Covariate quality has been primarily theory driven in propensity score matching with a general adversity to the interpretation of group prediction. However, effect sizes are well supported in the literature and may help to inform the method. Specifically, I index can be used as a measure of effect size in logistic regression to evaluate group prediction. As such, simulation was used to create 35 conditions of I, initial bias and sample size to examine statistical differences in (a) post-matching bias reduction and (b) treatment effect sensitivity. The results of this study suggest these conditions do not explain statistical differences in percent bias reduction of treatment likelihood after matching. However, I and sample size do explain statistical differences in treatment effect sensitivity. Treatment effect sensitivity was lower when sample sizes and I increased. However, this relationship was mitigated within smaller sample sizes as I increased above I = .50.

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. …

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 …

This study compared three methods of selecting the best discriminating test items and the resultant test reliability of mastery/nonmastery classifications. These three methods were (a) the agreement approach, (b) the phi coefficient approach, and (c) the random selection approach. Test responses from 1,836 students on a 50-item physical science test were used, from which 90 distinct data sets were generated for analysis. These 90 data sets contained 10 replications of the combination of three different sample sizes (75, 150, and 300) and three different numbers of test items (15, 25, and 35). The results of this study indicated that the agreement approach was an appropriate method to be used for selecting criterion-referenced test items at the classroom level, while the phi coefficient approach was an appropriate method to be used at the district and/or state levels. The random selection method did not have similar characteristics in selecting test items and produced the lowest reliabilities, when compared with the agreement and the phi coefficient approaches.

The present study is concerned with selected factors that may strongly influence classroom teachers to refer young children for possible placement in special classes when the children are functioning near the borderline for placement on the basis of intelligence test scores. Particular attention was given to the contribution of student attributes (i.e., sex, ethnic background, socioeconomic status, and classroom behavior) and teacher attributes (i.e., age, sex, ethnic background and teaching experience) to the referral patterns of teachers. Also considered were the size of school enrollment, school locale, and interactions among student, teacher, and school variables. It was concluded that the teachers in the population studied responded to the case histories on the basis of certain selective biases. However, the relationship of these biases to referral decisions was less obvious and considerably more complex than has been suggested previously in the professional literature. At the same time, the presence of any bias in the referral process seemingly warrants careful consideration and points to the -need for greater emphasis in pre-service and in-service training programs upon the objective evaluation of students as an integral part of educational planning.

The study seeks to determine the effect upon the F-statistic of violating the assumption of homogeneity of regression slopes in the one-way, fixed-effects analysis of covariance model. The study employs a Monte Carlo simulation technique to vary the degree of heterogeneity of regression slopes with varied sample sizes within experiments to determine the effect of such conditions. One hundred and eighty-three simulations were used.

The problem with which this investigation is concerned is that of developing (a) the concept of instructional claim, and (b) credible principles for instructional claim formulation and evaluation. The belief that these constructions are capable of contributing to the advancement of curricular and instructional research and practice is grounded in three major features. The first feature is that of increased precision of basic concepts and increased coherence among them. The second feature is the deliberate connecting of instructional strategies and goal-states and the connecting of instructional configurations with curricular configurations. The third feature is the introduction of fundamental logical principles as evaluative criteria and the framing of instructional plans in such a way as to be subject to empirical tests under the principles of hypothesis testing that are considered credible in the empirical sciences.

This study sought to determine the effect upon Tukey's Honestly Significant Difference (HSD) statistic of concurrently violating the assumptions of homogeneity of variance and equal sample sizes. Two forms for the unequal sample size problem were investigated. Kramer's form and the harmonic mean approach were the two unequal sample size procedures studied. The study employed a Monte Carlo simulation procedure which varied sample sizes with a heterogeneity of variance condition. Four thousand experiments were generated. Findings of this study were based upon the empirically obtained significance levels. Five conclusions were reached in this study. The first conclusion was that for the conditions of this study the Kramer form of the HSD statistic is not robust at the .05 or .01 nominal level of significance. A second conclusion was that the harmonic mean form of the HSD statistic is not robust at the .05 and .01 nominal level of significance. A general conclusion reached from all the findings formed the third conclusion. It was that the Kramer form of the HSD test is the preferred procedure under combined assumption violations of variance heterogeneity and unequal sample sizes. Two additional conclusions are based on related findings. The fourth conclusion was that for …

The present study examined the structural construct validity of the LoTi Digital-Age Survey, a measure of teacher instructional practices with technology in the classroom. Teacher responses (N = 2840) from across the United States were used to assess factor structure of the instrument using both exploratory and confirmatory analyses. Parallel analysis suggests retaining a five-factor solution compared to the MAP test that suggests retaining a three-factor solution. Both analyses (EFA and CFA) indicate that changes need to be made to the current factor structure of the survey. The last two factors were composed of items that did not cover or accurately measure the content of the latent trait. Problematic items, such as items with crossloadings, were discussed. Suggestions were provided to improve the factor structure, items, and scale of the survey.

