sas problem 8
Please copy and paste all the SAS code, log, and output, fill out the table.
About the project: the objective is to determine whether there is any difference between students who took this course this semester online compared to in-class.
- EXCEL datasets EPI626 Online and EPI626 InClass contains selected data collected from students enrolled in this course. Please use the data to fill out the table below.
- Table to be filled out*:
- General Grading of Final Assignment (required steps).
|
Student Characteristics |
All |
Online |
In Class |
p-value |
|
N = () |
N = () |
N = () |
||
|
Age |
||||
|
Gender |
||||
|
Female |
||||
|
Male |
||||
|
Degree program |
||||
|
MPH |
||||
|
MSPH |
||||
|
Other |
||||
|
Number of Languages |
||||
|
1 |
||||
|
2 |
||||
|
3 or more |
||||
|
Hold Breath for 45 seconds or longer |
||||
|
Yes |
||||
|
No |
*For continuous variables, report Mean (SD) or Median (IQR) as appropriate and specify which one you are reporting in the table.For categorical variables, report n and % of total N for each column (All, Online, In Class at top of table)
- Import data sets
- If you use Import Wizard, please be sure to request and save relevant SAS program
- Do NOT modify data in excel sheets by hand prior to import!
- Combine data sets
- Make sure that in the combined data set, there is a variable which allows you to classify a student as enrolled in the online vs in class course.
- Preparing and cleaning combined data set
- Please check variable names (and types!)
- Please use Dec 31 of this year as the reference date for age calculations for each respective group.
- Saving the cleaned data set as a permanent data set
- Please exclude anyone from the final cleaned data set if the person has a missing value for one or more of the final variables.
- Producing descriptive statistics (cover all characteristic)
- Producing p-values (cover all characteristic)
- Final report. The final report should address the following:
- Describe the process you went through to clean/recode the data. Essentially, your report should allow someone else to replicate your work, based on information provided in the report, and come to the same findings/conclusions.
- For each variable:
- How many observations have missing values
- How many observations have implausible values (e.g., age of 101)
- How many observations have inconsistencies to record the same value (e.g., f and F for female)
- What did you do to handle the above situations and why?
- What is the impact of the approaches you used to clean the data (e.g., observation excluded from analysis)?
- What are the attributes of the cleaned data set that you saved permanently?
- How many total included observations?
- Number of observations having missing values for one or more variables
- Distribution of each variable
- What procedures did you use to obtain the descriptive statistics and the p-values?
- Why?
- For each variable:
- Describe the process you went through to clean/recode the data. Essentially, your report should allow someone else to replicate your work, based on information provided in the report, and come to the same findings/conclusions.
