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Screening and Cleaning Data
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Week 2 lecture for stats
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Lecture Structure:Lecture Structure:
1. Outliers
2. Normality
3. Linearity
4. Homoscedasticity
5. Multicollinearity
6. Missing Data
Recommended Reading:
Tabachnick & Fidell, Chapter 4
Data Screening:
Importance: A critical step before serious data analysis. Ensures assumptions are met
and data is clean.
Key Principles:
1. Transparency: Make data and methods open for review.
2. Consistency: Use a systematic approach.
3. Clarity: Ensure clear documentation for reproducibility.
First Steps:
Check accuracy of data entry.
Look for out-of-range and implausible values.
Verify coding for categorical variables.
Outliers:
Definition: Extreme values in one (univariate) or multiple (multivariate) variables.
Causes:
o Data entry errors.
o Participants from different populations.
o Genuine extreme cases.
Detection:
o Univariate: Standardize the variable (compute z-scores). Values > 3.29 are
potential outliers. Visualize using histograms or boxplots.
o Multivariate: Use formal tools like Mahalanobis Distance (MD), calculated
with the Rj Editor module in JAMOVI. MD is compared to a critical value
using a χ² distribution.
Dealing with Outliers:
Check for errors.
Omit cases if not part of the target population (use caution).
Transform the variable, winsorize, or trim the data.
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