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Pooling correlation matrices corrected for selection bias: Implications for meta-analysis
Title
Pooling correlation matrices corrected for selection bias: Implications for meta-analysis.
Creator
Matthews, Kenneth, Sivo, Stephen, Bai, Haiyan, Hahs-Vaughn, Debbie, Butler, Malcolm, University of Central Florida
Abstract / Description
Selection effects systematically attenuate correlations and must be considered when performing meta-analyses. No research domain is immune to selection effects, evident whenever self-selection or attrition take place. In educational research, selection effects are unavoidable in studies of postsecondary admissions, placement testing, or teacher selection. While methods to correct for selection bias are well documented for univariate meta-analyses, they have gone unexamined in multivariate...
Show moreSelection effects systematically attenuate correlations and must be considered when performing meta-analyses. No research domain is immune to selection effects, evident whenever self-selection or attrition take place. In educational research, selection effects are unavoidable in studies of postsecondary admissions, placement testing, or teacher selection. While methods to correct for selection bias are well documented for univariate meta-analyses, they have gone unexamined in multivariate meta-analyses, which synthesize more than one correlation from each study (i.e., a correlation matrix). Multivariate meta-analyses of correlations provide opportunities to explore complex relationships and correcting for selection effects improves the summary effect estimates. I used Monte Carlo simulations to test two methods of correcting selection effects and evaluate a method for pooling the corrected matrices. First, I examined the performance of Thorndike's corrections (for both explicit and incidental selection) and Lawley's multivariate correction for selection on correlation matrices when explicit selection takes place on a single variable. Simulation conditions included a wide range of selection ratios, samples sizes, and population correlations. The results indicated that univariate and multivariate correction methods perform equivalently. I provide practical guidelines for choosing between the two methods. In a second Monte Carlo simulation, I examined the confidence interval coverage rates of a Robust Variance Estimation (RVE) procedure when it is used to pool correlation matrices corrected for selection effects under a random-effects model. The RVE procedure empirically estimates the standard errors of the corrected correlations and has the advantage of having no distributional assumptions. Simulation conditions included tau-squared ratio, within-study sample size, number of studies, and selection ratio. The results were mixed, with RVE performing well under higher selection ratios and larger unrestricted sample sizes. RVE performed consistently across values of tau-squared. I recommend applications of the results, especially for educational research, and opportunities for future research.
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Date Issued
2019
Identifier
CFE0007680, ucf:52483
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007680
IMPROVING PROJECT MANAGEMENT WITH SIMULATION AND COMPLETION DISTRIBUTION FUNCTIONS
Title
IMPROVING PROJECT MANAGEMENT WITH SIMULATION AND COMPLETION DISTRIBUTION FUNCTIONS.
Creator
Cates, Grant, Mollaghasemi, Mansooreh, University of Central Florida
Abstract / Description
Despite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. Uncertainty has been identified as a contributing factor in late projects. This uncertainty resides in activity duration estimates, unplanned upsetting events, and the potential unavailability of critical resources. This research developed a comprehensive simulation based methodology for conducting...
Show moreDespite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. Uncertainty has been identified as a contributing factor in late projects. This uncertainty resides in activity duration estimates, unplanned upsetting events, and the potential unavailability of critical resources. This research developed a comprehensive simulation based methodology for conducting quantitative project completion-time risk assessments. The methodology enables project stakeholders to visualize uncertainty or risk, i.e. the likelihood of their project completing late and the magnitude of the lateness, by providing them with a completion time distribution function of their projects. Discrete event simulation is used to determine a project's completion distribution function. The project simulation is populated with both deterministic and stochastic elements. Deterministic inputs include planned activities and resource requirements. Stochastic inputs include activity duration growth distributions, probabilities for unplanned upsetting events, and other dynamic constraints upon project activities. Stochastic inputs are based upon past data from similar projects. The time for an entity to complete the simulation network, subject to both the deterministic and stochastic factors, represents the time to complete the project. Multiple replications of the simulation are run to create the completion distribution function. The methodology was demonstrated to be effective for the on-going project to assemble the International Space Station. Approximately $500 million per month is being spent on this project, which is scheduled to complete by 2010. Project stakeholders participated in determining and managing completion distribution functions. The first result was improved project completion risk awareness. Secondly, mitigation options were analyzed to improve project completion performance and reduce total project cost.
