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Course Information
Meet the Teaching Team -
Course Dataset 1
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Course Dataset 2
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MODULE A1: INTRODUCTION TO STATISTICS USING R, STATA, AND SPSSA1.1 What is Statistics?
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A1.2.1a Introduction to Stata
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A1.2.2b: Introduction to R
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A1.2.2c: Introduction to SPSS
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A1.3: Descriptive Statistics
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A1.4: Estimates and Confidence Intervals
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A1.5: Hypothesis Testing
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A1.6: Transforming Variables
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End of Module A11 Quiz
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MODULE A2: POWER & SAMPLE SIZE CALCULATIONSA2.1 Key Concepts
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A2.2 Power calculations for a difference in means
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A2.3 Power Calculations for a difference in proportions
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A2.4 Sample Size Calculation for RCTs
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A2.5 Sample size calculations for cross-sectional studies (or surveys)
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A2.6 Sample size calculations for case-control studies
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End of Module A21 Quiz
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MODULE B1: LINEAR REGRESSIONB1.1 Correlation and Scatterplots
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B1.2 Differences Between Means (ANOVA 1)
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B1.3 Univariable Linear Regression
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B1.4 Multivariable Linear Regression
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B1.5 Model Selection and F-Tests
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B1.6 Regression Diagnostics
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End of Module B11 Quiz
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MODULE B2: MULTIPLE COMPARISONS & REPEATED MEASURESB2.1 ANOVA Revisited – Post-Hoc Testing
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B2.2 Correcting For Multiple Comparisons
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B2.3 Two-way ANOVA
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B2.4 Repeated Measures and the Paired T-Test
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B2.5 Repeated Measures ANOVA
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End of Module B21 Quiz
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MODULE B3: NON-PARAMETRIC MEASURESB3.1 The Parametric Assumptions
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B3.2 Mann-Whitney U Test
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B3.3 Kruskal-Wallis Test
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B3.4 Wilcoxon Signed Rank Test
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B3.5 Friedman Test
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B3.6 Spearman’s Rank Order Correlation
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End of Module B31 Quiz
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MODULE C1: BINARY OUTCOME DATA & LOGISTIC REGRESSIONC1.1 Introduction to Prevalence, Risk, Odds and Rates
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C1.2 The Chi-Square Test and the Test For Trend
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C1.3 Univariable Logistic Regression
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C1.4 Multivariable Logistic Regression
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End of Module C11 Quiz
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MODULE C2: SURVIVAL DATAC2.1 Introduction to Survival Data
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C2.2 Kaplan-Meier Survival Function & the Log Rank Test
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C2.3 Cox Proportional Hazards Regression
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C2.4 Poisson Regression
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End of Module C21 Quiz
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A Note about the Fossa Certificate
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 Errors and Power (6 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?Â
What information do you need to conduct a sample size estimate for difference between means?Â
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?Â
What information do you need to conduct a sample size estimate for difference between means?Â
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.
Simplified statistics
Great lectures
please repost a better version of this video. the editing is off at some points and you can tell we are missing some crucial points because of it.