We believed positive experiences with homosexual men and women would decrease participants’ negative attitudes toward gay men and lesbians. We found a moderately strong negative association (?=-.45, se = .07, p < .05) between quality of participants' interactions with gay and lesbian individuals and negative attitudes toward homosexual; thus, confirming our third hypothesis. A one unit increase in participants perceived positive experiences during their interactions with homosexual men and women decreased their sexual prejudice score by half a point. Moreover, we found significant correlations between positive experiences with gay men and lesbians and previous interactions with homosexual men and women (r = .26, se = .05, p < .05), as well as with participants' perceived similarities in their friends' attitudes toward gay men and lesbians (r = .24, se = .07, p < .05). While moderately low, the association between these three latent factors point to the multifaceted nature of participants' attitudes toward gay and lesbian people.
Our fourth hypothesis stated participants with stronger religious convictions would hold stronger negative attitudes toward gay men and lesbians. We found religiosity to be the strongest predictor of participants’ negative attitudes toward gay men and lesbians (?=.50, se = .11, p < .05). For every unit increase in participants' assessment of the importance of their religious beliefs in their lives, their sexual prejudice score increased by half a scale point.
All of our results recommend zero differences in the newest model’s street differ owed to participants’ gender
Considering the low-extreme forecast away from peers’ parallels within their perceptions into homosexuals, we attempted removing which path nevertheless the model is struggling to converge properly after five-hundred iterations. Thus, i remaining it factor in our very own model to be sure profitable design stability. The past model showed a keen Roentgen dos out-of 56% to own sexual prejudice’s variance.
Assessment to have gender outcomes
In order to test whether the exploratory structural model provided an equally good fit for males and females, we re-ran the structural model estimation procedures running each group’s covariance matrix simultaneously. All factor loadings, paths, and variances were constrained to be equal in the initial model. The sex differences model indicated a relatively acceptable fit for both sexes, [? 2 (141, N-males = 153, N-females = 207) = ; NFI = .88, NNFI = .93, CFI = .94, RMSEA = .055]. We then freed each path consecutively to test whether sex www.datingranking.net/local-hookup/boston-2/ differences existed between the significant latent-factors and sexual prejudice. After freeing the path for participants’ interaction with homosexuals and sexual prejudice, we found no difference across male and female participants (? ? 2 (1) = 1.27, n.s.). Subsequently, we freed the path between positive experiences with homosexuals and sexual prejudice but we found no difference by participants’ sex (? ? 2 (1) = .05, n.s.). Finally, we tested whether sex differences existed between religiosity and sexual prejudice but no difference was found (? ? 2 (1)= 0.27, n.s.).
Regardless if all of our analyses discover a good fit to your data, we examined whether or not some other model you may match the details just as well or greatest (MacCallum, Wegener, Uchino, & Fabrigar, 1993). Theoretically, it’s just because plausible that individuals having greater bad attitudes toward homosexuality do prevent interacting with gay males and you may lesbians, rating its affairs because the bad, seeing people they know just like the having more thinking towards gay somebody, otherwise look for encouragement about their philosophy within religiosity. Profile dos gifts it inversed causation alternate model below.
An option exploratory structural design: What if sexual prejudice predicts communications and you will positive experience that have homosexuals, seen resemblance with peers’ attitudes into homosexuality, and you can religiosity. Most of the strong lines show statistically extreme paths within .05 peak. Magnitudes away from organization is offered the high quality errors in the parentheses; X dos (61, N = 360) = . Normed (NFI), non-normed (NNFI), and you can relative (CFI) goodness-of-complement try .91, .91, .93, respectively; RMSEA are .09.
