_________________________________
2019 GERMAN USERS GROUP MEETING
Announcement and Program
_________________________________
Overview
========
Date/Venue/Cost
~~~~~~~~~~~~~~~
------------------------------------------------
Date: May 24, 2019
Venue: Ludwig-Maximilians-Universität Munich
Seidlvilla e.V.
Nikolaiplatz 1b
80802 Munich
[http://www.seidlvilla.de]
Cost: Meeting only: 45 EUR (students 35 EUR)
Workshop only: 65 EUR
Workshop and Meeting: 85 EUR
------------------------------------------------
Meeting
~~~~~~~
The 16th German Stata Users Group Meeting will be held at the
Seidlvilla Munic on Friday, May 24 2019. Everybody from anywhere who
is interested in using Stata is invited to attend this meeting. The
meeting will include presentations about causal models, general
statistics, and data management, both by researchers and by StataCorp
staff. The meeting will also include a "wishes and grumbles"
session, during which you may air your thoughts to Stata developers.
Workshop
~~~~~~~~
On the day before the conference, Jan-Paul Heisig, from the Social
Science Research Center Berlin (WZB) will hold a workshop on
"Multiple Imputation". Details about the workshop are given below
the program.
Conference Dinner
~~~~~~~~~~~~~~~~~
There is (at additional cost) the option of an informal meal at a
restaurant in Munich on Friday evening. Details about accommodations
and fees are given below the program
Language
~~~~~~~~
The conference language will be English because of the international
nature of the meeting and the participation of non-German guest
speakers.
Time table
~~~~~~~~~~
-------------------------------------------------------------------------------------
8:30--9:00 Registration
-------------------------------------------------------------------------------------
9:00--9:15 Welcome
Katrin Auspurg/Josef Brüderl
9:15--10:15 On the shoulders of giants, or not reinventing the wheel
Nicholas J. Cox
10:15--10:45 Stata export for metadata documentation
Anne Balz, Klaus Pforr, Florian Thirolf
-------------------------------------------------------------------------------------
10:45--11:00 Coffee
-------------------------------------------------------------------------------------
11:00--12:00 Agent Based Models in Mata
Maarten Buis
12:00--12:30 How to use Stata's -sem- command with nonnormal data?
Wolfgang Langer
-------------------------------------------------------------------------------------
12:30--13:30 Lunch
-------------------------------------------------------------------------------------
13:30--14:00 xtoaxaca: Extending Oaxaca-Blinder-Decomposition to longitudinal data
Hannes Kröger/Jörg Hartmann:
14:00--14:30 Linear Discrete-Time Hazard Estimation using Stata
Harald Tauchmann
14:30--15:00 Heat (and hexagon) plots in Stata
Ben Jann
-------------------------------------------------------------------------------------
15:00--15:30 Coffee
-------------------------------------------------------------------------------------
15:30--16:00 Extending the -label- commands (cont'd)
Daniel Klein
16:00--16:30 The production process of the Global MPI
Nicolai Suppa
-------------------------------------------------------------------------------------
16:30--16:45 Coffee
-------------------------------------------------------------------------------------
16:45--17:30 Performing and interpreting discrete choice analyses in Stata
Joerg Luedicke
17:30--18:00 Wishes and Grumbles
-------------------------------------------------------------------------------------
18:00 End of meeting
-------------------------------------------------------------------------------------
How to get to the venue
~~~~~~~~~~~~~~~~~~~~~~~
Google Route Planer: [http://www.seidlvilla.de/kontakt.html]
------------------------------------------------------------
From the airport
----------------
Take S1 or S8 (direction does not matter) to station "Marienplatz".
Change to U3 (direction "Moosach") or U6 (direction "Fröttmaning")
and leave at "Giselastraße" (3rd station). Leave the
subway-station. Follow "Leopoldstraße" north (about 200m), turn
right into "Nikolaistraße". After another 100m you reach
"Nicolaiplatz".
From the railway main station
-----------------------------
Take any S-Bahn direction "Marienplatz". At "Marienplatz" change
to U3 (direction "Moosach") or U6 (direction "Fröttmaning") and
leave at "Giselastraße" (3rd station). Leave the
subway-station. Follow "Leopoldstraße" north (about 200m), turn
right into "Nikolaistraße". After another 100m you reach
"Nicolaiplatz".
Abstracts
=========
9:00--9:15 Welcome
~~~~~~~~~~~~~~~~~~
Katrin Auspurg (Ludwigs-Maximilians-Universtiy Munich), Josef Brüderl
(Ludwigs-Maximilians-Universtiy Munich)
9:15--10:15 On the shoulders of giants, or not reinventing the wheel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~
Nicholas J. Cox (Department of Geography, University of Durham, UK)
[[email protected]]
/Abstract:/ Part of the art of coding is writing as little as possible
to do as much as possible. In this presentation, I expand on this
truism and give examples of Stata code to yield tables and graphs in
which most of the real work is delegated to workhorse commands. In
tabulations and listings, the better known commands sometimes seem to
fall short of what you want. However, some preparation commands (such
as generate, egen, collapse or contract) followed by list, tabdisp,
or _tab can get you a long way. In graphics, a key principle is
that graph twoway is the most general command, even when you do not
want rectangular axes. Variations on scatter and line plots are
precisely that, variations on scatter and line plots. More challenging
illustrations include commands for circular and triangular graphics,
in which x and y axes are omitted with an inevitable but manageable
cost in re-creating scaffolding, titles, labels, and other
elements. The examples range in scope from a few lines of interactive
code to fully developed programs. This presentation is thus pitched at
all levels of Stata users.
