Analyzing Categorical Data in SAS — PickAClass
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⏱ 3h 📚 30 lessons 🎧 Audio version

Analyzing Categorical Data in SAS

Learn to analyze categorical variables, perform exact tests, and interpret contingency tables using SAS to uncover meaningful patterns in your data.

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About this course

Categorical data is everywhere, from customer satisfaction surveys to clinical trial outcomes, but analyzing it requires specialized statistical methods. This text-only course guides you through the core concepts of categorical data analysis using SAS, starting with the absolute basics of data classification. You will transition from understanding simple frequency distributions to performing association tests and interpreting statistical relationships. Through clear written explanations and step-by-step code examples, you will gain the confidence to prepare, analyze, and report on qualitative data using standard SAS procedures. What you'll learn: 1. Identify and classify different types of categorical data, including nominal and ordinal variables. 2. Construct and analyze contingency tables using modern SAS Studio programming workflows. 3. Apply chi-square and exact tests to determine statistical significance between variables. 4. Measure the strength of associations using odds ratios and relative risk calculations. 5. Write clean, structured SAS code to clean and prepare categorical datasets. 6. Interpret statistical reports and output to make data-driven decisions. The course begins with foundational definitions and key terminology before guiding you through practical SAS syntax and output analysis. This course is designed for beginners who want to build a solid foundation in statistical analysis using SAS, with no prior programming experience required. Start reading today to unlock the power of categorical data analysis.

Course contents

6 modules · 30 lessons · 3h

1 Foundations of Categorical Data
Understand the nature of qualitative data and the SAS Studio environment. 5 lessons · 30 min
  1. Introduction to Categorical Data
    Categorical data represents groups or categories rather than numerical measurements.    5 min
  2. Nominal vs. Ordinal Variables
    Nominal variables have no inherent order, while ordinal variables follow a logical sequence.    6 min
  3. Understanding Frequency Distributions
    Frequencies count how often each category appears in a dataset.    6 min
  4. Navigating SAS Studio
    The SAS Studio interface allows you to write code and view results in a web browser.    7 min
  5. The Structure of a SAS Program
    SAS programs consist of DATA steps for preparation and PROC steps for analysis.    6 min
2 Data Preparation and Cleaning
Learn to format and clean categorical data for accurate analysis. 5 lessons · 31 min
  1. Importing Your Data
    Use PROC IMPORT to bring external files into the SAS environment.    6 min
  2. Cleaning Categorical Variables
    Standardize text entries to ensure consistency across categories.    6 min
  3. Creating Custom Formats
    Use PROC FORMAT to map numerical codes to meaningful text labels.    7 min
  4. Handling Missing Values
    Identify and manage missing categorical observations to avoid bias.    6 min
  5. Recoding Variables
    Collapse multiple categories into broader groups using IF-THEN logic.    6 min
3 Frequency Analysis and Contingency Tables
Master PROC FREQ to summarize relationships between variables. 5 lessons · 31 min
  1. Introduction to PROC FREQ
    PROC FREQ is the primary tool for descriptive statistics of categorical data.    5 min
  2. Generating One-Way Tables
    Create simple frequency counts and percentages for a single variable.    6 min
  3. Building Two-Way Contingency Tables
    Cross-tabulate two variables to see how they interact.    7 min
  4. Customizing Table Output
    Control the display of row, column, and total percentages in your tables.    6 min
  5. Interpreting Frequency Reports
    Read and explain the key components of a SAS frequency output.    7 min
4 Testing for Statistical Significance
Apply statistical tests to determine if relationships are real or due to chance. 5 lessons · 31 min
  1. The Concept of Independence
    Statistical independence means the value of one variable doesn't affect the other.    6 min
  2. The Chi-Square Test Theory
    The Chi-square test compares observed counts against expected counts.    6 min
  3. Running Chi-Square in SAS
    Add the CHISQ option to the TABLES statement in PROC FREQ.    6 min
  4. When to Use Fisher's Exact Test
    Use exact tests when sample sizes are small and Chi-square assumptions fail.    7 min
  5. Understanding P-Values
    The p-value tells you the probability of seeing your results by random chance.    6 min
5 Measuring Strength of Association
Go beyond significance to measure the magnitude of relationships. 5 lessons · 31 min
  1. Risk vs. Odds
    Distinguish between the probability of an event and the ratio of success to failure.    6 min
  2. Calculating Relative Risk
    Relative risk compares the probability of an outcome between two groups.    6 min
  3. Calculating Odds Ratios
    The odds ratio measures association strength, commonly used in case-control studies.    6 min
  4. Interpreting Measures of Association
    A value of 1 indicates no association, while values further from 1 indicate stronger effects.    7 min
  5. Confidence Intervals for Ratios
    Confidence intervals provide a range of likely values for your risk and odds estimates.    6 min
6 Reporting and Visualization
Communicate your findings effectively using SAS output. 5 lessons · 26 min
  1. Visualizing Categorical Data
    Use PROC SGPLOT to create bar charts that complement your tables.    6 min
  2. Exporting SAS Reports
    Save your output as PDF or Excel files for sharing with stakeholders.    5 min
  3. Best Practices for Reporting
    Focus on the most meaningful statistics and avoid 'data dumping'.    6 min
  4. Common Pitfalls to Avoid
    Watch out for small sample sizes and confusing correlation with causation.    5 min
  5. Course Summary and Next Steps
    Review the workflow from data classification to final reporting.    4 min
Final project

The Patient Outcome Analysis Project

You will be provided with a raw dataset of clinical trial participants. You must classify the variables, clean the data, generate a contingency table, test for significance between treatment and recovery, and calculate the Odds Ratio.

What you'll get

  • 📜 Certificate of completion
    Add it to your LinkedIn profile
  • 💬 Personal AI tutor
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  • 🎧 Audio version included
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  • ♾️ Lifetime access
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  • 📱 Phone or computer
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  • 💸 14-day refund
    No questions asked
  • ⚡ Short & focused
    3h of practical content

Certificate of completion

Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.

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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Analyzing Categorical Data in SAS
Skills demonstrated
✓
Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
✓
A/B test design
Proficient
1.7 hrs
✓
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Advanced
1.9 hrs
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PickAClass — Name Surname
Analyzing Categorical Data in SAS
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Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
Skill verification Verified Skill Path
Verify this credential
pickaclass.com/certificates/PCC-2026-X4F7-AP19
Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

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