Item Responses in Quantity-Frequency Questionnaires: Implications for Data Generalizability.

Alcohol consumption is an important predictor of a variety of negative outcomes. There is an extensive literature that examines the differences in the estimated level of alcohol consumption between types of assessments (e.g.

Gaussian model-based partitioning using iterated local search.

The emergence of Gaussian model-based partitioning as a viable alternative to K-means clustering fosters a need for discrete optimization methods that can be efficiently implemented using model-based criteria. A variety of alternative partitioning criteria have been proposed for more general data conditions that permit elliptical clusters, different spatial orientations for the clusters, and unequal cluster sizes. Unfortunately, many of these partitioning criteria are computationally demanding, which makes the multiple-restart (multistart) approach commonly used for K-means partitioning less effective as a heuristic solution strategy.

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous.

Local Optima in Mixture Modeling.

It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model.

Examining the effect of initialization strategies on the performance of Gaussian mixture modeling.

Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used.

Sex differences in the latent class structure of alcohol use disorder: Does (dis)aggregation of indicators matter?

Many researchers have argued for a differential presentation of alcohol use disorder (AUD) between men and women. Latent class analysis is the most commonly used analytic technique for modeling AUD subcategories, and latent class analyses have supported a variety of class structures of AUD. This article examines whether these differential results are, in part, an artifact of whether researchers have (a) analyzed men and women in the same analysis and (b) aggregated item-level symptoms into AUD diagnostic criteria prior to analysis.