10:15--10:45 Stata export for metadata documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~
Anne Balz (GESIS--Leipniz Institute for the Social Sciences), Klaus
Pforr (GESIS--Leipniz Institute for the Social Sciences), Florian
Thirolf (GESIS--Leipniz Institute for the Social Sciences)
[[email protected]]
/Abstract:/ Precise and detailed data documentation is essential for
the secondary analysis of scientific data, whether survey or official
microdata. Among the most important metadata in this perspective are
variable and category labels as well as frequency distributions and
descriptive statistics. To generate and publish these metadata from
Stata datafiles, an efficient export interface is essential. It must
be able to handle large and complex data sets, take into account the
specifics of different studies and generate flexible output formats
(depending on the requirements of the documentation system). As a
solution to the problem described above, we present the process
developed in the GML (German Microdata Lab) at GESIS. In the first
step, we show how an aggregated file with all required metadata can be
generated from the microdata. In the second step, this file is
transformed into a standardized DDI format. Additionally, we will
present the implementation for MISSY (the metadata information system
for official microdata at GESIS), which includes some practical
additions (e.g. communication with the MISSY database to retrieve
existing element identifiers, writing an output tailored to the MISSY
data model).
10:45--11:00 Coffee
~~~~~~~~~~~~~~~~~~~
11:00--12:00 Agent Based Models in Mata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Maarten Buis (University of Konstanz)
[[email protected]]
/Abstract:/ An Agent Based Model is a simulation in which agents, that
each follow simple rules, interact with one another and thus produce a
often surprising outcome at the macro level. The purpose of an ABM is
to explore mechanisms through which actions of the individual agents
add up to a macro outcome, by varying the rules that agents have to
follow or varying with whom the agent can interact (i.e. varying the
network).
A simple example of an ABM is Schelling's segregation model, in which
he showed that one does not need racists to produce segregated
neighbourhoods. The model starts with 25 red and 25 blue agents, each
of which live in a cell of a chessboard. They can have up to 8
neighbours. In order for an agent to be happy she needs to have some,
e.g. 30%, agents in the neighbourhood of the same color. If the agent
is unhappy, she will move to another empty cell that will make her
happy. If we repeat this till everybody is happy or nobody can move,
we will often end up with segregated neighbourhoods.
Implementing a new ABM will always require programming, but a lot of
the tasks will be similar across ABMs. For example, in many ABMs the
agents live on a square grid (like a chessboard), and can only
interact with their neighbours. I have created a set of Mata functions
that will do those task, and that someone can import in her or his own
ABM. In this talk I will illustrate how to build an ABM in Mata with
these functions.
12:00--12:30 How to use Stata's -sem- command with nonnormal data? A new nonnormality correction for the RMSEA and incremental Fit Indices, CFI and TLI
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~
Wolfgang Langer (University of Luxembourg and
Martin-Luther-Universität Halle-Wittenberg)
[[email protected]]
/Abstract:/ Traditional fit measures like RMSEA, TLI or CFI are based
on noncentral chi-square distribution assuming the multinormal
distribution of the observed indicators (Jöreskog 1970). If this
assumption is violated programs like Stata, EQS or LISREL calculate
the fit indices using the Sattora-Bentler correction. It rescales the
Likelihood-Ratio-chi2-test statistics of the baseline and the
hypothesized model (Satorra & Bentler 1994, Newitt & Hancock 2000).
Brosseu-Liard et al. (2012, 2014) and Savalei (2018) showed in their
simulation studies with nonnormal data two results: Firstly, they
demonstrated that the ad-hoc nonnormality corrections of the fit
indices provided by the SEM software made the fit worse, better or
unchanged as compared to their uncorrected counterparts. Secondly, the
authors proposed new robust versions of RMSEA, CFI and TLI which
performed very well in their simulation studies. They systematically
varied the sample size, the extend of misspecification and
nonnormality. Therefore the same rule of thumb or criteria which are
used for normal distributed data can be applied to assess the fit of
the strutural equation model.
My `robust_gof.ado' estimates the robust RMSEA, CFI and TLI fit
measures using the corrections proposed by Brosseu-Liard et al. and
Savalei. It also estimates a 90 percent confidence interval for the
Root-Mean-Squared-Error of Approximation. My `robust_gof.ado' can be
executed after the -sem- command with the `vce(sbentler)' option and
`estat gof, stats(all)' as a postestimation command by simply typing
`robust_gof'. It returns the estimated fit indices and scalars as -r-
containers. A survey example of islamophobia analysis in Germany will
be presented to demonstrate the usefulness of my `robust_gof.ado'.
