دورية أكاديمية
Assessing Measurement Invariance across Multiple Groups: When Is Fit Good Enough?
| العنوان: | Assessing Measurement Invariance across Multiple Groups: When Is Fit Good Enough? |
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| اللغة: | English |
| المؤلفون: | van Dijk, Wilhelmina (ORCID |
| المصدر: | Educational and Psychological Measurement. Jun 2022 82(3):482-505. |
| الإتاحة: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 24 |
| تاريخ النشر: | 2022 |
| Sponsoring Agency: | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH) |
| Contract Number: | R21HD072286 P50HD052120 R01HD095193 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Education Level: | Early Childhood Education Elementary Education Grade 1 Primary Education Kindergarten Grade 2 Grade 3 |
| Descriptors: | Sample Size, Data Analysis, Goodness of Fit, Measurement, Scores, Achievement Tests, Reading Tests, Grade 1, Kindergarten, Elementary School Students, Grade 2, Grade 3 |
| Assessment and Survey Identifiers: | Woodcock Johnson Tests of Achievement |
| DOI: | 10.1177/00131644211023567 |
| تدمد: | 0013-1644 |
| مستخلص: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2022 |
| رقم الأكسشن: | EJ1336702 |
| قاعدة البيانات: | ERIC |
Full Text Finder | تدمد: | 0013-1644 |
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| DOI: | 10.1177/00131644211023567 |