Descriptive Statistics II: Measures of Dispersion

1. Descriptive Statistics II: Measures of Dispersion

2. How typical is ‘typical’? • Mean, Median and Mode are measures designed to convey to the reader the “typical” observation. • Often useful for the reader to know just how typical the “typical” observation is! • If most of the observations fall in the mode, than the mode is very “typical” • If most of the observations are close to the mean or median observation than the mean/median is very “typical” or indicative of the distribution.

3. Measures of Dispersion • Measures of dispersion give us an idea of how representative the measures of central tendency (mode, median, mean) are of the entire distribution. • The idea is that the more the data is dispersed – or spread out – from the central measure (mode, median or mean), the less indicative the central measure. • In other words, a high measure of dispersion tells us that the mode/median/mean is not very typical and many observations are quite different!

4. Measures of Dispersion • Measures of dispersion – how dispersed are the observations. • Variation ratio • Range • Interquartile Range • Variance & Standard Deviation • Skewness, Kurtosis

5. Nominal: Variation Ratio • For nominal variables, the variation ratio is the percentage of cases which are not the mode. • =1-(number of observations in the mode) / total number of observations • Infrequently used since the variation ratio really does not tell the reader anything that the mode does not already tell the reader.

6. Example: Variation Ratio and Mode USA Today/Gallup Poll # 2009-02: January – Economy / Obama: What should be the primary goal for the United States in Afghanistan: 1 Building a stable democratic government in Afghanistan 300 (30.6%) 2 Weakening terrorists ability to stage attacks against the USA… 557 (56.8%)= Mode 3 Both equally… 123 (12.6%) Variation Ratio = 43.2% Note: Unweighted responses are not reflective of the population.

7. Range • Minimum value to maximum value • Useful when you want to know all the possible responses, for an aggregate policy measures like GDP or other interval/ratio data. • Not very useful for closed-ended survey responses. • In example above, range of real GDP is $338 to $48,589. • What does the range tell us about the mean of $9,089 or the median of $5,194?

8. Percentiles, Quantiles and Quartiles • By ordering the values in the distribution, one can classify observations by where they are in the distribution. • Percentiles divide the distribution into 100 equal parts. • Lowest values are in the 1st percentile, largest values are in the 99th or 100th percentile. • The median is the 50th percentile. • Quantiles divide the distribution into 10 equal parts. • Quartiles divide the distribution into 4 equal parts. • 1st Quartile = 25th Percentile, 2nd Quartile = Median, 3rd Quartile = 75th Percentile • This matters because quartiles provide us with a measure of dispersion…

9. Interquartile Range • For closed-ended survey responses, like rating Hillary Clinton, finding the interquartile range (or IQR) between the observation value at the 25th percentile and the observation value at the 75thpercentile provides more useful information than the full range. • IQR measures the range of the middle half of all observations. • A high IQR relative to the range tells the reader that there are many observations far from the median. • A low IQR relative to the range tells the reader that at least half of all observations are very close to the median.

10. Calculating the interquartile range • Order all of the responses. • Identify the observation at the 25th percentile. • Recall: 50th percentile = median. • Take the value of this observation. • Identify the observation at the 75thpercentile. • Take the value of this observation. • Interquartilerange= difference between the value of the observation at the 25th percentileand the observation at the 75thpercentile.

11. Example: Feelings towards Hillary Clinton (2008) 25th percentile Median (50th Percentile)= 6 75th percentile Source: American National Election Study, 2008, v093040 [recoded into 11 categories, and weighted]

12. Example: Feelings towards Hillary Clinton (2008) 25th percentile IQR = 9– 4= 5 75th percentile Source: American National Election Study, 2008, v093040 [recoded into 11 categories, and weighted]

13. Interpreting IQR • The interquartile range for opinions of the Hillary Clinton (2008) was 5. • An IQR of 5 (with a range of 11) tells us that most observations fall into a relatively narrow range of values. • There are few observations with extremely low views of Hillary Clinton.

14. Interpreting IQR: Real GDP Example • In example above, range of real GDP ran between $338 to $48,589= $48,251 • The median was $5,194. • The value of the observation at the 25th percentile is $2,018. • The value of the observation at the 75th percentile is $13,532. • The interquartile range is $13,532 - $2,018= $11,514. • This tells us that half of all observations are in a relatively narrow range since 11,000 is much smaller than 48,000. • Most countries are nowhere near as rich as the richest countries…

15. Interpreting IQR: % Pop on $2/day • Value of observation at 25th Percentile = 13.1% • Value of observation at 75th Percentile = 73.9% • What is the interquartile range? • Since the range was between 2% and 96.6%, what does the interquartile range tell us?

16. Variance • Rather than relying on the location of the value, variancemeasures dispersion by calculating how far observations are from the mean. • Variance = Average of the distance from the mean of each observation (squared). • High variance means that many/most observations are far from the meanbut could be heavily influenced by outliers. • Low variance means that many/most observations are close to the mean.

