1. Associate Collaborator for LISA Department of Statistics, VT
Analyzing Surveys
Marcos Carzolio
Associate Collaborator for LISA
PhD Student
Department of Statistics, VT
Laboratory for Interdisciplinary Statistical Analysis

2. Outline Data Cleaning and Preprocessing
Outlier Detection
Missing Value Imputation
Visualizing and Understanding Data
Boxplots, Histograms, and Scatterplots
Correlation Matrices
Analyzing Data
Contingency Tables
Analysis of Variance (ANOVA)
Regression

3. Laboratory for Interdisciplinary Statistical Analysis
LISA helps VT researchers benefit
from the use of Statistics
Experimental Design • Data Analysis • Interpreting Results Grant Proposals • Software (R, SAS, JMP, SPSS...)
Our goal is to improve the quality of research and the use of statistics at Virginia Tech.

4. How can LISA help? Formulate research question.
Screen data for integrity and unusual observations.
Implement graphical techniques to showcase the data – what is the story?
Develop and implement an analysis plan to address research question.
Help interpret results.
Communicate! Help with writing the report or giving the talk.
Identify future research directions.

5. Collaboration Walk-In Consulting Short Courses
Laboratory for Interdisciplinary Statistical Analysis
LISA helps VT researchers benefit from the use of Statistics
Designing Experiments • Analyzing Data • Interpreting Results Grant Proposals • Using Software (R, SAS, JMP, Minitab...)
Collaboration
From our website request a meeting for personalized statistical advice
Great advice right now: Meet with LISA before collecting your data
Walk-In Consulting
Monday—Friday 1-3 pm in 401 Hutcheson
Also, Tuesdays 1-3 pm in ICTAS Café X
& Thursdays 1-3 pm in GLC Video Conf. Room
for questions requiring <30 mins
Short Courses
Designed to help graduate students apply statistics in their research
All services are FREE for VT researchers.

6. Some Useful Resources R Statistical Computing Software
Can be downloaded for free from:
R Studio, a free Integrated Development Environment:
For a more interactive and user-friendly experience, try JMP
Downloadable from the Virginia Tech software library: /jmp/index.html
Amelia II: A Program for Missing Data
Visit:

7. Types of Survey Data Data Type Description Examples Statistics Nominal
Data with no intrinsic relative meaning behind labels
Strawberry, Banana, Hispanic
Mode
Ordinal
Data with an ordered structure
Small, Extra Large, Likert Scale*
Median and Percentiles
Interval (continuous or discrete)
Data with meaningful difference relations
Degrees in Celsius, Birthdates, GPS Coordinates
Mean, Standard Deviation, Correlation
Ratio (continuous or discrete)
Data with scale relations
Weight, Income, Length

8. Outlier Detection and Handling
Outliers are data points that deviate far from the main body of data so as to arouse suspicion about their origins
Visualize your data
Boxplots, histograms, and scatterplots
Only remove outliers that are verifiable errors
Extremeness in observations is not in itself cause for data removal
R Package ‘outliers’
Outlier

9. Missing Value Imputation
Imputation is the process of filling in the missing values of a dataset
Before considering imputation, try going after respondents for their true answers
Can be very tricky (Come to LISA for help)
If only one or two missing values are present in a vast dataset, use the mean of available values as a “best guess”
Honaker, James et al., AMELIA II: A Program for Missing Data

10. Visualizing Your Data Boxplots
SAS/GRAPH(R) 9.2: Statistical Graphics Procedures Guide, Second Edition

11. Visualizing Your Data
Histograms

12. Visualizing Your Data
Scatter Plots

13. Understanding Your Data
Correlation Matrices

14. Contingency Tables Tabulates the number of responses in each category
Helps to visualize the distribution of data
Use χ2 approximate test for independence
Pearson's Chi-squared test
data: tab
X-squared = , df = 2, p-value =
Warning message:
In chisq.test(tab) : Chi-squared approximation may be incorrect

15. Analysis of Variance
Technique used to test the differences between groups
Always plot your data before doing analyses
Call:
aov(formula = resp_height ~ gender)
Terms:
gender Residuals
Sum of Squares
Deg. of Freedom

16. Regression Actually a generalization of ANOVA
Again, always plot your data
Call:
lm(formula = exercise ~ dad_height)
Residuals:
Min Q Median Q Max
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)
dad_height
Residual standard error: on 37 degrees of freedom
(8 observations deleted due to missingness)
Multiple R-squared: , Adjusted R-squared:
F-statistic: on 1 and 37 DF, p-value:

17. Other Useful Resources
A PowerPoint on more automated outlier detection techniques:
2010/kdd10-outlier-tutorial.pdf
R Package ‘outliers’:
project.org/web/packages/outliers/outliers.pdf
On multiple imputation: