[{"key":"dc.contributor.author","value":"Fouladi, Rachel Tanya","language":null},{"key":"dc.date.accessioned","value":"2009-03-20T18:41:53Z","language":null},{"key":"dc.date.available","value":"2009-03-20T18:41:53Z","language":null},{"key":"dc.date.issued","value":"1996","language":null},{"key":"dc.identifier.uri","value":"http:\/\/hdl.handle.net\/2429\/6275","language":null},{"key":"dc.description.abstract","value":"A wide array of procedures have been proposed for testing correlation pattern. Many, but\r\nnot all, of the statistical techniques available for testing correlation pattern are derived under the\r\ndistributional condition of multivariate normality which does not always hold in the behavioral,\r\neducational and social sciences. Though a number of studies have explored the performance of\r\nstructure analysis techniques under conditions of multivariate nonnormality, very little is known\r\nabout the actual performance of many correlation structure analysis techniques under conditions\r\nof multivariate nonnormality. In addition, very little is known about the actual concurrent\r\nperformance of tests of multivariate normality.\r\nThe present investigation ascertains how tests of correlation pattern hypotheses and\r\nindicators of multivariate normality perform when data are from multivariate normal or nonnormal\r\nparent populations. This paper reviews and examines, using a Monte Carlo simulation study, the\r\nconcurrent performance of different approaches to testing (1) correlation pattern hypotheses,\r\nincluding, (i) normal theory (NT) and asymptotically distribution free (ADF) covariance structure\r\nanalysis techniques, (ii) NT and ADF correlation structure analysis techniques, (iii) correlation\r\npattern specific techniques; (2) the distributional assumption of multivariate normality using\r\nstatistics based on Mardia's measures of multivariate skewness and kurtosis. This paper also\r\nexamines the performance characteristics of test procedures based on joint consideration of tests\r\nof multivariate normality and structure analysis techniques. Performance of the covariance and\r\ncorrelation structure analysis techniques, tests of multivariate normality, and joint test procedures\r\nwas assessed across different types of correlation pattern models, numbers of variables, levels of\r\nskew and kurtosis, sample sizes, and nominal alpha levels, on the primary Neyman-Pearson\r\ncriterion for an optimal test, according to which an optimal procedure (1) controls\r\nexperimentwise Type I error rate at or below the nominal level, (2) maximizes power.","language":"en"},{"key":"dc.format.extent","value":"29618159 bytes","language":null},{"key":"dc.format.mimetype","value":"application\/pdf","language":null},{"key":"dc.language.iso","value":"eng","language":"en"},{"key":"dc.publisher","value":"University of British Columbia","language":null},{"key":"dc.rights","value":"For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https:\/\/open.library.ubc.ca\/terms_of_use.","language":null},{"key":"dc.title","value":"A study of procedures to examine correlation pattern hypotheses under conditions of multivariate normality and nonnormality","language":"en"},{"key":"dc.type","value":"Text","language":null},{"key":"dc.degree.name","value":"Doctor of Philosophy - PhD","language":"en"},{"key":"dc.degree.discipline","value":"Psychology","language":"en"},{"key":"dc.degree.grantor","value":"University of British Columbia","language":null},{"key":"dc.date.graduation","value":"1996-11","language":"en"},{"key":"dc.type.text","value":"Thesis\/Dissertation","language":null},{"key":"dc.description.affiliation","value":"Arts, Faculty of","language":null},{"key":"dc.description.affiliation","value":"Psychology, Department of","language":null},{"key":"dc.degree.campus","value":"UBCV","language":"en"},{"key":"dc.description.scholarlevel","value":"Graduate","language":"en"}]