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Direct linkQu, Wen; Liu, Haiyan; Zhang, Zhiyong – Grantee Submission, 2020
In social and behavioral sciences, data are typically not normally distributed, which can invalidate hypothesis testing and lead to unreliable results when being analyzed by methods developed for normal data. The existing methods of generating multivariate non-normal data typically create data according to specific univariate marginal measures…
Descriptors: Social Science Research, Multivariate Analysis, Statistical Distributions, Monte Carlo Methods
Qu, Wen; Liu, Haiyan; Zhang, Zhiyong – Grantee Submission, 2020
In social and behavioral sciences, data are typically not normally distributed, which can invalidate hypothesis testing and lead to unreliable results when being analyzed by methods developed for normal data. The existing methods of generating multivariate non-normal data typically create data according to specific univariate marginal measures…
Descriptors: Social Science Research, Statistical Distributions, Multivariate Analysis, Monte Carlo Methods
Tong, Xin; Zhang, Zhiyong – Grantee Submission, 2017
Growth curve models are widely used for investigating growth and change phenomena. Many studies in social and behavioral sciences have demonstrated that data without any outlying observation are rather an exception, especially for data collected longitudinally. Ignoring the existence of outlying observations may lead to inaccurate or even…
Descriptors: Observation, Models, Statistical Distributions, Monte Carlo Methods
Cain, Meghan K.; Zhang, Zhiyong; Yuan, Ke-Hai – Grantee Submission, 2017
Nonnormality of univariate data has been extensively examined previously (Blanca et al., 2013; Micceri, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of…
Descriptors: Multivariate Analysis, Probability, Statistical Distributions, Psychological Studies
