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  Partner: UNT Libraries
 Degree Discipline: Educational Research
 Degree Level: Doctoral
Ability Estimation Under Different Item Parameterization and Scoring Models

Ability Estimation Under Different Item Parameterization and Scoring Models

Date: May 2002
Creator: Si, Ching-Fung B.
Description: A Monte Carlo simulation study investigated the effect of scoring format, item parameterization, threshold configuration, and prior ability distribution on the accuracy of ability estimation given various IRT models. Item response data on 30 items from 1,000 examinees was simulated using known item parameters and ability estimates. The item response data sets were submitted to seven dichotomous or polytomous IRT models with different item parameterization to estimate examinee ability. The accuracy of the ability estimation for a given IRT model was assessed by the recovery rate and the root mean square errors. The results indicated that polytomous models produced more accurate ability estimates than the dichotomous models, under all combinations of research conditions, as indicated by higher recovery rates and lower root mean square errors. For the item parameterization models, the one-parameter model out-performed the two-parameter and three-parameter models under all research conditions. Among the polytomous models, the partial credit model had more accurate ability estimation than the other three polytomous models. The nominal categories model performed better than the general partial credit model and the multiple-choice model with the multiple-choice model the least accurate. The results further indicated that certain prior ability distributions had an effect on the accuracy ...
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Attenuation of the Squared Canonical Correlation Coefficient Under Varying Estimates of Score Reliability

Attenuation of the Squared Canonical Correlation Coefficient Under Varying Estimates of Score Reliability

Date: August 2010
Creator: Wilson, Celia M.
Description: Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability. Monte Carlo simulation methodology was used to fulfill the purpose of this study. Initially, data populations with various manipulated conditions were generated (N = 100,000). Subsequently, 500 random samples were drawn with replacement from each population, and data was subjected to canonical correlation analyses. The canonical correlation results were then analyzed using descriptive statistics and an ANOVA design to determine under which condition(s) the squared canonical correlation coefficient was most attenuated when compared to population Rc2 values. This information was analyzed and used to determine what effect, if any, the different conditions considered in this study had on Rc2. The results from this Monte Carlo investigation clearly illustrated the importance of score reliability when interpreting study results. As evidenced by the outcomes presented, the more measurement error (lower reliability) present in the variables included in an analysis, the more attenuation experienced by the effect size(s) produced in the analysis, in this ...
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Bias and Precision of the Squared Canonical Correlation Coefficient under Nonnormal Data Conditions

Bias and Precision of the Squared Canonical Correlation Coefficient under Nonnormal Data Conditions

Date: August 2006
Creator: Leach, Lesley Ann Freeny
Description: This dissertation: (a) investigated the degree to which the squared canonical correlation coefficient is biased in multivariate nonnormal distributions and (b) identified formulae that adjust the squared canonical correlation coefficient (Rc2) such that it most closely approximates the true population effect under normal and nonnormal data conditions. Five conditions were manipulated in a fully-crossed design to determine the degree of bias associated with Rc2: distribution shape, variable sets, sample size to variable ratios, and within- and between-set correlations. Very few of the condition combinations produced acceptable amounts of bias in Rc2, but those that did were all found with first function results. The sample size to variable ratio (n:v)was determined to have the greatest impact on the bias associated with the Rc2 for the first, second, and third functions. The variable set condition also affected the accuracy of Rc2, but for the second and third functions only. The kurtosis levels of the marginal distributions (b2), and the between- and within-set correlations demonstrated little or no impact on the bias associated with Rc2. Therefore, it is recommended that researchers use n:v ratios of at least 10:1 in canonical analyses, although greater n:v ratios have the potential to produce even less bias. ...
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A Comparison of IRT and Rasch Procedures in a Mixed-Item Format Test

A Comparison of IRT and Rasch Procedures in a Mixed-Item Format Test

Date: August 2003
Creator: Kinsey, Tari L.
Description: This study investigated the effects of test length (10, 20 and 30 items), scoring schema (proportion of dichotomous ad polytomous scoring) and item analysis model (IRT and Rasch) on the ability estimates, test information levels and optimization criteria of mixed item format tests. Polytomous item responses to 30 items for 1000 examinees were simulated using the generalized partial-credit model and SAS software. Portions of the data were re-coded dichotomously over 11 structured proportions to create 33 sets of test responses including mixed item format tests. MULTILOG software was used to calculate the examinee ability estimates, standard errors, item and test information, reliability and fit indices. A comparison of IRT and Rasch item analysis procedures was made using SPSS software across ability estimates and standard errors of ability estimates using a 3 x 11 x 2 fixed factorial ANOVA. Effect sizes and power were reported for each procedure. Scheffe post hoc procedures were conducted on significant factos. Test information was analyzed and compared across the range of ability levels for all 66-design combinations. The results indicated that both test length and the proportion of items scored polytomously had a significant impact on the amount of test information produced by mixed item ...
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A comparison of traditional and IRT factor analysis.

A comparison of traditional and IRT factor analysis.

