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The 2018 Stata Summer School
Venue: Hotel Birger Jarl Conference
Stockholm, Sweden  

Date:  August 13-17, 2018


Every year Metrika organizes a Stata Summer School in Stockholm. These summer schools represent a unique opportunity for students, academics, and professionals to expand their skills in data management and data analysis and to learn how these skills can be applied to their own fields. All courses combine teaching and problem solving, and there are ample opportunities for participants to ask questions and to receive individualized guidance. 

The 2018 Summer School is jointly organized by Metrika Consulting and Statistical Horizons and this makes it possible to offer an excellent set of courses taught by experienced Stata users and effective teachers of statistical methods:

  • Introduction to Stata. Dr. Peter Hedström, Linköping University/Metrika Consulting (August 13)
  • Linear Regression Analysis. Dr. Paul Allison, University of Pennsylvania/Statistical Horizons (August 14-15)
  • Structural Equation Modeling. Dr. Paul Allison, University of Pennsylvania/Statistical Horizons (August 16-17)


Please click here to sign up for the Stata Summer School.



Introduction to Stata (August 13)

This is a one-day introductory course for everyone who is interested in learning how to use Stata. No prior knowledge of Stata is required. The course offers a basic introduction to Stata and to data management using Stata. Once you have completed this course you will know the basics of Stata and be able to use it in your own research. The course also is an excellent foundation for the other courses in the Summer School. 


Course outline

  • How the Stata interface is organized: review window, variables window, results windows, do-file editor, data browser, etc.
  • The basic Stata commands.
  • Setting up your data: file management, recoding, and transforming data.
  • How to save your results in log files.
  • Reading data into Stata from non-Stata files.
  • Recoding and transforming variables.
  • Variable and value labels.
  • Reshaping your data.
  • Merging two or more data files.
  • How to work with do-files.
  • How to tabulate and graph your data.
  • How to estimate linear regression models.
  • How to calculate and graph the marginal effects of covariates in regression-like models.


Stata commands covered include use, save, import, export, list, browse, edit, describe, summarize, in, if, by, sort, generate, egen, replace, recode, regress, rename, drop, keep, reshape, merge, append, tabulate, graph, margins, marginsplot.



Peter Hedström is Professor of Analytical Sociology at Linköping University, and a Fellow of the Royal Swedish Academy of Sciences. He is the founder of Metrika Consulting, and an experienced user and teacher of Stata.



Linear Regression Analysis (August 14-15)


Linear regression is the most widely-used method for the statistical analysis of non-experimental (observational) data. It is also the essential foundation for understanding more advanced methods like logistic regression, survival analysis, multilevel modeling, and structural equation modeling.

If you have never had a course on linear regression, or if you took one so long ago that you have forgotten most of it, this seminar will get you up to speed. When it is over, you will be a knowledgeable and effective user of regression methods.

The seminar will begin by focusing on the two major goals of linear regression: prediction and hypothesis testing. We will look at several examples from published articles to see how linear regression is used in practice and how to interpret regression tables. Next we will consider all the things that can go wrong when using linear regression, and we’ll see how to critique the analyses done by others.

We will delve into the mathematical theory behind linear regression, focusing on the essential assumptions, and on the implied properties of the least squares method. We’ll also spend considerable time on techniques for building non-linearity into linear regression by way of transformations, interactions, and dummy (indicator) variables.

There will be hands-on exercises using Stata.


Course outline

  • What is linear regression and what is it good for?
  • Examples of published regression analyses and interpretation of results.
  • The mechanics of regression in Stata.
  • Bivariate and trivariate regression.
  • Assumptions of linear regression and properties of least squares estimation.
  • Evaluation of regression models.
  • What can go wrong in linear regression.
  • Regression, correlation, and standardized coefficients.
  • Nonlinearity and interaction.
  • Dummy (indicator) variables.
  • Multicollinearity.
  • Model building strategies.
  • Missing data.
  • Heteroscedastity.



