Basic Quantitative Toolkit

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ANOVA: Crash Course Statistics

This video introduces the ANOVA as a type of model which allows you to compare multiple groups.

Basic Theoretical Probability

These videos and exercises provide a solid foundation in probability theory, which might help you understand the results of statistical tests better.

Binomial Distribution

Practice calculating probabilities under the binomial distribution with this example-focused guide.

Calculating the variance and standard deviation

This lesson clarifies how the variance and standard deviation are different and what they can tell us about the spread of our data.

Carrying out a test for a population mean

A primer on when to use a z vs t statistic in significant testing.

Central Tendency: Mean, Median, Mode

How do conceptual understandings of the mean, median and mode translate to mathematical notations? This tutorial provides an explanation of the mathematical notations needed to calculate the mean, median and mode.

Code Academy- Learn R

A slow and steady introduction to data analysis and visualisation in R.

Confidence Intervals Using the z-Distribution

Learn how to calculate confidence intervals using the normal distribution curve.

Confidence Intervals: Crash Course Statistics

Learn how confidence intervals can help you make predictions about your data, with a pre-defined level of certainty.

Correlation Doesn't Equal Causation: Crash Course Statistics #8

When two variables in your data vary together, there could be several reasons why. This video explains this idea using everyday examples.

Creating a Data Frame from Vectors in R Programming (GeeksforGeeks)

Vectors are to dataframes what columns are to tables. This resource explains how to create a dataframe from separate vectors in R with helpful examples.

Data Cleaning with R and the Tidyverse: Detecting Missing Values

Learn how to write code that will help you identify missing values in your dataset.

Data Collection: Primary Vs. Secondary

Learn about the different data collection methods available in social science research.

Data Viz Checklist

This guide will help you choose the best way to visualise the data you have.

Data Wrangling Ex 2: Dealing with missing values

There are a few different ways to add missing data values in R—this blog post explains when and how through an exercise.

Data Wrangling in Stata: Introduction and Review

Learn the fundamentals of Stata syntax and apply it to data wrangling.

Data Wrangling with R

Learn how to manipulate data using two foundational packages from the tidyverse: dplyr and tidyr.

Dealing with Missing Values in R

Data collection is often an imperfect process—this guide describes how to make the most of the data you have and account for any missing values.

Dispersion: Variance and Standard Deviation

This textbook chapter will push your conceptual knowledge of measures of spread towards an understanding of their mathematical notations and calculations.

Disseminating your findings

Planning and thoughtfulness shouldn't end with data analysis—this guide advocates for a thoughtful approach to research dissemination and offers practical advice on how, depending on your audience and your medium.