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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Start date: 2000End date: 2009

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    The Impact of Model Selection on Loglinear Analysis of Contingency Tables
    (2009) Gao, Jing; Dayton, C. Mitchell; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    It is common practice for researchers in the social sciences and education to use model selection techniques to search for best fitting models and to carry out inference as if these models were given a priori. This study examined the effect of model selection on inference in the framework of loglinear modeling. The purposes were to (i) examine the consequences when the behavior of model selection is ignored; and (ii) investigate the performance of the estimator provided by the Bayesian model averaging method and evaluate the usefulness of the multi-model inference as opposed to the single model inference. The basic finding of this study was that inference based on a single "best fit" model chosen from a set of candidate models leads to underestimation of the sampling variability of the parameters estimates and induces additional bias in the estimates. The results of the simulation study showed that due to model uncertainty the post-model-selection parameter estimator has larger bias, standard error, and mean square error than the estimator under the true model assumption. The same results applied to the conditional odds ratio estimators. The primary reason for these results is that the sampling distribution of the post-model-selection estimator is, in actuality, a mixture of distributions from a set of candidate models. Thus, the variability of the post-model- selection estimator has a large component from selection bias. While these problems were alleviated with the increase of sample size, the interpretation of the p-value of the Z-statistic of the parameters was misleading even when sample size was quite large. To avoid the problem of inference based on a single best model, Bayesian model averaging adopts a multi-model inference method, treating the weighted mean of the estimates from each model in the set as a point estimator, where the weights are derived using Bayes' theorem.
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    The Inluence of Cultural Identity and Intergroup Contact on Adolescents' Evaluations of Arab-Jewish Peer Relationships
    (2009) Brenick, Alaina Faye; Killen, Melanie; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Recent research has documented the negative intergroup attitudes between Jewish and Arab youth and adults in the Middle East (Bar-Tal & Teichman, 2005; Brenick et al., 2007; Cole et al., 2003), yet little is known about how these negative intergroup biases manifest in the same cultural communities removed from the daily stress and tension of an intractable conflict, and living in the U.S. Moreover, while negative intergroup tensions between Jews and Arabs and, cultural stereotyping, prejudice, and discrimination towards Muslim and Arab groups have increased in the U.S. (Alliance of Civilizations, 2006; Sheridan, 2006), they may still benefit from increased opportunities to engage in intergroup contact, which has been shown to reduce intergroup prejudice (see Pettigrew & Tropp, 2005). However, these attitudes have yet to receive much empirical scrutiny in the developmental literature. The present study investigated age related changes in the influence of intergroup contact and cultural identification on evaluations of Arab-Jewish intergroup friendships. The focus of this study was on how Jewish-American, Arab-American, and unaffiliated (e.g., non-Jewish, non-Arab) American adolescents evaluate exclusion and inclusion in peer situations between Jewish and Arab youth in the peer, home, and community contexts. This study surveyed 953 ninth and twelfth graders (36 Arab participants, 306 Jewish participants, and 591 unaffiliated participants (259 in the Jewish comparison group and 332 in the Arab comparison group). Overall, all participants were primarily rejecting of intergroup exclusion, more so when the exclusion was based on cultural group membership than when no reason for the exclusion was specified. Further, males were more accepting of the intergroup exclusion and more accepting of including an ingroup member as compared to females. Context effects emerged revealing that intergroup exclusion was considered most acceptable in the community context and the least acceptable in the friendship context. The interactive influence of intergroup contact and cultural identification demonstrated that high levels of intergroup contact and high levels of identity commitment predicted less accepting ratings of intergroup exclusion, whereas high levels of intergroup contact and high levels of identity exploration, led to more accepting ratings of intergroup exclusion. These interactions varied by cultural group.
