Understanding the physical attractiveness literature: Qualitative reviews versus meta-analysis.

The target article is a qualitative review of selected findings in the physical attractiveness literature. This commentary explains why the meta-analytic approach, frequently used by other attractiveness reviewers, is preferable for drawing unbiased conclusions about the effects of attractiveness. The article's main contribution is affording a foundation for subsequent meta-analysis of the studies discussed in a subjective fashion.

Growth, Persistence, and Desistance of Alcohol Use for At-Risk Men in Their 30s.

BACKGROUND: Little is known about heterogeneity in men's drinking behaviors and their related consequences across mid-adulthood, and moreover, whether individual or social factors may predict such differences. This study examined 3 indicators of alcohol use, namely alcohol volume, heavy episodic drinking (HED), and drinking-related problems for men in their 30s. METHODS: Participants were 197 at-risk men from the Oregon Youth Study assessed 5 times across ages 29 to 38 years. Growth mixture modeling with count outcomes was used to examine unobserved heterogeneity in alcohol trajectories. Associations of latent classes of alcohol users with (i) classes for the other alcohol indicators, (ii) alcohol use by peers and romantic partners, (iii) alcohol classes previously extracted from ages 18 to 29 years, and (iv) past year alcohol use disorder (AUD) diagnostic status at ages 35 to 36 years were examined. RESULTS: A 3-class solution afforded the best fit for each alcohol indicator. Alcohol problems were relatively established in the 30s, with an ascending use class found only for volume. Although relatively few men were in higher classes for all 3 indicators, 45% of the sample was in the highest class on at least 2 indicators of use. Peer drunkenness was a robust predictor of the alcohol classes. Concordance among classes of alcohol users was seen from the 20s to the 30s, with prior desistance likely to be maintained for alcohol volume and HED. AUD diagnoses at ages 35 to 36 years were more common in the higher classes obtained for alcohol volume and alcohol problems. CONCLUSIONS: Many men in their 30s engaged in a high volume of alcohol use without frequent engagement in HED, likely relating to continuing alcohol problems. The convergence of men's alcohol use with that of their peers found at younger ages was maintained into early mid-adulthood.

Proximal vs. distal predictors of alcohol use disorders and treatment utilization in at-risk men in early middle age.

Understanding factors that predict both development and treatment of alcohol misuse and its consequences can inform prevention and treatment efforts. This study used measures of both proximal (e.g., behaviors) and distal (e.g., traits) risk factors that were predicted to relate to both an alcohol use disorder (AUD) and to treatment utilization among AUD-diagnosed men to test the hypothesis that both type of factors predict AUDs but only proximal factors influence treatment-seeking. Analyses of variance with contrasts were used to compare the means for risk factors between men with an AUD and AUD-free men and-given an AUD diagnosis-between men who were treated for an AUD and untreated men (n=181). As predicted, men with AUDs differed on a broad range of proximal and distal factors, including number of alcohol problems, alcohol-related influences of peers and partners, alcohol expectancies, familial factors, and psychopathology. As hypothesized, only proximal risk factors predicted treatment-seeking among the AUD men, particularly alcohol problems and related consequences.

Heterogeneity in growth and desistance of alcohol use for men in their 20s: prediction from early risk factors and association with treatment.

BACKGROUND: The course of men's alcohol use from ages 18 to 19 through 28 to 29 years was examined using growth mixture modeling (GMM) to determine alcohol trajectories for 3 conceptualizations of alcohol use: volume of use, heavy episodic drinking (HED), and drinking-related problems. Trajectory classes were validated against the young men's alcohol treatment history, and childhood/adolescent predictors of trajectory membership were examined. METHODS: Participants were 205 men from the Oregon Youth Study, an ongoing longitudinal study of predominantly White men recruited from higher crime neighborhoods who were assessed annually during their 20s. The multivariate association between 3 prospectively assessed risk factors-parental alcohol use, child antisocial behavior, and age at first drunken experience-and the latent classes extracted from the GMM were examined for each alcohol indicator. RESULTS: A 3-class-solution model best fit the data for each alcohol indicator. The classes for both HED and problematic drinking for the men were significantly associated with history of treatment for alcohol use. Overall, the findings indicated a relatively large class with persistently high volume of alcohol use across the 20s and a greater prevalence of desistance for HED and alcohol problems. Age at first intoxication was the best predictor of latent class membership, and men in the initially high-then-desisting alcohol classes had a high level of early risk. Concordance of trajectory class membership across alcohol indicators was moderate overall but particularly strong for higher problem groups, as almost all men in the increasing HED trajectory were also in the highest volume and alcohol problems trajectory classes. Levels of treatment were high for the higher and desisting HED and alcohol problems classes. CONCLUSIONS: Many of the men showed chronic alcohol use across the decade of the 20s and had problems resulting from their high usage. Whereas most of the men showed low and/or desisting HED across this period, desistance was less common for volume of use and for alcohol problems.

