mimic modeling
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2020 ◽  
Author(s):  
Tao Hong ◽  
William Oakes ◽  
Susan Maller ◽  
Carla Zoltowski

2019 ◽  
Vol 38 (2) ◽  
pp. 256-262 ◽  
Author(s):  
Linda Ruan-Iu ◽  
Laura L. Pendergast ◽  
Muneera Rasheed ◽  
Fahmida Tofail ◽  
Erling Svensen ◽  
...  

An adapted version of the Wechsler Preschool and Primary Scale of Intelligence—Third Edition (WPPSI-III) was administered to assess cognitive functioning among 1,253 5-year-old children from the Malnutrition and Enteric Disease (MAL-ED) study—an international, multisite study investigating multiple aspects of child development. In this study, the factor structure and invariance of the WPPSI-III were examined across seven international research sites located in Bangladesh, Brazil, India, Nepal, Pakistan, South Africa, and Tanzania. Using a multiple indicator multiple cause (MIMIC) modeling approach, the findings supported the validity of a fluid reasoning dimension (comprised of block design, matrix reasoning, and picture completion subscales) across each of the seven sites, although the scores were noninvariant. Accordingly, these scores are recommended for research purposes and understanding relationships between variables but not for mean comparisons or clinical purposes.


Psychology ◽  
2019 ◽  
Vol 10 (06) ◽  
pp. 799-818
Author(s):  
Ioannis Agaliotis ◽  
Panagiotis Varsamis

Medical Care ◽  
2017 ◽  
Vol 55 (4) ◽  
pp. e25-e35 ◽  
Author(s):  
Luk Bruyneel ◽  
Baoyue Li ◽  
Allison Squires ◽  
Sara Spotbeen ◽  
Bart Meuleman ◽  
...  

2011 ◽  
Vol 72 (3) ◽  
pp. 469-492 ◽  
Author(s):  
Eun Sook Kim ◽  
Myeongsun Yoon ◽  
Taehun Lee

Multiple-indicators multiple-causes (MIMIC) modeling is often used to test a latent group mean difference while assuming the equivalence of factor loadings and intercepts over groups. However, this study demonstrated that MIMIC was insensitive to the presence of factor loading noninvariance, which implies that factor loading invariance should be tested through other measurement invariance testing techniques. MIMIC modeling is also used for measurement invariance testing by allowing a direct path from a grouping covariate to each observed variable. This simulation study with both continuous and categorical variables investigated the performance of MIMIC in detecting noninvariant variables under various study conditions and showed that the likelihood ratio test of MIMIC with Oort adjustment not only controlled Type I error rates below the nominal level but also maintained high power across study conditions.


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