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    A MIXTURE RASCH MODEL WITH A COVARIATE:A SIMULATION STUDY VIA BAYESIAN MARKOV CHAIN MONTE CARLO ESTIMATION
    (2009) Dai, Yunyun; Mislevy, Robert J; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Mixtures of item response theory models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying structures. In particular, the impact of auxiliary variables, or covariates, for examinees in estimation has not been systematically explored. The goal of this dissertation is to carry out a systematically designed simulation study to investigate the performance of mixture Rasch model (MRM) under Bayesian estimation using Markov Chain Monte Carlo (MCMC) method. The dependent variables in this study are (1) the proportion of cases in which the generating mixture structure is recovered, and (2) among those cases in which the structure is recovered, the bias and root mean squared error of parameter estimates. The foci of the study are to use a flexible logistic regression model to parameterize the relation between latent class membership and the examinee covariate, to study MCMC estimation behavior in light of effect size, and to provide insights and suggestions on model application and model estimation.
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    AN INFORMATION CORRECTION METHOD FOR TESTLET-BASED TEST ANALYSIS: FROM THE PERSPECTIVES OF ITEM RESPONSE THEORY AND GENERALIZABILITY THEORY
    (2009) Li, Feifei; MIslevy, Robert J.; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    An information correction method for testlet-based tests is introduced in this dissertation. This method takes advantage of both generalizability theory (GT) and item response theory (IRT). The measurement error for the examinee proficiency parameter is often underestimated when a unidimensional conditional-independence IRT model is specified for a testlet dataset. By using a design effect ratio composed of random variances which can be easily derived from GT analysis, it becomes possible to adjust the underestimated measurement error from the unidimensional IRT models to a more appropriate level. It is demonstrated how the information correction method can be implemented in the context of a testlet design. Through the simulation study, it is shown that the underestimated measurement errors of proficiency parameters from IRT calibration could be adjusted to the appropriate level despite the varying magnitude of local item dependence (LID), testlet length, balance of testlet length and number of the item parameters in the model. Each of the three factors (i.e., LID, testlet length and balance of testlet length) and their interactions have statistically significant effects on error adjustment. The real data example provides more details about when and how the information correction should be used in a test analysis. Results are evaluated by comparing the measurement errors from the IRT model with those from the testlet response theory (TRT) model. Given the robustness of the variance ratio, estimation of the information correction should be adequate for practical work.
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    The Effects of Constructs of Motivation that Affirm and Undermine Reading Achievement Inside and Outside of School on Middle School Students' Reading Achievement
    (2009) Coddington, Cassandra Shular; Wigfield, Allan; Guthrie, John T; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The purpose of this study was to examine whether motivation for reading was multidimensional in two respects. First, central constructs were drawn from three major theories of motivation. Second, versions of each construct were formulated that were expected to correlate positively with achievement (affirming); and versions of each construct were formulated that were expected to correlate negatively with achievement (undermining). The goal of the study was to determine whether these reading motivation constructs were relatively independent and whether the multiple motivations contributed to predicting achievement. Constructs of motivation were derived from Self-Determination Theory (Deci, Vallerand, Pelletier, & Ryan, 1991), Social Cognitive Theory (Bandura, 1977, 2001) and Social Goals (Wentzel, 2002, 2004). Constructs of motivation that affirm reading achievement and constructs of motivation that undermine reading achievement were both examined. These constructs included, intrinsic motivation, avoidance, self-efficacy, perceived difficulty, prosocial interactions, and antisocial interactions. This study also investigated student motivations for reading for two reasons, school and outside school. Participants were 247 seventh grade students from two middle schools in a mid-Atlantic state. Students completed four measures, including the Gates-MacGinitie Reading Comprehension test, a measure of inferencing ability, a motivation questionnaire for school reading, and a motivation questionnaire for outside school reading. Reading/Language Arts grades were also obtained for all students. Four objectives were addressed through the results of six research questions. Factor analyses results supported the discussion of motivation as a multidimensional construct. Three factors emerged when examining the three constructs of motivation that affirm achievement and the three constructs of motivation that undermine achievement. In addition, factor analyses results supported the perspective that undermining motivations are uniquely predictive of achievement and not simply negatively valenced affirming motivations. Two factors emerged when analyzing the affirming and undermining constructs of motivation in theoretical pairs. Regression analyses indicated that undermining motivations are predictive of achievement even when affirming motivations have been taken into account statistically. Some differences in these results for the school and outside school constructs are discussed. Significance of the findings was discussed in terms of the theoretical importance of the simultaneous functioning of multiple motivations for reading among adolescent students.
