PEP 6305 Measurement in Health & Physical Education


Topic 9: Analysis of Variance (ANOVA)

Section 9.1


n   This Topic has 2 Sections.



n   Vincent & Weir, Statistics in Kinesiology, 4th ed. Chapter 11 “Simple Analysis of Variance: Comparing the Means Among Three or More Sets of Data”


n   To demonstrate the comparison of means using analysis of variance (ANOVA).


The ANOVA Null Hypothesis and the F Statistic


n   ANOVA compares multiple (≥ 2) means simultaneously. The purpose is to determine if the variation among the means is higher than would be expected by sampling error alone.

n   The ANOVA null hypothesis is that all of the means have the same value. If the number of means = k , the null hypothesis is: 


¨  where μ, the lowercase Greek letter mu, is a symbol for the population mean.

n   The statistic used in ANOVA is F, which is the ratio of the variation “between” the groups to the variation “within” the groups.

¨  The observed variance between groups is an estimate of the variation among the group means in the population. If all groups have the exact same mean, this variance = 0.

¨  The observed variance within groups is an estimate of the variation that would be expected to occur as a result of sampling error alone.

¨  If the observed (computed) value for F is significantly higher than the value expected by sampling variation alone, then the variance between groups is larger than would be expected by sampling error alone.

·       In other words, at least one mean differs from the others enough to cause large variation between the groups.


ANOVA versus t Test


n   If a t test compares two means, why not just test each pair of means instead of using ANOVA? (After all, t tests are easier to do, by hand anyway, and easier to interpret, right?)

n   Performing multiple t tests in one set of data causes a number of problems.

¨  Increases the type I error probability (α): If you conduct multiple tests in a single set of data, you are compounding error probability and increasing the chance of rejecting the null hypothesis when it is actually true (a type I error). (See here for an explanation.)

¨  Does not use all of the available information: t tests compare only two means, using an estimate of sampling error from the two groups being compared. ANOVA uses an estimate of sampling error from all groups. Recall from the Central Limit Theorem that sampling error decreases as sample size increases; thus, the ANOVA estimate of sampling error is typically more accurate than the estimates from t tests.

¨  Increases time and effort: Obviously, doing more tests take more time and you have to read more computer output. While this is not a tremendous concern if there are 3 or 4 groups, in complex studies the number of paired comparisons can be very large.

¨  Does not allow testing of complex hypotheses: Vincent does not mention this limitation, but it is important. In complex studies, we often have hypotheses about whether one factor influences the way that another factor influences the dependent variable. Using t tests to compare group means cannot answer these questions, meaning that you have not tested your hypothesis. The flexibility of ANOVA allows for testing these types of hypotheses directly.

n   ANOVA indicates whether one or more means differs from the others; it does not tell you which means differ from one another.

¨  Techniques known as post hoc (Latin for “after the fact”) tests are used to identify which means differ. Post hoc tests are discussed in the next section.


ANOVA Assumptions


n   Normality

¨  The dependent variable is normally distributed in the population being sampled.

¨  This assumption is needed to interpret variance properly.

¨  Normality of the dependent variable can be evaluated using a histogram and skewness and kurtosis statistics.

¨  ANOVA, however, is remarkably accurate ("robust") even if this assumption is not met.

n   Homogeneity of variance

¨  This means that the variances within each group are equal in the population being sampled.

¨  This assumption is needed to allow for computing a pooled (combined across all groups, sort of like an average) estimate of sampling variance. If one or more groups had a variance that was much larger or much smaller than the other groups, the “average” variance would be inaccurate (not representative of the population) for any of the groups (too large for some, too small for others). Since sampling variance is used to compute the F statistic, if the estimate of sampling variance is inaccurate, the accuracy of the F test is questionable.

¨  Homogeneity of variance can be evaluated using a variety of statistical tests, but the most straightforward method is to compare the within-group variances; one or more variances twice as large as other variances may be a problem.

¨  This assumption affects ANOVA more than normality, but only large differences in the variances (i.e., one twice as large as another) produce a noticeable effect.

n   Independence

¨  This means that the scores are not affected by other scores, which means that subjects in one group did not influence subjects in other groups.

¨  This assumption is needed to allow for computing the variance between groups. If one group influences scores in another group, then comparing scores between those two groups is biased—one group will always have higher or lower scores regardless of any treatment effect being present.

¨  Random sampling, random assignment to groups, and keeping the groups separated during the study ensures independence.

¨  This assumption is critical; violation invalidates the results.

¨  Repeated measures analyses (see Topic 10) are used when scores are not independent.


“Between” Variance and “Within” Variance


n   Group means are computed within each group by summing the scores in that group and dividing by the number of subjects in that group:

   where Xik is the score of a person (i) in group k and nGroup k is the number of subjects in group k.

n   A "grand" mean is computed across all groups by summing the scores of all subjects across all groups: 

   where N is the total number of subjects in the study (the sum of all nGroup).

n   The between group variance is the variation of the group means from the grand mean.

n   The within group variance is the variation of each subject from the group mean of the group to which they belong.

n   The total variance is the variation of each subject from the grand mean (the sample variance computed in Topic 4).

n   These three variance are related: VarianceTotal = VarianceBetween + VarianceWithin


Sum of Squares

n   A sum of squares (SS) is the sum of the squared deviations.

n   Each component (Between, Within, and Total) in ANOVA has a SS.

¨  Between SS, summing across all k groups, multiplying by n in each group to make SSB comparable to SSWithin and SSTotal ; this multiplication equalizes the scale of the SS values.

¨  Within SS, summing across the nGroup subjects in each group, then across all k groups, which means you are summing across (nGroup × k) = all N subjects.

¨  Total SS, summing across all N subjects.

n   Each of these SS is a measure of variability.

n   ANOVA compares the between-groups variability with the within-groups variability using the F statistic.

n   Because the SS depend on the number of elements being summed, and the number of elements for the Between SS is less than Within SS (you have fewer groups than you have subjects), then both SS measures are standardized by their respective degrees of freedom, creating Mean Squares.


Mean Squares and the F Statistic

n   A mean square (MS) is the mean of a series of squared deviations.

n   A MS is computed by dividing a SS by its degrees of freedom (df).

¨  df = (the number of elements being summed in the SS) – (the number of means subtracted in the SS)

¨  For each SS equation, compute the difference between the number of elements ahead of the subtraction sign from the number of elements behind the subtraction sign.

¨  dfBetween : How many elements are being summed in the SSB equation above? The k group means. How many means are subtracted from these k elements? One—the grand mean is subtracted from all k group means. So dfB = k – 1.

¨  dfWithin : How many elements are being summed in the SSW equation above? The (nGroup × k groups) = N subjects. How many means are subtracted from these N elements? The k group means are subtracted from the n subjects in each group. So dfW = N – k.

¨  dfT : What is the df for SSTotal? Note that dfT = dfB + dfW = (k – 1) + (N – k) = k – 1 + N – k = N – 1 .

n   Divide each SS by its df to find the corresponding MS.

¨  Between: 

¨  Within: 

n   FINALLY, we compute F :

  MSW represents the variation expected as a result of sampling error alone.

n   The sampling distribution of F in the population is known, so we can compare our observed sample F value to the distribution to determine the error probability for the observed value.

¨  If MSB is several times larger than MSW, we may conclude that the variation between groups is significantly larger than sampling error.

n   MSW is often called mean square error (MSE), referring to sampling error.  MSW and MSE are exactly the same thing.


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