# Post-hoc comparisons & two-way analysis of variance. Two-way ANOVA, II. Post-hoc testing for main effects. Post-hoc testing 9.

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1 Two-way ANOVA, II Post-hoc comparisons & two-way analysis of variance 9.7 4/9/4 Post-hoc testing As before, you can perform post-hoc tests whenever there s a significant F But don t bother if it s a main effect and has only two levels you already know the answer We ll just talk about the Tukey s HSD procedure Requires that the n s in all levels of a factor are equal obt Post-hoc testing for main effects This is just like post-hoc testing for the oneway ANOVA

2 Post-hoc testing for main effects is just like what we did for one-way ANOVA MS HSD q wn α = α is the Type I error rate (.5). α n q α MS wn n Is a value from a table of the studentized range statistic based on alpha, df W, and k, the number of levels in the factor you are testing Is the mean square within groups. Is the number of people in each group. I.E. how many numbers did you average to get each mean you are comparing? Our example from last time What effect do a workbook and consumption have on exam performance? Both main effects and the interaction were significant Factor A (the workbook) had only two levels. post-hoc testing required. The workbook helps. Factor B (the ) had three levels. We need to do post-hoc testing. Numbers from our example last time wn = 5.56, 3, MS n = 6 45, 5, 85 q k is a function of df wn and k 3, 4, dfwn = k = 3 So, from the table, qk = 3.77 for α=.5, 45, 55 4, 6, 65 9, 85, 75 Σx = 8 n B = 6 Σx = 345 n B = 6 Σx = 34 n B3 = 6 HSD for this example HSD = qk sqrt(ms wn /n) = 3.77 sqrt(5.56/6) =.7 Differences in means: Level : cups Level : cup Level 3: cups m =3 m = 57.5 m 3 = cups of differ significantly from both and cups of

3 Post-hoc testing for the interaction Involves comparing cell means But we don t compare every possible pair of cell means m = m = 6 m = 3 A) m = 4 Confounded & unconfounded comparisons m = m = 6 m = 3 A) m = 4 Confounded comparison, because the cells differ along more than one factor. If there s a difference, what s the explanation? Is it because of factor A or B? We can t tell, because there s a confound. Confounded & unconfounded comparisons m = m = 6 m = 3 () () A) m = 4 Unconfounded comparisons. The cells differ only in one factor. We can test these with post-hoc tests. Tukey s HSD for interactions. Compute HSD = qk sqrt(ms wn /n) Before, qk was a function of dfwn and k, the number of levels in the factor of interest = # of means being compared For the interaction, we use an adjusted k to account for the actual number of unconfounded comparisons (as opposed to all comparisons of cell means, some of which are confounded). Compare with unconfounded differences in means 3

4 Table from the handout TABLE Values of Adjusted k Design of Number of Cell Adjusted Study Means in Study Value of k x 4 3 x x x x x x 5 Figure by MIT OCW. What s going on here? k is sort of short hand for the number of means you d like to compare In one-way ANOVA or main effects analysis, e.g: 3 means -> + = 3 comparisons 5 means -> = comparisons What s going on here? Two-way interactions x -> 4 comparisons, k=3 is closest te t all stat books bother with this adjusted value of k many just use k = # cell means x3 -> 9 comparisons, k=5 is closest 4

5 Back to our example We had a 3x design, so the adjusted value of k = 5. df wn =. So q k = 4.5 for α=.5 MSwn = 5.56, n = # in each mean = 3, so HSD = 4.5 sqrt(5.56/3) = What unconfounded comparisons lead to differences larger than 37.33? 4 3 A) m = m = 4 5 m = m = 3 A) m = m = 4 m = 6 m = 3 m = m = 6 m = 3 m=36.67 A) m = 4 m=59.44 m=3 m=57.5 m=56.7 m = m = 6 m = 3 m=36.67 A) m = 4 m=59.44 m=3 m=57.5 m=56.7 All significant effects shown (blue = interaction, green = main). What is the interpretation of these results? Interpretation:. If the interaction is not significant, interpretation is easy it s just about what s significant in the main effects. In this case, with no significant interaction, we could say that or cups of are significantly better than cups, and using the workbook is significantly better than not using it. 5

