# One-Way Analysis of Variance

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1 One-Way Analysis of Variance Note: Much of the math here is tedious but straightforward. We ll skim over it in class but you should be sure to ask questions if you don t understand it. I. Overview A. We have previously compared two populations, testing hypotheses of the form H 0 : µ 1 = µ H A : µ 1 µ But in many situations, we may be interested in more than two populations. Examples: T Compare the average income of blacks, whites, and others. T Compare the educational attainment of Catholics, Protestants, Jews. B. Q: Why not ust compare pairwise - take each possible pairing, and see which are significant? A: Because by chance alone, some contrasts would be significant. For example, suppose we had 7 groups. The number of pairwise combinations is 7 C = 1. If α =.05, we expect one of the differences to be significant. Therefore, you want to simultaneously investigate differences between the means of several populations. C. To do this, you use ANOVA - Analysis of Variance. ANOVA is appropriate when T You have a dependent, interval level variable T You have or more populations, i.e. the independent variable is categorical. In the population case, ANOVA becomes equivalent to a -tailed T test ( sample tests, Case II, σ's unknown but assumed equal). D. Thus, with ANOVA you test H 0 : H A : µ 1 = µ = µ 3 =... = µ J The means are not all equal. E. Simple 1-factor model: Suppose we want to compare the means of J different populations. We have samples of size N. Any individual score can be written as follows: y i = µ + τ + ε i, where = 1, J (# groups) and i = 1,,..., N That is, an observation is the sum of three components: 1. The grand mean µ of the combined populations. For example, the overall average income might be \$15,000. One-Way Analysis of Variance - Page 1

2 . A treatment effect τ associated with the particular population from which the observation is taken; put another way, τ is the deviation of the group mean from the overall mean. For example, suppose the average White income is \$0,000. Then τ whites = \$5, A random error term ε i. This reflects variability within each population. Not everyone in the group will have the same value. For example, the average white income might be \$0,000, but some whites will make more, some will make less. (For a white who makes \$18,000, ε i = -,000.) F. An alternative way to write the model is y i = µ + ε i, where µ = mean of the th population = µ + τ. G. We are interested in testing the hypothesis H 0 : µ 1 = µ = µ 3 =... = µ J But if the J means are equal, this means that µ = µ, which means that there are no treatment effects. That is, the above hypothesis is equivalent to H 0 : τ 1 = τ = τ 3 =... = τ J = 0 H. Estimating the treatment effects: As usual, we use sample information to estimate the population parameters. It is pretty simple to estimate the treatment effects: J N N yi yi T =1 i=1 A i=1 ˆ µ = y =, ˆ µ = y = =, = - = y - y ˆ τ ˆ µ ˆ µ N N N Example: A firm wishes to compare four programs for training workers to perform a certain manual task. Twenty new employees are randomly assigned to the training programs, with 5 in each program. At the end of the training period, a test is conducted to see how quickly trainees can perform the task. The number of times the task is performed per minute is recorded for each trainee, with the following results: One-Way Analysis of Variance - Page

3 Observation Program 1 Program Program 3 Program T A = Σy i µˆ = T A /N Estimate the treatment effects for the four programs. Solution. Note that ΣΣy i = 07, so µˆ = 07/0 = Since ˆ τ ˆ µ ˆ µ, we get ˆ1 τ = = 1.45, ˆ τ = = -1.55, ˆ3 τ = = 1.85, ˆ4 τ = = = I. Computing the treatment effects is easy - but how do we test whether the differences in effects are significant??? Note the following: s total = s = ( y i N y ) = SS Total DF Total = MS Total where SS = Sum of squares (i.e. sum of the squared deviations from the mean), DF = degrees of freedom, and MS = Mean square. Also, Where SS Total = SS Within + SS ( yi - y ) = εˆ i Between = SS Within = SS Errors = SS Residual ( y y ) = = N = SS Between = SS Explained ˆ τ ˆ τ i i One-Way Analysis of Variance - Page 3