A Regression Framework for Effect Size Assessments in Longitudinal Modeling of Group Differences.

The use of growth modeling analysis (GMA)--particularly multilevel analysis and latent growth modeling--to test the significance of intervention effects has increased exponentially in prevention science, clinical psychology, and psychiatry over the past 15 years. Model-based effect sizes for differences in means between two independent groups in GMA can be expressed in the same metric (Cohen's d) commonly used in classical analysis and meta-analysis. This article first reviews conceptual issues regarding calculation of d for findings from GMA and then introduces an integrative framework for effect size assessments that subsumes GMA. The new approach uses the structure of the linear regression model, from which effect sizes for findings from diverse cross-sectional and longitudinal analyses can be calculated with familiar statistics, such as the regression coefficient, the standard deviation of the dependent measure, and study duration.

Regression-Equivalent Effect Sizes for Latent Growth Modeling and Associated Null Hypothesis Significance Tests.

The effect of an independent variable on random slopes in growth modeling with latent variables is conventionally used to examine predictors of change over the course of a study. This tutorial demonstrates that the same effect of a covariate on growth can be obtained by using final status centering for parameterization and regressing the random intercepts (or the intercept factor scores) on both the independent variable and a baseline covariate--the framework used to study change with classical regression analysis. Examples are provided that illustrate the application of an intercept-focused approach to obtain effect sizes--the unstandardized regression coefficient, the standardized regression coefficient, squared semi-partial correlation, and Cohen's f2 --that estimate the same parameters as respective effect sizes from a classical regression analysis. Moreover, statistical power to detect the effect of the predictor on growth was greater when using random intercepts than the conventionally used random slopes.

Stability of intimate partner violence by men across 12 years in young adulthood: effects of relationship transitions.

The present study examined the stability of young men's intimate partner violence (IPV) over a 12-year period as a function of relationship continuity or discontinuity. Multiwave measures of IPV (physical and psychological aggression) were obtained from 184 men at risk for delinquency and their women partners. The effects of relationship continuity versus transitions on change in IPV were examined using multilevel analyses. In general, men's IPV decreased over time. Men's physical aggression in their early 20s predicted levels of physical aggression about 7 years later, and men's psychological aggression in their early 20s predicted levels of psychological aggression about 10-12 years later. As hypothesized, higher stability in IPV was found for men who stayed with the same partners, whereas men experiencing relationship transitions showed greater change. The IPV of new partners was linked to the changes in men's IPV that occurred with repartnering. There was less change in men's IPV over time as men changed partners less frequently.

Effect of Parameterization on Statistical Power and Effect Size Estimation in Latent Growth Modeling.

The difference between groups in their random slopes is frequently examined in latent growth modeling to evaluate treatment efficacy. However, when end centering is used for model parameterization with a randomized design, the difference in the random intercepts is the model-estimated mean difference between the groups at the end of the study, which has the same expected value as the product of the coefficient for the slope difference and study duration. A Monte Carlo study found that (a) the statistical power to detect the treatment effect was greater when determined from the intercept instead of the slope difference, and (b) the standard error of the model-estimated mean difference was smaller when obtained from the intercept difference. Investigators may reduce Type II errors by comparing groups in random intercepts instead of random slopes to test treatment effects, and should therefore conduct power assessments using end centering to detect each difference.

Effect sizes for growth-modeling analysis for controlled clinical trials in the same metric as for classical analysis.

