1. Research Technology Consulting
Simo Goshev
Alex Storer
Steve Worthington
Ista Zahn
support@help.hmdc.harvard.edu
http://rtc.iq.harvard.edu

2. Consulting Goals
 Data analysis support and programming services
 Research project planning and guidance selecting
appropriate technology for research projects
 Facilitating appropriate organization, storage and
sharing of data
 Training on the use of both established software
packages and emerging tools

3. Scope
 Free!
 Support the entire social science
community
 Consults measured in hours rather
than weeks or months
 Currently doing outreach to
departments, student groups and
centers
 Drop-ins on Fridays at 1pm in the
training lab, Appointments, Help
Tickets and casual chats in K306

4. Who We
Scope Are

5. Simo Goshev
 BA – Sofia, Bulgaria
Applied Econometrics
 MS – McMaster University
Statistics
 PhD – McMaster University
Economics
Analysis: Tools:
Econometrics Mainly Stata
Applied Microeconometrics Some R
Panel Data
Applied statistics

6. Help with econometrics
 What model is most suitable for my data on
hospital IT innovation?
 I am looking at HIV in children. Can you help me
design an overlapping generations model?
 Why are the confidence intervals of my spline of
health care spending so wide/narrow?
 Could the interaction between an exogenous
and endogenous variable be exogenous?
 I am looking for a way to compare survival
between two cancer management programs.
Can you help me?

7. Help with computation/estimation
 I am trying to estimate a model but for
some reason the routine fails. Could you
have a look at my script ?
 I am working with a large dataset and my
machine is giving up on me. Do you have
any suggestions?
 Which routine is best for…?

8. Replication study in health
economics
•Graduate Student •Make sense of a study and Stata code
1
1
.8
.8
.6
.6
.4
.4
.2
.2
65 70 75 80 65 70 75 80

9. Predictors of hospital IT adoption
•Graduate Student, School of Public •Understand what factors facilitate/hinder
Health adoption of IT in US hospitals
 Data:
 Sample of hospitals clustered within states
 Count of IT’s adopted by a hospital in 3 consecutive years
 Modeling strategy:
 Three-level mixed effects model

10. Alex Storer
 BS,BA - UC Berkeley
Electrical Engineering & Computer
Science, Cognitive Science
 PhD – Boston University
Cognitive & Neural Systems
Analysis: Tools:
Machine Learning Matlab, R, Python
Signal Processing Emacs, LaTeX, Linux
Surface Based Techniques
Simulation
Optimization

11. Text Analysis
Topic
Models
Large
corpus
Prevalenc
e of
Sentiment
certain
terms

12. Text Analysis
Twitter:
#obamacare
Positive/Ne
gative
Opinions?

13. Text Analysis
Distinct
Content
Groupings
Congress
Speeches

14. Text Analysis
NY
Times
Archive
Term:
"Medicare"

15. Text Analysis
Topic
Models
 What models are appropriate to perform our
analysis?
 What software is appropriate?
Prevalenc
e of
Sentiment
certain
terms

16. Text Analysis
 Where do we obtain this corpus?
 How do we pre-process it so we can analyze
it?
Large
corpus

17. Federal Procurement Database

18. Federal Procurement Database
Only first 500 hits, only a few columns
All of the data, but…

19. Federal Procurement Database
Download atom feeds
Parse XML Tree structure
Python!
Search for union of entries
Output as CSV
For 20gb of data, there is no way to download by hand…

20. Steve Worthington
 BA / MS – Durham, UK
Anthropology & Archeology
 PhD – NYU
Biological Anthropology
Analysis: Tools:
Linear models (OLS, GLS, PLS, etc.) Mainly R
Resampling (permutation, bootstrap) Some SAS, SPSS
Ordination (PCA, LDA, CVA, etc.)

21. Cleaning / reshaping data
•Department of •171 files, 3 types (2 ascii •Parse messy data
Economics text, 1 binary) into a long-format Stata
•Daily Lat/Long data on •One file for each year data frame
rainfall in India (1951 – (containing 365 daily
2007) matrices)
June 21st 2007

22. Cleaning / reshaping data
• No common delimiter (spaces and tabs)
• Use regexp to parse each datum
• Use template to place each datum into correct row/column
Template

23. Cleaning / reshaping data
 Long format
data frame
in Stata
 Rainfall for
each day
and lat/long

24. Rainfall / CEO movie

25. Rainfall / CEO movie

26. Geospatial Analysis in R
 Spatial prediction: interpolation of data points
 Spatial autocorrelation analysis
Drug resistant TB
Moldova

27. Ista Zahn
 BS – University of Oregon
Psychology
 PhD (ABD) – University of Rochester
Social Psychology
Analysis: Tools:
Regression R, Stata, SAS, SPSS
Mixed Models Emacs, LaTeX, Linux
Scale Development

28. Workshops
(schedule at http://rtc.iq.harvard.edu)

29. IQSS Services
THE INSTITUTE FOR
Quantitative Social Science
at Harvard
University

30. Contact Us!
support@help.hmdc.harvard.edu
http://rtc.iq.harvard.edu/
CGIS-Knafel, Room K306
Fridays afternoons, K018