Manova

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Manova

  1. 1. MANOVA<br />
  2. 2. Multivariate analysis<br />When there is more than one dependent variable, it is inappropriate to do a series of univariate tests.<br /> Multivariate analysis of variance (MANOVA) is an extension of analysis of variance, used with two or more dependent variables<br />
  3. 3. MANOVA<br /><ul><li>Developed as a theoretical construct by Samual S. Wilks in 1932
  4. 4. An extension of univariate ANOVA procedures to situations in which there are two or more related dependent variables (ANOVA analyses only a single DV at a time)
  5. 5. The MANOVA procedure identifies (inferentially) whether:
  6. 6. Different levels of the IVs have a significant effect on a linear combination of each of the DVs
  7. 7. There are interactions between the IVs and a linear combination of the DVs.
  8. 8. There are significant univariate effects for each of the DVs separately.</li></li></ul><li>MANOVA USAGE<br /><ul><li>MANOVA is appropriate when we have several DVs which all measure different aspects of some cohesive theme, e.g., several different types of academic achievement (e.g., Maths, English, Science).
  9. 9. MANOVA works well in situations where there are moderate correlations between DVs. For very high or very low correlation in DVs, it is not suitable: if DVs are too correlated, there isn’t enough variance left over after the first DV is fit, and if DVs are uncorrelated, the multivariate test will lack power
  10. 10. "Because of the increase in complexity and ambiguity of results with MANOVA, one of the best overall recommendations is: Avoid it if you can." (Tabachnick & Fidell, 1983, p.230) </li></li></ul><li>Anova vs. Manova<br />Why not multiple Anovas?<br />Anovas run separately cannot take into account the pattern of covariation among the dependent measures<br />It may be possible that multiple Anovas may show no differences while the Manova brings them out<br />MANOVA is sensitive not only to mean differences but also to the direction and size of correlations among the dependents <br />
  11. 11. Anova vs. Manova<br />Consider the following 2 group and 3 group scenarios, regarding two DVs Y1 and Y2<br />If we just look at the marginal distributions of the groups on each separate DV, the overlap suggests a statistically significant difference would be hard to come by for either DV<br />However, considering the joint distributions of scores on Y1 and Y2 together (ellipses), we may see differences otherwise undetectable<br />
  12. 12. Anova vs. Manova<br />Now we can look for the greatest possible effect along some linear combination of Y1 and Y2<br />The linear combination of the DVs created makes the differences among group means on this new dimension look as large as possible<br />
  13. 13. Anova vs. Manova<br />So, by measuring multiple DVs you increase your chances for finding a group difference<br />In this sense, in many cases such a test has more power than the univariate procedure, but this is not necessarily true as some seem to believe<br />Also conducting multiple ANOVAs increases the chance for type 1 error and MANOVA can in some cases help control for the inflation<br />
  14. 14. MANOVA ASSUMPTIONS<br /><ul><li>Sample size
  15. 15. Rule of thumb: the n in each cell > the number of DVs
  16. 16. Larger samples make the procedure more robust to violation of assumptions
  17. 17. Normality
  18. 18. MANOVA sig. tests assume multivariate normality, however when cell size > ~20 to 30 the procedure is robust violating this assumption
  19. 19. Note that univariate normality is not a guarantee of multivariate normality, but it does help.
  20. 20. Check univariate normality via histograms, normal probability plots, skewness, kurtosis, etc
  21. 21. Linearity
  22. 22. Linear relationships among all pairs of DVs
  23. 23. Assess via scatterplots and bivariate correlations (check for each level of the IV(s
  24. 24. Homogeneity of regression
  25. 25. Homogeneity of variance-covariance matrix (Box's M)
  26. 26. Multicollinearityand Singularity
  27. 27. Outliers</li></li></ul><li>MANOVA ASSUMPTIONS<br /><ul><li>Homogeneity of regression
  28. 28. This assumption is only important if using stepdown analysis, i.e., there is reason for ordering the DVs.
  29. 29. Covariates must have a homogeneity of regression effect (must have equal effects on the DV across the groups)
  30. 30. Homogeneity of variance-covariance matrix (Box's M)
  31. 31. The F test from Box’s M statistics should be interpreted cautiously because it is a highly sensitive test of the violation of the multivariate normality assumption, particularly with large sample sizes.
  32. 32. MANOVA is fairly robust to this assumption where there are equal sample sizes for each cell.</li></li></ul><li>MANOVA ASSUMPTIONS<br /><ul><li>Multicollinearityand Singularity
  33. 33. MANOVA works best when the DVs are only moderately correlated.
  34. 34. When correlations are low, consider running separate ANOVAs
  35. 35. When there is strong multicollinearity, there are redundant DVs (singularity) which decreases statistical efficiency.
  36. 36. Correlations above .7, and particularly above .8 or .9 are reason for concern.