The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the intervention and control groups that captures the treatment effect is rarely reported. This article first reviews 2 classes of formulas for effect sizes associated with classical repeated-measures designs that use the standard deviation of either change scores or raw scores for the denominator. It then broadens the scope to subsume GMA and demonstrates that the independent groups, within-subjects, pretest-posttest control-group, and GMA designs all estimate the same effect size when the standard deviation of raw scores is uniformly used. Finally, the article shows that the correct effect size for treatment efficacy in GMA--the difference between the estimated means of the 2 groups at end of study (determined from the coefficient for the slope difference and length of study) divided by the baseline standard deviation--is not reported in clinical trials.

New Approaches for Estimation of Effect Sizes and their Confidence Intervals for Treatment Effects from Randomized Controlled Trials.

Although Cohen's d and the growth modeling analysis (GMA) d from linear models are common standardized effect sizes used to convey treatment effects, popular statistical software packages do not include them in their standard outputs. This article demonstrated the use of statistical software with user-prescribed parameter functions (e.g., Mplus) to produce d for treatment effects from both classical analysis and GMA--along with their associated standard errors (SEs) and confidence intervals (CIs). A Monte Carlo study was conducted to examine bias in the SE and CI for GMA d obtained with Mplus and found that both estimates were more accurate when calculated by the software with the standard bootstrap than with the delta method, but the delta method estimates were less biased than respective estimates from extant post hoc equations. Thus, users of many statistical software packages (including SAS, R, and LISREL) should obtain d or GMA d and associated CIs directly. Researchers employing less versatile software--and meta-analysts including ds and GMA ds in their syntheses of treatment effects--should continue to use the conventional post hoc equations. Biases in SEs and CIs for effect sizes obtained with them are ignorable and point estimates of d and GMA d are the same whether obtained directly from the software or with post hoc equations.

Time-Varying Effect Sizes for Quadratic Growth Models in Multilevel and Latent Growth Modeling.

Multilevel and latent growth modeling analysis (GMA) is often used to compare independent groups in linear random slopes of outcomes over time, particularly in randomized controlled trials. The unstandardized coefficient for the effect of group on the slope from a linear GMA can be transformed into a model-estimated effect size for the group difference at the end of a study. Because effect sizes vary non-linearly in quadratic GMA, the effect size at the end of a study using quadratic GMA cannot be derived from a single coefficient, and cannot be used to estimate effect sizes at intermediate time points with backwards extrapolation. This article formulates equations and associated input commands in Mplus for time-varying effect sizes for quadratic GMA. Illustrative analyses that produced these time-varying effect sizes were presented, and Monte Carlo study found that bias in the effect sizes and their confidence intervals was ignorable.

Mediation analysis with binary outcomes: Direct and indirect effects of pro-alcohol influences on alcohol use disorders.

A risk factor or intervention (an independent variable) may influence a substance abuse outcome (the dependent variable) indirectly, by affecting an intervening variable (a mediator) that in turn affects that outcome. Mediation analysis is a statistical method commonly used to examine the interrelations among independent, mediating, and dependent variables to obtain the direct and indirect effects of an independent variable on a continuous dependent variable. However, mediation analysis may also be used with binary outcomes, such as a diagnosis of an alcohol use disorder (AUD). Study 1 demonstrated methods of mediation analysis with binary outcomes by examining the direct and indirect effects of pro-alcohol social influences on an AUD, as a function of: (a) the distribution of the independent variable (binary vs. continuous), (b) the frequency of the outcome (non-rare vs. rare), and (c) the effect metric (probability vs. odds ratio). Study 2 was a Monte Carlo (simulation) study of bias in the indirect effects based on estimates from the first study. These methods have wide applicability in addictions research because many key outcomes are binary, and mediation analysis is frequently used to study the causal mechanisms by which interventions and risk factors affect substance abuse.

Associations of substance use problems with intimate partner violence for at-risk men in long-term relationships.

