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The 2017 Stata Summer School
Venue: Hotel Birger Jarl Conference
Stockholm, Sweden  

Date:  August 14-18, 2017

 

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 2017 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 14)
  • Missing Data. Dr. Paul Allison, University of Pennsylvania/Statistical Horizons (August 15-16)
  • Longitudinal Data Analysis Using Structural Equation Modeling. Dr. Paul Allison, University of Pennsylvania/Statistical Horizons (August 17-18)

 

Pleasclick here to sign upfor the Stata Summer School.

 

 

Introduction to Stata (August 14)

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 tha 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.

 

Instructor

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.

 

 

Missing Data (August 15-16)

If you’re using conventional methods for handling missing data, you may be missing out. Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems:
 
  • Inefficient use of the available information, leading to low power and Type II errors.
  • Biased estimates of standard errors, leading to incorrect p-values.
  • Biased parameter estimates, due to failure to adjust for selectivity in missing data.More

 

More accurate and reliable results can be obtained with maximum likelihood or multiple imputation.

These new methods for handling missing data have been around for at least a decade, but have only become practical in the last few years with the introduction of widely available and user friendly software. Maximum likelihood and multiple imputation have very similar statistical properties. If the assumptions are met, they are approximately unbiased and efficient--that is, they have minimum sampling variance.

What's remarkable is that these newer methods depend on less demanding assumptions than those required for conventional methods for handling missing data. Maximum likelihood is available for linear models, logistic regression and Cox regression. Multiple imputation can be used for virtually any statistical problem.

This course will cover the theory and practice of both maximum likelihood and multiple imputation using Stata. It will focus on the mi command for multiple imputation and the sem command for maximum likelihood.

 

Who should attend?

Virtually anyone who does statistical analysis can benefit from new methods for handling missing data. To take this course, you should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory.

 
Course outline

  • Assumptions for missing data methods
  • Problems with conventional methods
  • Maximum likelihood (ML)
  • ML with EM algorithm
  • Direct ML
  • ML for contingency tables
  • Multiple Imputation (MI)
  • MI under multivariate normal model
  • MI with Stata
  • MI with categorical and nonnormal data
  • Interactions and nonlinearities
  • Using auxiliary variables
  • Other parametric approaches to MI
  • Linear hypotheses and likelihood ratio tests
  • Nonparametric and partially parametric methods
  • Fully conditional models
  • MI and ML for nonignorable missing data

 

Instructor 

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.”

 

 

Longitudinal Data Analysis Using Structural Equation Modeling (August 17-18)

Panel data have two big attractions for making causal inferences with non-experimental data:

1. The ability to control for unobserved, time-invariant confounders.

2. The ability to determine the direction of causal relationships.

 

For several years, Professor Allison has been teaching his acclaimed two-day seminar “Longitudinal Data Analysis Using Stata”. In this two-day course he takes up where that course leaves off, with methods for analyzing panel data using structural equation modeling.

This seminar takes a deep dive into the ML-SEM method for estimating dynamic panel models, exploring the ins and outs of assumptions, model specification, software programming, model evaluation and interpretation of results. Several real data sets are analyzed in great detail, testing out alternative methods and working toward an optimal solution. Both the –sem- and the –gsem- commands will be explored. In addition, Professor Allison will explain his new –xtdpdml- command which radically reduces the programming necessary to run the panel data models.

This is an applied course with a minimal number of formulas and a maximal number of examples. Although the methodology is cutting edge, the emphasis is on how to actually do the analysis in order accomplish your objectives.

At the end of this seminar, you should be able to confidently apply the ML-SEM method for dynamic panel data to your own research projects. You will also have a thorough understanding of the rationale, assumptions, and interpretation of these methods. Note: the methods covered in this course require panel data with at least three time points, and the number of individuals should be substantially larger than the number of time points.

 

Who should attend?
 

This seminar is designed for those who want to analyze longitudinal data with three or more time points, and whose primary interest is in the effect of predictors that vary over time. You should have a solid understanding of basic principles of statistical inference, including such concepts as bias, sampling distributions, standard errors, confidence intervals, and hypothesis testing. You should also have a good working knowledge of the principles and practice of linear regression.

It is desirable, but not essential, to have previous training in either longitudinal data analysis, structural equation modeling, or both. If you’ve taken Professor Allison’s courses on either of these topics, you should be well prepared.

 

Course outline

  • Characteristics of panel data
  • Objectives of panel modeling methods
  • Review
  • Random effects models
  • Fixed effects models
  • Cross-lagged panel models
  • SEM modeling
  • Problems
  • Dependence among repeated measures
  • Lagged dependent variables
  • Problems with reciprocal causation
  • Difference scores for T=3
  • Econometric solutions
  • SEM for random effects
  • SEM for fixed effects
  • SEM with lagged endogenous and predetermined variables.
  • SEM compared with Arellano-Bond
  • SEM details
  • Evaluating model fit.
  • Relaxing constraints
  • Higher-order lags
  • Likelihood ratio tests
  • Allowing coefficients to vary with time
  • Linear and quadratic effects of time
  • Missing data by FIML
  • Higher-level clustering
  • Alternative estimators
  • Categorical outcomes

 

Instructor

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.”

 

 

Logistics

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.

 

Prices

Introduction to Stata

  • Academic and student                         3500 SEK
  • Non-academic                                      4800 SEK

 

Missing Data

  • Academic and student                         7000 SEK
  • Non-academic                                     11500 SEK

 

Longitudinal/SEM

  • Academic and student                         7000 SEK
  • Non-academic                                   11500 SEK

 

Introduction and Missing Data or Longitudinal/SEM

  • Academic and student                       9200 SEK
  • Non-academic                                 13800 SEK

 

Missing Data and Longitudinal/SEM

  • Academic and student                  11500 SEK
  • Non-academic                              16200 SEK

 

Introduction and Missing Data and Longitudinal/SEM

  • Academic and student                   15000 SEK
  • Non-academic                                21400 SEK

 

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.
  • 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.

 

 

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