Longitudinal data analysis has long played a significant role in empirical research within the developmental sciences. The past decade has given rise to a host of new and exciting analytic methods for studying between-person differences in within-person change. These methods are broadly organized under the term growth curve models. The historical lines of development leading to current growth models span multiple disciplines within both the social and statistical sciences, and this in turn makes it challenging for developmental researchers to gain a broader understanding of the current state of this literature. To help address this challenge, the authors pose 12 questions that frequently arise in growth curve modeling, particularly in applications within developmental psychology. They provide concise and nontechnical responses to each question and make specific recommendations for further readings.

Although several measures of obsessive-compulsive (OC) symptoms exist, most are limited in that they are not consistent with the most recent empirical findings on the nature and dimensional structure of obsessions and compulsions. In the present research, the authors developed and evaluated a measure called the Dimensional Obsessive-Compulsive Scale (DOCS) to address limitations of existing OC symptom measures. The DOCS is a 20-item measure that assesses the four dimensions of OC symptoms most reliably replicated in previous structural research. Factorial validity of the DOCS was supported by exploratory and confirmatory factor analyses of 3 samples, including individuals with OC disorder, those with other anxiety disorders, and nonclinical individuals. Scores on the DOCS displayed good performance on indices of reliability and validity, as well as sensitivity to treatment and diagnostic sensitivity, and hold promise as a measure of OC symptoms in clinical and research settings.

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In the context of an NIAAA/Fetzer Institute-funded study designed to look at the impact of spirituality in an inpatient alcohol treatment, this retrospective case control study investigated whether spiritual growth occurred during an inpatient phase of treatment for alcohol dependence, the degree to which spiritual gains (if noted) would be maintained at follow-up, and whether spiritual growth would be associated with follow-up sobriety. To accomplish this goal, thirty-six individuals who reported relapsing to alcohol at three-month follow-up were compared with thirty-six matched controls who reported abstinence at follow-up. Spiritual development and change was assessed via a set of six measures. Paired t-tests revealed that spiritual growth occurred across all measures during the treatment phase. Repeated measures analysis of variance (ANOVA) indicated that this growth was maintained at three-month follow-up. Two-way repeated measures ANOVA revealed that while non-relapsers maintained spiritual growth over the course of four weeks of treatment and in the three-month period following treatment, renewed alcohol use was associated with decreased spirituality.

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Structural equation mixture models (SEMMs), when applied as a semiparametric model (SPM), can adequately recover potentially nonlinear latent relationships without their specification. This SPM is useful for exploratory analysis when the form of the latent regression is unknown. The purpose of this article is to help users familiar with structural equation models to add SEMM to their toolkit of exploratory analytic options. We describe how the SEMM captures potential nonlinearity between latent variables, and how confidence bands (CBs; point wise and simultaneous) for the recovered latent function are constructed and interpreted. We then illustrate the usefulness of CBs for inference with an empirical example on the effect of emotions on cognitive processing. We also introduce a visualization tool that automatically generates plots of the latent regression and their CBs to promote user accessibility. Finally, we conclude with a discussion on the use of this SPM for exploratory research.Structural equation models (SEMs) are widely used in the educational, social, and behavioral sciences, as many phenomena of interest such as intelligence, emotions, and personality are latent in nature. Latent variable models have the advantage of accounting for measurement error and have the ability to model

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