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

Date:  February 20-24, 2017

 

For several years Metrika has organized a Stata summer school. For the last couple of years, several courses have been oversubscribed and we have been forced to turn down several interested participants. For this reason, we are now offering a winter school as well. The concept is the same as for the summer schools, and the courses 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 Winter School is jointly organized by Metrika Consulting and Statistical Horizons. Based in the United States, Statistical Horizons has established a reputation for providing outstanding training in statistics for researchers working in academia, business, non-profit organizations, and government.

The courses are taught by Paul Allison and Peter Hedström, both of whom are experienced Stata users, and effective teachers of statistical methods. 

All courses run between 9:00 and 17:00 every day.

Please click here to sign up.

 

Introduction to Stata (February 20)

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.

 

 

Longitudinal Data Analysis Using Stata (February 21-22)

In this two-day course, you will learn how to do regression analysis of panel data—the most common type of longitudinal data. Panel data contain measurements of predictor and response variables at two or more points in time for many individuals. Although panel data have many attractions, the downside is that repeated measurements typically violate assumptions of independence.


This seminar covers four methods for solving the problem of dependent observations: robust standard errors, generalized estimating equations, random effects (mixed) models and fixed effects models. You'll learn how to use these methods for quantitative outcomes, categorical outcomes, and count data outcomes.


Here are a few of the topics that will be covered:

  • How to use panel data to control for unobserved variables.
  • Why fixed effects methods often give very different results from random effects methods.
  • How to reshape data from long form to wide form and back again.
  • Why the default correlation structure for GEE is usually not the best.
  • The difference between maximum likelihood and restricted maximum likelihood.
  • How to estimate and interpret random coefficients models.
  • Why first-order autoregressive structures are usually unsatisfactory.
  • The difference between subject-specific and population-averaged coefficients, and why it matters.
  • How to do longitudinal analysis using ordered logit or multinomial logit.

 

We will cover the following Stata commands: reg, areg, xtreg, xtmixed, logit, ologit, clogit, mlogit, xtlogit, xtmelogit, xtgee, xtpoisson, xtnbreg, mixed, melogit, mepoisson, menbreg, meologit, meglm, sem, and reshape.

 

Course outline

1. Opportunities and challenges of panel data.
a. Data requirements
b. Control for unobservables
c. Determining causal order
d. Problem of dependence
e. Software considerations

2. Linear models
a. Robust standard errors
b. Generalized estimating equations
c. Random effects models
d. Fixed effects models
e. Hybrid models

3. Logistic regression models
a. Robust standard errors
b. Generalized estimating equations
c. Subject-specific vs. population averaged methods
d. Random effects models
e. Fixed effects models
f. Hybrid models

4. Count data models
a. Poisson models
b. Negative binomial models
c. Fixed and random effects

5. Linear structural equation models
a. Fixed and random effects in the SEM context
b. Models for reciprocal causation with lagged effects

 

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.”

 

 

Structural Equation Modelling Using Stata (February 23-24)

Structural Equation Modelling (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 modeling:
• Test complex causal theories with multiple pathways.
• 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).
• Analyze longitudinal data.
• Estimate fixed and random effects models in a comprehensive framework.
• Adjust for measurement error in predictor variables.

 

Because SEM is such a complex and wide-ranging methodology, learning how to use it can take a substantial investment of time and effort. Now, you have the opportunity to learn the basics of SEM in just two days.

 

This course will focus primarily on the sem command in Stata, which is restricted to linear models. There will also be an introduction to the gsem command which handles categorical and count data.

 

Course outline
1. Introduction to SEM
2. Path analysis
3. Direct and indirect effects
4. Identification problem in nonrecursive models
5. Reliability and validity
6. Multiple indicators of latent variables
7. Exploratory factor analysis
8. Confirmatory factor analysis
9. Goodness of fit measures
10. Structural relations among latent variables
11. Alternative estimation methods.
12. Multiple group analysis
13. Models for ordinal and nominal 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.”

 

 

Logistics

The Winter 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.

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

Stata 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

 

Longitudinal  

Academic and student                        7000 SEK

Non-academic                                     11500 SEK

 

SEM

Academic and student                          7000 SEK

Non-academic                                     11500 SEK

 

Introduction and Longitudinal or SEM

Academic and student                                9200 SEK

Non-academic                                            13800 SEK

 

Longitudinal and SEM  

Academic and student                         11500 SEK

Non-academic                                      16200 SEK

 

Introduction and Longitudinal and SEM 

Academic and student                         15000 SEK

Non-academic                                      21400 SEK

 

Please click here to sign up.

 

 

Please observe

  • 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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