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One of the challenges that is encountered in the analysis of program outcome results is having signficant power. You may recall that statistical power is the part of statistical conclusion validity in that the null hypothesis should be rejected, but in practice, we have accepted the null hypothesis. In outcome evaluation, most often, this translates in the intervention appearing to not be effective, when in fact, it is. Statistical power may be influenced by the sample size, the alpha level set to indicate that a result is significant, the robustness of the result, referred to as the effect size, and in some cases, the actual statistical procedure being used.
For this discussion select one of the scenarios below. Speculate on what would occur to statistical power given the recommended change in the scenario. Identify any practical challenges that making the change could incur. This could include demand on resources to implement the change, threat to statistical conclusion validity or even ethical concerns if relevant. There could be others that you think of that may apply.
Scenario A: Upon completion of an evaluation of the Templeton program the results were surprisingly “not significant” in terms of the reported improvement in client well-being. One issue that was noted that for the evaluation data was collected only on a sample of the participants during the evaluation cycle. It was recommended that in the next evaluation cycle all participants to the program be included in the evaluation.
Scenario B: In developing an evaluation plan for the upcoming cycle, the administration and evaluation team met to discuss the plan. Typical for most studies and evaluations the significance value of .05 was used to indicate that a result was statistically signficant when the data is analyzed with inferential statistics. During the meeting, one administrator noted that the program is unique, and after all “this isn’t data to be used in rocket science or of a life and death matter”—and recommended that a new significance value of .10 be used to indicate clinical significance.
Scenario C: In developing an evaluation plan for the upcoming cycle, the administration and evaluation team met to discuss the plan. Even though the previous evaluation’s results indicated that the results were not statistically significant, the administration noted that satisfaction surveys and their own discussions with participants indicated that the program was impactful in a positive way. And in fact, they argued that if it wasn’t for other factors in the process the actual “effect” of the program is larger than anticipated. In “statistics speak” they were indicating that the effect size for their program results should be “medium” as opposed to “small to medium.” They are suggesting a larger effect size
Use one reference from readings.
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