MANOVA: Multivariate Analysis of Variance1Review of ANOVA: Univariate Analysis of VarianceAn univariate analysis of variance looks for the causal impact of a nominal level independent variable (factor) on a single, interval or better level dependent variableThe basic question you seek to answer is whether or not there is a difference in scores on the dependent variable attributable to membership in one or the other category of the independent variableAnalysis of Variance (ANOVA): Required when there are three or more levels or conditions of the independent variable (but can be done when there are only two)What is the impact of ethnicity (IV) (Hispanic, African-American, Asian-Pacific Islander, Caucasian, etc) on annual salary (DV)?What is the impact of three different methods of meeting a potential mate (IV) (online dating service; speed dating; setup by friends) on likelihood of a second date (DV)2Basic Analysis of Variance ConceptsWe are going to make two estimates of the common population variance, 2The first estimate of the common variance 2 is called the “between” (or “among”) estimate and it involves the variance of the IV category means about the grand meanThe second is called the “within” estimate, which will be a weighted average of the variances within each of the IV categories. This is an unbiased estimate of 2The ANOVA test, called the F test, involves comparing the between estimate to the within estimateIf the null hypothesis, that the population means on the DV for the levels of the IV are equal to one another, is true, then the ratio of the between to the within estimate of 2 should be equal to one (that is, the between and within estimates should be the same)If the null hypothesis is false, and the population means are not equal, then the F ratio will be significantly greater than unity (one). 3Basic ANOVA OutputSome of the things that we learned to look for on the ANOVA output:A. The value of the F ratio (same line as the IV or “factor”)B. The significance of that F ratio (same line)C. The partial eta squared (an estimate of the amount of the “effect size” attributable to between-group differences (differences in levels of the IV (ranges from 0 to 1 where 1 is strongest)D. The power to detect the effect (ranges from 0 to 1 where 1 is strongest)The IV, fathers highest degreeABCD4More Review of ANOVAEven if we have obtained a significant value of F and the overall difference of means is significant, the F statistic isnt telling us anything about how the mean scores varied among the levels of the IV. We can do some pairwise tests after the fact in which we compare the means of the levels of the IVThe type of test we do depends on whether or not the variances of the groups (conditions or levels of the IV) are equalWe test this using the Levene statistic. If it is significant at p .05) so we use the Sheff test5Review of Factorial ANOVAoTwo-way ANOVA is applied to a situation in which you have two independent nominal-level variables and one interval or better dependent variableoEach of the independent variables may have any number of levels or conditions (e.g., Treatment 1, Treatment 2, Treatment 3 No Treatment)oIn a two-way ANOVA you will obtain 3 F ratiosnOne of these will tell you if your first independent variable has a significant main effect on the DVnA second will tell you if your second independent variable has a significant main effect on the DVnThe third will tell you if the interaction of the two independent variables has a significant effect on the DV, that is, if the impact of one IV depends on the level of the other 6Review: Factorial ANOVA ExampleTests of Hypotheses: (1)There is no significant main effect for education level (F(2, 58) = 1.685, p = .194, partial eta squared = .055) (red dots)(2)There is no significant main effect for marital status (F (1, 58) = .441, p = .509, partial eta squared = .008)(green dots)(3)There is a significant interaction effect of marital status and education level (F (2, 58) = 3.586, p = .034, partial eta squared = .110) (blue dots)7Plots of Interaction EffectsEducation Level is plotted along the horizontal axis and hours spent on the net is plotted along the vertical axis. The red and green lines show how marital status interacts with education level. Here we note that spending time on the Internet is strongest among the Post High School group for single people, but lowest among this group for married people8MANOVA: What Kinds of Hypotheses Can it Test?oA MANOVA or multivariate analysis of variance is a way to test the hypothesis that one or more independent variables, or factors, have an effect on a set of two or more dependent variablesnFor example, you might wish to test the hypothesis that sex and ethnicity interact to influence a set of job-related outcomes including attitudes toward co-workers, attitudes toward supervisors, feelings of belonging in the work environment, and identification with the corporate culturenAs another example, you might want to test