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10 - Data Processing

Invisible Decisions That Matter

from Part III - Expanding the Multiverse

Published online by Cambridge University Press:  28 February 2025

Cristobal Young  and
Erin Cumberworth
Cristobal Young
Affiliation:
Cornell University, New York
Erin Cumberworth
Affiliation:
Cornell University, New York
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Summary

Raw data require a great deal of cleaning, coding, and categorizing of observations. Vague standards for this data work can make it troublingly ad hoc, with much opportunity and temptation to influence the final results. Preprocessing rules and assumptions are not often seen as part of the model, but they can influence the result just as much as control variables or functional form assumptions. In this chapter, we discuss the main data processing decisions that analysts often face and how they can affect the results: coding and classifying of variables, processing anomalous and outlier observations, and the use of sample weights.

Keywords

data processingdata cleaningoutlierssampling weightsmissing dataanomalous observations
Type
Chapter
Information
Multiverse Analysis
Computational Methods for Robust Results
 , pp. 154 - 176
Publisher: Cambridge University Press
Print publication year: 2025

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