6 m = m = 6 m = 3 m=36.67 A) m = 4 m=59.44 m=3 m=57.5 m=56.7 m = m = 6 m = 3 m=36.67 A) m = 4 m=59.44 m=3 m=57.5 m=56.7 Interpretation: Interpretation:. However, if there is a significant interaction, then the main interpretation of the experiment has to do with the interaction. Would we still say that or cups of are better than? That using the workbook is better than not using it? NO. It depends on the level of the other factor. Increasing consumption improves exam scores, where without the workbook there s an improvement going from to cups, and with the workbook there s an improvement in going from to cups. The workbook leads to significant improvement in exam scores, but only for students drinking cups of. Within-subjects experimental design Within-subjects (one-way) ANOVA Also known as repeated-measures Instead of having a bunch of people each try out one tennis racket, so you can compare two kinds of racket (between-subjects), you instead have a bunch of people each try out both rackets (within-subjects) 6

7 Why within-subjects designs can be useful Subjects may differ in ways that influence the dependent variable, e.g. some may be better tennis players than others In a between-subjects design, these differences add to the noise in the experiment, i.e. they increase the variability we cannot account for by the independent variable. As a result, it can be more difficult to see a significant effect. In a within-subjects design, we can discount the variability due to subject differences, and thus perhaps improve the power of the significance test How to do a within-subjects ANOVA (and why we didn t cover it until now) A one-way within-subjects ANOVA looks an awful lot like the two-way ANOVA we did in (my) last lecture We just use a different measure for MS error, the denominator of our Fobt, and a corresponding different df error An example The data Factor A: Type of dress How does your style of dress affect your comfort level when you are acting as a greeter in a social situation? 3 styles of dress: casual, semiformal, and formal. 5 subjects. Each subject wears each style of dress, one on each of 3 days. Order is randomized. Comfort level is measured by a questionnaire Subj Subj Subj3 Subj4 Subj5 Casual Σx=5 Σx =33 Semiformal Σx=45 Σx =4 Formal Σx=6 Σx =66 Σx=7 Σx=4 Σx=6 Σx=7 Σx= Total: Σx=86 Σx =6 7

8 Two-way between-subjects vs. oneway within-subjects The table on the previous slide looks a lot like we re doing a two-way ANOVA, with subject as one of the factors However, cell (i, ) is not necessarily independent of cell (i, ) and cell (i, 3) Also, there is only, in this case, one data point per cell we can t calculate MSerror = MS wn the way we did with two-way ANOVA Sum of squared differences between the scores in each cell and the mean for that cell We have to estimate the error variance in some other way Error variance is the variation we can t explain by one of the other factors So it s clearly not variance in the data for the different levels of factor A, and it s not the variance in the data due to the different subjects We use as our estimate of the error variance the MS for the interaction between subject and factor A The difference between the cell means not accounted for by the main effects Steps. Compute SS A, as before (see other lectures for the equation) = 5 / /5 + 6 /5 86 /5 = Similarly, compute SSsubj = 7 /3 + 4 /3 + 6 /3 + 7 /3 + /3 86 /5 = Compute SStot as usual, = 6 86 /5 = 6.93 Steps 4. SS tot = SS A + SS subj + SS Axsubj -> SS Axsubj = SS tot SS A + SS subj = = Compute degrees of freedom: df A = k A = df Axsubj = (k A )(k subj ) = ()(4) = 8 8

9 Steps We are doing this to check whether there s a significant effect of factor A, so: 6. MS A = SS A /df A = 88.3/ = MSerror = MS Axsubj = SS Axsubj /df Axsubj = 3.87/8 = Compute F obt = MS A /MS error = Compare with Fcrit for df = (df A, df error ) = (, 8). In this case, we wont bother, because it s clearly significant. What if we had done this the between-subjects way? SS tot = 6.9, SS bn = SS A = 88.3 SS wn = SS tot SSbn = 8.79 df bn =, df wn = 5 3 = MSbn = 88.3/ = 44.7 MS wn = 8.79/ =.4 F obt = 8.37 Still, no doubt significant, but not as huge of an F value as before. The extent to which the within-subjects design will have more statistical power is a function of how dependent the samples are for the different conditions, for each subject Other two-sample parametric tests (Some of) what this course did not cover (This will not be on the exam; I just think it can be helpful to know what other sorts of tests are out there.) We talked about z- and t-tests for whether or not two means differ, assuming that the underlying distributions were approximately normal Recall that only two parameters are necessary to describe a normal distribution: mean and variance F-tests (which we used in ANOVA) can test whether the variances of two distributions differ significantly 9

