Basic Quantitative Toolkit

A clear illustration of an important point: even when two variables change in the same way, this tells you nothing about the relationship between them.

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

Compare primary and secondary data to learn what types of conclusions each type allows you to make.

This module explains the theory behind the ANOVA and how to conduct one by hand

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

Unsure how best to characterise your data in Stata? This resource is a definitive guide on several descriptive analyses relevant to social science research.

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

This is the definitive guide to data cleaning in R, with chapters organised by the different operations you can carry out on your data

How do you create meaning from a compilation of numbers and letters? This resource explains the necessity of tidying data, as a way to create meaning from it.

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.

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.

Learn how a sample relates to a population, and various types of samples available to you as a researcher

This resource meets you where you’re at in your algebra journey, going over prerequisites such as scientific notation before walking through focused algebra modules.

Are you unsure how to interpret correlation coefficients or what kinds of data are suitable for these calculations? This resource introduces coefficients and how to calculate one in R

A simple breakdown on how to interpret the regression tables produced by statistics software like Stata.

Descriptive statistics can summarise the properties of your data and show you what types of analyses would work best; this is crucial to know before conduct a full analysis.

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

This guide distinguishes between internal and external validity and the threats to each that may arise in your research

These handy cheatsheets make it easy to keep up with and refer to all the functions included in important R packages.

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

Knowledge translation is an important final step of your research process; this guide offers guidance on how to be an effective research communicator.

The most powerful data visualisations have a clear message- this guide provides thought-provoking questions and frameworks to help you decide what your message will be.

Learn how to install the tidyverse package and how to use it

The normal distribution is foundational to many statistical tests in social science research- this resource provides a basic introduction with intuitive examples.

When an event has two outcomes, it’s outcomes follow a binomial distribution; this resource explains important formulae related to the binomial distribution.

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

An example-driven resource that explains the concept of relational data in R

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

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

Learn how to manage and organise your data using Stata, or review familiar concepts using focused articles

The swirl package in R features short, self—paced and interactive tutorials that make learning R an active process.

How likely is it that an outcome has happened by chance? Significance testing gets at this idea and this tutorial explains this using an intuitive example

Even well-meaning scientists and students can fall prey to misrepresentations of statistical tests; this paper provides guidelines for avoiding common pitfalls

This chapter explains why validity is an important factor to consider when measuring constructs in social science.

When should we use a z-test and what conclusions can we make after we conduct one? This textbook chapter explains how to calculate a z statistic with an example.

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.

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

This article provides a thoughtful discussion of the difference between explanatory and predictive modelling, clarifying the distinct implications of each.

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

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.

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.

This resource presents hypothesis testing as a formal way to determine which hypothesis your data supports.

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

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

Learn how to build a regression model using an intuitive example

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

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.

This video introduces important terminology around normal distributions and how we can use them to compare parameters like the mean

This video is an engaging and intuitive explanation of the sampling distribution and its usefulness for making inferences.

This video explains a fundamental concept in statistics; the sampling distribution of the mean will always be normally distributed

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

This tutorial describes the hypothesis tests available to you, and how to choose which to use based on your data.

Learn how to distinguish between using Z statistics and T statistics, based on the data you have available to you.

Learn how to solve problems related to the Normal Distribution

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

Learn how to calculate confidence intervals using the normal distribution curve

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.

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

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

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.

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.

Learn how confidence intervals can move us from a calculation of a sample mean towards an estimate of the true population mean

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

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 to they differ and how to calculate them all.