the hypothesis that three different methods of teaching writing result in significant differences in ratings of student creativity, student acquisition of grammar, and assessments of writing quality by an independent panel of judges9Why Should You Do a MANOVA?oYou do a MANOVA instead of a series of one-at-a-time ANOVAs for two main reasonsnSupposedly to reduce the experiment-wise level of Type I error (8 F tests at .05 each means the experiment-wise probability of making a Type I error (rejecting the null hypothesis when it is in fact true) is 40%! The so-called overall test or omnibus test protects against this inflated error probability only when the null hypothesis is true. If you follow up a significant multivariate test with a bunch of ANOVAs on the individual variables without adjusting the error rates for the individual tests, theres no “protection”nAnother reasons to do MANOVA. None of the individual ANOVAs may produce a significant main effect on the DV, but in combination they might, which suggests that the variables are more meaningful taken together than considered separatelynMANOVA takes into account the intercorrelations among the DVs10Assumptions of MANOVAo1. Multivariate normalitynAll of the DVs must be distributed normally (can visualize this with histograms; tests are available for checking this out)nAny linear combination of the DVs must be distributed normallyoCheck out pairwise relationships among the DVs for nonlinear relationships using scatter plotsnAll subsets of the variables must have a multivariate normal distributionoThese requirements are rarely if ever tested in practiceoMANOVA is assumed to be a robust test that can stand up to departures from multivariate normality in terms of Type I error rateoStatistical power (power to detect a main or interaction effect) may be reduced when distributions are very plateau-like (platykurtic)11Assumptions of MANOVA, contdo2. Homogeneity of the covariance matrices nIn ANOVA we talked about the need for the variances of the dependent variable to be equal across levels of the independent variableoIn MANOVA, the univariate requirement of equal variances has to hold for each one of the dependent variables oIn MANOVA we extend this concept and require that the “covariance matrices” be homogeneousnComputations in MANOVA require the use of matrix algebra, and each persons “score” on the dependent variables is actually a “vector” of scores on DV1, DV2, DV3, . DVnnThe matrices of the covariances-the variance shared between any two variables-have to be equal across all levels of the independent variable12Assumptions of MANOVA, contdnThis homogeneity assumption is tested with a test that is similar to Levenes test for the ANOVA case. It is called Boxs M, and it works the same way: it tests the hypothesis that the covariance matrices of the dependent variables are significantly different across levels of the independent variableoPutting this in English, what you dont want is the case where if your IV, was, for example, ethnicity, all the people in the “other” category had scores on their 6 dependent variables clustered very tightly around their mean, whereas people in the “white” category had scores on the vector of 6 dependent variables clustered very loosely around the mean. You dont want a leptokurtic set of distributions for one level of the IV and a platykurtic set for another leveloIf Boxs M is significant, it means you have violated an assumption of MANOVA. This is not much of a problem if you have equal cell sizes and large N; it is a much bigger issue with small sample sizes and/or unequal cell sizes (in factorial anova if there are unequal cell sizes the sums of squares for the three sources (two main effects and interaction effect) wont add up to the Total SS)13Assumptions of MANOVA, contdo3. Independence of observationsnSubjects scores on the dependent measures should not be influenced by or related to scores of other subjects in the condition or level nCan be tested with an intraclass correlation coefficient if lack of independence of observations is suspected14MANOVA ExampleoLets test the hypothesis that region of the country (IV) has a significant impact on three DVs, Percent of people who are Christian adherents, Divorces per 1000 population, and Abortions per 1000 populations. The hypothesis is that there will be a significant multivariate main effect for region. Another way to put this is that the vectors of means for the three DVs are different among regions of the countryoThis is done with the General Linear Model/ Multivariate procedure in SPSS (we will look first at an example where the analysis has already been done)oComputations are done using matrix algebra to find the ratio of the variability of B (Between-Groups sums of squares and cross-products (SSCP) matrix) to that of the W (Within-Groups SSCP matrix) MY1 MY2 My3Vectors of means on the three DVs (Y1, Y2, Y3) for Regions South and Midwest MY1 MY2 My3SouthMidwest15MANOVA test of Our