multiple Groups: When Is Fit Good Enough?

Autor/inn/en	van Dijk, Wilhelmina; Schatschneider, Christopher; Al Otaiba, Stephanie; Hart, Sara A.
Titel	Assessing Measurement Invariance across Multiple Groups: When Is Fit Good Enough?
Quelle	In: Educational and Psychological Measurement, 82 (2022) 3, S.482-505 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (van Dijk, Wilhelmina)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644211023567
Schlagwörter	Sample Size; Data Analysis; Goodness of Fit; Measurement; Scores; Achievement Tests; Reading Tests; Grade 1; Kindergarten; Elementary School Students; Grade 2; Grade 3; Woodcock Johnson Tests of Achievement + Suchen Sie Ihr Suchwort? Auswertung; Messverfahren; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Lesetest; School year 01; 1. Schuljahr; Schuljahr 01; School year 02; 2. Schuljahr; Schuljahr 02; School year 03; 3. Schuljahr; Schuljahr 03
Abstract	Complex research questions often need large samples to obtain accurate estimates of parameters and adequate power. Combining extant data sets into a large, pooled data set is one way this can be accomplished without expending resources. Measurement invariance (MI) modeling is an established approach to ensure participant scores are on the same scale. There are two major problems when combining independent data sets through MI. First, sample sizes will often be large leading to small differences becoming noninvariant. Second, not all data sets may include the same combination of measures. In this article, we present a method that can deal with both these problems and is user friendly. It is a combination of generating random normal deviates for variables missing completely in combination with assessing model fit using the root mean square error of approximation "good enough principle," based on the hypothesis that the difference between groups is not zero but small. We demonstrate the method by examining MI across eight independent data sets and compare the MI decisions of the traditional and "good enough" approach. Our results show the approach has potential in combining educational data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01