Learn how to generate the smoking-related variables designed earlier in the data dictionary.
- [Instructor] First we will review the data dictionary…for the different smoking variables we need to make.…This was a rather complicated variable.…Next I will go through the code needed…to make the smoking variables.…We will make and check the variables.…Okay, let's go to our data dictionary.…Let's look at SMOKDAY2, our smoking variable.…We don't like that these unknowns need to be collapsed.…Also, Every Days and Some Days need to be collapsed.…Those will both be one in the new variable.…
So we need to make a grouping variable first…which we didn't have to do with age.…That will be called SMOKGRP.…I'll show you how.…But also, don't forget the SMOKE100 tab.…We have a bunch of never smokers in there,…and we need to make sure that they show up…as non-smokers rather than unknown in the grouping variable.…Let's scroll down a little and look at the smoking code.…First before we get to the grouping variable,…let's get our NEVERSMK variable going.…Remember if they said two, or No, to smoking…a hundred cigarettes in their lives,…
This detailed, practical course is designed to help those in the field of public health, medicine, and data science to edit, analyze, and interpret data. Learn how to code new variables, use the forward-stepwise modeling process, and document your decisions. Find out how to visualize results by generating charts and graphics, and how to add tables and figures to your documentation. This course helps equip you to independently design, develop, and execute a full BRFSS analysis, and even publish your results in scientific publications or journals.
- Reviewing survey data and documentation
- Conducting a BRFSS analysis
- Understanding naming conventions
- Editing variables
- Reviewing distributions
- Generating an analytic dataset
- Developing descriptive statistics to answer prespecified hypotheses
- Preparing publication-worthy tables and plots
Skill Level Advanced
1. What Is the BRFSS?
2. Designing Your Metadata
3. Reading in Data and Applying Exclusions
4. Preparing for Descriptive Analysis
5. Conducting Descriptive Analysis
Making a frequency macro4m 8s
6. Descriptive Analysis: Weights and Tests
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