Correlates of dysfunctional career thoughts in breast cancer survivors from the Bahamas.

Purpose: The purpose of this research was to examine the correlates of dysfunctional career thoughts (DCTs) in a sample of breast cancers survivors. Sample and design: A total of 195 breast cancer survivors from the Bahamas completed a cross-sectional survey. Methods: The Career Thoughts Inventory (CTI) and standard medical and demographic items were administered. Analysis of Variance and logistic regression models were used to examine associations. Findings: Total CTI scores differed by age group, marital status, surgical remedy, and employment prior to diagnosis. The odds of DCT were higher among younger (Odds Ratio [OR] = 4.4), married (OR = 2.4), who were unemployed prior to diagnosis (OR = 3.5). Conclusion: Dysfunctional career thoughts appear to be common in certain breast cancer survivors. Further research is needed to determine whether similar associations are observed in other samples. Implications for Providers: Providers should discuss DCTs after treatment.

Predicting group-level outcome variables: An empirical comparison of analysis strategies.

This study provides a review of two methods for analyzing multilevel data with group-level outcome variables and compares them in a simulation study. The analytical methods included an unadjusted ordinary least squares (OLS) analysis of group means and a two-step adjustment of the group means suggested by Croon and van Veldhoven (2007). The Type I error control, power, bias, standard errors, and RMSE in parameter estimates were compared across design conditions that included manipulations of number of predictor variables, level of correlation between predictors, level of intraclass correlation, predictor reliability, effect size, and sample size. The results suggested that an OLS analysis of the group means, with White's heteroscedasticity adjustment, provided more power for tests of group-level predictors, but less power for tests of individual-level predictors. Furthermore, this simple analysis avoided the extreme bias in parameter estimates and inadmissible solutions that were encountered with other strategies. These results were interpreted in terms of recommended analytical methods for applied researchers.

How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study.

Considering that the absence of measurement error in research is a rare phenomenon and its effects can be dramatic, we examine the impact of measurement error on propensity score (PS) analysis used to minimize selection bias in behavioral and social observational studies. A Monte Carlo study was conducted to explore the effects of measurement error on the treatment effect and balance estimates in PS analysis across seven different PS conditioning methods. In general, the results indicate that even low levels of measurement error in the covariates lead to substantial bias in estimates of treatment effects and concomitant reduction in confidence interval coverage across all methods of conditioning on the PS.

Use of design effects and sample weights in complex health survey data: a review of published articles using data from 3 commonly used adolescent health surveys.

OBJECTIVES: We assessed how frequently researchers reported the use of statistical techniques that take into account the complex sampling structure of survey data and sample weights in published peer-reviewed articles using data from 3 commonly used adolescent health surveys. METHODS: We performed a systematic review of 1003 published empirical research articles from 1995 to 2010 that used data from the National Longitudinal Study of Adolescent Health (n=765), Monitoring the Future (n=146), or Youth Risk Behavior Surveillance System (n=92) indexed in ERIC, PsycINFO, PubMed, and Web of Science. RESULTS: Across the data sources, 60% of articles reported accounting for design effects and 61% reported using sample weights. However, the frequency and clarity of reporting varied across databases, publication year, author affiliation with the data, and journal. CONCLUSIONS: Given the statistical bias that occurs when design effects of complex data are not incorporated or sample weights are omitted, this study calls for improvement in the dissemination of research findings based on complex sample data. Authors, editors, and reviewers need to work together to improve the transparency of published findings using complex sample data.

Comparing the Performance of Approaches for Testing the Homogeneity of Variance Assumption in One-Factor ANOVA Models.

Various tests to check the homogeneity of variance assumption have been proposed in the literature, yet there is no consensus as to their robustness when the assumption of normality does not hold. This simulation study evaluated the performance of 14 tests for the homogeneity of variance assumption in one-way ANOVA models in terms of Type I error control and statistical power. Seven factors were manipulated: number of groups, average number of observations per group, pattern of sample sizes in groups, pattern of population variances, maximum variance ratio, population distribution shape, and nominal alpha level for the test of variances. Overall, the Ramsey conditional, O'Brien, Brown-Forsythe, Bootstrap Brown-Forsythe, and Levene with squared deviations tests maintained adequate Type I error control, performing better than the others across all the conditions. The power for each of these five tests was acceptable and the power differences were subtle. Guidelines for selecting a valid test for assessing the tenability of this critical assumption are provided based on average cell size.