- Asparouhov, T. & Muthén, B. (2010): Simple second order chi-square
correction. Los Angels, Ca: MPLUS Working papers
- Borsseau-Liard, P.E., Savalei, V. & Li, L. (2012): An investigation
of the sample performance of two nonnormality corrections for
RMSEA. Multivariate Behavioral Research, 47, 6, pp. 904-930
- Borsseau-Liard, P.E. & Savalei, V. (2014): Adjusting incremental fit
indices for nonnormality. Multivariate Behavioral Research, 49, 5,
pp. 460--470
- Jöreskog, K.G. (1970): A general method for analysis of covariance
structures. Biometrika, 57,2, pp. 239--251
- Jöreskog, K.G., Olsson, U.H. & Wallentin, F.Y. (2016): Multivariate
Analysis with LISREL. Switzerland: Springer
[[email protected]]
mailto:[email protected]
13:30--14:00 -xtoaxaca-: Extending the Oaxaca-Blinder Decomposition Approach to longitudinal data analyses
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~
Hannes Kröger (DIW--German Institute for Economic Research, Berlin),
Jörg Hartman (University of Göttingen)
[[email protected]]
/Abstract:/ The Oaxaca-Blinder (Oaxaca, 1973) decomposition approach
has been widely used to attribute group level differences in an
outcome to differences in endowment, coefficients, and their
interactions. The method has been implemented for Stata in the popular
-oaxaca- program for cross-sectional analyses (Jann, 2008). However,
in the last decades research questions are more often focused on the
decomposition of group based differences in change over time,
e.g. diverging income trajectories, as well as decomposition of change
in differences between groups, e.g. change in the gender pay gap.
Another way in which decomposition analyses can be extended to
longitudinal data is repeated crosssectional decompositions and time
point specific decomposition of group levels differences based on
latent growth curve models. We propose to unify these different
research interest under a more general longitudinal perspective that
has each of the applications as a special case of the Oaxaca-Blinder
decomposition. We present this general view, give examples of applied
research questions that can be answered within the framework and
propose a first version of the program -xtoaxaca- which works as a
postestimation command in Stata in order maximize flexibility in
modeling and forms of longitudinal decompositions according to the
user's preferences.
- Jann, B. (2008). The Blinder-Oaxaca decomposition for linear
regression models. The Stata Journal, 8(4), 453–479.
- Oaxaca, R. (1973). Male-female wage differentials in urban labor
markets. International Economic Review, 693–709.
14:00--14:30 Linear Discrete-Time Hazard Estimation using Stata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~
Harald Tauchmann (Friedrich-Alexander-University, Erlangen-Nürnberg;
RWI--Leibniz Institute for Economic Research, Essen; CINCH -- Health
Economics Research Center, Essen)
[[email protected]]
/Abstract:/ Linear fixed-effects estimators (first-differences,
within-transformation) are workhorses of applied econometrics because
they straightforwardly allow for eliminating unobserved time-invariant
individual heterogeneity that otherwise may cause a bias. I show that
these popular estimators are, however, biased and inconsistent when
applied in a discrete-time hazard setting, that is with the outcome
variable being a binary dummy indicating an absorbing state. I suggest
an alternative, computationally simple, adjusted first-differences
estimator. This estimator is shown to be consistent in the considered
non-repeated event setting, under the assumption of unobserved
time-invariant individual heterogeneity being uncorrelated with the
changes in the explanatory variables. Using higher-order differences
instead of first-differences allows for consistent estimation under
weaker assumptions. Finally I introduce the new user written command
-xtlhazard- that implements the suggested estimation procedure in
Stata.
14:30--15:00 Heat (and hexagon) plots in Stata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Ben Jann (University of Bern)
[[email protected]]
/Abstract:/ In this talk I will present two new Stata commands to
produce heat plots. Generally speaking, a heat plot is a graph in
which one of the dimensions of the data is visualized using a color
gradient. One example of such a plot is a two-dimensional histogram in
which the frequencies of combinations of binned X and Y are displayed
as rectangular (or hexagonal) fields using a color gradient. Another
example is a plot of a trivariate distribution where the color
gradient is used to visualize the (average) value of Z within bins of
X and Y. Yet another example is a plot that displays the contents of a
matrix, say, a correlation matrix or a spacial weights matrix, using a
color gradient. The two commands I will present are called -heatplot-
and -hexplot-.
15:00--15:30 Coffee
~~~~~~~~~~~~~~~~~~~
15:30--16:00 Extending the -label- commands (cont'd)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~
Daniel Klein (INCHER --International Centre for Higher Education
Research, Kassel) [[email protected]]
/Abstract:/ Four years ago, I first suggested extending Stata’s label
commands to manipulate variable labels and value labels in a more
systematic way. By now, I have refined my earlier approach and
released a new suit of commands, -elabel-, that facilitate these
everyday data management tasks. In contrast to most existing
community-contributed commands to manipulate labels, -elabel- does not
focus on solving specific problems. Combined with any of Stata’s
-label- commands, it address any problem related to variable and value
labels. -elabel- accepts wildcard characters in value label names,
allows referring to value labels via variable names, selects subsets
of integer to text mappings, and applies any of Stata’s functions to
define new or modify existing labels. I demonstrate these features
drawing on various examples and show how to write new ado-files to
further extend the -elabel- commands.
