Underlying Distributions in Loglinear Models of Discrete Data

The implications of loglinear models based on underlying uniform and binomial distribution are assessed with respect to modeling eight distributions. Regarding statistical selection of the loglinear models’ parameterizations, results indicate that better fitting models are obtained when the distribution being modeled is dissimilar to the underlying distribution used. For loglinear models with predetermined numbers of parameters, results suggest that better fitting models can be obtained when the distribution being modeled is similar to the underlying distribution

A preferred vision for administering elementary and secondary schools : a reflective essay

As I think about the question of becoming an administrator, all sorts of things run through my mind. Things such as, am I really cut out to do this job ? Do I have what it takes to be an administrator? Will I fail or will I succeed? Will I have the support of my staff? Will I be able to perform the paper-pencil tasks of an administrator? Will I be able to handle the school\u27s budget ? I mean the questions just go on and on. So, as I look at the driving forces behind my decision to become an administrator, I feel very confident with the decision that I have made. I would like to share some of those driving forces with you in this reflective research paper

An Evaluation of Standard, Alternative, and Robust Slope Test Strategies

The robustness and power of nine strategies for testing the differences between two groups’ regression slopes under nonnormality and residual variance heterogeneity are compared. The results showed that three most robust slope test strategies were the combination of the trimmed and Winsorized slopes with the James second order test, the combination of Theil-Sen with James, and Theil-Sen with percentile bootstrapping. The slope tests based on Theil-Sen slopes were more powerful than those based on trimmed and Winsorized slopes

Testing For Aptitude-Treatment Interactions In Analysis Of Covariance And Randomized Block Designs Under Assumption Violations

This study compared the robustness of two analysis strategies designed to detect Aptitude-Treatment Interactions to two of their similarly-held assumptions, normality and residual variance homogeneity. The analysis strategies were the test of slope differences in analysis of covariance and the test of the Block-by- Treatment interaction in randomized block analysis of variance. With equal sample sizes in the treatment groups the results showed that residual variance heterogeneity has little effect on either strategy but nonnormality makes the test of slope differences liberal and the test of the Block-by-Treatment interaction conservative. With unequal sample sizes in the treatment groups the often-reported sample size-variance heterogeneity pairing is problematic for both strategies. The findings suggest that the randomized block strategy can be characterized as an overly-conservative alternative to the test of slope differences with respect to robustness

The Influence of Data Generation on Simulation Study Results: Tests of Mean Differences

Type I error and power of the standard independent samples t-test were compared with the trimmed and Winsorized t-test with respect to continuous distributions and various discrete distributions known to occur in applied data. The continuous and discrete distributions were generated with similar levels of skew and kurtosis but the discrete distributions had a variety of structural features not reflected in the continuous distributions. The results showed that the Type I error rates of the t-tests were not seriously affected, but the power rate of the trimmed and Winsorized t-test varied greatly across the considered distributions

Type I Error Rates For Rank-Based Tests Of Homogeneity Of Slopes

The purpose of this study was to explicate two issues concerning the standard and rank based test of homogeneity of slopes. Two alternative ranking methods intended to address nonnormality and additive treatment effect patterns were developed and compared in terms of their ability to control Type I error. The results replicated previous findings of inflated Type I error rates with leptokurtic curves and with rank based tests with some patterns of additive treatment effects. The new nonparametric procedures generally control Type I error although they were slightly inflated with skewed distributions

Banner News

https://openspace.dmacc.edu/banner_news/1165/thumbnail.jp

An XML based standard to enable bulk project data transfer between heterogeneous systems

The objective of the work reported in this paper was the development of an XML based standard which would enable project data, such as contacts, drawings, documentation, instructions and emails, to be transferred between collaborative systems provided by different software vendors and used by construction organisations. The primary aim being to create a transfer mechanism which would allow project data transfer without modification of existing collaborative system deployed by vendors. This paper discusses the business and technological needs for such a transfer capability between collaborative systems, by examining the current use of these tools and related problems encountered by clients. It sets out the main components that underlie the majority of construction specific collaborative systems which forms the basis of the generic collaborative system model which has been developed, contrasting this with previous data exchange efforts. This paper shows how the XML Schema was developed and the procedures undertaken to ensure that it could be utilised by the maximum possible set of vendors. It sets out the best practice procedure for implementations by vendor organisations and the required testing to confirm a successful transfer. The paper also highlights some of the practical problems that were encountered when transferring projects between heterogeneous systems during the project and in subsequent deployments of the solution. Finally, the paper concludes with methods of taking the work forward as a foundation to allow for greater interoperability between systems in the future