Family Studies Center Methods Workshop
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1 oncentral Family Studies Center Methods Workshop Temple University ovember 14, 2014 (Temple University) ovember 14, / 47
2 oncentral Understand the role of statistical power analysis in family studies research Introduce concept of statistical power Develop intuitions about factors affecting statistical power Learn applications of power analysis when sample size is fixed (Temple University) ovember 14, / 47
3 oncentral Research is difficult, time-consuming, and expensive to conduct Before we conduct a study, we want to be assured that we have a reasonable change of finding an effect if, in fact, one exists We must recruit sufficient numbers of subjects into our study We must also consider efforts (and potential for risk) of study participants early all studies entail at least some risk for participants (even after data are collected!) We must not recruit too many research subjects into our study (Temple University) ovember 14, / 47
4 oncentral When number of potential subjects is limited, need to identify design that gives us the best chance of answering our question When number of subjects is fixed in advance, need to know how big an effect we can detect in our data with desired probability (Temple University) ovember 14, / 47
5 oncentral Ways to be Wrong in True State of Affairs Decision H 0 True H 0 False AcceptH 0 Correct(1 ) β RejectH 0 Correct(1 β) (Temple University) ovember 14, / 47
6 oncentral Ways to be Wrong in True State of Affairs Decision o Effect Effect o Effect Correct(1 ) Type II Error Effect Type I Error Correct(1 β) (Temple University) ovember 14, / 47
7 oncentral Central and on-central oncentral (Temple University) ovember 14, / 47
8 oncentral oncentral Central and on-central Central distributions apply when the null hypothesis (H 0 ) is true They are standardized on-central distributions apply when H 0 is false They are not standardized on-centrality Parameter (CP, λ) reflects degree to which (H 0 ) is false on-centrality parameter can affect both location and shape of distribution. (Temple University) ovember 14, / 47
9 oncentral oncentral Central and on-central Central χ 2 distribution with df degrees of freedom can be generated by squaring and summing df different random normal variates with means of 0 and variances of 1 on-central χ 2 distribution with df degrees of freedom and CP = λ can be generated by squaring and summing df different random normal variates with means of λ µ = df (Temple University) ovember 14, / 47
10 oncentral Four variables are important for power analysis, (1 β) Effect Size, (, λ) Knowing any 3, solve for fourth Two other factors include choice of H 0 and Pr(H 0 is false) (Temple University) ovember 14, / 47
11 oncentral Conventions = (Some applications, may define minimum acceptable standards or heuristics for overall sample size, distinct from power conventions) Effect Size analyses are invaluable a priori, not so useful a posteriori ( (Temple University) ovember 14, / 47
12 Visualizing oncentral (Temple University) ovember 14, / 47
13 Visualizing oncentral (Temple University) ovember 14, / 47
14 Visualizing oncentral (Temple University) ovember 14, / 47
15 Visualizing oncentral (Temple University) ovember 14, / 47
16 Effects of Increasing Alpha oncentral (Temple University) ovember 14, / 47
17 Effects of Increasing Alpha oncentral (Temple University) ovember 14, / 47
18 Effects of Increasing Alpha oncentral (Temple University) ovember 14, / 47
19 Effects of Increasing Alpha oncentral (Temple University) ovember 14, / 47
20 Effects of Increasing Alpha oncentral (Temple University) ovember 14, / 47
21 Effects of Increasing Alpha oncentral (Temple University) ovember 14, / 47
22 Effects of Increasing Effect Size oncentral (Temple University) ovember 14, / 47
23 Effects of Increasing Effect Size oncentral (Temple University) ovember 14, / 47
24 Effects of Increasing Effect Size oncentral (Temple University) ovember 14, / 47
25 Effects of Increasing Effect Size oncentral (Temple University) ovember 14, / 47
26 Effects of Increasing Effect Size oncentral (Temple University) ovember 14, / 47
27 Discerning Patterns: Large Clockwise: (one, Small, Large, Moderate, = 1000) oncentral (Temple University) ovember 14, / 47
28 Discerning Patterns: Small Clockwise: (one, Small, Large, Moderate, = 10) oncentral (Temple University) ovember 14, / 47
29 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
30 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
31 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
32 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
33 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
34 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
35 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
36 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
37 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
38 Effects of Increasing or Decreasing /SE oncentral (Temple University) ovember 14, / 47
39 oncentral Summing Up of hypothesis test with significance level is probability we reject null hypothesis when the alternative is true is probability that data gathered will be sufficient to reject null hypothesis when it is false is of critical importance (Temple University) ovember 14, / 47
40 oncentral Summing Up Uses of power A priori: When designing study, select a sample size large enough to detect and effect of magnitude you believe is meaningful A posteriori: When test finds no significant difference/association, was there enough power to detect effect of meaningful magnitude? (Too little, too late. Can still be used to properly power next study.) See power/ for more. (Temple University) ovember 14, / 47
41 oncentral of a Test Significance testing is a balancing act Chance of making Type I error Chance β of making Type II error Reducing increases β, and thus reduces the power of a test. It might be tempting to emphasize greater power (the more the better) With too much power statistical significance may be clinically inconsequential A Type II error is not definitive since a failure to reject the null hypothesis does not imply that the null hypothesis is correct Since H 0 is either always true or false, we are only in danger of making one kind of error or the other (but we have no idea which one) (Temple University) ovember 14, / 47
42 oncentral Affecting Size of effect an important factor in determining power. Higher probability of detecting larger effects More conservative significance levels (lower ) yield lower power. Less power with =.01 than with =.05. Increasing the sample size decreases the spread of the sampling distribution and increases power, but there is a trade-off between gain in power and the time/expense of testing a larger sample Larger variance ( σ 2) implies a larger spread of the sampling distribution, (σ/ ). The larger the variance, the lower the power. is partly a property of the population, but can be reduced through careful study design. (Temple University) ovember 14, / 47
43 oncentral with Fixed Sample Size Many times, is fixed, either by resource constraints or with secondary data analysis In this context, power analysis serves a different function Minimum detectable effect (MDE) What is the smallest effect size I can detect with power = (1 β), sample size =, and alpha =? (Stata Users: db power) (Temple University) ovember 14, / 47
44 oncentral with Fixed Sample Size Accuracy in parameter estimation (AIPE: Bracketing effect sizes (half-width, w). For sample size, find range that give p% chance that the estimated interval will be 2 w The AIPE paradigm is a framework for managing width of confidence interval, independent of effect size) (Stata Users: findit aipe) (Temple University) ovember 14, / 47
45 oncentral Additional for (Books) (Too) Simple Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), %201992%20sample%20size.pdf Just Sufficient Cohen, J. (2013). power analysis for the behavioral sciences. Routledge Academic. Cohen,%201988%20(%20,% ).pdf (Temple University) ovember 14, / 47
46 oncentral Additional for (Books) More Contemporary Murphy, K. R., Myors, B., & Wolach, A. H. (2009). power analysis: A simple and general model for traditional and modern hypothesis tests. Routledge. Extensions Davey, A. (2009). power analysis with missing data: A structural equation modeling approach. Routledge. Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), RelatedArticles/Article 097.pdf (Temple University) ovember 14, / 47
47 oncentral Additional for (Software) G* R Package pwr R Package powermediation powermediation/powermediation.pdf (Temple University) ovember 14, / 47
POLS 205 Political Science as a Social Science. Making Inferences from Samples
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