Get Beyond Significance Testing: Reforming Data Analysis Methods PDF

By Rex B. Kline

ISBN-10: 1591471184

ISBN-13: 9781591471189

Deals integrative and transparent displays concerning the obstacles of statistical checks and experiences substitute tools of information research, reminiscent of influence dimension estimation (at either the crowd and case degrees) and period estimation.

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These issues are elaborated next. Now let us consider confidence intervals for contrasts between dependent means. Below we use the symbol MD to refer the average difference score when two dependent samples are compared. A difference score is computed as D = Xj - %2 for each of the n cases in a repeated-measures design or for each of the n pairs of cases in a matched-groups design. ) If D = 0, there is no difference; any other value indicates a higher score in one condition than in the other. The average of all the difference scores equals the dependent mean contrast, or MD = MI — M2.

Or on which hypothesis tests they occurred. 05 level in the same sample. 64 In other words, the risk of a making a Type I error across the whole set of 20 tests is 64%, given the assumptions just stated. There are two basic ways to control experimentwise Type I error: Reduce the number of tests or lower (X for each one. The former can be realized by honing one's questions down to the most substantively meaningful (prioritize the hypotheses). This also means that "fishing expeditions" where essentially every effect is tested are to be avoided.

The same author also makes the point that statistical tests cannot be conducted without a null hypothesis, but no hypothesis is required for a confidence interval. These ideas are elaborated in the next chapter. F TESTS FOR MEANS The £ statistic compares only two means. Such contrasts are focused comparisons, and they address specific questions, such as whether treatment is superior to control. All focused comparisons are single-df, directional effects. The F statistic can also analyze focused comparisons—recall that t = F for a mean contrast.

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Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research by Rex B. Kline


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