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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: B3.6 Spearman’s Rank Order Correlation

B3.6 PRACTICAL: R

For the last practical in this module, you are going to perform a test of correlation on non-parametric data using the Spearman’s Rank Order Correlation.

This can be conducted using the cor.test command, which has the structure:

cor.test(data, variable 1, variable 2, method=””)

We can specify spearman as the method to conduct a Spearman’s rank test. If the method is not specified, the default is Pearson’s correlation.

Question B3.6: What is the spearman correlation between Weight_end and BCS_end?

Answer

We can use the cor.test command with the structure specified above:

> cor_test(data, Weight_end, BCS_end, method = “spearman”)

This gives the following RStudio output:

We can see that there is a significant (p<0.05) correlation between the body condition score and the weight of the mice at the end of the study and their correlation coefficient is 0.81.

B3.6 PRACTICAL: Stata

For the last practical in this module, you are going to perform a test of correlation on non-parametric data using the Spearman’s Rank Order Correlation.

The command in Stata is

spearman [varlist] [if] [in] [, spearman_options]

We can put multiple variables on the command line where it states [varlist].

Question B3.6: What is the spearman correlation between Weight_end and BCS_end?

Answer

Here you would report an rs value of 0.814 and a significant correlation with P<0.001.

B3.6 PRACTICAL: SPSS

For the last practical in this module, you are going to perform a test of correlation on non-parametric data using the Spearman’s Rank Order Correlation.

Select

Analyze  >> Correlate >> Bivariate

Move the two variables you are interested in into the Test Variables box. Here we are going to look at BCS_end and Weight_end.

If you put more than two variables into the Test Variables box, SPSS will perform the selected test of correction on all possible combinations.

Make sure ‘Spearman’ is selected at the bottom of the box before you press ‘OK’ to run the test.

Answer

Here you would report an rs value of 0.814 and a significant correlation with P<0.001. SPSS automatically conducts all of the correlations both ways and the correlation of each variable against itself.

If this is confusing, you can get rid of this by clicking ‘show only lower triangle’ and then deselecting ‘show diagonal’ when setting up the test. Then your output will look like this.

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