4 SS Within captures variability within each group. If all group members had the same score, SS Within would equal 0. It is also called SS Errors or SS Residual, because it reflects variability that cannot be explained by group membership. Note that there are N degrees of freedom associated with each individual sample, so the total number of degrees of freedom within = Σ(N - 1) = N - J. SS Between captures variability between each group. If all groups had the same mean, SS Between would equal 0. The term SS Explained is also used because it reflects variability that is explained by group membership. Note that there are J samples, one grand mean, hence DF Between = J - 1. We further define MS Total = Proof (Optional): Note that SS Within MS Within= = DF Within SS Within, N - J SS Between SS Between MS Between = =, DF Between J - 1 SS Within + SS Between SS Total = =Total Variance N - 1 N - 1 y i = y i - y + y,and y i - y = y i - y + y - y We simply add and subtract y. Why do we do this? Note that individual's score from the group score = εˆ i ; and total score = τˆ. Hence, y y = deviation of the i y y = deviation of the group score from the SS Total = ( yi y ) = ( yi y + y y ) = ( ˆ ε i + ˆ τ ) = ˆ ε i + ˆ τ + ˆ ε i ˆ τ Let us deal with each term in turn: ( yi - y ) = εˆ i = SS Within = SS Errors = SS Residual SS Within captures variability within each group. If all group members had the same score, SS Within would equal 0. It is also called SS Errors or SS Residual, because it reflects variability One-Way Analysis of Variance - Page 4

5 that cannot be explained by group membership. Note that there are N degrees of freedom associated with each individual sample, so the total number of degrees of freedom within = Σ(N - 1) = N - J. ( y y ) = = N = SS Between = SS Explained ˆ τ ˆ τ i i (The third equation is valid because all cases within a group have the same value for y.) SS Between captures variability between each group. If all groups had the same mean, SS Between would equal 0. The term SS Explained is also used because it reflects variability that i s explained by group membership. Note that there are J samples, one grand mean, hence DF Between = J - 1. i ( y i y )( y y )= i ˆ ε i ˆ τ = ˆ τ i ˆ ε i = ˆ τ * 0 = 0 (The latter is true because the deviations from the mean must sum to 0). Hence, SS Total = SS Within + SS Between J. Now that we have these, what do we do with them? For hypothesis testing, we have to make certain assumptions. Recall that y i = µ + τ + ε i. ε i is referred to as a "random error te rm" or "disturbance." If we assume: (1) ε i - N(0, σ ), () σ is the same for all samples, (3) the random error terms are independent (Note that these assumptions basically mean that the ε 's are iid, independent and identically distributed); Then, if H 0 is true, MS Between F = ~ F(J -1, N - J), MS Within and E(F) =1 That is, if H 0 is true, then the test statistic F has an F distribution with J - 1 and N - J degrees of Freedom. See Appendix E, Table V (Hayes, pp ), for tables on the F distribution. See especially tables 5-3 (Q =.05) and 5-5 (Q =.01). One-Way Analysis of Variance - Page 5

6 K. Rationale: T The basic idea is to determine whether all of the variation in a set of data is attribut able to random error ( chance) or whether some of the variation is attributable to chance and some is attributable to differences in the means of the J populations of interest. T First, the sample variance for the entire set of data is computed and is seen to be composed of two parts: the numerator, which is a sum of squares, and the denominator, which is the degrees of freedom. T The total sum of squares can be partitioned into SS Between and SS Within, and the total degrees of freedom can be partitioned into d.f. between and d.f. Within. T By dividing each sum of squares by the respective d.f., MS between and MS within are determined; these represent the sample variability between the different samples and the sample variability within all the samples, respectively. T But the variability within the samples must be due to random error alone, according to the assumptions of the one-factor model. T The variability between the samples, on the other hand, may be attributable both to chance and to any differences in the J population means. T Thus, if MS Between is significantly greater than MS Within (as measured by the F-test), then the null hypothesis of zero treatment effects must be reected. L. Comments on the F distribution: T There are two sets of d.f., rather than 1. T F is not symmetric. All values are positive. T Like χ, we are only interested in values in the right-hand side of the tail. T In the tables, columns give the d.f. for MS Between (J - 1), while the rows give the d.f. for MS Within (N - J). T Look at Table 5-3, column 1; compare with Table 3 for the T distribution, the column labeled Q =.05. Note that F = T. A two sample test, case II, σ 1 = σ = σ, with a - tailed alternative hypothesis, can also be tested using ANOVA. One-Way Analysis of Variance - Page 6

