Analyzing the distributions of wages for whites, blacks and Hispanics reveals the existence of a wage gap throughout the distribution. There are also clear cognitive and noncognitive skill differences across groups. Do differences in the distributions of these skills explain differences in the distributions of wages? Do predicted distributions of wages resulting from rewarding blacks and Hispanics as if they were white help explain the observed wage gap? Using data from the NLSY79, I look at the impacts of noncognitive skills on wages for blacks, Hispanics and whites. I estimate the entire distribution of wages conditional on skills for blacks and Hispanics to see if there is a difference in wages individuals with the same level of cognitive and noncognitive skills. I find that all cognitive and noncognitive measures examined are important in explaining the wage penalty paid by blacks and Hispanics and that, for blacks, predicting their wages conditional on skills approximates the distribution of actual wages. Do employers recognize noncognitive skills at the onset (interview) or is there a learning process? How does learning about these noncognitive skills occur over time? This paper uses data from the NLSY79 to incorporate measures of noncognitive skills into a model of employer learning described originally by Altonji Pierret (2001). Measures of noncognitive skills include the Rosenberg Self Esteem Score, the Rotter Locus of Internal Control Score, the Coding Speed Score, and the CES-Depression Scale. I find that employers observe an initial signal of self esteem and schooling and that, over time, employers learn about cognitive skills and motivation, placing less emphasis on these initial observations. Does learning transfer perfectly across employers or is there a degree to which learning resets as employees change jobs throughout their careers? In this paper, I use data from the NLSY79 to look for evidence of asymmetric employer learning. I use tests developed by Schonberg (2007) and Pinkston (2009) to look for asymmetric learning in the model from Altonji Pierret (2001) augmented in Petre (2013b) to incorporate noncognitive skills in addition to cognitive skills. I find mixed evidence that learning done by a prior employer might not transfer completely to a new employer. / text

The Health Care Specialist Course trains Active Army, Army Reserves, Army National Guard and various international students in basic medical care, culminating in the possession of the EMT-B certification. The course is conducted in a stressful environment where students are required to be successful in both academic and nonacademic domains. Within the last decade, course administrators have noticed a higher rate of attrition and requested assistance with understanding why one-fifth of students fail to graduate with their original unit. A high rate of attrition results in an increased use of resources and it decreases the Army’s ability to provide qualified Health Care Specialists to forward units. The purpose of this study was to understand how noncognitive factors contribute to attirition in the Health Care Specialist Program with students who were within their first six months of training. This study specifically focuses on the experiences of the recyled student. The Modified Noncognitive Questionnarie (NCQ) and the Military Environment Noncognitive Adjustment Scale (MENAS), which focused on measuring noncognitive variables, were used with both passing and recycled students. In addition, an interview was used for recycled students to allow them the opportunity to elaborate on their personal experiences. This mixed methods explanatory research study revealed quantitatively, using the t-test, that a significant difference exists between the passing and recycled groups in their: level of motivation, realistic self-appraisal, battle buddy support, unit support, preference for long-term goals, ability to successfully handle racism, and their level of stress. Logistic regression revealed the following to be predictive of attrition for students participating in this course: low ST score, unrealistic self-appraisal, preference for shortterm goals, low perception of battle buddy support and unit support, a high level of stress and low motivation to complete the course. Qualitative results were consistent with quantitative results and added a deeper understanding of how students negotiated the academic and military environment. The results of this study will contribute to course administrators understanding of the challenges that student’s encounter while matriculating through this course.

