1985
DOI: 10.1177/0049124185013004003
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Multiple-Indicator, Multiple-Cause Models for a Single Latent Variable with Ordinal Indicators

Abstract: A MIMIC model is developed that contains a one-dimensional latent variable measured by ordinal indicators. The latent variable can be either discrete and ordered or continuous. Parameters for both the measurement and structural components of the model are estimated simultaneously using the EM algorithm. An example of a sociological application is presented.

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Cited by 8 publications
(4 citation statements)
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“…Large magnitudes of modification indices would suggest that which items are likely to show DIF (Bye et al . 1985). Afterwards, the paths from a probable DIF items to the indicator variables of interest were freely estimated, one at a time (in an iterative process); and in a case of significant direct effect, the items were distinguished as DIF.…”
Section: Discussionmentioning
confidence: 99%
“…Large magnitudes of modification indices would suggest that which items are likely to show DIF (Bye et al . 1985). Afterwards, the paths from a probable DIF items to the indicator variables of interest were freely estimated, one at a time (in an iterative process); and in a case of significant direct effect, the items were distinguished as DIF.…”
Section: Discussionmentioning
confidence: 99%
“…An item is considered to display uniform DIF when people from different groups have different probabilities in item responses, despite having the same underlying level of a latent trait (Green, ). MIMIC also has the advantage of allowing control of covariate effects when testing measurement invariance across a specific grouping variable (e.g., Bye, Gallicchio, & Dykacz, ; Muthén, ). As age, gender, and SES differed significantly across the seven countries in our study, we utilized the advantage of this model to assess measurement invariance across the countries while controlling the effect of above‐mentioned covariates.…”
Section: Methodsmentioning
confidence: 99%
“…If this model fits the data well, it would suggest an absence of noninvariant items; conversely, a poor‐fitting model would indicate sources of noninvariance. Large modification indices would indicate which parameters are likely sources of noninvariance, and each of these parameters may then be estimated one at a time (Bye et al., ).…”
Section: Methodsmentioning
confidence: 99%
“…W sensie statystycznym modele MIMIC stanowią połączenie dwóch rodzajów technik analitycznych: konfirmacyjnej analizy czynnikowej (confirmatory factor analysis, CFA) oraz analizy ścieżek (path analysis, PA). Składa się więc z komponentu pomiarowego (CFA) oraz regresyjnego (PA), zwanego także komponentem strukturalnym (Bye, Gallicchio i Dykacz, 1985). Komponent pomiarowy służy do estymacji niedającego się bezpośrednio zaobserwować poziomu interesującej nas cechy (np.…”
Section: Hipotezaunclassified