The purpose of this study was the examination of age bias in social work professionals who have direct and influential contact with a growing segment of the population: older adults. Those who work most closely with older people may be at risk for age bias, although much of the research on age bias has been conducted with students rather than with those who work with older people. This study adds to the research on prejudice; the sources from which attitudes, values, prejudices, and stereotypical thinking arise were addressed. Key experiences with older individuals were found to predict age bias. Because social work ethical principles closely align with conditions known to reduce prejudice, it was hypothesized that higher ethics adherence would be associated with less age bias. Specific experiential factors were found to influence prejudice toward older people. Influences from family beliefs and from television and other media were associated with a non-biased attitude, as were influences from caregiving to older people. These sources of one's values and beliefs about older individuals were also found to predict the extent of one's knowledge of aging processes. Although ethics adherence was not a predictor of age bias, the discovery of the influence of family beliefs, media portrayals, and caregiving experiences revealed a need for awareness of ageist beliefs in a professional population that works extensively with older adults. / Ph. D.

Basic facial emotion recognition is suggested to be negatively affected by puberty onset reflected in a “pubertal dip” in performance compared to pre- or post-puberty. However, findings remain inconclusive. Further, research points to an own-age bias, i.e., a superior emotion recognition for peer faces. We explored adolescents’ ability to recognize specific emotions. Ninety-five children and adolescents, aged 8–17 years, judged whether the emotions displayed by adolescent or adult faces were angry, sad, neutral, or happy. We assessed participants a priori by pubertal status while controlling for age. Results indicated no “pubertal dip”, but decreasing reaction times across adolescence. No own-age bias was found. Taken together, basic facial emotion recognition does not seem to be disrupted during puberty as compared to pre- and post-puberty.

The thesis investigates the impact of age-biased sampling on the accuracy of lifetime prediction models for traffic signs. The bias in question originates from age-biased sampling as a result of the inspection paradox. This phenomenon occurs because longer intervals have a higher probability of being observed compared to shorter intervals, leading to a skewed representation in the data. The research employs a dual approach: firstly, conducting an extensive analysis of real data on traffic sign longevity using a Weibull Survival Model. This analysis is based on the data set compiled by Saleh et al., (2023). Secondly, the study sets up a Monte Carlo simulation to systematically explore the effects of varying degrees and patterns of age bias on the sample. The simulation parameters are derived from the original Weibull Model parameters, obtained from the real dataset. This approach ensures that the simulations closely replicate the actual parameters and estimates. The comparison of the true shape, scale, intercept, and the coefficients associated with the covariates against the simulated estimates indicates a significant bias in the dataset. The study also examines the impact of this bias on the predictive capabilities of various models: Weibull Modeling, Cox Proportional Hazards, Kaplan Meier, and Random Survival Forest. This is done by comparing the true means and medians of the simulated data with the estimates from each model. The findings show that all models exhibit large deviations from the actual means and medians at varying bias levels in the simulated data. The accuracy of the predictions is measured using the Brier Score. This score also shows significant deviations from the prediction accuracy of the original Weibull Model applied to the real dataset, especially when the bias levels vary across simulated datasets. Given these findings, the study advises against using the aforementioned methods for lifetime modeling of traffic signs when there is age bias due to the inspection paradox.