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The Data Detective's Toolkit

Release Date: 2020/12/01
ISBN: 9781952363023
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Reduce the cost and time of cleaning, managing, and preparing research data while also improving data quality!

Have you ever wished there was an easy way to reduce your workload and improve the quality of your data? The Data Detective’s Toolkit: Cutting-Edge Techniques and SAS Macros to Clean, Prepare, and Manage Data will help you automate many of the labor-intensive tasks needed to turn raw data into high-quality, analysis-ready data. You will find the right tools and techniques in this book to reduce the amount of time needed to clean, edit, validate, and document your data. These tools include SAS macros as well as ingenious ways of using SAS procedures and functions.

The innovative logic built into the book’s macro programs enables you to monitor the quality of your data using information from the formats and labels created for the variables in your data set. The book explains how to harmonize data sets that need to be combined and automate data cleaning tasks to detect errors in data including out-of-range values, inconsistent flow through skip paths, missing data, no variation in values for a variable, and duplicates. By the end of this book, you will be able to automatically produce codebooks, crosswalks, and data catalogs.

Table of Contents

  1. About This Book
  2. About the Author
  3. Acknowledgments
  4. Chapter 1: Advantages of Using the Data Detective’s Toolkit
    1. Introduction
    2. An Overview of the Data Detective’s Toolkit
    3. %TK_codebook
    4. %TK_inventory
    5. %TK_xwalk
    6. %TK_find_dups
    7. %TK_harmony
    8. %TK_skip_edit
    9. %TK_max_length
    10. Summary
  5. Chapter 2: The Data Detective’s Toolkit and SAS
    1. Introduction
    2. Preparing Your SAS Data Set
    3. Types of Metadata
    4. Using SAS to add Metadata to Your Data Set
    5. Fundamental SAS Macro Concepts
    6. What is the Macro Language?
    7. Using the Data Detective’s Toolkit Macro Programs
    8. The Output Delivery System
    9. Summary
  6. Chapter 3: Codebooks: A Roadmap to Your Data
    1. Introduction
    2. Understanding Codebooks
    3. Using the %TK_codebook Macro
    4. Syntax
    5. A Word of Caution When Using Excel to Create Your Codebook
    6. Ordering Variables in Codebook
    7. Output Data Set
    8. Example 3-1: Create a Codebook with Potential Problem Reports
    9. Interpreting the Codebook
    10. Understanding the Potential Problem Reports
    11. Inside the Toolkit: %TK_codebook
    12. Summary
  7. Chapter 4: Customizing Codebooks
    1. Introduction
    2. Example 4-1: Embellishing Titles
    3. Example 4-2: Add a Logo to Your Codebook
    4. Example 4-3: Codebook Output Data Set and Default Design
    5. Understanding the Default Codebook Template
    6. Formatting Your Codebook with the Default Codebook Design
    7. Example 4-4: Create a Custom Design for Your Codebook
    8. Modifying the Default Codebook Template
    9. Updating the Design of Your Codebook
    10. Summary
  8. Chapter 5: Catalog Your Data
    1. Introduction
    2. Using the %TK_inventory Macro
    3. Syntax
    4. Arguments
    5. Output Data Set
    6. Example 5-1: Create an Inventory of Data Sets
    7. Inside the Toolkit: %TK_inventory
    8. Using the %TK_xwalk Macro
    9. Syntax
    10. Arguments
    11. Example 5-2: Creating a Crosswalk
    12. Inside the Toolkit: %TK_xwalk
    13. Summary
  9. Chapter 6: Detecting and Correcting Data Errors
    1. Introduction
    2. Harmonizing Data Sets: Using the %TK_harmony Macro
    3. Syntax
    4. Output Data Set
    5. Example 6-1: Harmonizing Two Data Sets
    6. Inside the Toolkit: How %TK_harmony Works
    7. Finding Duplicates: Using the %TK_find_dups Macro
    8. Syntax
    9. Example 6-2: Identifying Duplicates Based on Multiple Variables
    10. Inside the Toolkit: How %TK_find_dups Works
    11. Summary
  10. Chapter 7: Inspect and Edit Flow through Skip Patterns
    1. Introduction
    2. Understanding Skip Patterns
    3. Identifying Skip Patterns in a Survey
    4. Traditional Method of Auditing Skip Patterns
    5. Example 7-1: Using the %TK_skip_edit Macro
    6. Syntax
    7. Required Arguments
    8. Optional Arguments
    9. Tally Results Data Set
    10. Skip Formats
    11. How Skip Path Logic Is Implemented by %TK_skip_edit
    12. A Blueprint to Using %TK_skip_edit
    13. Example 7-2: Automated Method of Checking Skip Patterns
    14. Examining the Tally Report
    15. Examining the Edits Reported in the Crosstab Tables
    16. Inside the Toolkit: How %TK_skip_edit Works
    17. Summary
  11. Chapter 8: Create and Validate New Variables
    1. Introduction
    2. Coding Variables
    3. Coding Missing Values
    4. Using Formats to Recode Data Values
    5. Example 8-1: Using Formats to Recode Data Values
    6. Easy Ways to Check Variable Construction
    7. Example 8-2: Checking Indicator Variables Created from Ordinal Variables
    8. Example 8-3: Checking Categorical Variables Created from Continuous Variables
    9. Summary
  12. Appendix A: Your Part in the Data Life Cycle
    1. Introduction
    2. Understanding the Data Life Cycle
    3. Stage 1: Define Project
    4. Stage 2: Plan Data Management
    5. Stage 3: Acquire Data
    6. Stage 4: Prepare Data
    7. Stage 5: Analyze Data
    8. Stage 6: Publish Results
    9. Stage 7: Preserve Publication Data
    10. Stage 8: Share Data
    11. Stage 9: Archive Project
    12. Summary
  13. Appendix B: Skip Pattern Data Codebook
    1. Introduction
    2. SAS Program to Create Codebook
  14. Appendix C: Research Data Codebook
    1. Introduction
    2. SAS Program to Create Codebook
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