1. Chapter 12 Introduction to Analysis of Variance PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Eighth Edition by Frederick J. Gravetter and Larry B. Wallnau

2. Chapter 12 Learning Outcomes Explain purpose and logic of Analysis of Variance 1 Perform Analysis of Variance on data from single-factor study 2 Know when and why to use post hoc tests (posttests) 3 Compute Tukey’s HSD and Scheffé test post hoc tests 4 Compute η 2 to measure effect size 5

3. Tools You Will Need Variability (Chapter 4) – Sum of squares – Sample variance – Degrees of freedom Introduction to hypothesis testing (Chapter 8) – The logic of hypothesis testing Independent-measures t statistic (Chapter 10)

4. 12.1 Introduction to Analysis of Variance Analysis of variance – Used to evaluate mean differences between two or more treatments – Uses sample data as basis for drawing general conclusions about populations Clear advantage over a t test: it can be used to compare more than two treatments at the same time

5. Figure 12.1 Typical Situation for Using ANOVA

6. Terminology Factor – The independent (or quasi-independent) variable that designates the groups being compared Levels – Individual conditions or values that make up a factor Factorial design – A study that combines two or more factors

7. Figure 12.2 Two-Factor Research Design

8. Statistical Hypotheses for ANOVA Null hypothesis: the level or value on the factor does not affect the dependent variable – In the population, this is equivalent to saying that the means of the groups do not differ from each other

9. Alternate Hypothesis for ANOVA H 1 : There is at least one mean difference among the populations (Acceptable shorthand is “Not H 0 ”) Issue: how many ways can H 0 be wrong? – All means are different from every other mean – Some means are not different from some others, but other means do differ from some means

10. Test statistic for ANOVA F-ratio is based on variance instead of sample mean differences

11. Test statistic for ANOVA Not possible to compute a sample mean difference between more than two samples F-ratio based on variance instead of sample mean difference – Variance used to define and measure the size of differences among sample means (numerator) – Variance in the denominator measures the mean differences that would be expected if there is no treatment effect

12. Type I Errors and Multiple-Hypothesis tests Why ANOVA (if t can compare two means)? – Experiments often require multiple hypothesis tests—each with Type I error (testwise alpha) – Type I error for a set of tests accumulates testwise alpha  experimentwise alpha > testwise alpha ANOVA evaluates all mean differences simultaneously with one test—regardless of the number of means—and thereby avoids the problem of inflated experimentwise alpha

13. 12.2 Analysis of Variance Logic Between-treatments variance – Variability results from general differences between the treatment conditions – Variance between treatments measures differences among sample means Within-treatments variance – Variability within each sample – Individual scores are not the same within each sample

14. Sources of Variability Between Treatments Systematic differences caused by treatments Random, unsystematic differences – Individual differences – Experimental (measurement) error

15. Sources of Variability Within Treatments No systematic differences related to treatment groups occur within each group Random, unsystematic differences – Individual differences – Experimental (measurement) error

16. Figure 12.3 Total Variability Partitioned into Two Components

17. F-ratio If H 0 is true: – Size of treatment effect is near zero – F is near 1.00 If H 1 is true: – Size of treatment effect is more than 0. – F is noticeably larger than 1.00 Denominator of the F-ratio is called the error term

18. Learning Check Decide if each of the following statements is True or False ANOVA allows researchers to compare several treatment conditions without conducting several hypothesis tests T/F If the null hypothesis is true, the F-ratio for ANOVA is expected (on average) to have a value of 0 T/F

19. Learning Check - Answers Several conditions can be compared in one test True If the null hypothesis is true, the F-ratio will have a value near 1.00 False

20. 12.3 ANOVA Notation and Formulas Number of treatment conditions: k Number of scores in each treatment: n 1, n 2 … Total number of scores: N – When all samples are same size, N = kn Sum of scores (ΣX) for each treatment: T Grand total of all scores in study: G = ΣT No universally accepted notation for ANOVA; Other sources may use other symbols

21. Figure 12.4 ANOVA Calculation Structure and Sequence

22. Figure 12.5 Partitioning SS for Independent-measures ANOVA

23. ANOVA equations

24. Degrees of Freedom Analysis Total degrees of freedom df total = N – 1 Within-treatments degrees of freedom df within = N – k Between-treatments degrees of freedom df between = k – 1

