Assessing the Straightforwardly-Worded Brief Fear of Negative Evaluation Scale for Differential Item Functioning Across Gender and Ethnicity.

The Brief Fear of Negative Evaluation Scale (BFNE; Leary Personality and Social Psychology Bulletin, 9, 371-375, 1983) assesses fear and worry about receiving negative evaluation from others. Rodebaugh et al. Psychological Assessment, 16, 169-181, (2004) found that the BFNE is composed of a reverse-worded factor (BFNE-R) and straightforwardly-worded factor (BFNE-S). Further, they found the BFNE-S to have better psychometric properties and provide more information than the BFNE-R. Currently there is a lack of research regarding the measurement invariance of the BFNE-S across gender and ethnicity with respect to item thresholds. The present study uses item response theory (IRT) to test the BFNE-S for differential item functioning (DIF) related to gender and ethnicity (White, Asian, and Black). Six data sets consisting of clinical, community, and undergraduate participants were utilized (N=2,109). The factor structure of the BFNE-S was confirmed using categorical confirmatory factor analysis, IRT model assumptions were tested, and the BFNE-S was evaluated for DIF. Item nine demonstrated significant non-uniform DIF between White and Black participants. No other items showed significant uniform or non-uniform DIF across gender or ethnicity. Results suggest the BFNE-S can be used reliably with men and women and Asian and White participants. More research is needed to understand the implications of using the BFNE-S with Black participants.

How bandwidth selection algorithms impact exploratory data analysis using kernel density estimation.

Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (KDE). This article provides an introduction to KDE and examines alternative methods for specifying the smoothing bandwidth in terms of their ability to recover the true density. We also illustrate the comparison and use of KDE methods with 2 empirical examples. Simulations were carried out in which we compared 8 bandwidth selection methods (Sheather-Jones plug-in [SJDP], normal rule of thumb, Silverman's rule of thumb, least squares cross-validation, biased cross-validation, and 3 adaptive kernel estimators) using 5 true density shapes (standard normal, positively skewed, bimodal, skewed bimodal, and standard lognormal) and 9 sample sizes (15, 25, 50, 75, 100, 250, 500, 1,000, 2,000). Results indicate that, overall, SJDP outperformed all methods. However, for smaller sample sizes (25 to 100) either biased cross-validation or Silverman's rule of thumb was recommended, and for larger sample sizes the adaptive kernel estimator with SJDP was recommended. Information is provided about implementing the recommendations in the R computing language.