FoSSA: Fundamentals of Statistical Software & Analysis

Course Information
Meet the Teaching Team 
Course Dataset 1

Course Dataset 2

MODULE A1: INTRODUCTION TO STATISTICS USING R, STATA, AND SPSSA1.1 What is Statistics?

A1.2.1a Introduction to Stata

A1.2.2b: Introduction to R

A1.2.2c: Introduction to SPSS

A1.3: Descriptive Statistics

A1.4: Estimates and Confidence Intervals

A1.5: Hypothesis Testing

A1.6: Transforming Variables

End of Module A11 Quiz

MODULE A2: POWER & SAMPLE SIZE CALCULATIONSA2.1 Key Concepts

A2.2 Power calculations for a difference in means

A2.3 Power Calculations for a difference in proportions

A2.4 Sample Size Calculation for RCTs

A2.5 Sample size calculations for crosssectional studies (or surveys)

A2.6 Sample size calculations for casecontrol studies

End of Module A21 Quiz

MODULE B1: LINEAR REGRESSIONB1.1 Correlation and Scatterplots

B1.2 Differences Between Means (ANOVA 1)

B1.3 Univariable Linear Regression

B1.4 Multivariable Linear Regression

B1.5 Model Selection and FTests

B1.6 Regression Diagnostics

End of Module B11 Quiz

MODULE B2: MULTIPLE COMPARISONS & REPEATED MEASURESB2.1 ANOVA Revisited – PostHoc Testing

B2.2 Correcting For Multiple Comparisons

B2.3 Twoway ANOVA

B2.4 Repeated Measures and the Paired TTest

B2.5 Repeated Measures ANOVA

End of Module B21 Quiz

MODULE B3: NONPARAMETRIC MEASURESB3.1 The Parametric Assumptions

B3.2 MannWhitney U Test

B3.3 KruskalWallis Test

B3.4 Wilcoxon Signed Rank Test

B3.5 Friedman Test

B3.6 Spearman’s Rank Order Correlation

End of Module B31 Quiz

MODULE C1: BINARY OUTCOME DATA & LOGISTIC REGRESSIONC1.1 Introduction to Prevalence, Risk, Odds and Rates

C1.2 The ChiSquare Test and the Test For Trend

C1.3 Univariable Logistic Regression

C1.4 Multivariable Logistic Regression

End of Module C11 Quiz

MODULE C2: SURVIVAL DATAC2.1 Introduction to Survival Data

C2.2 KaplanMeier Survival Function & the Log Rank Test

C2.3 Cox Proportional Hazards Regression

C2.4 Poisson Regression

End of Module C21 Quiz
Participants 252
The quiz below is designed to test your knowledge of the material covered in Module A2. Best of luck!
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Question 1 of 10
1. Question
What is the definition of â€˜powerâ€™?
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Question 2 of 10
2. Question
What information/parameters do you need before you can estimate the sample size needed for a particular study? Choose all that apply.
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Question 3 of 10
3. Question
What information/parameters do you need before you can calculate the power of a given sample size and study? Choose all that apply.
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Question 4 of 10
4. Question
Estimate the total sample size needed to compare the mean total cholesterol between two groups of men, with versus without coronary heart disease (CHD). Based on a previous study, you think the mean total cholesterol will be 5.5 mmol/l (SD: 1 mmol/l) amongst men without CHD, and 6.0 mmol/l (SD: 1 mmol/l) amongst men with CHD. Assume you want 90% power, and a 5% significance level. (Answers below are approximate as different software will give slightly different answers, but they should be close)
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Question 5 of 10
5. Question
Which of the following increases the power of a study (when you are comparing a difference in means)? (choose all that apply)
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Question 6 of 10
6. Question
What is a Type II error?
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Question 7 of 10
7. Question
In order to estimate the sample size for a casecontrol study, you need to run a test for a difference in means.
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Question 8 of 10
8. Question
In order to calculate the needed sample size for a study to have 90% power, you need estimate the expected difference in means/proportions you are likely to find in the study. With this information, you can run a sample size calculation. How do you estimate the expected difference in means/proportions you are likely to find in your study? (choose all that apply)
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Question 9 of 10
9. Question
What is an acceptable level of power for a study? (choose all that apply)
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Question 10 of 10
10. Question
You are running a placebocontrolled randomised trial of a new therapeutic drug that does actually reduce the risk of having a heart attack. However, over the course of the trial, 10% of the participants in your intervention group stop taking the new drug due to side effects. Will this reduce the power of your study to detect an effect?
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