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An applied guide to quantitative methods with Stata

posted on 18.02.2021, 07:30 by Ylva B Almquist, Christoffer Åkesson, Lars Brännström

Changes in Version 1.1:

Minor corrections throughout the publication.


The purpose of this guide is to provide both a basic understanding of statistical concepts (know-why) as well as instructions for analysing quantitative data in Stata (know-how). The assumption is that you already have some data – therefore, there is only a limited discussion about study design and all the issues related to this. It should also be noted that this guide is positioned somewhere in the intersection between social sciences and medical sciences.

We wanted to keep the guide almost completely free of formulas (i.e. brain-freezing mathematical equations). In doing so, we have tried to explain everything at the most elementary level and only included aspects that we think are important for applied statistics. As such, this guide is pragmatic and research-oriented. Hopefully, you will find it useful.

This guide consists of three parts. In the first part, we introduce the guide (Chapter 1) and the Stata environment (Chapter 2), after which we discuss basic statistical concepts (Chapter 3) and different types of descriptive analysis (Chapter 4).

The second part starts with issues related to statistical significance (Chapter 5) and then continue with basic types of analysis, such as t-tests, ANOVA, chi-square, correlation analysis, and factor analysis (Chapters 6-8).

In the third part focuses on more advanced statistical analysis: we discuss some theoretical dimensions of statistical analysis (Chapter 9) and briefly explore different extensions of ANOVA (Chapter 10), before continuing to regression analysis (Chapters 11-17). In addition to this, we guide you through mediation analysis (Chapter 18) and interaction analysis (Chapter 19).


The following files are attached to this publication (both as .dta and .csv):

StataData1, StataData2, and TestData2.


Original language




Affiliation (institution of first SU-affiliated author)

333 Institutionen för folkhälsovetenskap | Department of Public Health Sciences