Social Determinants of Health and Adolescent Pregnancy: An Analysis From the National Longitudinal Study of Adolescent to Adult Health.

PURPOSE: Although rates of adolescent pregnancy are at an all-time low in the United States, racial/ethnic and geographic disparities persist. This research used National Longitudinal Study of Adolescent to Adult Health (Add Health) data to analyze empirical relationships between social determinants of health (SDoH) and adolescent pregnancy. Examining relationships between the SDoH and adolescent pregnancy provides support for funding priorities and interventions that expand on the current focus on individual- and interpersonal-level factors. METHODS: On the basis of the Healthy People 2020 Social Determinants of Health Framework, the identification of proxy measures for SDoH within the Add Health study allowed for an analysis of relationships to adolescent pregnancy (N = 9,204). Logistic regression examined associations between adolescent pregnancy and each measure of SDoH. RESULTS: Results indicated that 6 of 17 measures of SDoH had an empirical relationship with adolescent pregnancy. Measures negatively associated with adolescent pregnancy included the following: feeling close to others at school, receipt of high school diploma, enrollment in higher education, participation in volunteering or community service, reporting litter or trash in the neighborhood environment as a big problem, and living in a two-parent home. CONCLUSIONS: Findings from this study support the need for increased research and intervention focus in SDoH related to areas of education and social and community context. Results of this study provide information for the allocation of resources to best address SDoH that show a link with adolescent pregnancy. Areas of future research can further explore the areas in which SDoH show a relationship with adolescent pregnancy.

A Meta-Meta-Analysis: Empirical Review of Statistical Power, Type I Error Rates, Effect Sizes, and Model Selection of Meta-Analyses Published in Psychology.

This article uses meta-analyses published in Psychological Bulletin from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual moderators in multivariate analyses, and tests of residual variability within individual levels of categorical moderators had the lowest and most concerning levels of power. Using methods of calculating power prospectively for significance tests in meta-analysis, we illustrate how power varies as a function of the number of effect sizes, the average sample size per effect size, effect size magnitude, and level of heterogeneity of effect sizes. In most meta-analyses many significance tests were conducted, resulting in a sizable estimated probability of a Type I error, particularly for tests of means within levels of a moderator, univariate categorical moderators, and residual variability within individual levels of a moderator. Across all surveyed studies, the median effect size and the median difference between two levels of study level moderators were smaller than Cohen's (1988) conventions for a medium effect size for a correlation or difference between two correlations. The median Birge's (1932) ratio was larger than the convention of medium heterogeneity proposed by Hedges and Pigott (2001) and indicates that the typical meta-analysis shows variability in underlying effects well beyond that expected by sampling error alone. Fixed-effects models were used with greater frequency than random-effects models; however, random-effects models were used with increased frequency over time. Results related to model selection of this study are carefully compared with those from Schmidt, Oh, and Hayes (2009), who independently designed and produced a study similar to the one reported here. Recommendations for conducting future meta-analyses in light of the findings are provided.

A SAS macro for statistical power calculations in meta-analysis.

Although statistical power is often considered in the design of primary research studies, it is rarely considered in meta-analysis. Background and guidelines are provided for conducting power analysis in meta-analysis, followed by the presentation of a SAS macro that calculates power using the methods described by Hedges and Pigott (2001, 2004). Several detailed examples are given, including input statements and output. Practical issues in the application of power analysis to meta-analysis are discussed. The macro and examples may be downloaded as supplemental materials for this article from brm.psychonomic-journals.org/content/supplemental.

RETR_PWR: an SAS macro for retrospective statistical power analysis.

In contrast to prospective power analysis, retrospective power analysis provides an estimate of the statistical power of a hypothesis test after an investigation has been conducted rather than before. In this article, three approaches to obtaining point estimates of power and an interval estimation algorithm are delineated. Previous research on the bias and sampling error of these estimates is briefly reviewed. Finally, an SAS macro that calculates the point and interval estimates is described. The macro was developed to estimate the power of an F test (obtained from analysis of variance, multiple regression analysis, or any of several multivariate analyses), but it may be easily adapted for use with other statistics, such as chi-square tests or t tests.