16:00--16:30 The production process of the Global MPI
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~
Nicolai Suppa (Juan de la Cierva Research Fellow, Centre d'Estudis
Demogràfics, Spain)
[[email protected]]
/Abstract:/ The Global Multidmensional Poverty Index is a
cross-country poverty measure published by the Oxford Poverty and
Human Development Initiative since 2010. The estimation requires
household survey data, as multidimensional poverty measures seek to
exploit the joint distribution of deprivations in the identification
step of poverty measurement. Moreover, analyses of multidimensional
poverty draw on several aggregate measures (e.g, headcount ratio,
intensity) as well as on dimensional quantities (e.g, indicator
contributions). Robustness analyses of key parameters (e.g., poverty
cutoffs and weighting schemes) further increase the number of
estimates.
During the 2018 revision for the first time figures for 105 countries
were calculated in one round. For a large scale project like this, a
clear and efficient workflow is essential. This paper introduces key
elements of a workflow and presents solutions with Stata for
particular problems, including (i) the structure of a comprehensive
results file, which facilitates both analysis and production of
deliverables, (ii) the usability of the estimation files, (iii) the
collaborative nature of the project, (iv) the labelling of 1200
subnational units, and (v) the documentation of code and decisions.
This paper seeks to share the gained experienced and to subject both
the principal workflow and selected solutions to public scrutiny.
16:30--16:45 Coffee
~~~~~~~~~~~~~~~~~~~
16:45--17:30 Performing and interpreting discrete choice analyses in Stata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~
Joerg Luedicke (StataCorp)
/Abstract:/ Discrete choice models are used across a variety of
disciplines to analyze choices made by individuals or other
decision-making entities. Stata supports a variety of discrete choice
models such as multinomial logit and mixed logit models. While
applying these models to a given dataset can be straightforward, it is
often challenging to interpret their results. In this talk, I will
provide an overview of Stata's discrete choice modeling capabilities
and show how to use postestimation commands to get the most out of
these models and their interpretation.
17:30--18:00 Whishes and Grumbles
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Abstract:/ Users air their whishes and grumbles and StataCorp
responds
Registration and accommodations
===============================
Please travel at your own expense. The conference fee will be €45
(Students €35) to cover costs for coffee, teas, and luncheons. There
will also be an optional informal meal at a restaurant on Friday
evening at additional cost. You can enroll by contacting Elena
Tsittser ([[email protected]]) by email or by writing,
phoning, or faxing to:
-------------------------------
Elena Tsittser
Dittrich & Partner Consulting
Prinzenstrasse 2
42697 Solingen
Germany
Tel: +49 (0)212 260 6624
Fax: +49 (0)212 260 6666
-------------------------------
Scientific Organizers
=====================
The academic program of the meeting is being organized by Katrin
Auspurg and Josef Brüderl (LMU Munich), Johannes Giesecke (HU Berlin)
and Ulrich Kohler (University of Potsdam)
Logistics organizers
====================
The logistics are being organized by Dittrich and Partner
([http://www.dpc.de]), the distributor of Stata in several countries
including Germany, The Netherlands, Austria,
Workshop
========
Date/Venue/Costs
~~~~~~~~~~~~~~~~
-------------------------------------------------------------------
Date: May 23, 2019
Venue: Ludwig-Maximilians-Universität Munich
Department for Sociology
Konradstraße 6
80801 Munich
[https://www.en.soziologie.uni-muenchen.de/index.html]
Room Cip-Pool
4th floor, room nr. 409
Presenter Jan Paul Heisig [[email protected]]
Dr. Heisig is head of the research Group
"Health and Social Inequality" at the
Social Science Research Center, WZB
Cost: Workshop only: 65 EUR
Meeting only: 45 EUR (students 35 EUR)
Workshop and Meeting: 85 EUR
Register [[email protected]]
-------------------------------------------------------------------
Description
~~~~~~~~~~~
Missing data are a pervasive problem in the social sciences. Data for
a given unit may be missing entirely, for example, because a sampled
respondent refused to participate in a survey (survey
non-response). Alternatively, information may be missing only for a
subset of variables (item non-response), for example, because a
respondent refused to answer some of the questions in a survey. The
traditional way of dealing with item non-response, referred to as
"complete case analysis" (CCA) or "listwise deletion", excludes
any observation with missing information from the analysis. While easy
to implement, complete case analysis is wasteful and can lead to
biased estimates. Multiple imputation (MI) addresses these issues and
provides more efficient and unbiased estimates, if certain conditions
are met. Therefore, it is increasingly replacing CCA as the method of
choice for dealing with item non-response in applied quantitative work
in the social sciences. The goals of the course are to introduce
participants to the principles of MI and its implementation in Stata,
with a primary focus on MI using iterated chained equations (aka
"fully conditional specification").
Prerequisites
~~~~~~~~~~~~~
Participants should have a good working knowledge of Stata.
Lecturer
~~~~~~~~
Dr. Heisig is head of the research Group ``Health and Social
Inequality'' at the Social Science Research Center, WZB. Heisg's
recent nethodological contributions include
- Heisig, Jan Paul/Schaeffer, Merlin/Giesecke, Johannes (2017): The
Costs of Simplicity. Why Multilevel Models May Benefit from
Accounting for Cross-Cluster Differences in the Effects of
Controls. In: American Sociological Review, Vol. 82, No. 4,
S. 796--827.
2019 GERMAN USERS GROUP MEETING
Announcement and Program
_________________________________
Overview
========
Date/Venue/Cost
~~~~~~~~~~~~~~~
------------------------------------------------
Date: May 24, 2019
Venue: Ludwig-Maximilians-Universität Munich
Seidlvilla e.V.