Date: December 2004
Creator: Kay, Cheryl Ann
Description: This study investigated the item parameter recovery of two methods of factor analysis. The methods researched were a traditional factor analysis of tetrachoric correlation coefficients and an IRT approach to factor analysis which utilizes marginal maximum likelihood estimation using an EM algorithm (MMLE-EM). Dichotomous item response data was generated under the 2-parameter normal ogive model (2PNOM) using PARDSIM software. Examinee abilities were sampled from both the standard normal and uniform distributions. True item discrimination, a, was normal with a mean of .75 and a standard deviation of .10. True b, item difficulty, was specified as uniform [-2, 2]. The two distributions of abilities were completely crossed with three test lengths (n= 30, 60, and 100) and three sample sizes (N = 50, 500, and 1000). Each of the 18 conditions was replicated 5 times, resulting in 90 datasets. PRELIS software was used to conduct a traditional factor analysis on the tetrachoric correlations. The IRT approach to factor analysis was conducted using BILOG 3 software. Parameter recovery was evaluated in terms of root mean square error, average signed bias, and Pearson correlations between estimated and true item parameters. ANOVAs were conducted to identify systematic differences in error indices. Based on many ...
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Comparisons of Improvement-Over-Chance Effect Sizes for Two Groups Under Variance Heterogeneity and Prior Probabilities

Comparisons of Improvement-Over-Chance Effect Sizes for Two Groups Under Variance Heterogeneity and Prior Probabilities

Date: May 2003
Creator: Alexander, Erika D.
Description: The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the decision of which method to choose is primarily determined by variance pattern and prior probabilities. Under variance homogeneity, any of the methods may be recommended. However, LRA is recommended when priors are equal or extreme and linear PDA is recommended when priors are moderate. Under variance heterogeneity, selecting a recommended method is more complex. In many cases, more than one method could be used appropriately.
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Convergent Validity of Variables Residualized By a Single Covariate: the Role of Correlated Error in Populations and Samples

Convergent Validity of Variables Residualized By a Single Covariate: the Role of Correlated Error in Populations and Samples

Date: May 2013
Creator: Nimon, Kim
Description: This study examined the bias and precision of four residualized variable validity estimates (C0, C1, C2, C3) across a number of study conditions. Validity estimates that considered measurement error, correlations among error scores, and correlations between error scores and true scores (C3) performed the best, yielding no estimates that were practically significantly different than their respective population parameters, across study conditions. Validity estimates that considered measurement error and correlations among error scores (C2) did a good job in yielding unbiased, valid, and precise results. Only in a select number of study conditions were C2 estimates unable to be computed or produced results that had sufficient variance to affect interpretation of results. Validity estimates based on observed scores (C0) fared well in producing valid, precise, and unbiased results. Validity estimates based on observed scores that were only corrected for measurement error (C1) performed the worst. Not only did they not reliably produce estimates even when the level of modeled correlated error was low, C1 produced values higher than the theoretical limit of 1.0 across a number of study conditions. Estimates based on C1 also produced the greatest number of conditions that were practically significantly different than their population parameters.
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Determination of the optimal number of strata for bias reduction in propensity score matching.

Determination of the optimal number of strata for bias reduction in propensity score matching.

Date: May 2010
Creator: Akers, Allen
Description: Previous research implementing stratification on the propensity score has generally relied on using five strata, based on prior theoretical groundwork and minimal empirical evidence as to the suitability of quintiles to adequately reduce bias in all cases and across all sample sizes. This study investigates bias reduction across varying number of strata and sample sizes via a large-scale simulation to determine the adequacy of quintiles for bias reduction under all conditions. Sample sizes ranged from 100 to 50,000 and strata from 3 to 20. Both the percentage of bias reduction and the standardized selection bias were examined. The results show that while the particular covariates in the simulation met certain criteria with five strata that greater bias reduction could be achieved by increasing the number of strata, especially with larger sample sizes. Simulation code written in R is included.
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Establishing the utility of a classroom effectiveness index as a teacher accountability system.

Establishing the utility of a classroom effectiveness index as a teacher accountability system.

Date: May 2002
Creator: Bembry, Karen L.
Description: How to identify effective teachers who improve student achievement despite diverse student populations and school contexts is an ongoing discussion in public education. The need to show communities and parents how well teachers and schools improve student learning has led districts and states to seek a fair, equitable and valid measure of student growth using student achievement. This study investigated a two stage hierarchical model for estimating teacher effect on student achievement. This measure was entitled a Classroom Effectiveness Index (CEI). Consistency of this model over time, outlier influences in individual CEIs, variance among CEIs across four years, and correlations of second stage student residuals with first stage student residuals were analyzed. The statistical analysis used four years of student residual data from a state-mandated mathematics assessment (n=7086) and a state-mandated reading assessment (n=7572) aggregated by teacher. The study identified the following results. Four years of district grand slopes and grand intercepts were analyzed to show consistent results over time. Repeated measures analyses of grand slopes and intercepts in mathematics were statistically significant at the .01 level. Repeated measures analyses of grand slopes and intercepts in reading were not statistically significant. The analyses indicated consistent results over time for reading ...
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A Hierarchical Regression Analysis of the Relationship Between Blog Reading, Online Political Activity, and Voting During the 2008 Presidential Campaign

A Hierarchical Regression Analysis of the Relationship Between Blog Reading, Online Political Activity, and Voting During the 2008 Presidential Campaign

Date: December 2010
Creator: Lewis, Mitzi
Description: The advent of the Internet has increased access to information and impacted many aspects of life, including politics. The present study utilized Pew Internet & American Life survey data from the November 2008 presidential election time period to investigate the degree to which political blog reading predicted online political discussion, online political participation, whether or not a person voted, and voting choice, over and above the predication that could be explained by demographic measures of age, education level, gender, income, marital status, race/ethnicity, and region. Ordinary least squares hierarchical regression revealed that political blog reading was positively and statistically significantly related to online political discussion and online political participation. Hierarchical logistic regression analysis indicated that the odds of a political blog reader voting were 1.98 the odds of a nonreader voting, but vote choice was not predicted by reading political blogs. These results are interpreted within the uses and gratifications framework and the understanding that blogs add an interpersonal communication aspect to a mass medium. As more people use blogs and the nature of the blog-reading audience shifts, continuing to track and describe the blog audience with valid measures will be important for researchers and practitioners alike. Subsequent potential effects ...
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