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Case Study: Cleaning Data from a Paper-Based Survey

Data Cleaning Ebook

Before you begin analyzing your data, it is crucial to ensure that your data set is complete and correct. This guide shares simple, yet crucial, techniques to help you clean your data effectively.

There are eight chapters in this ebook, each covering a distinct aspect of data cleaning.

Chapter 8 will help you learn the following techniques:

  • How to clean and identify UIDs from a real survey
  • How to crosscheck a data set with survey questions
  • How to deal with mistakes in conditional questions
  • How to detect outliers and assess whether they are accurate
  • How to standardize notation for missing data

We’ve devoted a full chapter in our ebook to exploring this issue.

At SocialCops, our partners have collected data for hundreds of surveys on our mobile data collection tool — Collect. However, we've learned from experience that cleaning data from a paper-based survey comes with its unique challenges. SocialCops has worked on cleaning data in one such survey.

Use this case study to learn more about the challenges involved and the basic steps followed in cleaning data from this paper-based survey.

This ebook includes 8 chapters, each covering a distinct aspect of data cleaning.

1

The Building Blocks of Data Cleaning

Understanding how to structure your data into rows and columns is a crucial first step in cleaning any data set.

2

Run Quick Sanity Checks on Data

A handy checklist of basic data checks to help you rule out some obvious errors in your data.

3

Check Different Question Types

Learn about common question types and how to ensure data consistency for your survey responses.

4

Deal with Missing Data

Learn how to appropriately deal with missing data to ensure the best balance between data accuracy and minimal loss in sample size.

5

Handle Outlier Detection

Outliers can be accurate or inaccurate. Learn different ways to detect outliers and deal with inaccurate data better.

6

Tackle Conditional Questions

Conditional questions may add complexity to your data. Learn how to deal with such questions in your data cleaning processes.

7

Join, Delimit, or Concatenate Data

Before analyzing your data, check out these functions will help you make better sense of your data and draw better insights

8

Case Study: Cleaning Data from a Paper-Based Survey

This case study will help you understand the challenges involved and the basic steps followed in cleaning data from this paper-based survey.