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**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

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**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

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**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.

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**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.

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**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.

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**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:

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**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

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**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

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**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

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**Visualizing Your Data Boxplots**

SAS/GRAPH(R) 9.2: Statistical Graphics Procedures Guide, Second Edition

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Visualizing Your Data Histograms

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Visualizing Your Data Scatter Plots

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**Understanding Your Data**

Correlation Matrices

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**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

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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

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**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:

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**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:

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