Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen’s d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood-ratio test, BIC, and sample-size adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. The AIC and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.

Count data reflect the number of occurrences of a behavior in a fixed period of time (e.g., number of aggressive acts by children during a playground period). In cases in which the outcome variable is a count with a low arithmetic mean (typically < 10), standard ordinary least squares regression may produce biased results. We provide an introduction to regression models that provide appropriate analyses for count data. We introduce standard Poisson regression with an example and discuss its interpretation. Two variants of Poisson regression, overdispersed Poisson regression and negative binomial regression, are introduced that may provide more optimal results when a key assumption of standard Poisson regression is violated. We also discuss the problems of excess zeros in which a subgroup of respondents who would never display the behavior are included in the sample and truncated zeros in which respondents who have a zero count are excluded by the sampling plan. We provide computer syntax for our illustrations in SAS and SPSS. The Poisson family of regression models provides improved and now easy to implement analyses of count data. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Personality Assessment for the following free supplemental resources: the data set used to illustrate Poisson regression in this article, which is available in three formats-a text file, an SPSS database, or a SAS database.].

Business theories often specify the mediating mechanisms by which a predictor variable affects an outcome variable. In the last 30 years, investigations of mediating processes have become more widespread with corresponding developments in statistical methods to conduct these tests. The purpose of this article is to provide guidelines for mediation studies by focusing on decisions made prior to the research study that affect the clarity of conclusions from a mediation study, the statistical models for mediation analysis, and methods to improve interpretation of mediation results after the research study. Throughout this article, the importance of a program of experimental and observational research for investigating mediating mechanisms is emphasized.

This study examined family and neighborhood influences relevant to low-income status to determine how they combine to predict the parenting behaviors of Mexican–American mothers and fathers. The study also examined the role of parenting as a mediator of these contextual influences on adolescent internalizing and externalizing symptoms. Study hypotheses were examined in a diverse sample of Mexican–American families in which 750 mothers and 467 fathers reported on their own levels of parental warmth and harsh parenting. Family economic hardship, neighborhood familism values, and neighborhood risk indicators were all uniquely associated with maternal and paternal warmth, and maternal warmth mediated the effects of these contextual influences on adolescent externalizing symptoms in prospective analyses. Parents’ subjective perceptions of neighborhood danger interacted with objective indicators of neighborhood disadvantage to influence maternal and paternal warmth. Neighborhood familism values had unique direct effects on adolescent externalizing symptoms in prospective analyses, after accounting for all other context and parenting effects.

Findings build on the small but growing literature supporting the promising role of new technologies for expanding the delivery of behavioral parent training. (PsycINFO Database Record