Switching genders: identifying the evaluator in stereotype threat for men and women in a math context

The current study seeks to identify the source of evaluation that causes stereotype threat for men and women in a math context. In a 2 (participant gender: male vs. female) X 3 (gender label: Match, Mismatch, Control) factorial design, male and female participants that identified highly with math were asked to take a math test. Throughout the test, participants\u27 ostensible gender was displayed on the computer screen. The displayed gender was either the correct gender, the opposite gender, or Alabama. Although our results were unable to determine if stereotype threat is a self- or an outside evaluator-threat, we did observe a strong gender-math relationship in which being labeled with the opposite gender disrupted both men and women\u27s math performance. However, women were more affected in that they not only performed significantly lower on the math test, but also took a longer time, attempted fewer problems, and significantly disidentified from math

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Factor-Analytic Investigations of Personality Structure: Do Data Take the Shape of Your Measure?

Since the early 1960s, the primary tool for identifying individual differences in personality structure has included the use of factor analysis to identify a small but interpretable number of dimensions that summarize the inter-individual differences in the participants’ qualities. Many researchers have interpreted these factor-analytic dimensions as being psychological structures that exist in the mind of individual persons and are causally responsible for the observed variations in psychological characteristics. An alternative interpretation is that the analysis of language-based data primarily yields information about the structure of language. Semantic overlap between items contributes to the obtained correlations among test items and thus influences the resulting factor structure. In principle, the semantic overlap may be sufficient to account for the resulting factor structure. This latter possibility is tested in a novel manner in the present thesis using a computer simulation. At the outset of the simulation, each member of a population of respondents has no personality characteristics (Study 1) or they have a personality structure that is distinct from the most commonly accepted dimensional model of personality structure, the Big Five model (Study 2). Test item responses are then updated as a function of semantic overlap among test items. The empirical question addressed is whether this semantic updating is sufficient, subsequent to factor analysis, to generate traditional factor-analytic personality structures. The computer simulations do indeed show that in both populations, semantic connections between the items are sufficient to reliably produce a factor-analytic structure that largely coincides with the Big Five. These studies suggest that lexical redundancy or semantic overlap within a measure can indeed shape or re-shape the data collected to reflect the structure entailed within the measure, regardless of whether that structure was present in the original population