Associations of substance use problems in men--defined as a man's meeting at least 1 criterion of dependence on each of a number of substances by his mid-20s--with their perpetration of intimate partner violence (IPV) were examined in an at-risk community sample of 150 men in long-term relationships from their late adolescence to their late 20s. Men who had a problem with substances other than sedatives (especially cannabis and hallucinogens) committed more IPV than did men without such problems. Most of the men who had a problem with marijuana also had an alcohol problem, which explains why alcohol was found to have only an indirect association with IPV. The failure of previous alcohol-use studies to control for co-occurrence of alcohol and marijuana problems may explain the discrepancy with conclusions from past research that alcohol problems contribute directly to the perpetration of IPV.

Sequelae of aggression in acutely suicidal adolescents.

The consequences of aggression on problem course and suicide risk were examined in 270 acutely suicidal adolescents (ages 12-17 years; 184 girls). Participants were assessed during psychiatric hospitalization (T1), 6-months post-hospitalization (T2), and 15 or more months post-hospitalization (T3). Study variables included self- and parent-reported aggression; self-reported internalizing symptoms, suicidal ideation, suicide attempt, and adverse events; and clinician-rated suicidal behavior. Aggression was not directly related to suicide attempt concurrently or prospectively. However, among more aggressive youth, internalizing symptoms were more predictive of T3 suicide attempt than among less aggressive youth. T1 aggression predicted aggressive incidents and the likelihood of incarceration prior to T3. Two-level hierarchical linear modeling indicated that self-reported aggression and internalizing problems were linked in terms of severity and rates of decline over time. Overall, parent-reported aggression was negatively associated with suicidal ideation. Findings highlight (a) the continuity and consequences of aggression, (b) a possible role of aggression in worsening suicide risk factors and potentiating suicide attempt, and (c) the importance of ongoing research on subtypes of suicidal adolescents.

Meta-analysis with standardized effect sizes from multilevel and latent growth models.

OBJECTIVE: Findings from multilevel and latent growth modeling analysis (GMA) need to be included in literature reviews, and this article explicates 4 rarely discussed approaches for using GMA studies in meta-analysis. METHOD: Extant and new equations are presented for calculating the effect size (d) and its variance (v) from reported statistics from GMA studies with each method, and a fixed effects meta-analysis of results from 5 randomized clinical trials was conducted to demonstrate their applications. RESULTS: Two common problematic practices--one that introduces bias in effect sizes because of attrition, measurement errors, and probable violations of assumptions for classical analysis, and the other that confounds the treatment effect with the intraclass correlation--were both found to yield smaller effect sizes from retrieved studies than were obtained with a newer model-based framework and its associated GMA d statistic. CONCLUSIONS: The optimal strategy for including a GMA study in a meta-analysis is to use GMA d and its v calculated with the standard error of the unstandardized coefficient for the treatment effect. When that standard error is unknown, the use of GMA d and its v estimated with an alternative equation that requires only GMA d and sample size is recommended. (PsycINFO Database Record

Are black-white differences in females' body dissatisfaction decreasing? A meta-analytic review.

Proponents of the sociocultural model of eating disorders have suggested that ethnic differences in body dissatisfaction may be diminishing as the thin ideal of beauty becomes more widely disseminated among minority women. In a meta-analysis, the authors examined temporal trends in Black-White differences and also examined whether these differences generalize across various age groups and measures. Results confirmed more favorable body image evaluations among Black than White females, with the greatest differences at the age period of the early 20s. Although results confirmed that ethnic differences have diminished, this trend was limited to weight-focused measures. On more global body image measures, ethnic differences actually increased. These results suggest that the relationship between Black-White ethnicity and body image is more complex than previously suggested.

Confidence interval estimation for standardized effect sizes in multilevel and latent growth modeling.

OBJECTIVE: Multilevel and latent growth models are frequently used interchangeably to examine differences between groups in trajectories of outcomes from controlled clinical trials. The unstandardized coefficient for the effect from group to slope (the treatment effect) from such models can be converted to a standardized mean difference (Cohen's d) between the treatment and control groups at end of study. This article addresses the confidence interval (CI) for this effect size. METHOD: Two sets of equations for estimating the CI for the treatment effect size in multilevel models were derived, and the usage of each was illustrated with data from the National Youth Study (Elliott, Huizinga, & Menard, 1989). Validity of the CIs was examined with a Monte Carlo simulation study that manipulated effect potency and sample size. RESULTS: The equivalence of the 2 new CI estimation methods was demonstrated, and the Monte Carlo study found that bias in the CI for the effect size was not appreciably larger than bias in the CI for the widely used unstandardized coefficient. CONCLUSIONS: Investigators reporting this increasingly popular effect size can estimate its CI with equations presented in this article.

