The social accuracy model of interpersonal perception (SAM) is a componential model that estimates perceiver and target effects of different components of accuracy across traits simultaneously. For instance, Jane may be generally accurate in her perceptions of others and thus high in perceptive accuracy-the extent to which a particular perceiver's impressions are more or less accurate than other perceivers on average across different targets. Just as well, Jake may be accurately perceived by others and thus high in expressive accuracy-the extent to which a particular target is accurately perceived on average across different perceivers. Perceptive and expressive accuracy can be further decomposed into their constituent components of normative and distinctive accuracy. Thus the SAM represents an integration of Cronbach's componential approach with Kenny's (1994) social relations model. The SAM is illustrated using both a half-block as well as a round-robin design. Key findings include reliable individual differences in several specific aspects of interpersonal perceptions.

The coding of time in growth curve models has important implications for the interpretation of the resulting model that are sometimes not transparent. The authors develop a general framework that includes predictors of growth curve components to illustrate how parameter estimates and their standard errors are exactly determined as a function of receding time in growth curve models. Linear and quadratic growth model examples are provided, and the interpretation of estimates given a particular coding of time is illustrated. How and why the precision and statistical power of predictors of lower order growth curve components changes over time is illustrated and discussed. Recommendations include coding time to produce readily interpretable estimates and graphing lower order effects across time with appropriate confidence intervals to help illustrate and understand the growth process.

Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses ( Baron & Kenny, 1986 ; Sobel, 1982 ) have in recent years been supplemented by computationally intensive methods such as bootstrapping, the distribution of the product methods, and hierarchical Bayesian Markov chain Monte Carlo (MCMC) methods. These different approaches for assessing mediation are illustrated using data from Dunn, Biesanz, Human, and Finn (2007). However, little is known about how these methods perform relative to each other, particularly in more challenging situations, such as with data that are incomplete and/or nonnormal. This article presents an extensive Monte Carlo simulation evaluating a host of approaches for assessing mediation. We examine Type I error rates, power, and coverage. We study normal and nonnormal data as well as complete and incomplete data. In addition, we adapt a method, recently proposed in statistical literature, that does not rely on confidence intervals (CIs) to test the null hypothesis of no indirect effect. The results suggest that the new inferential method-the partial posterior p value-slightly outperforms existing ones in terms of maintaining Type I error rates while maximizing power, especially with incomplete data. Among confidence interval approaches, the bias-corrected accelerated (BC a ) bootstrapping approach often has inflated Type I error rates and inconsistent coverage and is not recommended; In contrast, the bootstrapped percentile confidence interval and the hierarchical Bayesian MCMC method perform best overall, maintaining Type I error rates, exhibiting reasonable power, and producing stable and accurate coverage rates.

Recurrent pains are a complex set of conditions that cause great discomfort and impairment in children and adults. The objectives of this study were to (a) describe the frequency of headache, stomachache, and backache in a representative Canadian adolescent sample and (b) determine whether a set of psychosocial factors, including background factors (i.e., sex, pubertal status, parent chronic pain), external events (i.e., injury, illness/hospitalization, stressful-life events), and emotional factors (i.e., anxiety/depression, self-esteem) were predictive of these types of recurrent pain. Statistics Canada's National Longitudinal Survey of Children and Youth was used to assess a cohort of 2488 10- to 11-year-old adolescents up to five times, every 2 years. Results showed that, across 12-19 years of age, weekly or more frequent rates ranged from 26.1%-31.8% for headache, 13.5-22.2% for stomachache, and 17.6-25.8% for backache. Chi-square tests indicated that girls had higher rates of pain than boys for all types of pain, at all time points. Structural equation modeling using latent growth curves showed that sex and anxiety/depression at age 10-11 years was predictive of the start- and end-point intercepts (i.e., trajectories that indicated high levels of pain across time) and/or slopes (i.e., trajectories of pain that increased over time) for all three types of pain. Although there were also other factors that predicted only certain pain types or certain trajectory types, overall the results of this study suggest that adolescent recurrent pain is very common and that psychosocial factors can predict trajectories of recurrent pain over time across adolescence.

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