Nikolaiplatz 1b
80802 Munich
[http://www.seidlvilla.de]
Cost: Meeting only: 45 EUR (students 35 EUR)
Workshop only: 65 EUR
Workshop and Meeting: 85 EUR
------------------------------------------------
Meeting
~~~~~~~
The 16th German Stata Users Group Meeting will be held at the
Seidlvilla Munic on Friday, May 24 2019. Everybody from anywhere who
is interested in using Stata is invited to attend this meeting. The
meeting will include presentations about causal models, general
statistics, and data management, both by researchers and by StataCorp
staff. The meeting will also include a "wishes and grumbles"
session, during which you may air your thoughts to Stata developers.
Workshop
~~~~~~~~
On the day before the conference, Jan-Paul Heisig, from the Social
Science Research Center Berlin (WZB) will hold a workshop on
"Multiple Imputation". Details about the workshop are given below
the program.
Conference Dinner
~~~~~~~~~~~~~~~~~
There is (at additional cost) the option of an informal meal at a
restaurant in Munich on Friday evening. Details about accommodations
and fees are given below the program
Language
~~~~~~~~
The conference language will be English because of the international
nature of the meeting and the participation of non-German guest
speakers.
Time table
~~~~~~~~~~
-------------------------------------------------------------------------------------
8:30--9:00 Registration
-------------------------------------------------------------------------------------
9:00--9:15 Welcome
Katrin Auspurg/Josef Brüderl
9:15--10:15 On the shoulders of giants, or not reinventing the wheel
Nicholas J. Cox
10:15--10:45 Stata export for metadata documentation
Anne Balz, Klaus Pforr, Florian Thirolf
-------------------------------------------------------------------------------------
10:45--11:00 Coffee
-------------------------------------------------------------------------------------
11:00--12:00 Agent Based Models in Mata
Maarten Buis
12:00--12:30 How to use Stata's -sem- command with nonnormal data?
Wolfgang Langer
-------------------------------------------------------------------------------------
12:30--13:30 Lunch
-------------------------------------------------------------------------------------
13:30--14:00 xtoaxaca: Extending Oaxaca-Blinder-Decomposition to longitudinal data
Hannes Kröger/Jörg Hartmann:
14:00--14:30 Linear Discrete-Time Hazard Estimation using Stata
Harald Tauchmann
14:30--15:00 Heat (and hexagon) plots in Stata
Ben Jann
-------------------------------------------------------------------------------------
15:00--15:30 Coffee
-------------------------------------------------------------------------------------
15:30--16:00 Extending the -label- commands (cont'd)
Daniel Klein
16:00--16:30 The production process of the Global MPI
Nicolai Suppa
-------------------------------------------------------------------------------------
16:30--16:45 Coffee
-------------------------------------------------------------------------------------
16:45--17:30 Performing and interpreting discrete choice analyses in Stata
Joerg Luedicke
17:30--18:00 Wishes and Grumbles
-------------------------------------------------------------------------------------
18:00 End of meeting
-------------------------------------------------------------------------------------
How to get to the venue
~~~~~~~~~~~~~~~~~~~~~~~
Google Route Planer: [http://www.seidlvilla.de/kontakt.html]
------------------------------------------------------------
From the airport
----------------
Take S1 or S8 (direction does not matter) to station "Marienplatz".
Change to U3 (direction "Moosach") or U6 (direction "Fröttmaning")
and leave at "Giselastraße" (3rd station). Leave the
subway-station. Follow "Leopoldstraße" north (about 200m), turn
right into "Nikolaistraße". After another 100m you reach
"Nicolaiplatz".
From the railway main station
-----------------------------
Take any S-Bahn direction "Marienplatz". At "Marienplatz" change
to U3 (direction "Moosach") or U6 (direction "Fröttmaning") and
leave at "Giselastraße" (3rd station). Leave the
subway-station. Follow "Leopoldstraße" north (about 200m), turn
right into "Nikolaistraße". After another 100m you reach
"Nicolaiplatz".
Abstracts
=========
9:00--9:15 Welcome
~~~~~~~~~~~~~~~~~~
Katrin Auspurg (Ludwigs-Maximilians-Universtiy Munich), Josef Brüderl
(Ludwigs-Maximilians-Universtiy Munich)
9:15--10:15 On the shoulders of giants, or not reinventing the wheel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~
Nicholas J. Cox (Department of Geography, University of Durham, UK)
[[email protected]]
/Abstract:/ Part of the art of coding is writing as little as possible
to do as much as possible. In this presentation, I expand on this
truism and give examples of Stata code to yield tables and graphs in
which most of the real work is delegated to workhorse commands. In
tabulations and listings, the better known commands sometimes seem to
fall short of what you want. However, some preparation commands (such
as generate, egen, collapse or contract) followed by list, tabdisp,
or _tab can get you a long way. In graphics, a key principle is
that graph twoway is the most general command, even when you do not
want rectangular axes. Variations on scatter and line plots are
precisely that, variations on scatter and line plots. More challenging
illustrations include commands for circular and triangular graphics,
in which x and y axes are omitted with an inevitable but manageable
cost in re-creating scaffolding, titles, labels, and other
elements. The examples range in scope from a few lines of interactive
code to fully developed programs. This presentation is thus pitched at
all levels of Stata users.