10 Multiple regression and correlation We ve talked about regression and correlation, in which we looked at linear prediction of Y given X, and how much of the variance in Y is accounted for by X (or vice versa) Sometimes several X variables help us more accurately predict Y E.G. height and practise both affect a person s ability to shoot baskets in basketball This is like fitting a best fit plane instead of a best fit line Multiple regression and correlation Put another way, sometimes we want to know the strength of relationship between 3 or more variables If we want to simultaneously study how several X variables affect the Y variable, we use multiple regression & multiple correlation n-linear regression And, as mentioned, you can fit curves other than lines and planes to the data Correlation for ranked data To what extent do two rankings agree? Spearman s rank correlation coefficient, or Kendall s Tau

11 Other variants on ANOVA We talked about one-way and two-way between subjects ANOVA, and one-way within-subjects ANOVA You can, of course, also do two-way withinsubjects ANOVA, and n-way ANOVA (though this gets complicated to interpret after n>3) Designs can also be mixed-design, meaning some factors are within-subjects factors, and others are between-subjects factors And there are all sorts of other complications as well What if our data don t meet the requirements for ANOVA? Recall for t-tests we talked about what to do when the data violate the assumption that the two groups have equal variance we adjusted the degrees of freedom to account for this For ANOVA, there is a similar adjustment if the equivalent sphericity assumption is violated n-parametric procedures like t- tests The chi-square test was, in a sense, a nonparametric version of a t-test A t-test tested whether a mean differed from what was expected, or whether two means were significantly different A chi-square test tests whether cell values differ significantly from predicted, or whether two distributions were significantly different Other non-parametric procedures like t-tests The equivalent of a t-test for ranked data is either the Mann-Whitney U test, or the rank sums test The Wilcoxon t-test is a test for related samples of ranked data E.G. rank subjects reaction times on each of two tasks. Each subject participates in both tasks.

12 n-parametric version of ANOVA In addition, in some special cases there are parametric techniques like t-tests that assume some distribution other than a normal distribution Kruskal-Wallis H test Like a one-way, between-subjects ANOVA for ranked data Friedman χ test Like a one-way, within-subjects ANOVA for ranked data There are also more advanced techniques ANCOVA: ANalysis of COVAriance Provides a type of after-the-fact control for one or more variables that may have affected the dependent variable in an experiment The aim of this technique is to find out what the analysis of variance results might have been like if these variables had been held constant

13 More on ANCOVA Suppose factor A affects the response Y, but Y is also affected by a nuisance variable, X Ideally, you d have run your experiment so that groups for different levels of factor A all had the same value of X But sometimes this isn t feasible, for whatever reason, & under certain conditions you can use ANCOVA to adjust things after the fact, as if X had been held constant E.G. After the fact, adjust for the effects of intelligence on a training program n-parametric bootstrapping techniques Pulling yourself up by your bootstraps Use information gained in the experiment (e.g. about the distribution of the data) to create a nonparametric test that s basically designed for the distribution of your data These are basically Montecarlo techniques they involve estimating the distribution of the data, and then generating multiple samples of new data with that distribution ( Montecarlo techniques are what you did in MATLAB in the beginning of class) Why bootstrapping? Because now we can This is a recent technique (97 s), made feasible by computers It s conceptually and computationally simple The distribution assumptions depend upon the observed distribution of actual data, instead of upon large sample approximations like most of our parametric tests Why not: distribution estimates will change from one run of the experiment to another, and if the data does closely follow, say, a normal distribution, this technique will not do as well 3

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