25. Figure 12.6 Partitioning Degrees of Freedom

26. Mean Squares and F-ratio

27. ANOVA Summary Table SourceSSdfMSF Between Treatments4022010 Within Treatments20102 Total6012 Concise method for presenting ANOVA results Helps organize and direct the analysis process Convenient for checking computations “Standard” statistical analysis program output

28. Learning Check An analysis of variance produces SS total = 80 and SS within = 30. For this analysis, what is SS between ? 50 A 110 B 2400 C More information is needed D

29. Learning Check - Answer An analysis of variance produces SS total = 80 and SS within = 30. For this analysis, what is SS between ? 50 A 110 B 2400 C More information is needed D

30. 12.4 Distribution of F-ratios If the null hypothesis is true, the value of F will be around 1.00 Because F-ratios are computed from two variances, they are always positive numbers Table of F values is organized by two df – df numerator (between) shown in table columns – df denominator (within) shown in table rows

31. Figure 12.7 Distribution of F-ratios

32. 12.5 Examples of Hypothesis Testing and Effect Size Hypothesis tests use the same four steps that have been used in earlier hypothesis tests. Computation of the test statistic F is done in stages – Compute SS total, SS between, SS within – Compute MS total, MS between, MS within – Compute F

33. Figure 12.8 Critical region for α=.01 in Distribution of F-ratios

34. Measuring Effect size for ANOVA Compute percentage of variance accounted for by the treatment conditions In published reports of ANOVA, effect size is usually called η 2 (“eta squared”) – r 2 concept (proportion of variance explained)

35. In the Literature Treatment means and standard deviations are presented in text, table or graph Results of ANOVA are summarized, including – F and df – p-value – η 2 E.g., F(3,20) = 6.45, p<.01, η 2 = 0.492

36. Figure 12.9 Visual Representation of Between & Within Variability

37. MS within and Pooled Variance In the t-statistic and in the F-ratio, the variances from the separate samples are pooled together to create one average value for the sample variance Numerator of F-ratio measures how much difference exists between treatment means. Denominator measures the variance of the scores inside each treatment

38. 12.6 post hoc Tests ANOVA compares all individual mean differences simultaneously, in one test A significant F-ratio indicates that at least one difference in means is statistically significant – Does not indicate which means differ significantly from each other! post hoc tests are follow up tests done to determine exactly which mean differences are significant, and which are not

39. Experimentwise Alpha post hoc tests compare two individual means at a time (pairwise comparison) – Each comparison includes risk of a Type I error – Risk of Type I error accumulates and is called the experimentwise alpha level. Increasing the number of hypothesis tests increases the total probability of a Type I error post hoc (“posttests”) use special methods to try to control experimentwise Type I error rate

40. Tukey’s Honestly Significant Difference A single value that determines the minimum difference between treatment means that is necessary to claim statistical significance–a difference large enough that p < α experimentwise – Honestly Significant Difference (HSD)

41. The Scheffé Test The Scheffé test is one of the safest of all possible post hoc tests – Uses an F-ratio to evaluate significance of the difference between two treatment conditions

42. Learning Check Which combination of factors is most likely to produce a large value for the F-ratio? large mean differences and large sample variances A large mean differences and small sample variances B small mean differences and large sample variances C small mean differences and small sample variances D

43. Learning Check - Answer Which combination of factors is most likely to produce a large value for the F-ratio? large mean differences and large sample variances A large mean differences and small sample variances B small mean differences and large sample variances C small mean differences and small sample variances D

44. Learning Check Decide if each of the following statements is True or False Post tests are needed if the decision from an analysis of variance is “fail to reject the null hypothesis” T/F A report shows ANOVA results: F(2, 27) = 5.36, p <.05. You can conclude that the study used a total of 30 participants T/F

45. Learning Check - Answers post hoc tests are needed only if you reject H0 (indicating at least one mean difference is significant) False Because dftotal = N-1 and Because df total = df between + df within True

46. 12.7 Relationship between ANOVA and t tests For two independent samples, either t or F can be used – Always result in same decision – F = t 2 For any value of α, (t critical ) 2 = F critical

47. Figure 12.10 Distribution of t and F statistics

48. Independent Measures ANOVA Assumptions The observations within each sample must be independent The population from which the samples are selected must be normal The populations from which the samples are selected must have equal variances (homogeneity of variance) Violating the assumption of homogeneity of variance risks invalid test results

49. Figure 12.11 Formulas for ANOVA

50. Figure 12.12 Distribution of t and F statistics