Human Development & Quantitative Methodology
Permanent URI for this communityhttp://hdl.handle.net/1903/2248
The departments within the College of Education were reorganized and renamed as of July 1, 2011. This department incorporates the former departments of Measurement, Statistics & Evaluation; Human Development; and the Institute for Child Study.
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Item CHILDREN’S CONCEPTIONS OF FAIRNESS: THE ROLE OF MENTAL STATE UNDERSTANDING AND GROUP IDENTITY(2021) D'Esterre, Alexander; Killen, Melanie; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Children’s everyday experiences occur against a backdrop that is rich in social information andwhich requires decisions involving considerations about fairness, intentionality, and social groups. With age, children improve in their ability to utilize intentional information in their judgments and have been shown to demonstrate preferences for fairness over group benefit. What has not been fully investigated is how children coordinate and weight these considerations at different ages. Moreover, mistaken intentions and a tendency to benefit the in-group over others can be seen even in adulthood – suggesting that these issues are not so easily overcome and have the potential to affect the evaluations and behaviors of individuals more than have been previously considered. Research designed to carefully investigate the impact of these social and cognitive factors on children’s fairness concepts can provide insight into the ways in which biases may begin to form and potentially inform our understanding of the underlying mechanisms present in prejudicial attitudes. The present dissertation contains a series of three empirical papers that are designed to investigate children’s responses to unintentional and intentional transgressions based on their cognitive ability to infer beliefs of others and their relationship to the group identity of the target. Empirical Study 1 demonstrated the value of using a morally-relevant theory of mind measure embedded directly into the context when predicting children’s responses to unintentional and intentional transgressions. Empirical Study 2 investigated the ways in which children’s assessment of fair and unfair advantages were influenced by the group identity of the character who created the advantage. Empirical Study 3 explored the types of retributive justice that children would endorse in light of various types of intentional and unintentional transgressions, revealing differences based on group identity and the impact that the retributive justice would present to the functioning of the group. The results of these studies together suggest that children’s fairness concepts are heavily influenced by the context in which children find themselves and are far from static. Better understanding the relationship between these factors will provide increased insight into the ways in which prejudice and bias may develop in childhood and suggest potential areas for intervention.Item EFFECTS OF UNMODELED LATENT CLASSES ON MULTILEVEL GROWTH MIXTURE ESTIMATION IN VALUE-ADDED MODELING(2011) Yumoto, Futoshi; Hancock, Gregory R; Mislevy, Robert J; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fairness is necessary to successful evaluation, whether the context is simple and concrete or complex and abstract. Fair evaluation must begin with careful data collection, with clear operationalization of variables whose relationship(s) will represent the outcome(s) of interest. In particular, articulating what it is in the data that needs to be modeled, as well as the relationships of interest, must be specified before conducting any research; these two features will inform both study design and data collection. Heterogeneity is a key characteristic of data that can complicate the data collection design, and especially analysis and interpretation, interfering with or influencing the perception of the relationship(s) that the data will be used to investigate or evaluate. However, planning for, and planning to account for, heterogeneities in data are also critical to the research process, to support valid interpretation of results from any statistical analysis. The multilevel growth mixture model is a new analytic method specifically developed to accommodate heterogeneity so as to minimize the effect of variability on precision in estimation and to reduce bias that may arise in hierarchical data. This is particularly important in the Value Added Model context - where decisions and evaluations about teaching effectiveness are made, because estimates could be contaminated, biased, or simply less precise when data are modeled inappropriately. This research will investigate the effects of un-accounted for heterogeneity at level 1 on the precision of level-2 estimates in multilevel data utilizing the multilevel growth mixture model and multilevel linear growth model.