10:15--10:45 Stata export for metadata documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~
Anne Balz (GESIS--Leipniz Institute for the Social Sciences), Klaus
Pforr (GESIS--Leipniz Institute for the Social Sciences), Florian
Thirolf (GESIS--Leipniz Institute for the Social Sciences)
[[email protected]]
/Abstract:/ Precise and detailed data documentation is essential for
the secondary analysis of scientific data, whether survey or official
microdata. Among the most important metadata in this perspective are
variable and category labels as well as frequency distributions and
descriptive statistics. To generate and publish these metadata from
Stata datafiles, an efficient export interface is essential. It must
be able to handle large and complex data sets, take into account the
specifics of different studies and generate flexible output formats
(depending on the requirements of the documentation system). As a
solution to the problem described above, we present the process
developed in the GML (German Microdata Lab) at GESIS. In the first
step, we show how an aggregated file with all required metadata can be
generated from the microdata. In the second step, this file is
transformed into a standardized DDI format. Additionally, we will
present the implementation for MISSY (the metadata information system
for official microdata at GESIS), which includes some practical
additions (e.g. communication with the MISSY database to retrieve
existing element identifiers, writing an output tailored to the MISSY
data model).
10:45--11:00 Coffee
~~~~~~~~~~~~~~~~~~~
11:00--12:00 Agent Based Models in Mata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Maarten Buis (University of Konstanz)
[[email protected]]
/Abstract:/ An Agent Based Model is a simulation in which agents, that
each follow simple rules, interact with one another and thus produce a
often surprising outcome at the macro level. The purpose of an ABM is
to explore mechanisms through which actions of the individual agents
add up to a macro outcome, by varying the rules that agents have to
follow or varying with whom the agent can interact (i.e. varying the
network).
A simple example of an ABM is Schelling's segregation model, in which
he showed that one does not need racists to produce segregated
neighbourhoods. The model starts with 25 red and 25 blue agents, each
of which live in a cell of a chessboard. They can have up to 8
neighbours. In order for an agent to be happy she needs to have some,
e.g. 30%, agents in the neighbourhood of the same color. If the agent
is unhappy, she will move to another empty cell that will make her
happy. If we repeat this till everybody is happy or nobody can move,
we will often end up with segregated neighbourhoods.
Implementing a new ABM will always require programming, but a lot of
the tasks will be similar across ABMs. For example, in many ABMs the
agents live on a square grid (like a chessboard), and can only
interact with their neighbours. I have created a set of Mata functions
that will do those task, and that someone can import in her or his own
ABM. In this talk I will illustrate how to build an ABM in Mata with
these functions.
12:00--12:30 How to use Stata's -sem- command with nonnormal data? A new nonnormality correction for the RMSEA and incremental Fit Indices, CFI and TLI
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~
Wolfgang Langer (University of Luxembourg and
Martin-Luther-Universität Halle-Wittenberg)
[[email protected]]
/Abstract:/ Traditional fit measures like RMSEA, TLI or CFI are based
on noncentral chi-square distribution assuming the multinormal
distribution of the observed indicators (Jöreskog 1970). If this
assumption is violated programs like Stata, EQS or LISREL calculate
the fit indices using the Sattora-Bentler correction. It rescales the
Likelihood-Ratio-chi2-test statistics of the baseline and the
hypothesized model (Satorra & Bentler 1994, Newitt & Hancock 2000).
Brosseu-Liard et al. (2012, 2014) and Savalei (2018) showed in their
simulation studies with nonnormal data two results: Firstly, they
demonstrated that the ad-hoc nonnormality corrections of the fit
indices provided by the SEM software made the fit worse, better or
unchanged as compared to their uncorrected counterparts. Secondly, the
authors proposed new robust versions of RMSEA, CFI and TLI which
performed very well in their simulation studies. They systematically
varied the sample size, the extend of misspecification and
nonnormality. Therefore the same rule of thumb or criteria which are
used for normal distributed data can be applied to assess the fit of
the strutural equation model.
My `robust_gof.ado' estimates the robust RMSEA, CFI and TLI fit
measures using the corrections proposed by Brosseu-Liard et al. and
Savalei. It also estimates a 90 percent confidence interval for the
Root-Mean-Squared-Error of Approximation. My `robust_gof.ado' can be
executed after the -sem- command with the `vce(sbentler)' option and
`estat gof, stats(all)' as a postestimation command by simply typing
`robust_gof'. It returns the estimated fit indices and scalars as -r-
containers. A survey example of islamophobia analysis in Germany will
be presented to demonstrate the usefulness of my `robust_gof.ado'.
