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 of distributions.

As awareness of the costs of workplace injury and illness continues to grow, there has been an increased demand for effective ergonomic interventions to reduce the prevalence of musculoskeletal disorders (MSDs). The goal of ergonomic interventions is to reduce exposures (mechanical and psychosocial); however there is conflicting evidence about the impact of these interventions as many studies produce inconclusive or conflicting results. In order to provide a clearer picture of the effectiveness of these interventions, we must find out if methodological issues, particularly statistical power, are limiting this research. The purpose of this study was to review and examine factors influencing statistical power in ergonomic intervention papers from five peer reviewed journals in 2008. A standardized review was performed by two reviewers. Twenty eight ergonomic intervention papers met the inclusion criteria and were fully reviewed. Data and trends from the reviewed papers were summarized specifically looking at the research designs used, the outcome measures used, if statistical power was mentioned, if a rationale for sample size was reported, if standardized and un-standardized effect sizes were reported, if confidence intervals were reported, the alpha levels used, if pair-wise correlation values were provided, if mean values and standard deviations were provided for all measures and the location of the studies. Also, the studies were rated based on the outcomes of their intervention into one of three categories (shown to be effective, inconclusive and not shown to be effective). Between these three groupings comparisons of post hoc power, standardized effect sizes, un-standardized effect sizes and coefficients of variation were made. The results indicate that in general, a lack of statistical power is indeed a concern and may be due to the sample sizes used, effect sizes produced, extremely high variability in some of the measures, the lack of attention paid to statistical power during research design and the lack of appropriate statistical reporting guidelines in journals where ergonomic intervention research may be published. A total of 69.6% of studies reviewed had a majority of measures with less than .50 power and 71.4% of all measures used had CVs of > .20.

As awareness of the costs of workplace injury and illness continues to grow, there has been an increased demand for effective ergonomic interventions to reduce the prevalence of musculoskeletal disorders (MSDs). The goal of ergonomic interventions is to reduce exposures (mechanical and psychosocial); however there is conflicting evidence about the impact of these interventions as many studies produce inconclusive or conflicting results. In order to provide a clearer picture of the effectiveness of these interventions, we must find out if methodological issues, particularly statistical power, are limiting this research. The purpose of this study was to review and examine factors influencing statistical power in ergonomic intervention papers from five peer reviewed journals in 2008. A standardized review was performed by two reviewers. Twenty eight ergonomic intervention papers met the inclusion criteria and were fully reviewed. Data and trends from the reviewed papers were summarized specifically looking at the research designs used, the outcome measures used, if statistical power was mentioned, if a rationale for sample size was reported, if standardized and un-standardized effect sizes were reported, if confidence intervals were reported, the alpha levels used, if pair-wise correlation values were provided, if mean values and standard deviations were provided for all measures and the location of the studies. Also, the studies were rated based on the outcomes of their intervention into one of three categories (shown to be effective, inconclusive and not shown to be effective). Between these three groupings comparisons of post hoc power, standardized effect sizes, un-standardized effect sizes and coefficients of variation were made. The results indicate that in general, a lack of statistical power is indeed a concern and may be due to the sample sizes used, effect sizes produced, extremely high variability in some of the measures, the lack of attention paid to statistical power during research design and the lack of appropriate statistical reporting guidelines in journals where ergonomic intervention research may be published. A total of 69.6% of studies reviewed had a majority of measures with less than .50 power and 71.4% of all measures used had CVs of > .20.

This dissertation illustrates how to detect the rater centrality effect in a simulation study that approximates data collected in large scale performance assessment settings. It addresses three research questions that: (1) which of several centrality-detection indices are most sensitive to the difference between effect raters and non-effect raters; (2) how accurate (and inaccurate), in terms of Type I error rate and statistical power, each centrality-detection index is in flagging effect raters; and (3) how the features of the data collection design (i.e., the independent variables including the level of centrality strength, the double-scoring rate, and the number of raters and ratees) influence the accuracy of rater classifications by these centrality-detection indices. The results reveal that the measure-residual correlation, the expected-residual correlation, and the standardized deviation of assigned scores perform better than the point-measure correlation. The mean-square fit statistics, traditionally viewed as potential indicators of rater centrality, perform poorly in terms of differentiating central raters from normal raters. Along with the rater slope index, the mean-square fit statistics did not appear to be sensitive to the rater centrality effect. All of these indices provided reasonable protection against Type I errors when all responses were double scored, and that higher statistical power was achieved when responses were 100% double scored in comparison to only 10% being double scored. With a consideration on balancing both Type I error and statistical power, I recommend the measure-residual correlation and the expected-residual correlation for detecting the centrality effect. I suggest using the point-measure correlation only when responses are 100% double scored. The four parameters evaluated in the experimental simulations had different impact on the accuracy of rater classification. The results show that improving the classification accuracy for non-effect raters may come at a cost of reducing the classification accuracy for effect raters. Some simple guidelines for the expected impact of classification accuracy when a higher-order interaction exists summarized from the analyses offer a glimpse of the "pros" and "cons" in adjusting the magnitude of the parameters when we evaluate the impact of the four experimental parameters on the outcomes of rater classification. / Ph. D.

Made available in DSpace on 2016-03-15T19:32:48Z (GMT). No. of bitstreams: 1 Hugo Crespi Junior.pdf: 1552051 bytes, checksum: 34b1ca02581c6a8240a8bd4702d15a71 (MD5) Previous issue date: 2014-02-14 / One of the most important ways to finance consumers in the Brazilian market is the consumer credit offered in store. Provided by independent or captive finance companies, the consumer credit is normally granted or denied without taking into account its effect on the retailer s profitability. Denying credit to finance a high profit margin product is more damaging to the companies profits than if such refusal is about the sale of lower margin product. This suggests that there is an opportunity to improve the profitability in this sales channel. The objective of this research was to check the possibility to increase the Brazilian retailers profitability by introducing the retail products operating margin as an additional parameter of the consumer credit analysis. Simulations using tangents to ROC curves, as proposed by Stein (2005), made possible to confirm, through a new balance of type I and type II errors, that the maximization of corporate earnings occurs when using operating retail margins for determining cutoffs in consumer credit models. / Uma das mais importantes ferramentas de financiamento no varejo brasileiro é o crédito direto ao consumidor oferecido nos estabelecimentos por ocasião da compra. Operado através de financeiras cativas ou parceiras, o crédito é, normalmente, concedido ou negado sem que se leve em conta seu efeito na lucratividade do varejista. Quando se recusa o financiamento para um produto de grande margem de lucro, o efeito sobre os ganhos da empresa é evidentemente maior do que quando essa recusa inibe a venda de produto de menor margem, o que sugere haver ineficácia no processo. Esta pesquisa visou verificar se há espaço para aumentar a rentabilidade no varejo brasileiro, introduzindo a margem operacional nos critérios utilizados para concessão de crédito direto ao consumidor. Utilizando a curva ROC e a abordagem oferecida por Stein (2005), construíram-se simulações em torno de valores reais praticados no mercado, que permitiram confirmar, através de um novo balanceamento dos erros tipo I e tipo II, que ocorre a maximização de ganhos empresarias quando as margens operacionais do varejo são consideradas para a determinação de pontos de corte em modelos de crédito direto ao consumidor.