Chapter+11+Power+final+6262010+done

Chapter+11+Power+final+6262010+done - Chapter 11 2 CHAPTER...

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Chapter 11 CHAPTER 11: META-ANALYSIS AND POWER ANALYSIS: EFFECT SIZE, TYPE 1 ERROR AND ALPHA, TYPE 2 ERROR AND BETA, AND DETERMINING n 11.1 PREVIEW AND INTRODUCTION To market a new drug for depression or schizophrenia, its developers must show that it is safe and efficacious. From the point of view of the pharmaceutical company, it would be best if all these experiments yielded significant results, showing the drug more effective than placebo and/or competing products. Nonsignificant findings are not, in the long run, good for the company’s balance sheet. So, pone wants to run a big enough study to find an effect, if it is there. But neither is it good for the balance sheet to run massive clinical trials with thousands of research participants. While studies with larger numbers of research participants are more sensitive and more likely to provide significant results, they are also more expensive. So even in the industrial sector, where money and resources are often more easily available than they are for academic research, there are limits on the size of experiments. One use of power analysis involves designating how many research participants to use in a specific experiment. Power analysis has other uses as well. For example, it can help us interpret a study that yields nonsignificant results, helping us decide whether to do a larger, more sensitive study or to abandon a line of research. Another use of the principles underlying power analysis involves determining how much difference an intervention makes. Unless the null hypothesis is absolutely true, a rare event in the biomedical sciences, if you run a large enough study you will get statistically significant results. But have you changed anything? Hypothesis testing statistics can tell us whether an experimental group differs from its control. But what about the individual? Which interventions make a big difference for many individuals and which do not? Based on the work of Jacob Cohen, whose most well known book underlies this chapter 1 , we calculate an effect size by simply dividing the difference between the means of each treatment and control groups by the estimated standard deviation, the square root of MS W . By placing that difference on a normal curve, we can determine how the typical person in the active treatment group compares to the average person in the control group. (A similar calculation can be performed on studies using Pearson’s correlation coefficient.) Additionally, one can compute an average effect size across all relevant studies of an intervention. This technique is called meta- analysis. Articles reviewing areas of research for such journals as Psychological Bulletin , the premier venue for review articles in Psychology, now routinely use meta-analysis as their basic method of evaluating the effect of an independent variable. We will begin by calculating effect sizes and then use effect sizes to perform meta-analyses. Then we will look at Type 1 and Type 2 error and learn how to calculate the number of participants needed for a good study. Definition 11.1: Power analysis
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This note was uploaded on 11/17/2011 for the course PSYCHOLOGY 830:452 taught by Professor Staff during the Fall '11 term at Rutgers.

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Chapter+11+Power+final+6262010+done - Chapter 11 2 CHAPTER...

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