The study seeks to determine the effect upon the Marascuilo Ú₀ statistic of violating the small sample assumption. The study employed a Monte Carlo simulation technique to vary the degree of sample size and unequal sample sizes within experiments to determine the effect of such conditions, Twenty-two simulations, with 1200 trials each, were used. The following conclusion appeared to be appropriate: The Marascuilo Ú₀ statistic should not be used with small sample sizes and it is recommended that the statistic be used only if sample sizes are larger than ten.

The mitigation of outliers serves to increase the strength of a relationship between variables. This study defined outliers in three different ways and used five regression procedures to describe the effects of outliers on 50 data sets. This study also examined the relationship among the shape of the distribution, skewness, and outliers.

This study compared three methods of detecting test item bias, the chi-square approach, the transformed item difficulties approach, and the Linn-Harnish three-parameter item response approach which is the only Item Response Theory (IRT) method that can be utilized with minority samples relatively small in size. The items on two tests which measured writing and reading skills were examined for evidence of sex and ethnic bias. Eight sets of samples, four from each test, were randomly selected from the population (N=7287) of sixth, seventh, and eighth grade students enrolled in a large, urban school district in the southwestern United States. Each set of samples, male/female, White/Hispanic, White/Black, and White/White, contained 800 examinees in the majority group and 200 in the minority group. In an attempt to control differences in ability that may have existed between the various population groups, examinees with scores greater or less than two standard deviations from their group's mean were eliminated. Ethnic samples contained equal numbers of each sex. The White/White sets of samples were utilized to provide baseline bias estimates because the tests could not logically be biased against these groups. Bias indices were then calculated for each set of samples with each of the three …

The effects of random guessing as a measurement disturbance on Rasch fit statistics (unweighted total, weighted total, and unweighted ability between) and the Logit Residual Index (LRI) were examined through simulated data sets of varying sample sizes, test lengths, and distribution types. Three test lengths (25, 50, and 100), three sample sizes (25, 50, and 100), two item difficulty distributions (normal and uniform), and three levels of guessing (no guessing (0%), 25%, and 50%) were used in the simulations, resulting in 54 experimental conditions. The mean logit person ability for each experiment was +1. Each experimental condition was simulated once in an effort to approximate what could happen on the single administration of a four option per item multiple choice test to a group of relatively high ability persons. Previous research has shown that varying item and person parameters have no effect on Rasch fit statistics. Consequently, these parameters were used in the present study to establish realistic test conditions, but were not interpreted as effect factors in determining the results of this study.

This study was designed to determine whether chis-quared based tests for independence give reliable estimates (as compared to the exact values provided by Fisher's exact probabilities test) of the probability of a relationship between the variables in 3 X 3, 3 X 4 , and 3 X 5 contingency tables when the sample size is 10, 20, or 30. In addition to the classical (uncorrected) chi-squared test, four methods for continuity correction were compared to Fisher's exact probabilities test. The four methods were Yates' correction, two corrections attributed to Cochran, and Mantel's correction. The study was modeled after a similar comparison conducted on 2 X 2 contingency tables and published by Michael Haber.

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.

The aim of this study was to validate an instrument that can be used by instructors or social scientist who are interested in evaluating statistics anxiety. The psychometric properties of the English version of the Statistical Anxiety Scale (SAS) was examined through a confirmatory factor analysis of scores from a sample of 323 undergraduate social science majors enrolled in colleges and universities in the United States. In previous studies, the psychometric properties of the Spanish and Italian versions of the SAS were validated; however, the English version of the SAS had never been assessed. Inconsistent with previous studies, scores on the English version of the SAS did not produce psychometrically acceptable values of validity. However, the results of this study suggested the potential value of a revised two-factor model SAS to measure statistics anxiety. Additionally, the Attitudes Towards Statistics (ATS) scale was used to examine the convergent and discriminant validities of the two-factor SAS. As expected, the correlation between the two factors of the SAS and the two factors of the ATS uncovered a moderately negative correlation between examination anxiety and attitudes towards the course. Additionally, the results of a structural regression model of attitudes towards statistics as a predictor …

College student satisfaction is a concept that has become more prevalent in higher education research journals. Little attention has been given to the psychometric properties of previous instrumentation, and few studies have investigated the structure of current satisfaction instrumentation. This dissertation: (a) investigated the tenability of the theoretical dimensional structure of the Noel-Levitz Student Satisfaction Inventory™ (SSI), (b) investigated an alternative factor structure using explanatory factor analyses (EFA), and (c) used multiple-group CFA procedures to determine whether an alternative SSI factor structure would be invariant for three demographic variables: gender (men/women), race/ethnicity (Caucasian/Other), and undergraduate classification level (lower level/upper level). For this study, there was little evidence for the multidimensional structure of the SSI. A single factor, termed General Satisfaction with College, was the lone unidimensional construct that emerged from the iterative CFA and EFA procedures. A revised 20-item model was developed, and a series of multigroup CFAs were used to detect measurement invariance for three variables: student gender, race/ethnicity, and class level. No measurement invariance was noted for the revised 20-item model. Results for the invariance tests indicated equivalence across the comparison groups for (a) the number of factors, (b) the pattern of indicator-factor loadings, (c) the factor loadings, …