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This research gives a multidimensional investigation into community policies that are becoming more prevalent in American society. In Chapter 1, I apply multiple Value-Added Models (VAM) of achievement to data from the North Carolina Education Research Data Center (NCERDC) to determine the academic impacts of repeating a year (or more) with the same teacher on student achievement in math and reading. Given the growing trend in schools and teaching practices, like looping, that pair teachers and students for multiple years, this research finds contrasting results about the gains in academic achievement associated with repeating with a teacher. Specifically, while there is evidence that students on average have higher scores when repeating with a teacher, this effect is mitigated when one controls for teacher quality. Using limited probability models, I find students are 29\%-34\% and 42\%-46\% more likely to repeat with a teacher whose Value-Added estimate is in the top 20\% of teacher-quality compared to a teacher in the bottom 20\% in math and reading, respectively. This nonrandom assignment of students to teachers, creates upward bias in the estimated achievement effects of repeating with a teacher that have previously been unaccounted for. In chapters 1 and 2, I account for nonrandom assignment finding non-significant gains in achievement associated with repeating with a teacher. While Chapter 1 finds non-significant gains to student achievement, Chapter 2 investigates if there are any non-cognitive gains students experience when they repeat with a teacher for another year. Using the same longitudinal data from the NCERDC, Chapter 2’s results indicate increases in character-trait measures associated with teacher and student perceptions of academic success and effort. Using multiple partial persistence VAMs that include controls for student heterogeneity and for teacher quality, the estimated effects on a teacher's subjective scoring of a student's academic success, student's anticipated grade for the year, and student attendance are all significantly greater than zero. Taken together, the positive effects from students repeating with the same teacher reveal themselves prevalently on character-trait improvements rather than on contemporaneous achievement scores. In Chapter 3, I investigate the causal direction of a popular policing policy. Although there are a large number of studies testing Broken Windows Theory (BWT) (Wilson & Kelling, 1982), the reverse theoretical pathway has never been assessed (risk perceptions predicting incivilities perceptions). It is estimated in Chapter 3 using panel data from Baltimore. Results show lagged, multilevel impacts of risk perceptions on changes in incivilities perceptions. Further, results show the impact of risk perceptions on seeing later changes in neighborhood problems varies significantly across street blocks. Findings support Harcourt’s (2001) assertion that “disorder” is not a fixed and unambiguous label; rather, it is dependent upon a person defining his or her surroundings. People who feel a high degree of crime risk are “biased” (Hipp, 2010; Wallace, 2011) toward defining neighborhood features as more problematic than those who do not. / Economics

The assessment of noncognitive constructs poses a number of challenges that set it apart from traditional cognitive ability measurement. Of particular concern is the influence of response biases and response styles that can influence the accuracy of scale scores. One strategy to address these concerns is to use alternative item presentation formats (such as multidimensional forced choice (MFC) pairs, triads, and tetrads) that may provide resistance to such biases. A variety of strategies for constructing and scoring these forced choice measured have been proposed, though they often require large sample sizes, are limited in the way that statements can vary in location, and (in some cases) require a separate precalibration phase prior to the scoring of forced-choice responses. This dissertation introduces new item response theory models for estimating item and person parameters from rank-order responses indicating preferences among two or more alternatives representing, for example, different personality dimensions. Parameters for this new model, called the Hyperbolic Cosine Model for Rank order responses (HCM-RANK), can be estimated using Markov chain Monte Carlo (MCMC) methods that allow for the simultaneous evaluation of item properties and person scores. The efficacy of the MCMC parameter estimation procedures for these new models was examined via three studies. Study 1 was a Monte Carlo simulation examining the efficacy of parameter recovery across levels of sample size, dimensionality, and approaches to item calibration and scoring. It was found that estimation accuracy improves with sample size, and trait scores and location parameters can be estimated reasonably well in small samples. Study 2 was a simulation examining the robustness of trait estimation to error introduced by substituting subject matter expert (SME) estimates of statement location for MCMC item parameter estimates and true item parameters. Only small decreases in accuracy relative to the true parameters were observed, suggesting that using SME ratings of statement location for scoring might be a viable short-term way of expediting MFC test deployment in field settings. Study 3 was included primarily to illustrate the use of the newly developed IRT models and estimation methods with real data. An empirical investigation comparing validities of personality measures using different item formats yielded mixed results and raised questions about multidimensional test construction practices that will be explored in future research. The presentation concludes with a discussion of MFC methods and potential applications in educational and workforce contexts.

The purpose of this study is to examine the evidence underlying the claim that scores from CampusReady, a diagnostic measure of student college and career readiness, are valid indicators of student college and career readiness. Participants included 4,649 ninth through twelfth grade students from 19 schools who completed CampusReady in the 2012-13 school year. The first research question tested my hypothesis that grade level would have an effect on CampusReady scores. There were statistically significant effects of grade level on scores in two subscales, and I controlled for grade level in subsequent analyses on those subscales. The second, third and fourth research questions examined the differences in scores for subgroups of students to explore the evidence supporting the assumption that scores are free of sources of systematic error that would bias interpretation of student scores as indicators of college and career readiness. My hypothesis that students' background characteristics would have little to no effect on scores was confirmed for race/ethnicity and first language but not for mothers' education, which had medium effects on scores. The fifth and six research questions explored the assumption that students with higher CampusReady scores are more prepared for college and careers. My hypothesis that there would be small to moderate effects of students' aspirations for after high school on CampusReady scores was confirmed, with higher scores for students who aspired to attend college than for students with other plans. My hypothesis that there would be small to moderate relationships between CampusReady scores and grade point average was also confirmed. I conclude with a discussion of the implications and limitations of these results for the argument supporting the validity of CampusReady score interpretation as well as the implications of these results for future CampusReady validation research. This study concludes with the suggestion that measures of metacognitive learning skills, such as the CampusReady survey, show promise for measuring student preparation for college and careers when triangulated with other measures of college and career preparation.

