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Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The

Random Forests is a statistical learning method which has been proposed for propensity score estimation models that involve complex interactions, nonlinear relationships, or both of the covariates. In this dissertation I conducted a simulation study to examine the effects of three Random Forests model specifications in propensity score analysis. The results suggested that, depending on the nature of data, optimal specification of (1) decision rules to select the covariate and its split value in a Classification Tree, (2) the number of covariates randomly sampled for selection, and (3) methods of estimating Random Forests propensity scores could potentially produce an unbiased average treatment effect estimate after propensity scores weighting by the odds adjustment. Compared to the logistic regression estimation model using the true propensity score model, Random Forests had an additional advantage in producing unbiased estimated standard error and correct statistical inference of the average treatment effect. The relationship between the balance on the covariates' means and the bias of average treatment effect estimate was examined both within and between conditions of the simulation. Within conditions, across repeated samples there was no noticeable correlation between the covariates' mean differences and the magnitude of bias of average treatment effect estimate for the covariates that were imbalanced before adjustment. Between conditions, small mean differences of covariates after propensity score adjustment were not sensitive enough to identify the optimal Random Forests model specification for propensity score analysis.
ContributorsCham, Hei Ning (Author) / Tein, Jenn-Yun (Thesis advisor) / Enders, Stephen G (Thesis advisor) / Enders, Craig K. (Committee member) / Mackinnon, David P (Committee member) / Arizona State University (Publisher)
Created2013
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Description
The current study employs item difficulty modeling procedures to evaluate the feasibility of potential generative item features for nonword repetition. Specifically, the extent to which the manipulated item features affect the theoretical mechanisms that underlie nonword repetition accuracy was estimated. Generative item features were based on the phonological loop component

The current study employs item difficulty modeling procedures to evaluate the feasibility of potential generative item features for nonword repetition. Specifically, the extent to which the manipulated item features affect the theoretical mechanisms that underlie nonword repetition accuracy was estimated. Generative item features were based on the phonological loop component of Baddelely's model of working memory which addresses phonological short-term memory (Baddeley, 2000, 2003; Baddeley & Hitch, 1974). Using researcher developed software, nonwords were generated to adhere to the phonological constraints of Spanish. Thirty-six nonwords were chosen based on the set item features identified by the proposed cognitive processing model. Using a planned missing data design, two-hundred fifteen Spanish-English bilingual children were administered 24 of the 36 generated nonwords. Multiple regression and explanatory item response modeling techniques (e.g., linear logistic test model, LLTM; Fischer, 1973) were used to estimate the impact of item features on item difficulty. The final LLTM included three item radicals and two item incidentals. Results indicated that the LLTM predicted item difficulties were highly correlated with the Rasch item difficulties (r = .89) and accounted for a substantial amount of the variance in item difficulty (R2 = .79). The findings are discussed in terms of validity evidence in support of using the phonological loop component of Baddeley's model (2000) as a cognitive processing model for nonword repetition items and the feasibility of using the proposed radical structure as an item blueprint for the future generation of nonword repetition items.
ContributorsMorgan, Gareth Philip (Author) / Gorin, Joanna (Thesis advisor) / Levy, Roy (Committee member) / Gray, Shelley (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Identification of primary language impairment (PLI) in sequential bilingual children is challenging because of the interaction between PLI and second language (L2) proficiency. An important step in improving the accurate diagnosis of PLI in bilingual children is to investigate how differences in L2 performance are affected by a length of

Identification of primary language impairment (PLI) in sequential bilingual children is challenging because of the interaction between PLI and second language (L2) proficiency. An important step in improving the accurate diagnosis of PLI in bilingual children is to investigate how differences in L2 performance are affected by a length of L2 exposure and how L2 assessment contributes to differentiation between children with and without PLI at different L2 proficiency levels. Sixty one children with typical language development (TD) ages 5;3-8 years and 12 children with PLI ages 5;5-7;8 years participated. Results revealed that bilingual children with and without PLI, who had between 1 and 3 years of L2 exposure, did not differ in mean length of utterance (MLU), number of different words, percent of maze words, and performance on expressive and receptive grammatical tasks in L2. Performance on a grammaticality judgment task by children with and without PLI demonstrated the largest effect size, indicating that it may potentially contribute to identification of PLI in bilingual populations. In addition, children with PLI did not demonstrate any association between the length of exposure and L2 proficiency, suggesting that they do not develop their L2 proficiency in relation to length of exposure in the same manner as children with TD. Results also indicated that comprehension of grammatical structures and expressive grammatical task in L2 may contribute to differentiation between the language ability groups at the low and intermediate-high proficiency levels. The discriminant analysis with the entire sample of bilingual children with and without PLI revealed that among L2 measures, only MLU contributed to the discrimination between the language ability groups. However, poor classification accuracy suggested that MLU alone is not a sufficient predictor of PLI. There were significant differences among L2 proficiency levels in children with TD in MLU, number of different words, and performance on the expressive and receptive grammatical tasks in L2, indicating that L2 proficiency level may potentially impact the differentiation between language difficulties due to typical L2 acquisition processes and PLI.
ContributorsSmyk, Ekaterina (Author) / Restrepo, Maria Adelaida (Thesis advisor) / Gorin, Joanna (Committee member) / Gray, Shelley (Committee member) / Arizona State University (Publisher)
Created2012
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Description
The use of exams for classification purposes has become prevalent across many fields including professional assessment for employment screening and standards based testing in educational settings. Classification exams assign individuals to performance groups based on the comparison of their observed test scores to a pre-selected criterion (e.g. masters vs. nonmasters

The use of exams for classification purposes has become prevalent across many fields including professional assessment for employment screening and standards based testing in educational settings. Classification exams assign individuals to performance groups based on the comparison of their observed test scores to a pre-selected criterion (e.g. masters vs. nonmasters in dichotomous classification scenarios). The successful use of exams for classification purposes assumes at least minimal levels of accuracy of these classifications. Classification accuracy is an index that reflects the rate of correct classification of individuals into the same category which contains their true ability score. Traditional methods estimate classification accuracy via methods which assume that true scores follow a four-parameter beta-binomial distribution. Recent research suggests that Item Response Theory may be a preferable alternative framework for estimating examinees' true scores and may return more accurate classifications based on these scores. Researchers hypothesized that test length, the location of the cut score, the distribution of items, and the distribution of examinee ability would impact the recovery of accurate estimates of classification accuracy. The current simulation study manipulated these factors to assess their potential influence on classification accuracy. Observed classification as masters vs. nonmasters, true classification accuracy, estimated classification accuracy, BIAS, and RMSE were analyzed. In addition, Analysis of Variance tests were conducted to determine whether an interrelationship existed between levels of the four manipulated factors. Results showed small values of estimated classification accuracy and increased BIAS in accuracy estimates with few items, mismatched distributions of item difficulty and examinee ability, and extreme cut scores. A significant four-way interaction between manipulated variables was observed. In additional to interpretations of these findings and explanation of potential causes for the recovered values, recommendations that inform practice and avenues of future research are provided.
ContributorsKunze, Katie (Author) / Gorin, Joanna (Thesis advisor) / Levy, Roy (Thesis advisor) / Green, Samuel (Committee member) / Arizona State University (Publisher)
Created2013