The supply of non-physician clinicians (NPCs), such as physician assistant (PAs), could significantly influence demand requirements in medical workforce projections. This study predicts supply of and demand for PAs from 2006 to 2020. The PA supply model utilized the number of certified PAs, the educational capacity (at 10% and 25% expansion) with assumed attrition rates, and retirement assumptions. Gross domestic product (GDP) chained in 2000 dollar and US population were utilized in a transfer function trend analyses with the number of PAs as the dependent variable for the PA demand model. Historical analyses revealed strong correlations between GDP and US population with the number of PAs. The number of currently certified PAs represents approximately 75% of the projected demand. At 10% growth, the supply and demand equilibrium for PAs will be reached in 2012. A 25% increase in new entrants causes equilibrium to be met one year earlier. Robust application trends in PA education enrollment (2.2 applicants per seat for PAs is the same as for allopathic medical school applicants) support predicted increases. However, other implications for the PA educational institutions include recruitment and retention of qualified faculty, clinical site maintenance and diversity of matriculates. Further research on factors affecting …

This study dealt with shrinkage in multiple correlation coefficients computed for sample data when these coefficients are compared to the multiple correlation coefficients for populations and the effect of the ratio of utilized predictors to original predictors on the shrinkage in R square. The study sought to provide the rationale for selection of the shrinkage formula when the correlations between the predictors and the criterion are known and determine which of the three shrinkage formulas (Browne, Darlington, or Wherry) will yield the R square from sample data that is closest to the R square for the population data.

The problem considered involved assessing willingness of educators to participate in a study offering monetary incentives. Determination of willingness was implemented by sending educators a packet requesting return of a postcard to indicate willingness to participate. The purpose was twofold: to determine the effect of a monetary incentive upon willingness of educators to participate in a research study, and to analyze implications for mail questionnaire studies. A sample of 600 educators was chosen from directories of eleven public schools in north Texas. It included equal numbers of male and female teachers and male and female administrators. Subjects were assigned to one of twelve groups. No two from a school were assigned to different levels of the inducement variable.

The study seeks to determine the degree of accumulation of Type II error rates, while violating the assumptions of normality, for different specified levels of power among sample means. The study employs a Monte Carlo simulation procedure with three different specified levels of power, methodologies, and population distributions. On the basis of the comparisons of actual and observed error rates, the following conclusions appear to be appropriate. 1. Under the strict criteria for evaluation of the hypotheses, Type II experimentwise error does accumulate at a rate that the probability of accepting at least one null hypothesis in a family of tests, when in theory all of the alternate hypotheses are true, is high, precluding valid tests at the beginning of the study. 2. The Dunn-Bonferroni procedure of setting the critical value based on the beta value per contrast did not significantly reduce the probability of committing a Type II error in a family of tests. 3. The use of an adequate sample size and orthogonal contrasts, or limiting the number of pairwise comparisons to the number of means, is the best method to control for the accumulation of Type II errors. 4. The accumulation of Type II error is irrespective …

This study examined the statistical consequences of employing various methods of computing and cumulating effect sizes in meta-analysis. Six methods of computing effect size, and three techniques for combining study outcomes, were compared. Effect size metrics were calculated with one-group and pooled standardizing denominators, corrected for bias and for unreliability of measurement, and weighted by sample size and by sample variance. Cumulating techniques employed as units of analysis the effect size, the study, and an average study effect. In order to determine whether outcomes might vary with the size of the meta-analysis, mean effect sizes were also compared for two smaller subsets of studies. An existing meta-analysis of 60 studies examining the effectiveness of computer-based instruction was used as a data base for this investigation. Recomputation of the original study data under the six different effect size formulas showed no significant difference among the metrics. Maintaining the independence of the data by using only one effect size per study, whether a single or averaged effect, produced a higher mean effect size than averaging all effect sizes together, although the difference did not reach statistical significance. The sampling distribution of effect size means approached that of the population of 60 studies …

The present study examined the factor structure and measurement invariance of the revised version of the Athletic Coping Skills Inventory (ACSI-28), following adjustment of the wording of items such that they were appropriate to assess Coping Skills in an educational setting. A sample of middle school students (n = 1,037) completed the revised inventory. An initial confirmatory factor analysis led to the hypothesis of a better fitting model with two items removed. Reliability of the subscales and the instrument as a whole was acceptable. Items were examined for sex invariance with differential item functioning (DIF) using item response theory, and five items were flagged for significant sex non-invariance. Following removal of these items, comparison of the mean differences between male and female coping scores revealed that there was no significant difference between the two groups. Further examination of the generalizability of the coping construct and the potential transfer of psychosocial skills between athletic and academic settings are warranted.

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 …