Thesis advisor: Henry I. Braun / Addressing the call to provide hard evidence on undergraduate student outcomes and make comparisons across higher education institutions for accountability purposes, this study extends current efforts in measuring higher education outcomes and explores the differences in three value-added methodologies. Using the CIRP freshman and senior survey data from 2002 and 2006, this study examines noncognitive higher education outcomes with a focus on civic engagement. The three value-added methodologies examined are: an OLS-based cross-sectional method, an HLM-based cross-sectional method, and an HLM-based longitudinal method. Rather than seek to establish which methodology is superior, this study intends to provide empirical evidence concerning the similarities and differences in estimating institutional effectiveness with regard to civic engagement. First, several student-level and institution-level covariates were found to be associated with a measure of civic engagement in the senior year after adjusting for their level of civic engagement as freshmen. The model comparison further revealed some advantages in the HLM-based longitudinal method over the other two methods, such as providing a more accurate institutional value-added estimate and the ability to account for a relatively large percent of the total variance in the civic engagement measure when using the same covariates. Next, among all pairs of model comparisons, results from the two HLM-based methods agreed the most (r=.80). However, institutional rankings fluctuate dramatically, even when comparing institutions within small peer groups. Finally, the findings highlighted great divergences among different value-added methodologies in identifying institutions that perform significantly differently from the average for accountability purposes. / Thesis (PhD) — Boston College, 2012. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement, and Evaluation.

The problem studied was the poor academic achievement of middle school students in the Commonwealth of the Northern Mariana Islands (CNMI). Research indicates that a growth mindset positively affects a student's academic achievement and motivation to learn. However, despite the importance of mindset in fostering student success and enhancing learning, mindset remains underexplored in the CNMI. The purpose of this generic qualitative study was to fill this gap in knowledge by investigating teachers' knowledge, perceptions, and practices concerning mindset in the CNMI. Three research questions examined teachers' knowledge and perceptions of mindset in the CNMI and how teachers described and demonstrated the use of mindset in their practices. Dweck's seminal work on mindset served as the conceptual framework. Social constructivism guided the study process. Qualitative data were collected from 15 purposively sampled teachers at a local CNMI middle school. Data were analyzed through categorization and codification, from which emerging themes were used to answer research questions. Results indicated that teachers in the local middle school have limited knowledge and inaccurate perceptions regarding the mindset concept. Accordingly, the analysis recommended the need for and served as the basis for the design of a professional development workshop about mindset for teachers throughout the CNMI to enhance teacher instruction and improve student learning, thus promoting positive social change.

Research has shown that emotional intelligence is becoming an essential area of study to determine a person's potential for overall success as well as success in the workplace. Although noncognitive skills are not always a factor that leaders use in determining workplace efficiency, these skills may impact productivity. The purpose of this single case study was to explore the impact of noncognitive skills and the perception administrators and employees have regarding skills that are critical to their jobs and work performance. The conceptual framework used for this study was the emotional intelligence theory authored by Daniel Goleman in 1995, which studied personality qualities. The population consisted of 5 managerial staff members and 20 employees within the public university sector. The selection criteria consisted of managers, faculty and staff members. All participants with at least 1 year of experience were eligible to participate in the study. The data collection process included an open-ended online questionnaire for the managerial staff and a short online survey for employees. The focus of this study comprised 6 skills: communication, self-confidence, teamwork, work ethics, problem-solving ability, and leadership. Several patterns and themes emerged relating to the effects of emotional intelligence on job performance and productivity. The findings from this study could be vital for understanding the current and future impact of noncognitive skills in the work environment and could positively impact social change. Organizational leaders could use the results to develop strategies that would enhance their decision-making process and develop tools to promote employee productivity.

This study investigated the efficacy of the lz person fit statistic for detecting aberrant responding with unidimensional pairwise preference (UPP) measures, constructed and scored based on the Zinnes-Griggs (ZG, 1974) IRT model, which has been used for a variety of recent noncognitive testing applications. Because UPP measures are used to collect both "self-" and "other-" reports, I explored the capability of lz to detect two of the most common and potentially detrimental response sets, namely fake good and random responding. The effectiveness of lz was studied using empirical and theoretical critical values for classification, along with test length, test information, the type of statement parameters, and the percentage of items answered aberrantly (20%, 50%, 100%). We found that lz was ineffective in detecting fake good responding, with power approaching zero in the 100% aberrance conditions. However, lz was highly effective in detecting random responding, with power approaching 1.0 in long-test, high information conditions, and there was no diminution in efficacy when using marginal maximum likelihood estimates of statement parameters in place of the true values. Although using empirical critical values for classification provided slightly higher power and more accurate Type I error rates, theoretical critical values, corresponding to a standard normal distribution, provided nearly as good results.