The purpose of this research was to investigate the relation between measurement bias at the item level (differential item functioning, dif) and predictive bias at the test score level. Dif was defined as a difference in the probability of getting a test item correct for examinees with the same ability but from different subgroups. Predictive bias was defined as a difference in subgroup regression intercepts and/or slopes in predicting a criterion. Data were simulated by computer. Two hypothetical subgroups (a reference group and a focal group) were used. The predictor was a composite score on a dimensionally complex test with 60 items. Sample size (35, 70, and 105 per group), validity coefficient (.3 or .5), and the mean difference on the predictor (0, .33, .66, and 1 standard deviation, sd) and the criterion (0 and .35 sd) were manipulated. The percentage of items showing dif (0%, 15%, and 30%) and the effect size of dif (small = .3, medium = .6, and large = .9) were also manipulated. Each of the 432 conditions in the 3 x 2 x 4 x 2 x 3 x 3 design was replicated 500 times. For each replication, a predictive bias analysis was conducted, and the detection of predictive bias against each subgroup was the dependent variable. The percentage of dif and the effect size of dif were hypothesized to influence the detection of predictive bias; hypotheses were also advanced about the influence of sample size and mean subgroup differences on the predictor and criterion. Results indicated that dif was not related to the probability of detecting predictive bias against any subgroup. Results were inconsistent with the notion that measurement bias and predictive bias are mutually supportive, i.e., the presence (or absence) of one type of bias is evidence in support of the presence (or absence) of the other type of bias. Sample size and mean differences on the predictor/criterion had direct and indirect effects on the probability of detecting predictive bias against both reference and focal groups. Implications for future research are discussed.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Two broad influences have converged to shape a new environment in which universities must now compete and operate. Shrinking financial resources and a global economy have arguably compelled universities to adapt. The concept of academic capitalism helps explain the new realities and places universities in the context of a global, knowledge-based economy (Slaughter & Leslie, 1997). Prior to this theory, the role of universities in the knowledge economy was largely undocumented. Academic capitalism is a measurable concept defined by the mechanisms and behaviors of universities that seek to generate new sources of revenue and are best revealed through faculty work. This study was designed to create empirical evidence of academic capitalism through the behaviors of faculty members at research universities. Using a large-scale, national database, the researcher created a new measure—an estimate of academic capitalism—at the individual faculty member level and then used multi-level analysis to explain variation among these individual faculty members. This study will increase our understanding of the changing nature of faculty work, will lead to future studies on academic capitalism that involve longitudinal analysis and important sub-populations, and will likely influence institutional and public policy. / Ph. D.