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    Accuracy and consistency in discovering dimensionality by correlation constraint analysis and common factor analysis
    (2009) Tractenberg, Rochelle Elaine; Hancock, Gregory R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    An important application of multivariate analysis is the estimation of the underlying dimensions of an instrument or set of variables. Estimation of dimensions is often pursued with the objective of finding the single factor or dimension to which each observed variable belongs or by which it is most strongly influenced. This can involve estimating the loadings of observed variables on a pre-specified number of factors, achieved by common factor analysis (CFA) of the covariance or correlational structure of the observed variables. Another method, correlation constraint analysis (CCA), operates on the determinants of all 2x2 submatrices of the covariance matrix of the variables. CCA software also determines if partialling out the effects of any observed variable affects observed correlations, the only exploratory method to specifically rule out (or identify) observed variables as being the cause of correlations among observed variables. CFA estimates the strengths of associations between factors, hypothesized to underlie or cause observed correlations, and the observed variables; CCA does not estimate factor loadings but can uncover mathematical evidence of the causal relationships hypothesized between factors and observed variables. These are philosophically and analytically diverse methods for estimating the dimensionality of a set of variables, and each can be useful in understanding the simple structure in multivariate data. This dissertation studied the performances of these methods at uncovering the dimensionality of simulated data under conditions of varying sample size and model complexity, the presence of a weak factor, and correlated vs. independent factors. CCA was sensitive (performed significantly worse) when these conditions were present in terms of omitting more factors, and omitting and mis-assigning more indicators. CFA was also found to be sensitive to all but one condition (whether factors were correlated or not) in terms of omitting factors; it was sensitive to all conditions in terms of omitting and mis-assigning indicators, and it also found extra factors depending on the number of factors in the population, the purity of factors and the presence of a weak factor. This is the first study of CCA in data with these specific features of complexity, which are common in multivariate data.
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    AN INVESTIGATION OF GROWTH MIXTURE MODELS WHEN DATA ARE COLLECTED WITH UNEQUAL SELECTION PROBABILITIES: A MONTE CARLO STUDY
    (2009) Hamilton, Jennifer; Hancock, Gregory R.; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    As researchers begin to use Growth Mixture Models (GMM) with data from nationally representative samples, it becomes increasingly critical for researchers to understand the difficulties associated with modeling data that come from complex sample designs. If researchers naively apply GMM to nationally representative data sets without adjusting for the way in which the sample was selected, the resulting parameter estimates, standard errors and tests of significant may not be trustworthy. Therefore, the objective of the current study was to quantify the accuracy of parameter estimates and class assignment when subjects are sampled with unequal probabilities of selection. To this end, a series of Monte Carlo simulations empirically investigated the ability of GMM to recover known growth parameters of distinct populations when various adjustments are applied to the statistical model. Specifically, the current research compared the performance of GMM that 1) ignores the sample design; 2) accounts for the sample design via weighting; 3) accounts for the sample design via explicitly modeling the stratification variable; and 4) accounts for the sample design by using weights and modeling the stratification variable. Results suggested that a model-based approach does not improve the accuracy of parameter estimates when individuals are sampled with disproportionate sampling probabilities. Not only does this method often fail to converge, when it did converge the parameter estimates exhibited an unacceptable amount of bias. The weighted model performed the best out of all of the models tested, but still resulted in parameter estimates with unacceptably high percentages of bias. It is possible that the distributions of the manifest variables overlap too much, and the aggregate distribution may be unimodal, making it potentially difficult to distinguish among the latent classes and thus affecting the accuracy of parameter estimates. In sum, the current research indicates that GMM should not be used when data are sampled with disproportionate probabilities. Researchers should therefore attend to the study design and data collection strategies when considering the use of a Growth Mixture Model in the analysis phase.