- Asparouhov, T. & Muthén, B. (2010): Simple second order chi-square
correction. Los Angels, Ca: MPLUS Working papers
- Borsseau-Liard, P.E., Savalei, V. & Li, L. (2012): An investigation
of the sample performance of two nonnormality corrections for
RMSEA. Multivariate Behavioral Research, 47, 6, pp. 904-930
- Borsseau-Liard, P.E. & Savalei, V. (2014): Adjusting incremental fit
indices for nonnormality. Multivariate Behavioral Research, 49, 5,
pp. 460--470
- Jöreskog, K.G. (1970): A general method for analysis of covariance
structures. Biometrika, 57,2, pp. 239--251
- Jöreskog, K.G., Olsson, U.H. & Wallentin, F.Y. (2016): Multivariate
Analysis with LISREL. Switzerland: Springer
[[email protected]]
mailto:[email protected]
13:30--14:00 -xtoaxaca-: Extending the Oaxaca-Blinder Decomposition Approach to longitudinal data analyses
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~
Hannes Kröger (DIW--German Institute for Economic Research, Berlin),
Jörg Hartman (University of Göttingen)
[[email protected]]
/Abstract:/ The Oaxaca-Blinder (Oaxaca, 1973) decomposition approach
has been widely used to attribute group level differences in an
outcome to differences in endowment, coefficients, and their
interactions. The method has been implemented for Stata in the popular
-oaxaca- program for cross-sectional analyses (Jann, 2008). However,
in the last decades research questions are more often focused on the
decomposition of group based differences in change over time,
e.g. diverging income trajectories, as well as decomposition of change
in differences between groups, e.g. change in the gender pay gap.
Another way in which decomposition analyses can be extended to
longitudinal data is repeated crosssectional decompositions and time
point specific decomposition of group levels differences based on
latent growth curve models. We propose to unify these different
research interest under a more general longitudinal perspective that
has each of the applications as a special case of the Oaxaca-Blinder
decomposition. We present this general view, give examples of applied
research questions that can be answered within the framework and
propose a first version of the program -xtoaxaca- which works as a
postestimation command in Stata in order maximize flexibility in
modeling and forms of longitudinal decompositions according to the
user's preferences.
- Jann, B. (2008). The Blinder-Oaxaca decomposition for linear
regression models. The Stata Journal, 8(4), 453–479.
- Oaxaca, R. (1973). Male-female wage differentials in urban labor
markets. International Economic Review, 693–709.
14:00--14:30 Linear Discrete-Time Hazard Estimation using Stata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~
Harald Tauchmann (Friedrich-Alexander-University, Erlangen-Nürnberg;
RWI--Leibniz Institute for Economic Research, Essen; CINCH -- Health
Economics Research Center, Essen)
[[email protected]]
/Abstract:/ Linear fixed-effects estimators (first-differences,
within-transformation) are workhorses of applied econometrics because
they straightforwardly allow for eliminating unobserved time-invariant
individual heterogeneity that otherwise may cause a bias. I show that
these popular estimators are, however, biased and inconsistent when
applied in a discrete-time hazard setting, that is with the outcome
variable being a binary dummy indicating an absorbing state. I suggest
an alternative, computationally simple, adjusted first-differences
estimator. This estimator is shown to be consistent in the considered
non-repeated event setting, under the assumption of unobserved
time-invariant individual heterogeneity being uncorrelated with the
changes in the explanatory variables. Using higher-order differences
instead of first-differences allows for consistent estimation under
weaker assumptions. Finally I introduce the new user written command
-xtlhazard- that implements the suggested estimation procedure in
Stata.
14:30--15:00 Heat (and hexagon) plots in Stata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Ben Jann (University of Bern)
[[email protected]]
/Abstract:/ In this talk I will present two new Stata commands to
produce heat plots. Generally speaking, a heat plot is a graph in
which one of the dimensions of the data is visualized using a color
gradient. One example of such a plot is a two-dimensional histogram in
which the frequencies of combinations of binned X and Y are displayed
as rectangular (or hexagonal) fields using a color gradient. Another
example is a plot of a trivariate distribution where the color
gradient is used to visualize the (average) value of Z within bins of
X and Y. Yet another example is a plot that displays the contents of a
matrix, say, a correlation matrix or a spacial weights matrix, using a
color gradient. The two commands I will present are called -heatplot-
and -hexplot-.
15:00--15:30 Coffee
~~~~~~~~~~~~~~~~~~~
15:30--16:00 Extending the -label- commands (cont'd)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~
Daniel Klein (INCHER --International Centre for Higher Education
Research, Kassel) [[email protected]]
/Abstract:/ Four years ago, I first suggested extending Stata’s label
commands to manipulate variable labels and value labels in a more
systematic way. By now, I have refined my earlier approach and
released a new suit of commands, -elabel-, that facilitate these
everyday data management tasks. In contrast to most existing
community-contributed commands to manipulate labels, -elabel- does not
focus on solving specific problems. Combined with any of Stata’s
-label- commands, it address any problem related to variable and value
labels. -elabel- accepts wildcard characters in value label names,
allows referring to value labels via variable names, selects subsets
of integer to text mappings, and applies any of Stata’s functions to
define new or modify existing labels. I demonstrate these features
drawing on various examples and show how to write new ado-files to
further extend the -elabel- commands.