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Date Issued
2004
Identifier
CFE0000209, ucf:46243
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000209
A RELATIONSHIP STUDY OF STUDENT SATISFACTION WITH LEARNING ONLINE AND COGNITIVE LOAD
Title
A RELATIONSHIP STUDY OF STUDENT SATISFACTION WITH LEARNING ONLINE AND COGNITIVE LOAD.
Creator
Bradford, George, Dziuban, Charles, University of Central Florida
Abstract / Description
This study sought to explore if a relationship exists between cognitive load and student satisfaction with learning online. The study separates academic performance (a.k.a., ÂÂ"learningÂÂ") from cognitive load and satisfaction to better distinguish influences on cognition (from cognitive load) and motivation (from satisfaction). Considerations that remain critical to the field of instructional design, as they apply to learning online, were described and used to guide a review of the...
Show moreThis study sought to explore if a relationship exists between cognitive load and student satisfaction with learning online. The study separates academic performance (a.k.a., ÂÂ"learningÂÂ") from cognitive load and satisfaction to better distinguish influences on cognition (from cognitive load) and motivation (from satisfaction). Considerations that remain critical to the field of instructional design, as they apply to learning online, were described and used to guide a review of the literature to find directions to fulfill the goal of this study. A survey was conducted and 1,401 students responded to an instrument that contained 24 items. Multiple analysis techniques found a positive, moderate, and significant (p < .01) correlation between cognitive load and satisfaction. Most importantly, the results found that approximately 25% of the variance in student satisfaction with learning online can be explained by cognitive load. New constructs emerged from a Principal Components Analysis that suggest a refined view of student perspectives and potential improvement to guide instructional design. Further, a correlation, even a moderate one, has not previously been found between cognitive load and satisfaction. The significance of this finding presents new opportunities to study and improve online instruction. Multiple opportunities for future research are briefly discussed and guidelines for developing online course designs using interpretations of the emerged factors are made.
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Date Issued
2010
Identifier
CFE0003164, ucf:48599
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003164
COUNTER-TERRORISM: WHEN DO STATES ADOPT NEW ANTI-TERROR LEGISLATION?
Title
COUNTER-TERRORISM: WHEN DO STATES ADOPT NEW ANTI-TERROR LEGISLATION?.
Creator
Clesca, Princelee, Dolan, Thomas, University of Central Florida
Abstract / Description
The intent of this thesis is to research the anti-terror legislation of 15 countries and the history of terrorist incidents within those countries. Both the anti-terror legislation and the history of terrorist incidents will be researched within the time period of 1980 to 2009, a 30 year span. This thesis will seek to establish a relationship between the occurrence of terrorist events and when states change their anti-terror legislation. Legislation enacted can vary greatly. Common changes in...
Show moreThe intent of this thesis is to research the anti-terror legislation of 15 countries and the history of terrorist incidents within those countries. Both the anti-terror legislation and the history of terrorist incidents will be researched within the time period of 1980 to 2009, a 30 year span. This thesis will seek to establish a relationship between the occurrence of terrorist events and when states change their anti-terror legislation. Legislation enacted can vary greatly. Common changes in legislation seek to undercut the financing of terrorist organizations, criminalize behaviors, or empower state surveillance capabilities. A quantitative analysis will be performed to establish a relationship between terrorist attacks and legislative changes. A qualitative discussion will follow to analyze specific anti-terror legislation passed by states in response to terrorist events.
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Date Issued
2015
Identifier
CFH0004851, ucf:45451
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH0004851