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| Time | Mon, Nov 17, 2025 12:00 pm to 1:00 pm |
| Location | Zoom Only |
| Presenter(s) | Emily Lim, Postdoc in Department of Biobehavioral Health, Penn State University |
| Description |
Emily Lim, Postdoc in Department of Biobehavioral Health, Penn State University Title: The Association between Perceived Discrimination and Loneliness Among Older Adults: Friendship as a Moderator Abstract: Loneliness is a growing global public health concern due to its detrimental effects on older adults’ health and well-being. Older adults who experience discrimination are especially vulnerable, as perceived discrimination can heighten feelings of alienation and reduce their sense of belonging. Friendships, however, may serve as a protective factor that mitigates these negative effects. Yet, little is known about the moderating role of friendship in this relationship. Using data from adults aged 50 and older (N=2,902) across seven mid-Atlantic U.S. states, this study employed linear regression analyses to examine the associations between perceived discrimination, friendship, and loneliness. Results showed that higher levels of perceived discrimination were associated with greater loneliness (B=0.55, SE=0.03, p< .001), while stronger friendships were linked to lower loneliness (B=-0.07, SE=0.01, p< .001). Importantly, friendship significantly moderated the relationship between perceived discrimination and loneliness (B=-0.02, SE=0.01, p<.01), suggesting that friendship may buffer the adverse effects of discrimination. These findings highlight the importance of fostering and maintaining friendships in later life as a means of protecting older adults’ emotional well-being. Social interventions and policies aimed at strengthening social connections may help reduce loneliness among those facing discrimination.
Siyang Ni, PhD Candidate in Criminology and Social Data Analytics, Penn State University Title: Is Self Control Fixed? Measurement, Development, and Heterogeneity of Self-control from Ages Three to Seventeen Abstract: Objective. The General Theory of Crime asserts that self control is largely fixed by late childhood. We test this stability claim using six repeated measures of self control at ages 3, 5, 7, 11, 14, and 17 in a nationally representative United Kingdom cohort (N = 10,591), Method. We estimated survey weighted growth mixture models with freely estimated time scores and compared one, two, three, and four class solutions using information criteria and classification diagnostics. We then use early life individual and social factors to predict latent class membership using a three step multinomial logistic regression. Results. A three class solution provided the best balance of model fit and interpretability. Most youths (about 94 percent) showed increasing self control from ages 3 to 17. Sixty six percent began at average levels and increased steadily (average/steady increase); twenty eight percent began higher than average and increased sharply (high/fast increase); and a small group (about 6 percent) began much lower than average and declined (very low/decline). Intercept–slope covariances were negative, indicating catch up among children who started lower. Early life individual and social factors strongly predicted class membership: females, children from higher income families, those with parental college education, and children with higher cognitive ability at age 3 were more likely to be in the high/fast increase group, whereas heavy prenatal alcohol exposure was associated with near zero probability of membership in the high/fast increase group. Compared to the three-class model, the two class model compressed meaningful heterogeneity, while the four class model showed over extraction. Discussion. This study improves upon prior tests of the General Theory of Crime by (1) modeling latent developmental heterogeneity in self control from age 3 to 17, rather than treating self control as time invariant or population homogeneous; (2) using early life factors to predict self-control growth trajectory membership using a three step procedure; (3) conducting design based inference in a probability sample with weights, strata, and clusters. We look forward to seeing you there! Best, Xue |
| Contact Person | Kristina Brant |
| Contact Email | kbrant@psu.edu |