16:00--16:30 The production process of the Global MPI
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~
Nicolai Suppa (Juan de la Cierva Research Fellow, Centre d'Estudis
Demogràfics, Spain)
[[email protected]]
/Abstract:/ The Global Multidmensional Poverty Index is a
cross-country poverty measure published by the Oxford Poverty and
Human Development Initiative since 2010. The estimation requires
household survey data, as multidimensional poverty measures seek to
exploit the joint distribution of deprivations in the identification
step of poverty measurement. Moreover, analyses of multidimensional
poverty draw on several aggregate measures (e.g, headcount ratio,
intensity) as well as on dimensional quantities (e.g, indicator
contributions). Robustness analyses of key parameters (e.g., poverty
cutoffs and weighting schemes) further increase the number of
estimates.
During the 2018 revision for the first time figures for 105 countries
were calculated in one round. For a large scale project like this, a
clear and efficient workflow is essential. This paper introduces key
elements of a workflow and presents solutions with Stata for
particular problems, including (i) the structure of a comprehensive
results file, which facilitates both analysis and production of
deliverables, (ii) the usability of the estimation files, (iii) the
collaborative nature of the project, (iv) the labelling of 1200
subnational units, and (v) the documentation of code and decisions.
This paper seeks to share the gained experienced and to subject both
the principal workflow and selected solutions to public scrutiny.
16:30--16:45 Coffee
~~~~~~~~~~~~~~~~~~~
16:45--17:30 Performing and interpreting discrete choice analyses in Stata
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~
Joerg Luedicke (StataCorp)
/Abstract:/ Discrete choice models are used across a variety of
disciplines to analyze choices made by individuals or other
decision-making entities. Stata supports a variety of discrete choice
models such as multinomial logit and mixed logit models. While
applying these models to a given dataset can be straightforward, it is
often challenging to interpret their results. In this talk, I will
provide an overview of Stata's discrete choice modeling capabilities
and show how to use postestimation commands to get the most out of
these models and their interpretation.
17:30--18:00 Whishes and Grumbles
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/Abstract:/ Users air their whishes and grumbles and StataCorp
responds
Registration and accommodations
===============================
Please travel at your own expense. The conference fee will be €45
(Students €35) to cover costs for coffee, teas, and luncheons. There
will also be an optional informal meal at a restaurant on Friday
evening at additional cost. You can enroll by contacting Elena
Tsittser ([[email protected]]) by email or by writing,
phoning, or faxing to:
-------------------------------
Elena Tsittser
Dittrich & Partner Consulting
Prinzenstrasse 2
42697 Solingen
Germany
Tel: +49 (0)212 260 6624
Fax: +49 (0)212 260 6666
-------------------------------
Scientific Organizers
=====================
The academic program of the meeting is being organized by Katrin
Auspurg and Josef Brüderl (LMU Munich), Johannes Giesecke (HU Berlin)
and Ulrich Kohler (University of Potsdam)
Logistics organizers
====================
The logistics are being organized by Dittrich and Partner
([http://www.dpc.de]), the distributor of Stata in several countries
including Germany, The Netherlands, Austria,
Workshop
========
Date/Venue/Costs
~~~~~~~~~~~~~~~~
-------------------------------------------------------------------
Date: May 23, 2019
Venue: Ludwig-Maximilians-Universität Munich
Department for Sociology
Konradstraße 6
80801 Munich
[https://www.en.soziologie.uni-muenchen.de/index.html]
Room Cip-Pool
4th floor, room nr. 409
Presenter Jan Paul Heisig [[email protected]]
Dr. Heisig is head of the research Group
"Health and Social Inequality" at the
Social Science Research Center, WZB
Cost: Workshop only: 65 EUR
Meeting only: 45 EUR (students 35 EUR)
Workshop and Meeting: 85 EUR
Register [[email protected]]
-------------------------------------------------------------------
Description
~~~~~~~~~~~
Missing data are a pervasive problem in the social sciences. Data for
a given unit may be missing entirely, for example, because a sampled
respondent refused to participate in a survey (survey
non-response). Alternatively, information may be missing only for a
subset of variables (item non-response), for example, because a
respondent refused to answer some of the questions in a survey. The
traditional way of dealing with item non-response, referred to as
"complete case analysis" (CCA) or "listwise deletion", excludes
any observation with missing information from the analysis. While easy
to implement, complete case analysis is wasteful and can lead to
biased estimates. Multiple imputation (MI) addresses these issues and
provides more efficient and unbiased estimates, if certain conditions
are met. Therefore, it is increasingly replacing CCA as the method of
choice for dealing with item non-response in applied quantitative work
in the social sciences. The goals of the course are to introduce
participants to the principles of MI and its implementation in Stata,
with a primary focus on MI using iterated chained equations (aka
"fully conditional specification").
Prerequisites
~~~~~~~~~~~~~
Participants should have a good working knowledge of Stata.
Lecturer
~~~~~~~~
Dr. Heisig is head of the research Group ``Health and Social
Inequality'' at the Social Science Research Center, WZB. Heisg's
recent nethodological contributions include
- Heisig, Jan Paul/Schaeffer, Merlin/Giesecke, Johannes (2017): The
Costs of Simplicity. Why Multilevel Models May Benefit from
Accounting for Cross-Cluster Differences in the Effects of
Controls. In: American Sociological Review, Vol. 82, No. 4,
S. 796--827.
Comment