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Presentation at a Course on Principles of Epidemiology Health Research Faculty of Medicine, King Fahad Medical City October 11-12, 2017 by Professor Omar Hasan Kasule Sr. MB ChB (MUK). MPH (Harvard), DrPH (Harvard) Chairman of the Institutional Review Board / Research Ethics Committee at King Fahad Medical City, Riyadh.
LECTURE # 9 CONTINUOUS DATA ANALYSIS
OVERVIEW OF PARAMETRIC ANALYSIS:
• Inference on numeric continuous data is based on the comparison of sample means. Three test statistics are commonly used: z, t- and F-statistics.
• The z-statistic is used for large samples.
• The t and F are used for small or moderate samples.
• The z-statistic and the t-statistic are used to compare 2 samples.
• The F statistic is used to compare 3 or more samples.
OVERVIEW OF NON-PARAMETRIC ANALYSIS FOR CONTINUOUS DATA:
• Non-parametric methods were first introduced as rough, quick and dirty methods and became popular because of being un-constrained by normality assumptions.
• They are about 95% as efficient as the more complicated and involved parametric methods. They are simple, easy to understand, and easy to use.
• Generally non-parametric methods are used where parametric methods are not suitable.
NON-PARAMETRIC TESTS:
• The sign test, the signed rank test, and the rank sum tests are based on the median. The sign test is used for analysis of 1 sample median. The signed rank test is used for 2 paired sample medians. The rank sum test is used for 2 independent sample medians.
NON-PARAMETRIC TESTS, Con’t:
• The Kruskall-Wallis test is a 1-way test for 3 or more independent sample medians. The Friedman test is a 2-way test for 3 or more independent sample medians.
• Note that the Mann-Whitney test gives results equivalent to those of the signed rank test.
• The Kendall test gives results equivalent to those of the Spearman correlation coefficient.