Stata - Experiences and Needs
of a Professional Services Organisation
Dr. Andreas Stiehler
Berlecon Research GmbH
Oranienburger Str. 32
10117 Berlin
as@berlecon.de
Fon/Fax: +49 30 285296-0/-29
Outline
I.
Intro: Berlecon Research
II.
Requirements for statistic software
III.
Deploying Stata 8: main challenges
IV. Conclusions and wish list
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I. Intro: Berlecon Research GmbH
We analyze the potential of new technologies.
 Analyses are typically published as reports
Our clients are demanding and have high standards.
 IT executives from large and medium-sized companies
 Technology and consulting companies
 Public institutions (EU commission, BMWA etc.)
Empirical work makes up a significant part of the research.
 About 3 - 5 surveys per year (increasing importance)
 Data typically delivered by market research companies
 Requirements for statistics software differ from that of typical
Stata users (scientists).
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II. Requirements for statistics software
4
II. Requirements for statistics
software
We need less sophisticated statistics…
 > 90% binomial and ordinal data
 mainly descriptive statistics needed (tabulate, tabsum)
…but high quality graphs and tables!
 layout of the reports is a competitive factor
We only have a limited amount of time…
 for both exploring software opportunities and computing
…but need highly reliable data!
 errors in data analysis would seriously damage our reputation
 Stata 8 has the potential to fulfil many of these requirements.
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II. Requirements for statistics
software
July 2004: Start of internal project with two objectives:
 Choosing a new statistics software and - based on it  Finding a way to streamline the creation of tables and graphs
Decision in favour of Stata 8, decisive features are:
 Programming opportunities: allows for customisation of outputs
and exploiting economies of scale
 Intuitive user interface: facilitates the exploration of possibilities
that Stata offers.
 Graphs: high quality, no additional software needed
 Further features: Statalist, Mac support, price
 But: Exploiting the potentials for our needs is challenging
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III. Tables and Graphs: main challenges
7
III. Tables : Main Challenges
Goal: Preparation of data for custom tables
Overview tables typically compile results from several tabulate(s)
Often further transformation of results needed (e.g., summary data
for general approval = fully agree + somewhat agree).
Companies
Solution 1
Solution 2
Solution 3
All
By country
Germany
UK....
By education
High-skilled
Low-skilled
 C & P from “tabulate” (s) is too time-consuming and error prone.
8
III. Tables : Main Challenges
Solution today: Processing of data via matrices
1. Tabulate results to matrix: tab A B, col nofreq matcell (AB)
2. Transformation of data using matrix operations:
percentages, aggregation, compiling for the presentation in
table.
3. Matrix to data table: svmat (AB), names (newsol1)
4. Copying of table-ready data to Excel or Word
Advantages
 Significant time savings (half of the time needed compared
to transfer of results on a case-by-case basis)
 Error reduction (copying only once)
 Stata 8 allows for creating table-ready results in an efficient way.
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Programming of table-ready data
matrix def e = 1,1,1,1,1
foreach var of varlist q31* {
/*********Country******************************/
tabulate `var' country, matcell(c`var')
matrix define gesc`var'= e * c`var'
matrix define c`var' = c`var' * inv(diag(gesc`var')) * 100
/***********Education*************************/
tabulate `var' edu, matcell(e`var')
matrix define gese`var'= e * e`var'
matrix define e`var' = e`var' * inv(diag(gese`var')) * 100
/***************All together***************************/
matrix define `var' = c`var''\e`var'‘
matrix define `var' = `var'* (1,1,0,0,0)'
svmat `var', names (z`var')
foreach var of varlist z`var'* {
replace `var' = round(`var')
}
}
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III. Graphs: Main challenges
Graphs at Berlecon:
About 7 different graph
types in use
Customisation time:
about 2 weeks (GUI ”Submit” - big help)
Use of program files
makes it economic (reuse of do.files)
Extensive use of
overlaid two-way charts
 Challenge: Many graphs needed by Berlecon Research are non-
standard in Stata!
