ArticleEducational and psychological measurement2022
Assessing Measurement Invariance Across Multiple Groups: When Is Fit Good Enough?
Article in Educational and psychological measurement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Mediating effects of student-perceived instructional practices in the digital gaming-reading performance relationship: a multilevel analysis of PISA 2018.Frontiers in psychology · 2026Article
- Online shopping self-efficacy and emotional closeness to shopping-circle members as dual pathways: the association between product familiarity and Generation Z consumers' willingness to disclose.Frontiers in psychology · 2026Article
- Student Behavior Ratings and Response to Tier 1 Reading Intervention: Which Students Do Not Benefit?Journal of research on educational effectiveness · 2024Article
- Exploring Individual Differences in Response to Reading Intervention: Data from Project KIDS (Kids and Individual Differences in Schools).Journal of open psychology data · 2022Article
- Age and Sex Invariance of the Woodcock-Johnson IV Tests of Cognitive Abilities: Evidence from Psychometric Network Modeling.Journal of Intelligence · 2021Article
- A Community Data Sharing Resource: The LDbase Data Repository.Journal of learning disabilitiesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.