The A Priori Procedure for estimating the mean in both log-normal and gamma populations and robustness for assumption violations
Although the literature on the a priori procedure, designed to help researchers determine the sample sizes they should use in their substantive research, is expanding rapidly, there are two important limitations. First, there is a need to expand to new popular distributions, log-normal and gamma dis...
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Published in | Methodology Vol. 18; no. 1; pp. 24 - 43 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
PsychOpen GOLD/ Leibniz Institute for Psychology
31.03.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Although the literature on the a priori procedure, designed to help researchers determine the sample sizes they should use in their substantive research, is expanding rapidly, there are two important limitations. First, there is a need to expand to new popular distributions, log-normal and gamma distributions, and the present work provides those expansions. Second, there is a need to test the consequences of wrong distributional assumptions; for example, assuming a log-normal distribution when the population follows a gamma distribution, or the reverse. The present work addresses the limitations with respect to estimating population means, and it includes computer simulations, links to free and user-friendly programs that researchers can utilize for their own research, and two examples involving real data sets for illustrations of our main results. |
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ISSN: | 1614-2241 1614-2241 |
DOI: | 10.5964/meth.7321 |