Advances in behavioral genetics modeling using Mplus: Applications of factor mixture modeling to twin data.

This article discusses new latent variable techniques developed by the authors. As an illustration, a new factor mixture model is applied to the monozygotic-dizygotic twin analysis of binary items measuring alcohol-use disorder. In this model, heritability is simultaneously studied with respect to latent class membership and within-class severity dimensions. Different latent classes of individuals are allowed to have different heritability for the severity dimensions. The factor mixture approach appears to have great potential for the genetic analyses of heterogeneous populations. Generalizations for longitudinal data are also outlined

Longitudinal genetic analysis for loneliness in Dutch twins.

In previous studies we obtained evidence that variation in loneliness has a genetic component. Based on adult twin data, the heritability estimate for loneliness, which was assessed as an ordinal trait, was 48%. These analyses were done on loneliness scores averaged over items ('I feel lonely' and 'Nobody loves me') and over time points. In this article we present a longitudinal analysis of loneliness data assessed in 5 surveys (1991 through 2002) in Dutch twins (N = 8389) for the two separate items of the loneliness scale. From the longitudinal growth modeling it was found sufficient to have non-zero variance for the intercept only, while the other effects (linear, quadratic and cubic slope) had zero variance. For the item 'I feel lonely' we observed an increasing age trend up to age 30, followed by a decline to age 50. Heritability for individual differences in the intercept was estimated at 77%. For the item 'Nobody loves me' no significant trend over age was seen; the heritability of the intercept was estimated at 70%

Measurement invariance in the social sciences: Historical development, methodological challenges, state of the art, and future perspectives

This review summarizes the current state of the art of statistical and (survey) methodological research on measurement (non)invariance, which is considered a core challenge for the comparative social sciences. After outlining the historical roots, conceptual details, and standard procedures for measurement invariance testing, the paper focuses in particular on the statistical developments that have been achieved in the last 10 years. These include Bayesian approximate measurement invariance, the alignment method, measurement invariance testing within the multilevel modeling framework, mixture multigroup factor analysis, the measurement invariance explorer, and the response shift-true change decomposition approach. Furthermore, the contribution of survey methodological research to the construction of invariant measurement instruments is explicitly addressed and highlighted, including the issues of design decisions, pretesting, scale adoption, and translation. The paper ends with an outlook on future research perspectives

Sequential fixed width confidence intervals

We consider the problem of constructing confidence intervals of fixed width d and confidence level γ for the success probability p in Bernoulli trials. Algorithms are given for calculating numerical lower bounds on the average expected sample size required and an asymptotic lower bound is obtained as d -> 0. Sequential and two-stage procedures are proposed that attain the asymptotic lower bounds and nearly attain the numerical lower bounds. Asymptotically optimal sequential and two-stage confidence intervals of fixed width and confidence level are proposed for the mean in a general (non-Bernoulli) context