1. Global PaedSurg Research Training Fellowship6
Data Cleaning and Analysis
Dr. Emily Smith 4/25/2019

2. At the end of the day, you want a clean and precise dataset.
The question is – How to get there?
Especially in global health
Overarching Goal

3. Next Steps
Think about the data that you have collected or will collect as part of your research project
What is your research question?
What are you trying to get your data to “say”?
Which statistical tests will best help you answer your research question?
Contact the research coordinator to discuss how to analyze your data!

4. Data Analysis Process

5. Step 1: Creating an analysis plan
Formulate your plan according to your research objective and setting
Collect the data
Now that you have your data, how to you assess its accuracy and precision?

6. Create a data dictionary
Step 2: Managing Data
Create a data dictionary

7. Excel is a great resource for a data dictionary
Make sure to add a column describing how you code the missing values

8. Few datasets are free of errors and missing values.
It is important to review the dataset to identify errors before beginning analysis.
Document, document, document ANY changes you make.
This is an iterative process.
Before you begin, make a copy of the original dataset!! KEY POINT!
Step 3: Cleaning Data

11. Few datasets are 100% complete or accurate.
Usually there are a few weird or missing values.
Sometimes missing data occurs randomly and sometimes it occurs in patterns.
Step 4: Detecting and Correcting Missing, Miscoded or Out-of-Range Values

12. Types of missing data
MCAR (Missing completely at random): Missing are independent of variables and occur at random.
MAR (Missing at random): Missing-ness is related to a particular variable, but is not related to the value of the variable that has missing data (Accidently omitting an answer on a questionnaire).
MNAR (Missing not at random): Missing for a reason!
How do you know?

14. Identifying Missing Values
Best way to identify missing values is assessing the frequency distributions for each variable.

15. HANDLING MISSING DATA
Complete-case analysis (complete-participant analysis)
Remove everyone with missing data on one or more variables.
Often reduces precision
Unbiased in a wide range of circumstances
Imputation
Default value imputation
Mean imputation
Regression imputation
Multiple imputation
Inverse probability weighting

16. Identifying Records with Out-of-Range Values
Some variables may contain values that are outliers (or out of range) compared to the responses of the other participatns.
Often, these are numerical values that may have been incorrectly coded.
To identify these, make a scatter-plot of the variables.
Identifying Records with Out-of-Range Values

17. Making a scatterplot illustrates the value of one variable on the X axis and the value of the other on the Y axis.

18. Now that you have cleaned data (missing data, outliers, etc), you’re ready to analyze!
Remember you should at this point have an original dataset (pre-cleaned) and the dataset you have cleaned.
The analysis should be done on this dataset!
Step 5: Data Analysis

19. Types of Statistics/Analyses
Descriptive Statistics
Describing an association/relationship
How many? How much?
Inferences about an association/relationship
Proving or disproving theories
Associations between phenomena
If sample relates to the larger population
Frequencies
Basic measurements
Inferential Statistics
Hypothesis Testing
Correlation
Confidence Intervals
Significance Testing
Prediction

20. Descriptive Statistics
Descriptive statistics can be used to summarize and describe a single variable – univariate
Frequencies (counts) & Percentages
Categorical (nominal) data
Means & Standard Deviations
Continuous (interval/ratio) data

21. Frequencies & Percentages
How to display frequencies and percentages:
Pie chart
Table
Bar chart

22. Distributions
The distribution can be displayed using Box and Whiskers Plots and Histograms

23. Continuous  Categorical
You can aggregate data into categories from continuous data.
Collect continuous if you can, rather than categories in your raw data!

24. INFERENTIAL STATISTICS
Inferential statistics can be used to test theories, determine associations between variables, and determine if findings are significant
Types include:
Correlation
T-tests/ANOVA
Chi-square
Logistic Regression
INFERENTIAL STATISTICS

25. Analysis of Categorical/Nominal Data
Chi-square
Logistic Regression
Analysis of Continuous Data
Correlation T-tests
T-tests
Type of Data & Analysis

26. Correlation When to use it? What does it tell you?
When you want to know about the association or relationship between two continuous variables
Example: blood pressure and medication
What does it tell you?
If a linear relationship exists between two variables through the Pearson’s r, and how strong that relationship is
Ranges from -1 to +1

27. Correlation 0 – 0.25 = Little or no relationship
Interpreting strength of correlations:
0 – 0.25 = Little or no relationship
0.25 – 0.50 = Fair degree of relationship
= Moderate degree of relationship
0.75 – 1.0 = Strong relationship
1.0 = perfect correlation

28. T-tests What does a t-test tell you?
If there is a statistically significant difference between the mean score (or value) of two groups
What do the results look like?
Student’s t
Look at at corresponding p-value
If p < .05, means are significantly different from each other
If p > 0.05, means are not significantly different from each other

29. Chi-square When to use it? How to interpret?
When you want to know if there is an association between an exposure and outcome
Ex) Mortality (yes/no) and lung cancer (yes/no)
How to interpret?
If the observed frequencies of occurrence in each group are significantly different from expected frequencies (i.e., a difference of proportions)
Usually, the higher the chi-square statistic, the greater likelihood the finding is significant, but you must look at the corresponding p-value to determine significance

30. Logistic Regression When to use it? How do you interpret the results?
When you want to measure the strength and direction of the association between two variables (exposure and outcome)
Where the dependent or outcome variable is categorical (e.g., yes/no)
When you want to predict the likelihood of an outcome while controlling for confounders
How do you interpret the results?
Significance can be inferred using by looking at confidence intervals:
If the confidence interval does not cross 1 then the result is significant
If OR > 1  The outcome is that many times MORE likely to occur – Risk factor
2.0 = twice as likely
If OR < 1  The outcome is that many times LESS likely to occur – Protective factor
0.50 = 50% less likely to experience the event

31. Summary of Statistical Tests
Statistic Test
Type of Data Needed
Test Statistic
Example
Correlation
Two continuous variables
Pearson’s r
Are blood pressure and weight correlated?
T-tests/ANOVA
Means from a continuous variable taken from two or more groups
Student’s t
Do normal weight (group 1) patients have lower blood pressure than obese patients (group 2)?
Chi-square
Two categorical variables
Chi-square X2
Are obese individuals (obese vs. not obese) significantly more likely to have a stroke (stroke vs. no stroke)?
Logistic Regression
A dichotomous variable as the outcome
Odds Ratios (OR) & 95% Confidence Intervals (CI)
Does obesity predict stroke (stroke vs. no stroke) when controlling for other variables?

32. Descriptive statistics can be used with nominal, ordinal, interval and ratio data
Frequencies and percentages describe categorical data and means and standard deviations describe continuous variables
Inferential statistics can be used to determine associations between variables and predict the likelihood of outcomes or events
Inferential statistics tell us if our findings are significant and if we can infer from our sample to the larger population
Summary

33. References
Essential Medical Statistics. Kirkwood & Sterne, 2nd Edition
1/Association.aspx?Tutorial=AP
Background to Statistics for Non-Statisticians. Powerpoint Lecture. Dr. Craig Jackson , Prof. Occupational Health Psychology , Faculty of Education, Law & Social Sciences, BCU. ww.hcc.uce.ac.uk/craigjackson/Basic%20Statistics.ppt.

34. Thank you for listening, any questions?
Naomi Wright:
@PaedsSurgeon @GlobalPaedSurg #GlobalPaedSurg