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IV. Conclusions and wish list
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IV. Conclusions
Stata has the potential to serve as a helpful tool
for the specific needs of professional services organisations.
 Ability to streamlined and less error prone generation of tableready data.
 High quality graphs - no additional software needed.
 Intuitive user interface and collaboration via Statalist facilitates
programming for non-experts.
There are several challenges:
 Standard output typically not sufficient for further processing in
tables and graphs.
 Finding appropriate solutions is challenging - but it’s worth it.
 Tools facilitating the customisation process could help to win
further professional services organisations as customers.
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IV. Wish list
Tables:
Option to:
 Design tables within the Stata program!!!
 Save percentages in matrices
 Display “zero” rows/columns to matrices in case of empty cells
Graphs:
 Option to define colours for value labels in stacked bar charts as
standard (as it is possible for pie charts)
 “Text” options for labels on y/x-axis (including line breaks, size…)
 Book “A visual guide to Stata” for non-standard graphs
 Thanks for your attention!
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Programming of graph-ready data
/***Tabulate results to data editor*********/
matrix define e7 = 1,1,1,1,1,1,1
foreach var of varlist f7* {
tabulate `var' [fweight=rweight], matcell(`var')
matrix define ges`var' = e7 * `var'
matrix define `var' = `var' * inv(ges`var')* 100
}
matrix define y7 = f7a,f7b,f7c
matrix define y7 = y7'
svmat y7
/*Stack bars*/
gen y7stack7 =
gen y7stack6 =
gen y7stack5 =
gen y7stack4 =
gen y7stack3 =
gen y7stack2 =
gen y7stack1 =
}
y71+y72+y73+y74+y75+y76+y77
y71+y72+y73+y74+y75+y76
y71+y72+y73+y74+y75
y71+y72+y73+y74
y71+y72+y73
y71+y72
y71
/*counting*/
matrix input zaehl3 = (1\2\3)
svmat zaehl3
/*Rounded scatter values*/
forvalues i = 1/7 {
gen y7scat`i' = y7stack`i'-y7`i'/2
replace y7`i' = round(y7`i')
}
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Programming of overlaid charts
twoway ///
(bar y7stack7 zaehl31, horizontal blcolor(black) bfcolor(gs13) barwidth(0.5)) ///
(scatter zaehl31 y7scat7, msymbol(none) mlabel(y77) mlabsize(medsmall) ///
mlabcolor(black) mlabposition(0)) ///
///
(bar y7stack6 zaehl31, horizontal blcolor(black) bfcolor(gs3) barwidth(0.5)) ///
(scatter zaehl31 y7scat6, msymbol(none) mlabel(y76) mlabsize(medsmall) ///
mlabcolor(white) mlabposition(0)) ///
///
(bar y7stack5 zaehl31, horizontal blcolor(black) bfcolor("204 255 204") barwidth(0.5)) ///
(scatter zaehl31 y7scat5, msymbol(none) mlabel(y75) mlabsize(medsmall) ///
mlabcolor(black) mlabposition(0)) ///
///
(bar y7stack4 zaehl31, horizontal blcolor(black) bfcolor("0 204 0") barwidth(0.5)) ///
(scatter zaehl31 y7scat4 .........
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Labelling of bars
....///
, ylabel( ///
0.85 "Cost " ///
1.15 "leadership " ///
1.4 "..........................." ///
1.85 "Quality " ///
2.15 "leadership " ///
2.4 "..........................." ///
2.85 "Innovation " ///
3.15 "leadership " ///
3.4 "..........................." ///
3.85 "Market " ///
4.15 "leadership " ///
4.4 "..........................." ///
4.85 "Vertical " ///
5.15 "focus " ///
5.4 "..........................." ///
5.85 "Focus on " ///
6.16 "enterprise size" ///
, .....
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