Paul Allison is Professor of Sociology at the University of Pennsylvania, and the President of Statistical Horizons. He is a Fellow of the American Statistical Association, and a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.”


Paul Allison is also the author of the popular text, Multiple Regression, which provides a very practical, intuitive, and non-mathematical introduction to the topic of linear regression.



Structural Equation Modeling (August 16-17)

This two-day course covers both the theory and practice of Structural Equation Modelling (SEM). SEM is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences. First introduced in the 1970s, SEM is a marriage of psychometrics and econometrics. On the psychometric side, SEM allows for latent variables with multiple indicators. On the econometric side, SEM allows for multiple equations, possibly with feedback loops. In today’s SEM software, the models are so general that they encompass most of the statistical methods that are currently used in the social and behavioral sciences.


Here are a Few Things You Can Do With Structural Equation Modelling

  • Test the implications of causal theories.
  • Estimate simultaneous equations with reciprocal effects.
  • Incorporate latent variables with multiple indicators.
  • Investigate mediation and moderation in a systematic way.
  • Handle missing data by maximum likelihood (better than multiple imputation).
  • Adjust for measurement error in predictor variables.
  • Estimate and compare models across multiple groups of individuals.
  • Represent causal theories with rigorous diagrams.
  • Investigate the properties of multiple-item scales.ndent covariates.


This course will emphasize linear models that can be estimated with the sem command in Stata. However, it will also consider more advanced models that are available with the gsem command.

Each day will include hands-on exercises to provide practice in the methodology.


Course Outline

  • Introduction to SEM.
  • Path analysis.
  • Direct and indirect effects.
  • Identification problem in nonrecursive models.
  • Reliability and validity.
  • Multiple indicators of latent variables.
  • Exploratory factor analysis.
  • Confirmatory factor analysis.
  • Goodness of fit measures.
  • Structural relations among latent variables.
  • Alternative estimation methods.
  • Multiple group analysis.
  • Models for ordinal and nominal data.



Paul Allison is Professor of Sociology at the University of Pennsylvania, and the President of Statistical Horizons. He is a Fellow of the American Statistical Association, and a two-time winner of the American Statistical Association’s award for “Excellence in Continuing Education.”




The Summer School is held at Hotel Birger Jarl and they offer discounted accommodation for all course participants (please contact us for further details). 

Please register for the courses you want to attend by sending us an email.

Attendance is limited and places are allocated on a first come, first serve basis. Please register long in advance to guarantee your place.

Stata 15 software is provided free of charge to all participants during the courses but participants are assumed to bring their own laptops.



Introduction to Stata

  • Academic and student            435 USD
  • Non-academic                         600 USD


Linear Regression  Analysis or Structural Equation Modeling

  • Academic and student            870 USD
  • Non-academic                       1450 USD


Introduction and (Linear Regression Analysis or Structural Equation Modeling)

  • Academic and student           1150 USD
  • Non-academic                       1730 USD


Linear Regression Analysis and Structural Equation Modeling

  • Academic and student           1459 USD
  • Non-academic                        2020 USD


Introduction and Linear Regression Analysis and Structural Equation Modeling

  • Academic and student            1900 USD
  • Non-academic                         2700 USD


Please click here to sign up.

Please observe

  • All courses start at 9:00 and end at 17:00.
  • These prices do not include Swedish VAT/moms and they will be charged in SEK as of the day's currency exchange rate according to Sveriges Riskbank.
  • Faculty members and students must provide a proof of their current university affiliation at the time of booking (valid university email address for academics, and valid student ID card or official letter of enrollment for students)
  • The price includes course materials, lunch and refreshments (you will need to notify us well in advance in case of special dietary requirements)
  • These courses will be given in English.


Terms & conditions

  • Only paid participants are guaranteed places in the courses.
  • 100% of the fee is returned for cancellations made over six weeks prior to start of the course.
  • 50% of the fee is returned for cancellations made three weeks prior to the start of the course.
  • No fee is returned for cancellations made less than three weeks prior to the start of the course.