Same-Sex Kissing and Having a Gay or Lesbian Child: A Bridge Too Far? Parent-Child Similarities in Homophobic Attitudes and Observed Parental Discomfort.

This study examined parent-child similarities in homophobic attitudes and observed parental discomfort with coming-out vignettes in interactions with their adolescent children (14-18 years old). Based on gender schema theory and the family process model we expected parent-child similarities in homophobic attitudes to be stronger in same-gender dyads. Further, we expected that observed parental discomfort with coming-out vignettes would occur and is stronger when the gender of the parent, child, and character in the vignette match. We used questionnaires and observation data from 199 White Dutch families in the Netherlands. Our results showed that parents' homophobic attitudes were associated with their children's homophobic attitudes. For same-sex kissing and (imagining) having a gay son, these associations were stronger between parents and children of the same gender. Further, parental discomfort with coming-out vignettes occurred and was stronger when parents and children had the same gender, regardless of the gender of the vignette character. In conclusion, policies aiming at gay and lesbian inclusion should not be limited to accepting gay/lesbian identities, but also pay attention to the acceptance of same-sex intimacy expressions, having gay or lesbian family members, and normalizing discussions about gay/lesbian lives.

Demystifying omega squared: Practical guidance for effect size in common analysis of variance designs.

Omega squared (ω^2) is a measure of effect size for analysis of variance (ANOVA) designs. It is less biased than eta squared, but reported less often. This is in part due to lack of clear guidance on how to calculate it. In this paper, we discuss the logic behind effect size measures, the problem with eta squared, the history of omega squared, and why it has been underused. We then provide a user-friendly guide to omega squared and partial omega squared for ANOVA designs with fixed factors, including one-way, two-way, and three-way designs, using within-subjects factors and/or between-subjects factors. We show how to calculate omega squared using output from SPSS. We provide information on the calculation of confidence intervals. We examine the problems of nonadditivity, and intrinsic versus extrinsic factors. We argue that statistical package developers could play an important role in making the calculation of omega squared easier. Finally, we recommend that researchers report the formulas used in calculating effect sizes, include confidence intervals if possible, and include ANOVA tables in the online supplemental materials of their work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).