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Kang, Yoonjeong; Harring, Jeffrey R.; Li, Ming – Journal of Experimental Education, 2015
The authors performed a Monte Carlo simulation to empirically investigate the robustness and power of 4 methods in testing mean differences for 2 independent groups under conditions in which 2 populations may not demonstrate the same pattern of nonnormality. The approaches considered were the t test, Wilcoxon rank-sum test, Welch-James test with…
Descriptors: Comparative Analysis, Monte Carlo Methods, Statistical Analysis, Robustness (Statistics)
Williams, Joseph J.; Griffiths, Thomas L. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
Errors in detecting randomness are often explained in terms of biases and misconceptions. We propose and provide evidence for an account that characterizes the contribution of the inherent statistical difficulty of the task. Our account is based on a Bayesian statistical analysis, focusing on the fact that a random process is a special case of…
Descriptors: Experimental Psychology, Bias, Misconceptions, Statistical Analysis
Peer reviewedKinnucan, Mark T.; Wolfram, Dietmar – Information Processing and Management, 1990
Describes a technique for statistically comparing bibliometric models and illustrates its use with two examples using Lotka's hypothesis of author productivity and one example using library circulation frequencies. Topics discussed include nested statistical models, analysis of variance, regression, log-linear models, and the likelihood ratio…
Descriptors: Analysis of Variance, Bibliometrics, Chi Square, Comparative Analysis

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