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Nonparametric statistics. Four levels of measurement Nominal Ordinal Interval Ratio  Nominal: the lowest level  Ordinal  Interval  Ratio: the highest.

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Presentation on theme: "Nonparametric statistics. Four levels of measurement Nominal Ordinal Interval Ratio  Nominal: the lowest level  Ordinal  Interval  Ratio: the highest."— Presentation transcript:

1 Nonparametric statistics

2 Four levels of measurement Nominal Ordinal Interval Ratio  Nominal: the lowest level  Ordinal  Interval  Ratio: the highest level

3 Parametric Assumptions The observations must be independent The observations must be drawn from normally distributed populations These populations must have the same variances

4 Classic Parametric Setting For the Parametric test, we assume we are sampling from normally distributed populations with identical variances. From here we test the hypothesis: H(0):μ1= μ2 = μ3 (All distributions are identical) or at least one is not identical

5 Settings for Nonparametric Tests If you suspect the variable is not normally distributed in the population When you have nominal/ordinal data and are interested in comparing distributions and their relationships. If other assumptions of parametric tests are violated (e.g. homogeneity of variance, ordinal Data)‏ Can provide approximations for small sample sizes

6 A Guide to Testing Differences Nature Sample Type NominalOrdinalInterval 2 Independent Samples Chi-square TestMann-Whitney U Test Independent Samples T-test 2 Related Samples -- Wilcoxon Matched Pairs Test Paired Samples T- test >2 Independent Samples Chi-square TestKruskall-Wallis Test One-way ANOVA Correlation test Rank Spearman's Test Pearson Correlation

7 Mann-Whitney U Test Nonparametric alternative to two-sample t-test Actual measurements not used – ranks of the measurements used Data can be ranked from highest to lowest or lowest to highest values Calculate Mann-Whitney U statistic U = n 1 n 2 + n 1 (n 1 +1) – R 1 2

8 Example of Mann-Whitney U test Two tailed null hypothesis that there is no difference between the heights of male and female students (5% significance level)‏ H o : Male and female students are the same height H A : Male and female students are not the same height

9 R 2 = 48R 1 = 30n 2 = 5n 1 = 7 9170 6178 125163180 114165183 103168185 82173188 71175193 Ranks of female heights Ranks of male heights Heights of females (cm)‏ Heights of males (cm)‏ U = n 1 n 2 + n 1 (n 1 +1) – R 1 2 U=(7)(5) + (7)(8) – 30 2 U = 35 + 28 – 30 U = 33 U 0.025,7,5 = U 0.025,5,7 = 6 As 33 > 6, H o is rejected Zar, 1996

10 The Kruskal-Wallis Test This test is used to test whether several populations have the same mean. It is a nonparametric substitute for a one-factor ANOVA.

11 where n j is the number of observations in the j th sample, n is the total number of observations, and R j is the sum of ranks for the j th sample.

12 Kruskal-Wallis Test Example: Test at the 5% level whether average employee performance is the same at 3 firms, using the following standardized test scores for 20 employees. n 3 =7n 2 = 6n 1 = 7 7380 607290 706275 936187 508485 657795 826878 rankscorerankscorerankscore Firm 3Firm 2Firm 1

13 f(  2 )‏ acceptance region crit. reg..05 5.991 From the  2 table, we see that the 5% critical value for a  2 with 2 dof is 5.991. Since our value for K was 6.641, we reject H 0 that the means are the same and accept H 1 that the means are different.

14 We rank all the scores. Then we sum the ranks for each firm. Then we calculate the K statistic. R 3 = 57n 3 =7R 2 = 47n 2 = 6R 1 = 106n 1 = 7 9731380 2608721890 7704621075 19933611787 15015841685 56511772095 14826681278 rankscorerankscorerankscore Firm 3Firm 2Firm 1


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