7 M. Computational procedures for ANOVA. The above formulas are, in practice, a little awkward to deal with. When doing computations by hand, the following procedure is generally easier: One Way Anova: Computational Procedures Formula Explanation T A N = y ( y (1)= N i i ) i = N Y T A = the sum of the scores in group A, where A 1 = first group, A = second group, etc. Add up the values for the observations for group A 1, then A, etc. Also sometimes called ust T. Sum all the observations. Square the result. Divide by the total number of observations. ()= y i Square each observation. Sum the squared observations. (3)= T N A A Square T A1, and divide by N A1. Repeat for each of the J groups, and add the results together. SS Total = () - (1) SS Between = (3) - (1). Or, if treatment effects have been computed, use SS Within = () - (3) N ˆ τ MS Total = SS Total / (N - 1) MS Between = SS Between / (J - 1) MS Within = SS Within / (N - J) Total Sum of Squares Between Sum of Squares. This is also sometimes called SS A, SS Treatment, or SS Explained Within sum of squares. Also called SS error, or SS Residual Mean square total. Same as s, the sample variance. Mean square between. Also called MS A, MS Treatment, or MS Explained Mean Square Within. Also called MS error or MS Residual F = MS Between / MS Within Test statistic. d.f. = (J - 1, N - J) One-Way Analysis of Variance - Page 7

8 N. The ANOVA Table. The results of an analysis of variance are often presented in a table that looks something like the following (with the appropriate values filled in): Source SS D.F. Mean Square F A (or Treatment, or Explained) SS Between J - 1 SS Between/ (J - 1) MS Between MS Within Error (or Residual) SS Within N - J SS Within / (N - J) Total SS Total N - 1 SS Total / (N - 1) O. Hypothesis testing using ANOVA. As usual, we determine the critical value of the test statistic for a given value of α. If the test statistic is less than the critical value, we accept H 0, if it is greater than the critical value we reect H 0. EXAMPLES: 1. Again consider this problem: A firm wishes to compare four programs for training workers to perform a certain manual task. Twenty new employees are randomly assigned to the training programs, with 5 in each program. At the end of the training period, a test is conducted to see how quickly trainees can perform the task. The number of times the task is performed per minute is recorded for each trainee, with the following results: Program 1: 9, 1, 14, 11, 13 Program : 10, 6, 9, 9, 10 Program 3: 1, 14, 11, 13, 11 Program 4: 9, 8, 11, 7, 8 (a) (b) Construct the ANOVA table Using α =.05, determine whether the treatments differ in their effectiveness. Solution. (a) As we saw before, T A1 = 59, T A = 44, T A3 = 61, T A4 = 43. Also, ( (1)= y N i ) = 07 0 = () = y = = 39 (3)= T N i A A = = One-Way Analysis of Variance - Page 8

