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FoSSA: Fundamentals of Statistical Software & Analysis

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  1. Course Information

    Meet the Teaching Team
  2. Course Dataset 1
  3. Course Dataset 2
  4. MODULE A1: INTRODUCTION TO STATISTICS USING R, STATA, AND SPSS
    A1.1 What is Statistics?
  5. A1.2.1a Introduction to Stata
  6. A1.2.2b: Introduction to R
  7. A1.2.2c: Introduction to SPSS
  8. A1.3: Descriptive Statistics
  9. A1.4: Estimates and Confidence Intervals
  10. A1.5: Hypothesis Testing
  11. A1.6: Transforming Variables
  12. End of Module A1
    1 Quiz
  13. MODULE A2: POWER & SAMPLE SIZE CALCULATIONS
    A2.1 Key Concepts
  14. A2.2 Power calculations for a difference in means
  15. A2.3 Power Calculations for a difference in proportions
  16. A2.4 Sample Size Calculation for RCTs
  17. A2.5 Sample size calculations for cross-sectional studies (or surveys)
  18. A2.6 Sample size calculations for case-control studies
  19. End of Module A2
    1 Quiz
  20. MODULE B1: LINEAR REGRESSION
    B1.1 Correlation and Scatterplots
  21. B1.2 Differences Between Means (ANOVA 1)
  22. B1.3 Univariable Linear Regression
  23. B1.4 Multivariable Linear Regression
  24. B1.5 Model Selection and F-Tests
  25. B1.6 Regression Diagnostics
  26. End of Module B1
    1 Quiz
  27. MODULE B2: MULTIPLE COMPARISONS & REPEATED MEASURES
    B2.1 ANOVA Revisited - Post-Hoc Testing
  28. B2.2 Correcting For Multiple Comparisons
  29. B2.3 Two-way ANOVA
  30. B2.4 Repeated Measures and the Paired T-Test
  31. B2.5 Repeated Measures ANOVA
  32. End of Module B2
    1 Quiz
  33. MODULE B3: NON-PARAMETRIC MEASURES
    B3.1 The Parametric Assumptions
  34. B3.2 Mann-Whitney U Test
  35. B3.3 Kruskal-Wallis Test
  36. B3.4 Wilcoxon Signed Rank Test
  37. B3.5 Friedman Test
  38. B3.6 Spearman's Rank Order Correlation
  39. End of Module B3
    1 Quiz
  40. MODULE C1: BINARY OUTCOME DATA & LOGISTIC REGRESSION
    C1.1 Introduction to Prevalence, Risk, Odds and Rates
  41. C1.2 The Chi-Square Test and the Test For Trend
  42. C1.3 Univariable Logistic Regression
  43. C1.4 Multivariable Logistic Regression
  44. End of Module C1
    1 Quiz
  45. MODULE C2: SURVIVAL DATA
    C2.1 Introduction to Survival Data
  46. C2.2 Kaplan-Meier Survival Function & the Log Rank Test
  47. C2.3 Cox Proportional Hazards Regression
  48. C2.4 Poisson Regression
  49. End of Module C2
    1 Quiz

Learning Outcomes

By the end of this section, students will be able to:

  • Explain the importance of the parametric assumptions and determine if they have been met
  • Explain the basic principles of rank based non-parametric statistical tests
  • Describe the use of a range of common non-parametric tests
  • Conduct and interpret common non-parametric tests

You can download a copy of the slides here: B3.3 Kruskal-Wallis Test

B3.3 PRACTICAL: R

We can use the kruskal_test command for a comparison between more than two groups. As there is no constraint on the number of groups for the test, we only need to specify the data, followed by the dependent and independent variables in the same was as the previous module.

Question B3.3: Is there a significant difference between all three mouse strains on their BCS_baseline score?

Answer

The R code we use is:

> kruskal_test(mice, BCS_baseline ~ Strain)

The RStudio output looks like:

These results show that there is a significant difference (p<0.05) in the baseline body condition score when comparing all three strains.

B3.3 PRACTICAL: Stata

Following on from the previous practical (B3.2), you can use the Kruskal-Wallis Test to check for differences in baseline body condition score (BCS_baseline) between all three mouse strains.

The command is:

kwallis outcome_variable, by(grouping variable)

Question B3.3: Is there a significant difference between all three mouse strains on their BCS_baseline score?

Answer

We look at the chi-squared value with ties, which is p<0.05. The test indicates the median baseline body composition score of mice was not the same (X2=7.6, p=0.02). Therefore, there is a significant difference in the baseline body condition score when comparing all three groups.

B3.3 PRACTICAL: SPSS

Following on from the previous practical (B3.2), you can use the Kruskal-Wallis Test to check for differences in baseline body condition score (BCS_baseline) between all three mouse strains.

Select

Analyze >> Nonparametric Tests  >> Legacy Dialogs >> K Independent Samples

Move the variable of interest (BCS_baseline) into the Test Variable List.

Assign ‘Strain_group’ as the grouping variable and then click ‘Define Range’. Here you need to add the numerical grouping value of the highest and lowest groups of the range you wish to test. As we only have 3 groups here, these values are 1 and 3, but in larger data sets this can be used to specify a subset of groups to compare.

Make sure ‘Kruskal-Wallis H’ is selected at the bottom of the box before you press ‘OK’ to run the test.

Answer

These results show that there is a significant difference in the baseline body condition score when comparing all three groups.

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