Sexual Self-Concept: Testing a Hypothetical Model for Men and Women

One theoretical concept receiving modest attention in contemporary sex research is the sexual self-concept (SSC). However, a lack of cohesion within this research has culminated in a collection of SSC models which overlap one another but which are not exactly the same. Therefore, a unified conceptual model of SSC needs to be established. In addition, little research has examined potential differences between genders in SSC, as most SSC research has focused on women. Using Buzwell and Rosenthal's 1996 Buzwell , S. , & Rosenthal , D. (1996). Constructing a sexual self: Adolescents’ sexual self-perceptions and sexual risk-taking. Journal of Research on Adolescence , 6, 489–513.[Web of Science ®] , [Google Scholar] sexual selves model as a theoretical basis, a six-factor higher-order latent SSC model was tested using confirmatory factor analysis. Lower-order factors for this model included multidimensional sexual self-esteem and sexual self-efficacy factors, as well as unidimensional arousal, anxiety, exploration, and commitment factors. A five-factor latent model (after removing the commitment and the resistance sexual self-efficacy factors) was the best-fitting model. This model was then tested for measurement and structural invariance between genders. Results indicated that while the measurement of SSC was similar between men and women, structural invariance did not hold, as men had a significantly higher latent SSC score compared to women. These findings have important implications for sexual self-concept research, as well as contributing to better understanding of human sexuality.     

Measuring effect size: a robust heteroscedastic approach for two or more groups

Motivated by involvement in an intervention study, the paper proposes a robust, heteroscedastic generalization of what is popularly known as Cohen's d. The approach has the additional advantage of being readily extended to situations where the goal is to compare more than two groups. The method arises quite naturally from a regression perspective in conjunction with a robust version of explanatory power. Moreover, it provides a single numeric summary of how the groups compare in contrast to other strategies aimed at dealing with heteroscedasticity. Kulinskaya and Staudte [16 Rousseeuw, P. J. and Leroy, A. M. 1987. Robust Regression & Outlier Detection, New York: Wiley. [Crossref] [Google Scholar]] studied a heteroscedastic measure of effect size similar to the one proposed here, but their measure of effect size depends on the sample sizes making it difficult for applied researchers to interpret the results. The approach used here is based on a generalization of Cohen's d that obviates the issue of unequal sample sizes. Simulations and illustrations demonstrate that the new measure of effect size can make a practical difference regarding the conclusions reached.     

A step-down heteroscedastic multiple comparison procedure

All-pairs power in a one-way ANOVA is the probability of detecting all true differences between pairs of means. Ramsey (1978) found that for normal distributions having equal variances, step-down multiple comparison procedures can have substantially more all-pairs power than single-step procedures, such as Tukey’s HSD, when equal sample sizes are randomly sampled from each group. This paper suggests a step-down procedure for the case of unequal variances and compares it to Dunnett's T3 technique. The new procedure is similar in spirit to one of the heteroscedastic procedures described by Hochberg and Tamhane (1987), but it has certain advantages that are discussed in the paper. Included are results on unequal sample sizes.     

Robust ANCOVA: Some Small-sample Results when there are Multiple Groups and Multiple Covariates

Numerous methods have been proposed for dealing with the serious practical problems associated with the conventional analysis of covariance method, with an emphasis on comparing two groups when there is a single covariate. Recently, Wilcox (2005a: section 11.8.2) outlined a method for handling multiple covariates that allows nonlinearity and heteroscedasticity. The method is readily extended to multiple groups, but nothing is known about its small-sample properties. This paper compares three variations of the method, each method based on one of three measures of location: means, medians and 20% trimmed means. The methods based on a 20% trimmed mean or median are found to avoid Type I error probabilities well above the nominal level, but the method based on medians can be too conservative in various situations; using a 20% trimmed mean gave the best results in terms of Type I errors. The methods are based in part on a running interval smoother approximation of the regression surface. Included are comments on required sample sizes that are relevant to the so-called curse of dimensionality.     

Comparing J independent groups with a method based on trimmed means

The ANOVA-F test is the most popular and commonly used procedure for comparing J independent groups. However, it is well known that this method is very sensitive to non-normality, which has led to the derivation of alternative techniques based on robust estimators. In this work, ANOVA-F-test, trimmed mean Welch test, bootstrap-t trimmed mean Welch test, Schrader and Hettmansperger method with trimmed means, a percentile bootstrap method with trimmed means and a newly proposed method were compared in terms of both the Type I error probability and power. The proposed method compares well with ANOVA-F and other alternatives under various situations.