A Monte Carlo Study of the Robustness and Power of Analysis of Covariance Using Rank Transformation to Violation of Normality with Restricted Score Ranges for Selected Group Sizes

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The study seeks to determine the robustness and power of parametric analysis of covariance and analysis of covariance using rank transformation to violation of the assumption of normality. The study employs a Monte Carlo simulation procedure with varying conditions of population distribution, group size, equality of group size, scale length, regression slope, and Y-intercept. The procedure was performed on raw data and ranked data with untied ranks and tied ranks.

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v, 301 leaves : ill.

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Wongla, Ruangdet December 1984.

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  • Wongla, Ruangdet

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The study seeks to determine the robustness and power of parametric analysis of covariance and analysis of covariance using rank transformation to violation of the assumption of normality. The study employs a Monte Carlo simulation procedure with varying conditions of population distribution, group size, equality of group size, scale length, regression slope, and Y-intercept. The procedure was performed on raw data and ranked data with untied ranks and tied ranks.

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v, 301 leaves : ill.

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UNT Theses and Dissertations

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  • December 1984

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  • Aug. 22, 2014, 6 p.m.

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  • Jan. 10, 2018, 2:23 p.m.

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Wongla, Ruangdet. A Monte Carlo Study of the Robustness and Power of Analysis of Covariance Using Rank Transformation to Violation of Normality with Restricted Score Ranges for Selected Group Sizes, dissertation, December 1984; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc332218/: accessed August 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .