Agreement in interpersonal perception as a predictor of group performance

The present work deals with quantifying group characteristics. Specifically, dyadic measures of interpersonal perceptions were used to forecast group performance. 46 groups of students, 24 of four and 22 of five people, were studied in a real educational assignment context and marks were gathered as an indicator of group performance. Our results show that dyadic measures of interpersonal perceptions account for final marks. By means of linear regression analysis 85% and 85.6% of group performance was respectively explained for group sizes equal to four and five. Results found in the scientific literature based on the individualistic approach are no larger than 18%. The results of the present study support the utility of dyadic approaches for predicting group performance in social contexts.

Duration and compliance with antidepressant treatment in immigrant and native-born populations in Spain: a four year follow-up descriptive study

Background: Non-compliance with antidepressant treatment continues to be a complex problem in mental health care. In immigrant populations non-compliance is one of several barriers to adequate management of mental illness; some data suggest greater difficulties in adhering to pharmacological treatment in these groups and an increased risk of therapeutic failure. The aim of this study is to assess differences in the duration and compliance with antidepressant treatment among immigrants and natives in a Spanish health region. Methods: Population-based (n = 206,603), retrospective cohort study including all subjects prescribed ADT between 2007 and 2009 and recorded in the national pharmacy claims database. Compliance was considered adequate when the duration was longer than 4 months and when patients withdrew more than 80% of the packs required. Results: 5334 subjects (8.5% of them being immigrants) initiated ADT. Half of the immigrants abandoned treatment during the second month (median for natives = 3 months). Of the immigrants who continued, only 29.5% presented good compliance (compared with 38.8% in natives). The estimated risk of abandoning/ending treatment in the immigrant group compared with the native group, adjusted for age and sex, was 1.28 (95%CI 1.16-1.42). Conclusions: In the region under study, immigrants of all origins present higher percentages of early discontinuation of ADT and lower median treatment durations than the native population. Although this is a complex, multifactor situation, the finding of differences between natives and immigrants in the same region suggests the need to investigate the causes in greater depth and to introduce new strategies and interventions in this population group.

The effect of skewness and kurtosis on the Kenward-Roger approximation when group distributions differ

This study examined the independent effect of skewness and kurtosis on the robustness of the linear mixed model (LMM), with the Kenward-Roger (KR) procedure, when group distributions are different, sample sizes are small, and sphericity cannot be assumed. Methods: A Monte Carlo simulation study considering a split-plot design involving three groups and four repeated measures was performed. Results: The results showed that when group distributions are different, the effect of skewness on KR robustness is greater than that of kurtosis for the corresponding values. Furthermore, the pairings of skewness and kurtosis with group size were found to be relevant variables when applying this procedure. Conclusions: With sample sizes of 45 and 60, KR is a suitable option for analyzing data when the distributions are: (a) mesokurtic and not highly or extremely skewed, and (b) symmetric with different degrees of kurtosis. With total sample sizes of 30, it is adequate when group sizes are equal and the distributions are: (a) mesokurtic and slightly or moderately skewed, and sphericity is assumed; and (b) symmetric with a moderate or high/extreme violation of kurtosis. Alternative analyses should be considered when the distributions are highly or extremely skewed and samples sizes are small.