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We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To obtain imputations reflecting uncertainty about the unknown model parameters, we use a nonparametric bootstrap procedure as an alternative to the more common full Bayesian approach. The proposed multiple imputation method, which is implemented in Latent GOLD software for latent class analysis, is illustrated with two examples. In a simulated data example, we compare the new method to well-established methods such as maximum likelihood estimation with incomplete data and multiple imputation using a saturated log-linear model. This example shows that the proposed method yields unbiased parameter estimates and standard errors. The second example concerns an application using a typical social sciences data set. It contains 79 variables that are all included in the imputation model. The proposed method is especially useful for such large data sets because standard methods for dealing with missing data in categorical variables break down when the number of variables is so large.

In this paper we test the hypothesis that beliefs about the ideal mother are convergent across cultures and that these beliefs overlap considerably with attachment theory’s notion of the sensitive mother. In a sample including 26 cultural groups from 15 countries around the globe, 751 mothers sorted the Maternal Behavior Q-Set to reflect their ideas about the ideal mother. The results show strong convergence between maternal beliefs about the ideal mother and attachment theory’s description of the sensitive mother across groups. Cultural group membership significantly predicted variations in maternal sensitivity belief scores, but this effect was substantially accounted for by group variations in socio-demographic factors. Mothers living in rural versus urban areas, with a low family income, and with more children were less likely to describe the ideal mother as highly sensitive. Cultural group membership did remain a significant predictor of variations in maternal sensitivity belief scores above and beyond socio-demographic predictors. The findings are discussed in terms of the universal and culture-specific aspects of the sensitivity construct., info:eu-repo/semantics/publishedVersion