HypothesisFirst we will look at the overall F test (over all three dependent variables). What we are most interested in is a statistic called Wilks lambda (), and the F value associated with that. Lambda is a measure of the percent of variance in the DVs that is *not explained* by differences in the level of the independent variable. Lambda varies between 1 and zero, and we want it to be near zero (e.g, no variance that is not explained by the IV). In the case of our IV, REGION, Wilks lambda is .465, and has an associated F of 3.90, which is significant at p. 001. Lambda is the ratio of W to T (Total SSCP matrix)16MANOVA Test of our Hypothesis, contdContinuing to examine our output, we find that the partial eta squared associated with the main effect of region is .225 and the power to detect the main effect is .964. These are very good results!We would write this up in the following way: “A one-way MANOVA revealed a significant multivariate main effect for region, Wilks = .465, F (9, 95.066) = 3.9, p . 001, partial eta squared = .225. Power to detect the effect was .964. Thus hypothesis 1 was confirmed.”17Boxs Test of Equality of Covariance MatricesChecking out the Boxs M test we find that the test is significant (which means that there are significant differences among the regions in the covariance matrices). If we had low power that might be a problem, but we dont have low power. However, when Boxs test finds that the covariance matrices are significantly different across levels of the IV that may indicate an increased possibility of Type I error, so you might want to make a smaller error region. If you redid the analysis with a confidence level of .001, you would still get a significant result, so its probably OK. You should report the results of the Boxs M, though.18Looking at the Individual Dependent VariablesoIf the overall F test is significant, then its common practice to go ahead and look at the individual dependent variables with separate ANOVA tests nThe experimentwise alpha protection provided by the overall or omnibus F test does not extend to the univariate tests. You should divide your confidence levels by the number of tests you intend to perform, so in this case if you expect to look at F tests for the three dependent variables you should require that p .017 (.05/3)oThis procedure ignores the fact the variables may be intercorrelated and that the separate ANOVAS do not take these intercorrelations into accountnYou could get three significant F ratios but if the variables are highly correlated youre basically getting the same result over and over19Univariate ANOVA tests of Three Dependent VariablesAbove is a portion of the output table reporting the ANOVA tests on the three dependent variables, abortions per 1000, divorces per 1000, and % Christian adherents. Note that only the F values for %Christian adherents and Divorces per 1000 population are significant at your criterion of .017. (Note: the MANOVA procedure doesnt seem to let you set different p levels for the overall test and the univariate tests, so the power here is higher than it would be if you did these tests separately in a ANOVA procedure and set p to .017 before you did the tests.)20Writing up More of Your ResultsoSo far you have written the following:n“A one-way MANOVA revealed a significant multivariate main effect for region, Wilks = .465, F (9, 95.066) = 3.9, p . 001, partial eta squared = .225. Power to detect the effect was .964. Thus hypothesis 1 was confirmed.”oYou continue to write:n“Given the significance of the overall test, the univariate main effects were examined. Significant univariate main effects for region were obtained for percentage of Christian adherents, F (3, 41 ) = 3.944, p .015 , partial eta square =.224, power = .794 ; and number of divorces per 1000 population, F (3,41 ) = 8.789 , p .001 , partial eta square = .391, power = .991”21Finally, Post-hoc Comparisons with Sheff Test for the DVs that had Significant Univariate ANOVAsThe Levenes statistics for the two DVs that had significant univariate ANOVAs are all non-significant, meaning that the group variances were equal, so you can use the Sheff tests for comparing pairwise group means, e.g., do the South and the West differ significantly on % of Christian adherents and number of divorces. 22Significant Pairwise Regional Differences on the Two Significant DVsYou might want to set your confidence level cutoff even lower since you are going to be doing 12 tests here (4(3)/2) for each variable23Writing up All of Your MANOVA ResultsoYour final paragraph will look like thiso“A one-way MANOVA revealed a significant multivariate main effect for region, Wilks = .465, F (9, 95.066) = 3.9, p . 