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Moments Skew and Kurtosis APPENDIX 3.3: CALCULATING SKEW AND KURTOSIS 1 We mentioned in Chapter 1 that parameters are simply yy= numbers that characterize the scores of populations. 2 yy= * y The mean (µ) and variance (σ2) are two of the most 3 important parameters associated with statistical yy= **yy 4 analysis in psychology. In subsequent chapters, we will yy= ***yyy. use statistics to estimate these important parameters. Although µ and σ2 will be our primary concern, skew So, the exponents simply tell us how many times to and kurtosis are parameters in the same way as µ multiply a number by itself. and σ2. Skew and kurtosis can be computed from the scores in populations in a manner very similar to the Skew and Kurtosis computation of the mean and variance. Therefore, we will take a moment to comment on how skew and The second, third, and fourth central moments are related kurtosis are computed in populations and samples. to skew and kurtosis. In a population, skew is defined as Moments skew θ3 = 3 (3.A3.1) σ At the beginning of Chapter 3, we noted that the mean and variance had very similar definitions. The mean where σ is the population standard deviation (or the square of a population is the sum of all scores divided by N. root of the second central moment, θ2). Skew, as defined Statisticians sometimes call this the first raw moment in equation 3.A3.1, can take on positive and negative of the distribution. The variance in a population is the values. Symmetrical distributions (such as in Figures 3.3a sum of squared deviations from the mean, divided by and 3.4a) have zero skew. Distributions that are skewed N. Statisticians call this the second central moment of to the right yield positive skew values, and those that are a distribution. Central moments are computed by sub- skewed to the left yield negative skew values. The right- tracting the mean from all scores and then raising these skewed distribution in Figure 3.3 has a skew of about 3.6, deviation scores to some power. (Raw moments do and the left-skewed distribution has a skew of about -3.6. not subtract the mean from all scores.) The following Kurtosis in a population is defined as expressions define the second, third, and forth central moments: θ kurtosis = 4 . (3.A3.2) σ4 ∑()y − µ 2 θ = 2 N Kurtosis, as defined in equation 3.A3.2, can take 3 on only positive values. The larger the values, the ∑()y − µ θ3 = more leptokurtic the distribution. However, when N statisticians talk about kurtosis, they often mean excess ∑()y − µ 4 θ = . kurtosis. A normal distribution has a kurtosis of 3, when 4 N defined using equation 3.A3.2. Statisticians define 3 as normal kurtosis. Excess kurtosis is the difference The symbol θ is pronounced theta. Therefore, θ1, θ2, between kurtosis and normal kurtosis. Therefore, θ3, and θ4 represent the first, second, third, and fourth excess kurtosis is defined as follows: central moments of a distribution, respectively. All are moments computed in the same way, and they differ θ4 excess kurtosis =−3. (3.A3.3) only in the power to which the differences between σ4 y and µ are raised. You may not be familiar with exponents other than 2 (squaring), but they are really A normal distribution has an excess kurtosis of 0. not complicated, as shown in the following examples: The leptokurtic distribution in Figure 3.4b has an 1 2 Statistics for Research in Psychology excess kurtosis of 3, and the platykurtic distribution bad. The blue term is called a correction factor, which in Figure 3.4c has an excess kurtosis of -1. The flattest we already discussed when talking about the sample possible distribution (most platykurtic) is the uniform, variance. If you play around with the correction factor, or rectangular, distribution. A uniform distribution is you’ll notice that it gets closer to 1 as n (sample size) one for which the densities are equal for all possible gets larger, because n-1 and n-2 get closer and closer 2 numbers between some minimum and maximum. For to n, making̭ (n-1)(n-2) closer and closer to n . This example, a uniform distribution may have min = 0 means that θ s3 needs less correction as sample size 3/ and max = 1, and all values between min and max are increases. In statistics, we like large samples! equally probable. Uniform distributions have excess The formula for excess kurtosis for a sample