**Survey Data Analytics**

Public data collected through surveys are widely
used in marketing, social science, public health, economics, political and many
other research areas. Different from unstructured data cumulated through
Internet, survey data are deliberately collected through well-designed complex
surveys usually by big organization such as Census Bureau, Bureau of Labor
Statistics and National Opinion Research Center. Every data point is targeted
and sampled through probability sampling, which allows the surveys to represent
the whole population accurately. But for most researchers, it takes days even
months to understand the survey data, the major problem could be:

·
How to figure out the survey design to
calculate the right estimates, like what do these sampling stratifications,
clusters, survey weights, imputation flags stand for and how to use them in my
models.

·
How to identify and extrapolate the interested
subset and variables

·
How to interpret the survey questionnaire
correctly

·
How to analyze the survey data using survey
specific statistical models.

We are a team of professional
survey statisticians and big data analysts. We could solve your problems of
survey data related problems with the shortest time and the most economic
solution. Please write to us at wangfang227@gmail.com with any
questions. Thanks for visiting our webpage!

**Fang Wang Resume**

**EXPERIENCE**

**National Opinion Research
Center, at the University of Chicago **

*Survey
Statistician III Dec,
2014-Present*

*Survey
Statistician II***
****Dec, 2011 ****– ****Dec, 2014
**

*Survey
Statistician I***
****Dec, 2009 ****–**** Dec, 2011**

·
Applied text
mining and machine learning methods to analyze and visualize text open-ended
survey answers.

·
Served as Primary
Statistician of Medicare Current Beneficiary Survey (MCBS) imputation and
weighting; analyzed, imputed and weighted incomplete medical claim data,
insurance payment data and patient demographic data; developed internal and
external reports.

·
Served as PI of
the NORC research project “Improving NORC missing data imputation procedure”:
developed NORC SAS missing data imputation macro; implemented hot-deck and
Bayesian model-based multiple imputations.

·
Conducted
disclosure risk analysis for National Immunization Survey; generated public use
file and reports to CDC.

·
Developed SAS
macros for complex survey linear and logistic model selection; built complex
logistic models.

·
Developed R and
SAS programs of sampling, predictive modeling, variance estimation, data
cleaning and data quality control for Survey of Doctorate Recipient (SDR).

·
Participated in
the International Survey of Doctorate Recipient survey questionnaire design.

**National Opinion Research
Center, at the University of Chicago
**Jul,
2009 – Dec, 2009

*Graduate Research Assistant II*

·
Predicted US
number of children in a household using multinomial logistic model for 2008
NIS.

·
Developed 2008
NIS R, SAS and SUDAAN data read-in programs and data analysis sample code.

**Computational Institute,
University of Chicago & Argonne National Lab
**May,
2009 – Dec, 2009

*Graduate Research Assistant*

·
Modeled yearly
global population and urban rural classification using time series models.

·
Predicted and
visualized US land use change using generalized probit models and Bayesian
methods.

**Other
Experience **

· Predicted the 2012 Presidential Election results using
a Hierarchy Bayesian model and MCMC simulation, Oct, 2012

· Teaching Assistant, Department of Statistics,
University of Chicago, Jan,
2009 – Jun, 2009

· Teaching Assistant, Department of Mathematics, USTC, Sep,
2006 – Jan, 2007

· Conference Coordinator, Pacific Rim Complex Geometry
Conference, USTC, May,
2006– Aug, 2006

**EDUCATION**

**University of Chicago, **Chicago, IL

*M.S. in Statistics *Dec, 2009

**University of Science and Technology of China (USTC), **Hefei, China

*B.S.** in Computational Mathematics * * *July, 2006
* *

**AWARDS**

SAS Midwest User’s Group Jr
Professional Award (First Class), 2011

NORC CESR Research Award
2010, 2011; NORC Excellent Employee Award 2012; NORC Idea Lab Research Award,
2014

**SKILLS**

l
Survey Skills:
Sampling Design, Questionnaire Design, Weighting, Complex Survey Data Analysis,
Survey Management, Multiple Imputation, Total Survey Error Control

l Statistical Skills: Regression, Design of Experiments,
Categorical Data Analysis, Time Series, Machine learning

l Computer Skills: SAS (Macro), R, SQL, Excel, ArcGIS,
SUDAAN, SPSS, Python(basic)