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    LOW-INCOME TEEN FATHERS' TRAJECTORY OF INVOLVEMENT: THE INFLUENCE OF INDIVIDUAL, CONTEXTUAL, AND COPARENTAL FACTORS
    (2009) Holmes, Allison; Harden, Brenda J; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    While teen births are on the rise and marriage rates are on the decline, fathers have become a recent focus. However, there is a dearth of literature on teen fathers' parenting behaviors. The current study provided a portrait of Early Head Start teen fathers' involvement throughout early childhood and salient influences on that involvement. This study maximized developmental and life course perspectives by employing a longitudinal analysis (i.e., Latent Growth Curve Model) that emphasized time-effects. The majority of teen fathers were involved with children initially, but their involvement decreased over time. Consistent with extant literature, teen fathers who were prenatally engaged, resident after the birth, and in romantic coparental relationships at 14- and 24-months were more involved in their children's lives initially. Teen fathers who were in romantic coparental relationships at 36- and 64- months were less likely to decrease their involvement over the course of early childhood. Surprisingly, age, race, employment, and school status were not significant influences on father involvement. Although the present study had its limitations, trends were noted and should be considered in future studies. Teen fathers are a unique population facing several challenges to meeting their own developmental needs and enacting their father role. Some conceptual factors shown to be influential for father involvement with adult and married fathers (i.e., age, employment) do not hold the same meaning and impact among teen fathers. The conceptual and ultimately practical meaning of behaviors and characteristics must be contextualized within teen fathers' developmental trajectory and ecological settings. Similarly, examination of teen fathers within a dynamic, longitudinal framework emphasized the need to address fatherhood in a different way. Previous studies have examined longitudinal data, but not examined the patterns of involvement for individual fathers. This different perspective (i.e., person-centered) revealed unique patterns for teen fathers. Further analyses will allow when and how to best intervene with teen fathers. Teen fathers may be at-risk, but they are involved with their children and can positively benefit both children and mothers. Head Start and Early Head Start could continue to support teen fatherhood through its mission to serve low-income children and parents; availability from pregnancy through 5-years; and mission to adapt to the needs of the community and family. But without support or intervention, the cycle of teen of parenthood is perpetuated.
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    The nature of bi-ethnic identity in young adults of Asian and European descent and their perceptions of familial influences on its development
    (2009) Wagner Hoa, Amanda Laurel; Wigfield, Allan; Human Development; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The purpose of this study was to identify the key constructs of bi-ethnic identity, the key familial influences, and other salient influences on bi-ethnic identity as perceived by young adults of Asian and European descent. The rapidly changing demographics of the United States provide an impetus for research on the developmental processes of bi-ethnic individuals. In this qualitative study, participants were interviewed about their bi-ethnic identities and possible influences on bi-ethnic identity development. Data analysis for this study incorporated techniques from grounded theory (Strauss & Corbin, 1990) and analytic induction (LeCompte & Preissle, 1993). Five bi-ethnic identity types emerged from participants' responses to interview questions: majority identity, minority identity, dual identity, integrated identity, and unresolved identity. These identity types are a unique contribution to the literature in that they specify how individuals of Asian and European descent define themselves. Additionally, this study identified four facets of bi-ethnic identity that indicate how bi-ethnic individuals think and feel about their background: centrality, self-label, affirmation, and affect. Six categories of influences on bi-ethnic identity development emerged from responses to interview questions (parental, extended family, personal, peer, environmental, discrimination), with 18 subcategories. This study is important because most prior research on bi-ethnic identity has focused on uncovering developmental stages, while we lack understanding of the nature of bi-ethnic identity and influences on its development. This study was important given the dearth of research on bi-ethnic Asians, although future research is needed with other bi-ethnic groups.