You are here

Applied Statistics Using Stata

Applied Statistics Using Stata
A Guide for the Social Sciences

First Edition
Additional resources:

November 2016 | 376 pages | SAGE Publications Ltd

Clear, intuitive and written with the social science student in mind, this book represents the ideal combination of statistical theory and practice. It focuses on questions that can be answered using statistics and addresses common themes and problems in a straightforward, easy-to-follow manner. The book carefully combines the conceptual aspects of statistics with detailed technical advice providing both the ‘why’ of statistics and the ‘how’.

Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are encouraged to see the impact of theory whilst simultaneously learning how to manipulate software to meet their needs.

The book also provides:

  • Original case studies and data sets
  • Practical guidance on how to run and test models in Stata
  • Downloadable Stata programmes created to work alongside chapters
  • A wide range of detailed applications using Stata
  • Step-by-step notes on writing the relevant code.

This excellent text will give anyone doing statistical research in the social sciences the theoretical, technical and applied knowledge needed to succeed.

Research and statistics
1.1 The methodology of statistical research

1.2 The statistical method

1.3 The logic behind statistical inference

1.4 General laws and theories

1.5 Quantitative research papers

2. Introduction to Stata
2.1 What is Stata?

2.2 Entering and importing data into Stata

2.3 Data management

2.4 Descriptive statistics and graphs

2.5 Bivariate inferential statistics

3. Simple (bivariate) regression
3.1 What is regression analysis?

3.2 Simple linear regression analysis

3.3 Example in Stata

4. Multiple regression
4.1 Multiple linear regression analysis

4.2 Example in Stata

5. Dummy-Variable Regression
5.1 Why dummy-variable regression?

5.2 Regression with one dummy variable

5.3 Regression with one dummy variable and a covariate

5.4 Regression with more than one dummy variable

5.5 Regression with more than one dummy variable and a covariate

5.6 Regression with two separate sets of dummy variables

6. Interaction/moderation effects using regression
6.1 Interaction/moderation effect

6.2 Product-term approach

7. Linear regression assumptions and diagnostics
7.1 Correct specification of the model

7.2 Assumptions about residuals

7.3 Influential observations

8. Logistic regression
8.1 What is logistic regression?

8.2 Assumptions of logistic regression

8.3 Conditional effects

8.4 Diagnostics

8.5 Multinomial logistic regression

8.6 Ordered logistic regression

9. Multilevel analysis
9.1 Multilevel data

9.2 Empty or intercept-only model

9.3 Variance partition / intraclass correlation

9.4 Random intercept model

9.5 Level-2 explanatory variables

9.6 Logistic multilevel model

9.7 Random coefficient (slope) model

9.8 Interaction effects

9.9 Three-level models

10. Panel data analysis
10.1 Panel data

10.2 Pooled OLS

10.3 Between effects

10.4 Fixed effects (within estimator)

10.5 Random effects

10.6 Time-series cross-section methods

10.7 Binary dependent variables

11. Exploratory factor analysis
11.1 What is factor analysis?

11.2 Factor analysis process

11.3 Composite scores and reliability test

11.4 Example in Stata

12. Structural equation modelling and confirmatory factor analysis
12.1 What is structural equation modelling?

12.2 Confirmatory factor analysis

12.3 Latent path analysis

13. Critical issues
13.1 Transformation of variables

13.2 Weighting cases

13.3 Robust regression

13.4 Missing data


Practically orientated with a plethora of examples and an engaging narrative, this book is a must have for all those studying applied social statistics.

Franz Buscha
Reader in Economics and Quantitative Methods, Westminster Business School

This book provides an extraordinary and very readable account of the applied statistics methods needed in the social sciences. With its captivating didactical exposition, the book will be an invaluable resource for the novice as well as the advanced researcher.

Sergio Venturini
Lecturer of Statistics, Università Commerciale Luigi Bocconi

Stata users, especially social scientists, will find helpful advice in fitting statistical models to a diverse set of examples encountered when investigating the complexity and subtlety of real data. The authors emphasize the importance of assumptions behind the models and present clear exposition of the tools embedded in Stata to test these assumptions.  

James L Rosenberger
Professor of Statistics, Penn State University

This book addresses in an entertaining and instructive way multivariate analysis in the context of the Stata program. Well structured in relation to the themes and the main techniques of multivariate analysis, it is an easy-to-read book that helps you not only with statistics but also with using stata in investigations. 

Jorge Omar Cabrera Lavernia
Gastroenterology Service

I believe this is an excellent textbook for methods at the Master’s level. The sample of methods and approaches is very good. In addition to treating the “ordinary” techniques like linear and logistic regression, the book also deals with multilevel analysis, panel data analysis, factor analysis, and structural equation model. This covers the quantitative methods that are relevant for today’s Master’s students.

The material is presented at an acceptable advanced level where the necessary formulas are presented, and at the same time explained in an accessible manner. The highlighting of how to perform an analysis combined with examples from Stata makes the material easy to access for the students. This is definitely a book that I would like to use in my teaching.

Per Arne Tufte
Associate Professor, Oslo and Akershus University College