9 SS Total = () - (1) = = 96.55, SS Between = (3) - (1) = = 54.95; or, SS Between = τ = 5 * * * * 1.75 = N ˆ SS Within = () - (3) = = 41.6, MS Total = SS Total/ (N - 1) = / 19 = 5.08, MS Between = SS Between/ (J - 1) = 54.95/3 = 18.3, MS Within = SS Within/ (N - J) = 41.6/16 =.6, F = MS Between / MS Within = 18.3 /.6 = 7.04 The ANOVA Table therefore looks like this: Source SS D.F. Mean Square F A (or Treatment, or Explained) SS Between = J - 1 = 3 SS Between/ (J - 1) = 18.3 MS Between = MS Within Error (or Residual) SS Within = 41.6 N - J = 16 SS Within / (N - J) = Total SS Total = N - 1 = 19 SS Total / (N - 1) = 5.08 NOTE: Most computer programs would not be nice enough to spell out "SS Between =", etc.; that is, you would have to know from the location of the number in the table whether it was SS Between, MS Within, or whatever. See the SPSS examples below. (b) For α =.05, the critical value for an F with d.f. (3, 16) is 3.4. Ergo, we reect the null hypothesis. More formally, Step 1: H 0 : H A : µ 1 = µ = µ 3 = µ 4, i.e. treatments are equally effective The means are not all equal. Step : An F statistic is appropriate, since the dependent variable is continuous and there are or more groups. Step 3: Since α =.05 and d.f. = 3, 16, accept H 0 if F 3,16 # 3.4 Step 4: The computed value of the F statistic is 7.04 Step 5: Reect H 0. The treatments are not equally effective. One-Way Analysis of Variance - Page 9

10 There are several SPSS routines that can do one-way Anova. These include ANOVA (which, alas, requires that you enter the syntax directly rather than use menus; but it will give you the MCA table if you want it), MEANS, and ONEWAY. Which you use depends on any additional information you might like as well as the format you happen to like best. I ll use ONEWAY but feel free to try the others. If using the SPSS pull-down menus, after entering the data select ANALYZE/ COMPARE MEANS/ ONE WAY ANOVA. * Problem 1. Employee training. DATA LIST FREE / program score. BEGIN DATA END DATA. ONEWAY score BY program /STATISTICS DESCRIPTIVES /MISSING ANALYSIS. Descriptives SCORE Total 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum One-Way Analysis of Variance - Page 10

11 ANOVA SCORE Between Groups Within Groups Total Sum of Squares df Mean Square F Sig For each of the following, indicate whether H 0 should be accepted or reected. a. A researcher has collected data from 1 Catholics, 1 Protestants, and 1 Jews. She wants to see whether the groups significantly differ at the.05 level in their incomes. Her computed F = 3.0. Solution. Note that n = 63, = 3. Hence, d.f. = 3-1, 63-3 =, 60. Looking at table V, we see that for α =.05 we should accept H 0 if F # Since the researcher got an F of 3.0, she should accept H 0. b. A manager wants to test (using α =.05) whether the mean delivery time of components supplied by 5 outside contractors is the same. He draws a random sample of 5 delivery times for each of the 5 contractors. He computes the following: SS Between = 4 SS Within = 50 Solution. Note that n = 5 (5 delivery times for each of 5 contractors) and J = 5 (5 contractors). Hence MS Between = SS Between/(J - 1) = 4/4 = 1 MS Within = SS Within/(N - J) = 50/0 =.5 F = MS Between/MS Within = 1/.5 =.4 D.F. = (J - 1, N - J) = (4, 0) For α =.05, accept H 0 if F # Therefore, accept H 0. One-Way Analysis of Variance - Page 11

12 3. An economist wants to test whether mean housing prices are the same regardless of which of 3 air-pollution levels typically prevails. A random sample of house purchases in 3 areas yields the price data below. MEAN HOUSING PRICES (THOUSANDS OF DOLLARS): Pollution Level Observation Low Mod High Σ (a) (b) (c) pollution. Solution. (a) Compute the treatment effects Construct the ANOVA Table At the.05 level of significance, test whether housing prices differ by level of ˆ µ = 81., ˆ µ = 77, ˆ µ = 55.4, ˆ µ = 71. ˆ τ = = ˆ τ = = 5.8 ˆ τ = = One-Way Analysis of Variance - Page 1