  37. 37. Outliers
  38. 38. MANOVA is sensitive to the effect of outliers (they impact on the Type I error rate)
  39. 39. MANOVA can tolerate a few outliers, particularly if their scores are not too extreme and there is a reasonable N. If there are too many outliers, or very extreme scores, consider deleting these cases or transforming the variables involved (see Tabachnick & Fidell)</li></li></ul><li>DECISION TREE<br />
  40. 40. DECISION TREE<br />
  41. 41. Multivariate test statistics<br /><ul><li>Roy's greatest characteristic root
  42. 42. Tests for differences on only the first discriminant function
  43. 43. Most appropriate when DVs are strongly interrelated on a single dimension
  44. 44. Highly sensitive to violation of assumptions - most powerful when all assumptions are met
  45. 45. Wilks' lambda (λ)
  46. 46. Most commonly used statistic for overall significance
  47. 47. Considers differences over all the characteristic roots
  48. 48. The smaller the value of Wilks' lambda, the larger the between-groups dispersion</li></li></ul><li>Multivariate test statistics<br /><ul><li>Hotelling'strace
  49. 49. Considers differences over all the characteristic roots
  50. 50. Pillai's criterion
  51. 51. Considers differences over all the characteristic roots
  52. 52. More robust than Wilks'; should be used when sample size decreases, unequal cell sizes or homogeneity of covariances is violated</li></li></ul><li>Test statistics - Preferences<br /><ul><li>Pillai’s criterion or wilk’s lambda is the preferred measure when the basic design considerations( adequate sample size, no violations of assumptions, approx. equal cell sizes) are met
  53. 53. Pillai’s criterion is considered more robust and should be used if sample size decreases, unequal cell sizes appear or homogeneity of covariances is violated
  54. 54. Roy’s gcris a more powerful test statistic if the researcher is confident that all assumptions are strictly met and the dependent measures are representative of a single dimension of effects
  55. 55. In a vast majority of situations, all of statistical measures provide similar conclusions</li></li></ul><li>MANOVA - ADVANTAGES<br /><ul><li>It tests the effects of several independent variables and several outcome (dependent) variables within a single analysis
  56. 56. It has the power of convergence (no single operationally defined dependent variable is likely to capture perfectly the conceptual variable of interest)
  57. 57. independent variables of interest are likely to affect a number of different conceptual variables</li></ul>- for example: an organisation's non-smoking policy will affect satisfaction, production, absenteeism, health insurance claims, etc<br /><ul><li>It can provide a more powerful test of significance than available when using univariate tests
  58. 58. It reduces error rate compared with performing a series of univariate tests
  59. 59. It provides interpretive advantages over a series of univariate ANOVAs
  60. 60. Since only ‘one’ dependent variable is tested, the researcher is protected against inflating the type 1 error due to multiple comparisons. </li></li></ul><li>MANOVA - DISADVANTAGES<br /><ul><li>Discriminantfunctions are not always easy to interpret - they are designed to separate groups, not to make conceptual sense. In MANOVA, each effect evaluated for significance uses different discriminant functions (Factor A may be found to influence a combination of dependent variables totally different from the combination most affected by Factor B or the interaction between Factors A and B).
  61. 61. Like discriminant analysis, the assumptions on which it is based are numerous and difficult to assess and meet.</li></li></ul><li>HOW TO AVOID MANOVA <br /><ul><li>Combine or eliminate dependent variables so that only one dependent variable need be analyzed
  62. 62. Use factor analysis to find orthogonal factors that make up the dependent variables, then use univariate ANOVAs on each factor (because the factors are orthogonal each univariate analysis should be unrelated)</li></li></ul><li>MANOVA - LIMITATIONS<br /><ul><li>The number of people in the smallest cell should be larger than the total number of dependent variables
  63. 63. It can be very sensitive to outliers, for small N
  64. 64. It assumes a linear relationship (some sort of correlation) between the dependent variables
  65. 65. MANOVA won't give you the interaction effects between the main effect and the repeated factor</li></li></ul><li>MANOVA Question<br /><ul><li>A researcher randomly assigns 33 subjects to one of three groups. The first group receives technical dietary information interactively from an on-line website. Group 2 receives the same information in from a nurse practitioner, while group 3 receives the information from a video tape made by the same nurse practitioner. The researcher looks at three different ratings of the presentation, difficulty, useful and importance, to determine if there is a difference in the modes of presentation. In particular, the researcher is interested in whether the interactive website is superior because that is the most cost-effective way of delivering the information.</li>

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