Basic Quantitative Toolkit
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How to know which statistical test to use for hypothesis testing
This tutorial describes the hypothesis tests available to you, and how to choose which to use based on your data.
Hypothesis testing
This resource presents hypothesis testing as a formal way to determine which hypothesis your data supports.
Hypothesis Testing - Analysis of Variance (ANOVA)
This module explains the theory behind the ANOVA and how to conduct one by hand.
Internal vs. External Validity | Understanding Differences & Threats
This guide distinguishes between internal and external validity and the threats to each that may arise in your research.
Interpreting Regression Output
A simple breakdown on how to interpret the regression tables produced by statistics software like Stata.
Introduction to confidence intervals
Learn how confidence intervals can move us from a calculation of a sample mean towards an estimate of the true population mean.
Introduction to Normal Distributions
The normal distribution is foundational to many statistical tests in social science research—this resource provides a basic introduction with intuitive examples.
Mean, Median, and Mode: Measures of Central Tendency: Crash Course Statistics #3
You may have an intuitive sense of what an average is, but may be unsure how different measures of central tendency help you arrive at that value. This video clarifies how to calculate the mean, median and mode.
Measures of Spread: Crash Course Statistics
How well does your mean represent the data? Calculating a measure of spread will help to answer this question and this video shows you how.
Measuring center in quantitative data
How do you distinguish between all the various measures of central tendency? Which of them is best for your data? This tutorial explains, describes how they differ and how to calculate them all.
Merging Datasets in R
Joining multiple datasets is essential when working with data from multiple sources. This tutorial explains how to merge data in R in a way that preserves as much information as possible.
Normal Distribution
Are you unsure how to use the concept of the normal distribution to calculate the probability of a given event? This resource explains how areas under the normal distribution curve relate to probabilities.
Normal Distribution Explained with Examples
Learn how to solve problems related to the Normal Distribution.
Populations and samples
When is it necessary to sample a subset of a population rather than collect data from the entire population? This chapter provides a thoughtful discussion on how to choose.
Posit Cheatsheets
These handy cheatsheets make it easy to keep up with and refer to all the functions included in important R packages.
Posit Recipes- Transform Tables
These short, informative lessons are targeted towards specific questions you may have about data tidying. Code snippets throughout make it clearer how to apply them to your own data.
Primary vs Secondary Data: 15 Key Differences
Compare primary and secondary data to learn what types of conclusions each type allows you to make.
R for Data Science- Wrangle
This is the definitive guide to data cleaning in R, with chapters organised by the different operations you can carry out on your data.
R for reproducible scientific analysis- Vectors & Data frames
Learn how to structure your data for powerful analyses in R. This resource provides functions that will save you time on data manipulation so you can spend more time analysing and thinking about what your data means.
R tutorial: Descriptive Statistics
Yet another handy capability of the dplyr package is its summarisation and grouping functions. With these tools, you can explore your data at a glance and gather descriptive statistics.
