Workflow of Statistical Analysis with STATA: Data Management, Analysis and Visualization

Prof. Dr. Kai-Uwe Schnapp



Liebe TeilnehmerInnen am STATA-Workshop der GS Wiso im Februar dieses Jahres,

ich habe jetzt alle Leistungen die mir vorlagen eingetragen. Wenn jemand von Ihnen Leistungen geschickt, aber keinen Eintrag erhalten hat, dann melden Sie sich bitte nochmal bei mir, damit wir das korrigieren können.

Beste Grüße

Announcement created on: 26/04/2016 13:22

Liebe TeilnehmerInnen,

da bis gestern keine Stimmen eingegangen sind, die die Vorverschiebung des Kurses um einen Tag abgelehnt haben, möchte ich mit dieser Mail bekanntgeben, dass der Kurs nun wie folgt stattfinden wird:

Montag, den 22.2. bis Donnerstag, den 25.2.
Wir beginnen Montag um 10:30, wie es im Plan steht.

Der Montag verfolgt drei Ziele:
10:30-12:00: Denen, die noch nie STATA geöffnet haben einen allerersten Eindruck zu geben.
13:00-14:30 Kenntnisse der Regressionsanalyse aufzufrischen, damit wir uns im eigentlichen Kurs voll auf STATA konzentrieren können.
15:00-16:00 Kenntnisse der Faktorenanalyse aufzufrischen, damit wir uns im eigentlichen Kurs voll auf STATA konzentrieren können.

Der Montag kann also selektiv oder auch gar nicht genutzt werden. Am Dienstag geht es dann pünktlich um 9:00 für alle los.

Wenn jemand vorab in ein Buch schauen möchte folgende Empfehlung: Kohler/Kreuter: "Datenanalyse mit STATA" (gern auch in der englischen Version). Eine hervorragende Einführung in das Programm wie auch in wichtige Grundlagen der Statistik. Unbedingt versuchen, die neueste Auflage zu bekommen, weil die Dinge enthält, die STATA früher noch gar nicht konnte, und die daher in den älteren Auflagen auch nicht enthalten sind.

Beste Grüße, schöne Weihnachten und einen guten Rutsch in's neue Jahr


Announcement created on: 23/12/2015 15:38


Contents STATA for Beginners, Course at the Wiso Graduate Program

June 2015


In their education
students do regularly get to know statistics to a certain extent. Ideally,
some data analysis will be done with data sets ready made for teaching. The resulting
knowledge of statistics differs from university to university, but usually it
is sufficient for at least simple tasks in data analysis. Actually starting
with an analysis of once own, however, all too often is a bit rocky. The
reason for this being: Just knowing statistics is not enough. Why is that so?
To being with data usually do not come as ready-made as they appear in
statistics classes. They need to be adapted, transformed, aggregated or
disaggregated, thoroughly documented and saved in meaning- and useful
partitions. This involves a large number of tiny steps and decisions in the
work process. It is all too easy to loses track of what has been done when,
how, why and with which result. Why has X been filtered, why has Y been
aggregated the way it has been aggregated and where does the correction in Z
come from and how has it been justified? Often within days it is not clear
any longer, why a variable does now look the way it does. And be it for the
reason, that the seed number for some random number generator has either not
even been set or at least not been saved. It gets much more inconvenient
later on, when an article is ready for publication and the journal is asking
for documentation or even a replication data set. Or when in interested
reader is sending an e-mail, asking politely for more detailed information on
data preparation and analysis. Because it is now, the search starts for
information that has been lost along the way.

Many of those
problems can be avoided by a well thought plan for data manipulation and
analysis accompanied by extensive documentation of every step in the work
process. Most if not all of the things one has been doing can be kept within
reach when the steps in the work process are clear (and to the extent
possible standardized) and when saving and documenting becomes part of the
daily routine of working with data.

This course will try
to introduce students to such a way of working with data and at the same time
do the first steps of data manipulation and analysis with STATA. The aim is
not, however, to actually teach any statistics. It is assumed that students
already have at least a basic knowledge of statistics with at least some
descriptive and inferential methods known to everybody. For those lacking
this knowledge or not really remembering, what they learned earlier there
will be a refresher in the beginning of the weak.

In addition to an
introduction into the graphical user interface of STATA, the structure of its
command language, work process and documentation this course will teach some
tricks and give hints how to deal with some problems in data manipulation and
how to achieve almost directly publishable output. A special focus will be
put on graphical output.


Who will get to

- STATA’s user interface

- basic knowledge in data handling and data manipulation with STATA

- basic knowledge oft the structure and workings of STATA’s commands for data

- basic knowledge of how to quickly produce publishable output with STATA

- basic knowledge of efficient process management and documentation using

After all the course
is a language course of sorts. You will get to know STATA as a language to
code your data analysis.


The course will be
held in a computer lab. All steps will be demonstrated by the instruktor an
directly applied by the students. There will be room for free but guided

There will be a
brief (90 minutes) intro into the Graphical User Interface of STATA for
people, how did never work with STATA before in the first day of the course
week. In the same day there will be two 90-minute refreshers on regression
analyse and factor analysis, so that people will be able to follow data
examples later in the course without much need for further explanation.


As introductory
literature and a good guide book for further work I suggest Kohler/Kreuter
„Data analysis using STATA“ (meanwhile in its third edition) or K/K
„Datenanalyse mit STATA“ (here the fourth edition from 2012 is strongly

Hinweise zur Prüfung

In order to earn
credits students will have to complete a homework of about 5-10 hours
(depending on individual work pace) with some data manipulation and analysis,
the production of some output that is (almost) ready for publication and a
thorough documentation of the whole process.

General Data

  • Abbreviation
  • Semester
    winter semester 15/16
  • Target Groups
    WiSo doctoral study program
  • Course Type
  • Course Language
    German or English, upon agreement
  • Departments
    Faculty of Economics and Social Sciences

Place and Time

  • Place
    Von Melle Park 9 Raum A514
  • Time
    from 22/02/2016 to 22/02/2016 from 10:30 to 16:00
  • Place
    Von Melle Park 9 Raum A514
  • Time
    from 23/02/2016 to 23/02/2016 from 09:00 to 15:00
  • Place
    Von Melle Park 9 Raum A514
  • Time
    from 24/02/2016 to 24/02/2016 from 09:00 to 15:00
  • Place
    Von Melle Park 9 Raum A514
  • Time
    from 25/02/2016 to 25/02/2016 from 09:00 to 15:00

Recognition Modalities

  • Number of Semester Hours
  • Amount of Credit Points
  • Creditable as
    • WiSo doctoral program: WiSo methods for Economics
    • WiSo doctoral program: WiSo methods for Social Economics
    • WiSo doctoral program: WiSo methods for Social Siences

Registration Modalities

  • Type of Place Allocation
    Manual Place Allocation (after the registration deadline)
  • Information about Registration
  • Max. Number of Participants