Analysis of ordinal data to detect population differences.

Gregoire, T. G.; Driver, B. L.

doi:10.1037/0033-2909.101.1.159

“…When the analysis is restricted to studies meeting perceived quality criteria, the initial state of equipoise described above remained. Gregoire & Driver (1987) pointed out that such ordinal data frequently result from questionnaire surveys in behavioral science investigations. Sheu (2002) noted that ordered categorical variables play an important role in psychological studies because precise measurement is not always possible.…”

Section: Simultaneous Analysis Of Rrr Using Hierarchical Random Effecmentioning

confidence: 99%

Vol. 8, No. 2 (Full Issue)

2009

J. Mod. App. Stat. Meth.

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“…It should be noted that their aim was to compare the methods rather than to explore the impact of scale discreteness. Rasmussen (1988) extended (and corrected) the analysis by Gregoire and Driver (1987), and demonstrated that the Type I and Type II error rates were not seriously compromised by the use of discrete data.…”

Section: Introductionmentioning

confidence: 99%

“…However, Bevan et al (1974) did not examine how power or alternative methods were affected by continuity violation. Gregoire and Driver (1987) tested the performance of selected parametric (including the F test) and nonparametric tests of location on the basis of sampling results from simulated Likert-type data and concluded that there was no clear-cut superiority for either type of test. It should be noted that their aim was to compare the methods rather than to explore the impact of scale discreteness.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Continuity Violation on ANOVA and Alternative Methods

Lantz

¹

2013

J. Mod. App. Stat. Meth.

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“…It should be noted that their aim was to compare the methods rather than to explore the impact of scale discreteness. Rasmussen (1988) extended (and corrected) the analysis by Gregoire and Driver (1987), and demonstrated that the Type I and Type II error rates were not seriously compromised by the use of discrete data.The impact of continuity violation on the significance and power of statistical methods commonly used to compare locations across several groups is explored in the one-way ANOVA layout and its robust alternatives, the BrownForsythe test, the Welch test, and the non-parametric Kruskal-Wallis test. The one-way ANOVA is based on the idea that the true means in groups are more likely to be equal if the variation between the groups is small compared to the variation within the groups.…”

mentioning

confidence: 99%

“…However, Bevan et al (1974) did not examine how power or alternative methods were affected by continuity violation. Gregoire and Driver (1987) tested the performance of selected parametric (including the F test) and nonparametric tests of location on the basis of sampling results from simulated Likert-type data and concluded that there was no clear-cut superiority for either type of test. It should be noted that their aim was to compare the methods rather than to explore the impact of scale discreteness.…”

mentioning

confidence: 99%

Vol. 12, No. 2 (Full Issue)

2013

J. Mod. App. Stat. Meth.

0

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EDITORIAL ASSISTANCEJMASM (ISSN 1538−9472, http://digitalcommons.wayne.edu/jmasm) is an independent, open access electronic journal, published biannually in May and November by JMASM Inc. (PO Box 48023, Oak Park, MI, 48237) in collaboration with the Wayne State University Library System. JMASM seeks to publish (1) new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods, (2) the study of nonparametric, robust, permutation, exact, and approximate randomization methods, and (3) applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.Journal correspondence (other than manuscript submissions) and requests for advertising may be forwarded to ea@jmasm.com. See back matter for instructions for authors. Normality is a distributional requirement of classical test statistics. In order for the test statistic to provide valid results leading to sound and reliable conclusions this requirement must be satisfied. In the not too distant past, it was claimed that violations of normality would not likely jeopardize scientific findings (See Hsu & Feldt, 1969; Lunney, 1970). Recent revelations suggest otherwise (See e.g., Micceri, 1989; Keselman, Huberty, Lix et al., 1998;Erceg-Hurn, Wilcox, & Keselman, 2013; Wilcox and Keselman, 2003; Wilcox, 2012a, b). Unfortunately the data obtained in psychological investigations rarely, if ever, meet the requirement of normally distributed data (Micceri, 1989; Wilcox, 2012a, b). Consequently, it could be the case that the results from many of the investigations conducted in psychology provide invalid results. Accordingly, authors recommend that researchers attempt to assess the validity of assuming data are normal in form prior to conducting a test of significance (Erceg-Hurn, et al., 2013; Keselman, et al., 1998). Present evidence suggests that a popular fit-statistic, the Kolmogorov-Smirnov test does a poor job of evaluating whether data are normal. Our investigation based on this statistic and other fit-statistics provides a more favorable picture of preliminary testing for normality. Keywords:Assessing normality, fit statistics, g-and-h non-normal skewed and kurtotic data, contaminated mixed-normal distributions; outlying value(s), Likert scales IntroductionPsychological researchers are often reminded that the validity of their statistical tests and the conclusions derived from these tests depends to a great extent on whether the derivational assumptions of the test procedures have been satisfied (e.g., See Keselman, Huberty, Lix et al., 1998; Wilcox, 2012a, b; Wilcox & KESELMAN ET AL 3 Keselman, 2003). Consequently, though not a common practice, researchers are still reminded about assessing derivational assumptions (See Erce...

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Analysis of ordinal data to detect population differences.

Cited by 50 publications

References 20 publications

Vol. 8, No. 2 (Full Issue)

Vol. 8, No. 2 (Full Issue)

The Impact of Continuity Violation on ANOVA and Alternative Methods

Vol. 12, No. 2 (Full Issue)

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