Changes in the Associations of Heavy Drinking and Drug Use with Intimate Partner Violence in Early Adulthood.

The hypothesis that the disinhibitory effects induced by alcohol consumption contribute to domestic violence has gained support from meta-analyses of mainly cross-sectional studies that examined the association between alcohol abuse and perpetration of intimate partner violence (IPV). However, findings from multilevel analyses of longitudinal data investigating the time-varying effects of heavy episodic drinking (HED) on physical IPV have been equivocal. This 12-year prospective study used multilevel analysis to examine the effects of HED and illicit drug use on perpetration of both physical and psychological IPV during early adulthood. Participants were 157 romantic couples who were assessed biennially 2 to 6 times for substance misuse and IPV. The analyses found no significant main effect of either HED or drug use on perpetration of IPV but there were significant interactions of both HED and drug use with age. Moreover, the developmental trends in substance use effects on IPV typically varied by gender and type of IPV.

A placebo controlled trial of bupropion for smoking cessation in schizophrenia.

BACKGROUND: Schizophrenic patients have high rates of cigarette smoking compared with the general population. We compared sustained-release (SR) bupropion with placebo for smoking cessation in patients with schizophrenic disorders. We also examined how antipsychotic class predicts smoking cessation outcomes with bupropion. METHODS: Thirty-two subjects meeting DSM-IV criteria for schizophrenia or schizoaffective disorder and nicotine dependence were randomized to bupropion SR (BUP, 300 mg/day) or placebo (PLA). Outcomes included treatment retention, smoking abstinence rates, expired breath carbon monoxide (CO) levels, psychotic symptoms, and medication side effects. RESULTS: Bupropion significantly increased trial endpoint 7-day point prevalence smoking abstinence rates compared with placebo [BUP, 8/16 (50.0%), PLA, 2/16 (12.5%); chi(2) = 5.24, df = 1, p <.05], and reduced CO levels during the trial [Medication x Time interaction; Z = 3.09, p <.01]. Positive schizophrenia symptoms were not altered by BUP, but negative symptoms were significantly reduced. Atypical antipsychotic drug treatment enhanced smoking cessation responses to BUP. Major side effects were dry mouth, gastrointestinal symptoms, headache, and insomnia. CONCLUSIONS: Our results suggest that 1) BUP enhances smoking abstinence rates compared with PLA in nicotine-dependent schizophrenic smokers; 2) BUP is well-tolerated and safe for use in these patients; and 3) atypical antipsychotics may enhance smoking cessation outcomes with BUP.

Morphological and latency abnormalities of the mid-latency auditory evoked responses in schizophrenia: a preliminary report.

INTRODUCTION: Evoked potential (EP) amplitude and latency abnormalities have been extensively examined in schizophrenia. Morphological abnormalities of the mid-latency auditory evoked responses (MLAERs; P50, N100, P200), on the other hand, received very little attention. METHODS: Based on a priori defined set of morphological criteria, the morphology and latency of the MLAERs were blindly compared between stable outpatients with schizophrenia (N=27) and age- and gender-matched healthy control subjects (N=22). The morphology of the MLAERs was considered abnormal if one or more of the components fell outside the expected latency range, if one or more of the components were missing, or if a later occurring component was smaller in amplitude than an earlier occurring one. RESULTS: Of the 27 schizophrenia subjects, 20 had waveforms that were deemed atypical, while only 8 from the control group were classified as atypical (chi(2)=5.52, p<0.02). The latencies of the P50 and N100 components, identified based on morphology, were significantly prolonged in schizophrenia patients. CONCLUSIONS: These preliminary data suggest that morphological abnormalities of the MLAERs in schizophrenia patients are significant and should be taken into consideration when examining the MLAERs of this patient population.