Discussion: Categorical Variables
This week you will revisit some basic concepts from previous biostatistics classes in order to understand and manipulate data. As you know, databases contain values organized in one or multiple variables. Those variables have different characteristics that will not only help you to classify data, but also help you decide what type of analyses could be performed. Therefore, learning how to identify, manipulate, and analyze the different variables in your database will prove to be an invaluable task in your data analysis processes.
For this week’s Discussion, you will evaluate the classification of variables as categorical.
To prepare:
- Select two categorical variables from the data sets provided for the Scholar-Practitioner Project.
- Consider why the researchers may have chosen to make them categorical variables and decide whether you would have made the same classification.
By Day 3
Post an evaluation of the two variables you selected. Include the following in your post:
- A brief description of the two categorical variables you selected from the database
- An explanation of why you believe the variables were created as categorical variables instead of continuous variables
- An explanation of whether you believe this is a correct classification for this study (provide a rationale)
Support your post with the Learning Resources and current literature. Use APA formatting for your Discussion and to cite your resources.
Expert Solution Preview
Introduction:
In this assignment, we will be discussing the classification of variables as categorical in biostatistics. As a medical professor, it is important to understand the different types of variables and how they can be manipulated and analyzed. We will evaluate two categorical variables from the provided data sets for the Scholar-Practitioner Project and discuss why they were created as categorical instead of continuous variables.
Answer:
The two categorical variables I selected from the database are “smoking status” and “age group”. “Smoking status” is categorized into three groups: current smoker, former smoker, and never smoker. “Age group” is categorized into four groups: 18-29 years, 30-44 years, 45-59 years, and 60 or more years.
I believe the researchers created “smoking status” as a categorical variable because smoking is a binary variable – either one has smoked or they have not. However, in this study, the researchers further classified smoking status into three categories. This classification allows for more detailed analysis and understanding of the relationship between smoking and health outcomes. Similarly, “age group” was created as a categorical variable to classify participants into different age ranges for analysis purposes. Age is a continuous variable, but categorizing it into groups makes it easier to interpret and analyze the data.
I believe that the classification of both variables as categorical is correct for this study. Categorizing smoking status and age group allows for comparisons to be made between different groups and can provide insight into the relationship between these variables and health outcomes. Additionally, these classifications are commonly used in research studies, making it easier to compare and replicate findings.
Overall, understanding the classification of variables as categorical is crucial in biostatistics. Categorical variables allow for more detailed analysis and interpretation of data.
Expert Solution Preview
The two categorical variables selected from the database are “smoking status” and “age group”. “Smoking status” is categorized into three groups: current smoker, former smoker, and never smoker. “Age group” is categorized into four groups: 18-29 years, 30-44 years, 45-59 years, and 60 or more years.
The researchers created “smoking status” as a categorical variable because smoking is a binary variable- either one has smoked or they have not. However, in this study, the researchers further classified smoking status into three categories. This classification allows for more detailed analysis and understanding of the relationship between smoking and health outcomes. Similarly, “age group” was created as a categorical variable to classify participants into different age ranges for analysis purposes. Age is a continuous variable, but categorizing it into groups makes it easier to interpret and analyze the data.
I believe that the classification of both variables as categorical is correct for this study. Categorizing smoking status and age group allows for comparisons to be made between different groups and can provide insight into the relationship between these variables and health outcomes. Additionally, these classifications are commonly used in research studies, making it easier to compare and replicate findings.
In conclusion, understanding the classification of variables as categorical is crucial in biostatistics. It allows for more detailed analysis and interpretation of data.
