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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 session, students will be able to:

  • Continue practicing basic software commands
  • Learn how to explore the dataset, identifying the different types of variable stored
  • Calculate the different measures of location and spread
  • Plot frequency distributions and histograms

You can download a copy of the slides here: A2.1 Key Concepts

Video A2.1 Introduction to Power and Sample Size Calculation (11 minutes)

A2.1 PRACTICAL: R

The power package

We can estimate sample size and power using the R package ‘pwr’. First you need to install the package and load the library:

install.packages(“pwr”)

library(“pwr”)

Once this package is installed, we can start calculating our needed sample size to test hypotheses or we can estimate the amount of power a study had to detect a difference is one existed. The next sections will show you how to do this.

Look at the help file for the function pwr.t.test. What is the default value for significance level? 

What information do you need to conduct a sample size estimate for difference between means? 

Answer

The default value for significance level (denoted as ‘sig.level‘) in the function is 0.05. This is indicated by sig.level=0.05, and can be changed by specifying a different number in the function. So you can work out the sample size you would need for different significance levels. 

To conduct a sample size estimate between means you need the power and alpha values you have decided on, the estimated mean of each group in the population, or the estimated difference between groups in the population, and the estimated population standard deviation. 

A2.1 PRACTICAL: Stata

The power command

Calculations for power and sample size in Stata can be performed using the power command. If you look at the help file, you will see that you can use this command to compute a sample size, the power or an effect size. You do not need to have a data set loaded.

Look at the setup of the power command:

power method …, n(numlist) [power_options …]

You need to choose the method you are calculating a power estimate for. To do this, ask yourself the following questions: do you have 1 sample, 2 independent samples or 2 paired samples? Additionally, do you want to compare means from continuous outcomes or proportions from binary/categorical outcomes? The practical today will explore the power command and the one on Session 28 will familiarise you with some of the other options of this command.

Question A2.1: What are Stata’s default values for power and significance level in the power command? Can you see how to change them?

Answer

The default is for 80% power and a 5% significance level (denoted as ‘alpha’). You can change these using the options power() and alpha().

A2.1 PRACTICAL: SPSS

Calculations for power and sample size in SPSS are performed using the ‘Power Analysis’ option under the ‘Analyze’ menu.

Take some time to have a look through the different test types which you can estimate power and sample size for.

You need to choose the method you are calculating a power estimate for. To do this, ask yourself the following questions: do you have 1 sample, 2 independent samples or 2 paired samples? Additionally, do you want to compare means from continuous outcomes or proportions from binary/categorical outcomes?

Take some time to open some of the power analysis tests in SPSS and have a look through them.

What is the default value for significance level? 

What information do you need to conduct a sample size estimate for difference between means? 

Answer

The default value for significance level (denoted as ‘alpha‘) in SPSS is 0.05. This is easily changed in the Power Analysis window once you have selected your test type. So you can work out the sample size you would need for different alpha values.

To conduct a sample size estimate between means you need the power and alpha values you have decided on, the estimated mean of each group in the population, or the estimated difference between groups in the population, and the estimated population standard deviation.

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