17. Formula: Variance • Where N is the total number of observations • is the value of each observation • is the mean of the set of data • The difference between each observation’s value and the mean is squared before being added to eliminate negative signs. • Result tends to be large relative to the value of the observations.

18. Standard deviation • Takes square root of variance to put measure in the same unit as the observations. • Example: The average rating of the Conservatives is 4.8 and the standard deviation is 2.8. • This tells us that the average amount that the ratings differ from the mean is 2.8 points on the 11 point scale used to measure feeling towards the Conservative Party. • In contrast, the variance is 8.0, which can be interpreted as 8 squared points on the 11 point scale. This explanation is confusing and has little intuitive power.

19. Formula: Standard Deviation • Standard deviation is the square root of variance (S= ), so the calculations (and symbols)are exactly the same. • N is the total number of observations • is the value of each observation • is the mean of the set of data • The difference between each observation’s value and the mean is squared before being added to eliminate negative signs.

20. Skewness • If observations are symmetric around the mean there are as many observations less than the mean than there are observations greater than the mean • Skewness measures the extent to which the observations are asymmetric. • In other words, skewness tells us whether there are many more observations above or below the mean. • Except skew does not count the observations, skewness considers the values of the observations. • Like mean, skew is sensitive to extreme values.

21. Skewness Implications • Skewness could have normative implications for policy outcomes and public opinion. • Some bi- and multivariate analyses become more complicated with a skewed distribution.

22. Interpreting Skewness • Negative skew= most of the observation values are above the mean. • Usually this means that most of the observations (including the median) are below the mean. • Positive skew= most of the observation values are below the mean. • Usually this means that most of the observations (including the median) are below the mean. • Skew values close to zero mean that the distribution is nearly symmetrical.

23. Feelings towards Hillary Clinton Are more observations above or below the mean? Mean = 5.9 Source: American National Election Study, 2008, v093040 [recoded into 11 categories, and weighted]

24. Feelings towards Hillary Clinton More above; therefore skew is negative! Median>Mean Source: American National Election Study, 2008, v093040 [recoded into 11 categories, and weighted]

25. Conservative Party Skew More above; therefore skew is negative! Mean < Median Source: Canadian Election Study, 2008, CES_MBS_I10a [National Weight]

26. Real GDP Skew Here, mean > median & skew is positive (1.35). Median = $5,194.48 Mean = $9,089.82 Source: Gleditsch, K. S. 2002 via Quality of Government (QoG) v6, April 2011

27. Kurtosis • Kurtosis measures how tall or flat is the distribution of the variable. • Even with the same variance, some distributions will have more observations in a tall peak near the mean and then be more spread out than a distribution with the observations more concentrated in a shallower, broad peak near the mean. • Rarely used in social science.

28. Kurtosis – Illustrated • Relative to a ‘normal’ mesokurtic distribution (kurtosis=0) • Positive kurtosis (“leptokurtic”) means that the observations have tall peak near the mean. • Negative kurtosis values (“platykurtic” – sounds like ‘flat’) means that the observations are very spread apart with a broad, shallow peak.

29. Using Descriptive Statistics to Make Comparisons

30. Compare distributions Responses are opinions of Canadian adults.

31. How would you? • Describe the opinions portrayed in the previous slide. What would you say? • It may not be very easy. • There is no clear, standard or normal way to make the descriptions. • This is where descriptive statistics proves its use. • It is possible to discuss the overall distribution, the mode, any apparent differences.

32. How much have these institutions done to help resolve the conflict in Lebanon? Scale: 1 = A lot 2 = A little 3 = Not very much 4 = Nothing at all Note: the median = 3 for all three variables

33. Comparing Medians • The median for all these variables is three, indicating that: • Most Canadians think that the UN, EU and UN are not doing much or nothing at all • There are NOT large differences in opinion between variables. • But there are some differences, and the table clearly indicates what those differences are in a concise manner.

34. Comparing Means The mean for the U.N. is lower than the mean response for USA and EU, telling us that Canadians thought that the U.N. was doing [slightly] more to resolve the conflict than the EU and the USA. Scale: 1 = A lot 2 = A little 3 = Not very much 4 = Nothing at all The low UN mean is sensitive to the relatively high number of respondents who said the UN was doing “a lot.”

35. Comparing dispersion The standard deviation is about the same, indicating that the dispersion of opinion is about the same. Scale: 1 = A lot 2 = A little 3 = Not very much 4 = Nothing at all

36. How much have these institutions done to help resolve the conflict in Lebanon? All three variables skew negative, indicating that more opinions are “above” the mean. With the scale used for this variable, this means that more than half of all respondents thought that the UN, US and EU were doing “not very much” or “nothing at all.” In particular, the U.S.A., was seen by many as not doing very much. Can you see this in the chart? Scale: 1 = A lot 2 = A little 3 = Not very much 4 = Nothing at all

37. Comparing attitudes • Which politicians, on average, were the most popular in 2008? Least popular? • Towards which of the politicians is the widest range of feelings? Source: American National Election Study, 2008, V083036-V083041 [pre-election weight]