001, partial eta squared = .225. Power to detect the effect was .964. Thus Hypothesis 1 was confirmed. Given the significance of the overall test, the univariate main effects were examined. Significant univariate main effects for region were obtained for percentage of Christian adherents, F (3, 41 ) = 3.944, p .015 , partial eta square =.224, power = .794 ; and number of divorces per 1000 population, F (3,41 ) =8.789 , p .001 , partial eta square = .391, power = .991. Significant regional pairwise differences were obtained in number of divorces per 1000 population between the West and both the Northeast and Midwest. The mean number of divorces per 1000 population were 5.59 in the West, 3.6 in the Northeast, and 3.74 in the Midwest.” You can present the pairwise results and the MANOVA overall F results and univariate F results in separate tables24Now You Try It!oGo here to download the file statelevelmodified.savoLets test the hypothesis that region of the country and availability of an educated workforce have an impact on three dependent variables: % union members, per capita income, and unemployment rateoAlthough a test will be performed for an interaction between region and workforce education level, no specific effect is hypothesizedoGo to SPSS Data Editor 25Running a MANOVA in SPSSoGo to Analyze/General Linear Model/ MultivariateoMove Census Region and HS Educ into the Fixed Factors category (this is where the IVs go)oMove per capita income, unemployment rate, and % of workers who are union members into the Dependent Variables categoryoUnder Plots, create four plots, one for each of the two main effects (region, HS educ) and two for their interaction. Use the Add button to add each new plotnMove region into the horizontal axis window and click the Add buttonnMove hscat4 (HS educ) into the horizontal axis window and click the Add buttonnMove region into the horizontal axis window and hscat 4 into the separate lines window and click AddnMove hscat4 (HS educ) into the horizontal axis window and region into the separate lines window and click Add, then click Continue26Setting up MANOVA in SPSSnUnder Options, move all of the factors including the interactions into the Display Means for window nSelect descriptive statistics, estimates of effect size, observed power, and homogeneity testsnSet the confidence level to .05 and click continuenClick OKoCompare your output to the next several slides27MANOVA Main and Interaction EffectsNote that there are significant main effects for both region (green) and hscat4 (red) but not for their interaction (blue). Note the values of Wilks lambda; only .237 of the variance is unexplained by region. Thats a very good result. Boxs M is significant which is not so good but we do have high power. If you redid the analysis with a lower significance level you would “lose” hscat428Univariate Tests: ANOVAs on each of the Three DVs for Region, HS EducSince we have obtained a significant multivariate main effect for each factor, we can go ahead and do the univariate F tests where we look at each DV in turn to see if the two IVS have a significant impact on them separately. Since we are doing six tests here we are going to reguire an experiment-wise alpha rate of .05, so we will divide it by six to get an acceptable confidence level for each of the six tests, so we will set the alpha level to p .008. By that criterion, the only significant univariate result is for the effect of region on unemployment rate. With a more lenient criterion of .05 (and a greater probability of Type I error), three other univariate tests would have been significant29Pairwise Comparisons on the Significant Univariate TestsoWe found that the only significant univariate main effect was for the effect of region on unemployment rate. Now lets ask the question, what are the differences between regions in unemployment rate, considered two at a time?oWhat does the Levenes statistic say about the kind of post-hoc test we can do with respect to the region variable?oAccording to the output, the group variances on unemployment rate are not significantly different, so we can do a Sheff test30Pairwise Difference of MeansSince we are doing 6 significance tests (K(k-1)/2) looking at the pairwise tests comparing the employment rate by region, we can use the smaller confidence level again to protect against inflated alpha error, so lets divide the .05 by 6 and set .008 as our error level. By this standard, the South and Midwest and the West and Midwest are significantly different in unemployment rate.31Reporting the DifferencesSignificant mean differences in unemployment rate were obtained between the Midwest (M = 3.917) and the West (6.294) and Midwest and the South (M = 5.076)32Lab # 9oDuplicate the preceding data analysis in SPSS. Write up the results (the tests of the hypothesis about the main effects of region and HS Educ on the three dependent variables of per capita income, unemployment rate, and % union members, as if you were writing for publication. Put your paragraph in a Word document, and illustrate your results with tables from the output as appropriate (for example, the overall multivariate F table and the table of mean scores broken down by regions). You can also use plots to illustrate significant effects33