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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.

Although job analysis is basic to almost all human resource functions, little attention has been given to the response format and scoring strategy of job analysis instruments. This study investigated three approaches to scoring polychotomous and dichotomous responses from the frequency and importance scales of the Common Metric Questionnaire (CMQ). Factor, latent trait, and number endorsed scores were estimated from the responses of 2684 job incumbents in six organizations. Scores from four of the CMQ scales were used in linear and nonlinear multiple regression equations to predict pay. The results demonstrated that: (a) simple number endorsed scoring of dichotomous responses was superior to the other scoring strategies; (b) Scoring of dichotomous responses was superior to scoring of polychotomous responses for each scoring technique; (c) scores estimated from the importance scale were better predictors of pay then scores from the frequency scale; (d) the relationship between latent trait and factor scores is nonlinear; (e) latent trait scores estimated with the two-parameter logistic model were superior to latent trait scores from the three parameter model; (f) test information functions for each scale demonstrated that the CMQ scales accurately measured a relatively narrow range of theta; (g) the reliability of factor scores estimated from dichotomous data is superior to factor scores from polychotomous data. Issues regarding the construction of job analysis instruments and the use of item response theory are discussed. / Ph. D.

Item Response Theory (IRT) methodologies were employed in order to examine the Myers-Briggs Type Indicator (MBTI) for differential item functioning (DIF) on the basis of crossed gender and ethnicity variables. White males were the reference group, and the focal groups were: black females, black males, and white females. The MBTI was predicted to show DIF in all comparisons. In particular, DIF on the Thinking-Feeling scale was hypothesized especially in the comparisons between white males and black females and between white males and white females. A sample of 10,775 managers who took the MBTI at assessment centers provided the data for the present experiment. The Mantel-Haenszel procedure and an IRT-based area technique were the methods of DIF-detection. Results showed several biased items on all scales for all comparisons. Ethnicitybased bias was seen in the white male vs. black female and white male vs. black male comparisons. Gender-based bias was seen particularly in the white male vs. white female comparisons. Consequently, the Thinking-Feeling showed the least DIF of all scales across comparisons, and only one of the items differentially scored by gender was found to be biased. Findings indicate that the gender-based differential scoring system is not defensible in managerial samples, and there is a need for further research into the study of differential item functioning with regards to ethnicity. / Master of Science

In this study, an improved correlational procedure for factor-analyzing dichotomously-scored item response data is described and tested. The procedure involves (a) replacing the dichotomous input values with continuous probability values obtained through Rasch analysis; (b) calculating interitem product-moment correlations among the probabilities; and (c) subjecting the correlations to unweighted least-squares factor analysis. Two simulated data sets and an empirical data set (Kentucky Comprehensive Listening Test responses) were used to compare the new procedure with two more traditional techniques, using (a) phi and (b) tetrachoric correlations calculated directly from the dichotomous item-response values. The three methods were compared on three criterion measures: (a) maximum internal correlation; (b) product of the two largest factor loadings; and (c) proportion of variance accounted for. The Rasch-based procedure is recommended for subjecting dichotomous item response data to latent-variable analysis.

This study was performed to show advantages of Item Response THeory (IRT) over Classical Test Theory (CTT). Item Response THeory is a complex theory with many applications. This study used one application, test analysis. Ten items from a social psychology midterm were analyzed in order to show how IRT is more accurate than CTT, because IRT has the ability to add and delete individual items. Also, IRT features the Item Characteristic Curve (ICC) to give an easy to read interpretation of the results. The results showed the levels of the three indexes, item discrimination, difficulty, and guessing. The results indicated in which area each item was weak or strong. With this information, suggestions can be made to improve the item and ultimately improve the measurement accuracy of the entire test. Classical Test Theory cannot do this on individual item basis without changing the accuracy of the entire test. The results of this study confirm that IRT can be used to analyze individual items and allow for the improvement or revision of the item. This means IRT can be used for test analysis in a more efficient and accurate manner than CTT. This study provides an introduction to Item Response Theory in the hopes that more research will be performed to establish IRT as a commonly used tool for improving testing measurement.