13 (b) T A1 = 406, T A = 385, T A3 = 77, ( y (1)= N i ) = = () = y = = (3)= T N A i A = SS Total = () - (1) = = , SS Between = (3) - (1) = = ; or, SS Between = τ = 5 * * * = , N ˆ SS Within = () - (3) = = 598, MS Total = SS Total/ (N - 1) = / 14 = 564., MS Between = SS Between/ (J - 1) = / = 958., MS Within = SS Within / (N - J) = 598 / 1 = 498.5, F = MS Between / MS Within = 958. / = 1.9 =77958 Source SS D.F. Mean Square F A (or Treatment, or Explained) SS Between = J - 1 = SS Between/ (J - 1) = 958. MS Between = MS Within Error (or Residual) SS Within = N - J = 1 SS Within / (N - J) = Total SS Total = N - 1 = 14 SS Total / (N - 1) = 564. (c) For α =.05 and df =, 1, accept H 0 if the computed F is # Since F = 1.9, do not reect H 0. More formally, Step 1. Step. H 0 : The τ's all = 0 (i.e. prices are the same in each area) H A : The τ's are not all equal (prices not all the same) Appropriate stat is F = MS Between/ MS Within. Since n = 15 and = 3, d.f. =, 1. Step 3. For α =.05, accept H 0 if F # 5.10 One-Way Analysis of Variance - Page 13

14 Step 4. Compute test stat. As shown above, F = 1.9 Step 5. Do not reect H 0 [NOTE: the SPSS solutions follows later] Here is how you could solve this problem using SPSS. If using the SPSS pull-down menus, after entering the data select ANALYZE/ COMPARE MEANS/ ONE WAY ANOVA. * Problem 3. Housing Prices. DATA LIST FREE / plevel price. BEGIN DATA END DATA. ONEWAY price BY plevel /STATISTICS DESCRIPTIVES /MISSING ANALYSIS. Oneway Descriptives PRICE Total 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum ANOVA PRICE Between Groups Within Groups Total Sum of Squares df Mean Square F Sig One-Way Analysis of Variance - Page 14

15 Comment: Some Anova routines would also report that R =.43. Note that R = SS Between / SS Total = / =.43. That is, R = Explained Variance divided by total variance. We will talk more about R later. F Test versus T Test. Finally, for good measure, we will do an F-Test vs. T-Test comparison. We will do a modified version of problem 1, combining treatments 1 and 3 (the most effective), and and 4 (the least effective). We ll let SPSS do the work. * F test versus T-test comparison. DATA LIST FREE / program score. BEGIN DATA END DATA. RECODE PROGRAM (1, 3 = 1) (, 4 = ). ONEWAY score BY program /STATISTICS DESCRIPTIVES /MISSING ANALYSIS. Oneway Descriptives SCORE Total 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum One-Way Analysis of Variance - Page 15

16 ANOVA SCORE Between Groups Within Groups Total Sum of Squares df Mean Square F Sig Note that the F value is 3.8. T-TEST / GROUPS PROGRAM (1, ) / VARIABLES SCORE. T-Test Group Statistics SCORE PROGRAM Std. Error N Mean Std. Deviation Mean Independent Samples Test SCORE Equal variances assumed Equal variances not assumed Levene's Test for Equality of Variances F Sig. t df Sig. (-tailed) t-test for Equality of Means Mean Difference 95% Confidence Interval of the Std. Error Difference Difference Lower Upper COMMENT: Note that 4.8 = 3.8 (approximately), i.e. t = F. When you only have two groups, both the F test and the T-Test are testing H 0 : µ 1 = µ H A : µ 1 µ Not surprisingly, then, both tests yield the same conclusion. One-Way Analysis of Variance - Page 16

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