is kurtosis of -1.2. ̭ θ nn2 ()+1 excess kurtosis = 4 * Estimating Skew and Kurtosis s4 ()nn−−12()()n−3 2 (3.A3.5) ()n−1 The definitions of skew and excess kurtosis in equa- −3* , tions 3.A3.1 and 3.A3.3 are parameters. These for- ()n−2 (n−3) mulas should not be applied to samples to estimate the population parameters. Just as the definition of the wherḙ n is sample size, s is the sample standard deviation, sample variance differs from that of the population and θ4 is computed exactly like θ4 but from the scores̭ in variance, the definitions of the sample skew and sample a sample rather than scores in a population; i.e., θ 4 = Σ(y kurtosis differ from those of the population skew and - m)4/n. If equation 3.A3.4 looks horrible, then equation kurtosis. In all cases, the differences in the formulas 3.A3.5 looks positively ghastly. But notice again that the have to do with making the statistics good estimators black terms in equation 3.A3.5 look exactly like equation of the parameters. Once again, I promise this will be 3.A3.3, except that θ4 and s are computed from scores explained in Chapter 5. in the sample. The correction factors in blue behave the The formula for skew for a sample is same way as the correction factor in the sample variance ̭ and the sample skew. As sample size increases, the θ n2 skew = 3 * , (3.A3.4) correction factors get closer to 1. s3 (n−1)(n−2) We are rarely interested in estimating skew and kurtosis for their own sake. Rather, we use skew where n is samplḙ size, s is the sample standard and kurtosis computed from a sample to assess the deviation, and θ3 is computed exactly like θ3 but from normality of the population from which the sample the scores̭ in a sample rather than scores in a population; was drawn. When a sample is drawn from a normal 3 i.e., θ3 = Σ(y - m) /n. Equation 3.A3.4 looks horrible. population, then skew and excess kurtosis should (If I’d seen something like this in my first statistics be close to 0. Large departures from 0 (positive or course, I would have had an anxiety attack. Sorry.) negative) would suggest that our sample was not drawn Notice, however, that the black term in equatioṋ 3.A3.4 from a normal population. Just how large a departure looks exactly like equation 3.A3.1, except that θ3 and s from 0 would be cause for concern is something we will are computed from scores in the sample. That’s not so discuss in later chapters. LEARNING CHECK 1 1. If y = {2, 2, 5, 5, 5, 11} is a small population of (d) θ3, (e) θ4, (f) skew, (g) kurtosis, and (h) excess scores, calculate the following: (a) µ, (b) σ, (c) θ2, kurtosis. Answers 1. (a) µ = 5. (b) σ = 3. (c) θ2 = 9. (d) θ3 = 27. (e) θ4 = 243. 243/81 = 3. (h) Excess kurtosis = kurtosis - 3 = 3 - = 3 = / = = 4 = = (f) Skew θ3/σ 27 27 1. (g) Kurtosis θ4/σ 3 0. Chapter 3 • Online Appendices 3 APPENDIX 3.4: THE IMPORTANCE OF THE MEAN AND STANDARD DEVIATION In Chapter 2, we saw that probability distributions FIGURE 3.A4.1 ■ Chebyshev’s Theorem convey the relative standing of scores within a sample or population. That is, knowing how scores are 0.20 distributed allows us to determine the proportion of the 12.5% 75% 12.5% distribution at or below any score of interest. We can 0.16 say that a score is extreme or unusual if there are very few scores above it (thus an extremely high score) or 0.12 Skewed very few scores below it (thus an extremely low score). Normal Density Bimodal In Chapter 3, we saw that the mean is an important 0.08 measure of central tendency for a distribution, and 0.04 the standard deviation is an important measure of dispersion. There is an important connection between 0.00 the mean and standard deviation of a distribution, and 52 56 60 64 68 72 76 80 84 the relative standing of scores in a distribution. Test Scores Looking back at the distributions in Figures 3.3, 3.4, An illustration of the meaning of Chebyshev’s theorem for and 3.5, you might notice that scores tend to fall close the case of k = 2. The theorem says that no matter what shape to the mean, on average. In fact, it is unusual for a score the distribution has, at least 75% lies in the interval µ ± 2(σ), to fall a long way from the mean. In later chapters, we which means that at most 12.5% of it lies above µ + 2(σ), and will use the standard deviation of a distribution as our at most 12.5% lies below µ - 2(σ).
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