Excel

 
Part 1: Check your dataset and get to know your variables

1. There are 2 tabs in the Excel file which you can access from the bottom left of your screen called “Dataset” and “Codebook”. First scroll through the Dataset and visually examine whether there are any unusual data points or outliers. Then switch over to the Codebook tab and familiarize yourself with how the variables were coded.
2. (1 point) The variables in this Dataset that are useful for analysis are age, gender, race, educ, BMI, stress, yoga, and sleep. For each of them, is it continuous or categorical? Fill in this table with all variables by its correct type. The first row is an example.

Continuous variables	Categorical variables
1. Age 2.	1. Gender 2.

 
Part 2: Provide some descriptive statistics on your variables

1. (1 point) Depending on whether a variable is continuous or categorical, use the appropriate commands in Excel to compute the following statistics. (There are 110 participants in this dataset, so hand-calculating is absolutely NOT recommended.) The first row is an example. You will need to create additional rows for “n (%)” depending on the number categories each variable actually has.

For continuous variables only	For categorical variables only
Variable 1 name: Age Mean = 35.2 years Median = 35 years Standard deviation = 3.14 years	Variable 1 name: Gender n (%) for 0 or male = 57 (51.8%) n (%) for 1 or female = 53 (48.2%)
Variable 2 name: Mean = Median = Standard deviation =	Variable 2 name: n (%) n (%) etc.
	Insert additional rows as needed. Don’t know how to insert rows in Word? Read this: Add a cell, row, or column to a table (microsoft.com)

 

2. The table above is NOT how results should actually be presented. They are just the results from your calculations. In reality, your descriptive statistics results should be filled into a “Table 1” that is professionally formatted.

(2 points) Using the template provided below, complete the Table 1 with the appropriate category names and the data that you have calculated above. The information for age and gender have been provided as examples. (Note: If there are extra rows that you don’t need, remember to delete the blank rows appropriately.)
 
Table 1. Demographics and participant characteristics of 110 adults in cross-sectional survey to examine the effects of yoga and sleep patterns on stress.

Demographics and characteristics	Mean (SD) or n (%)
Age in years	35.2 (3.14)
Gender
Male	57 (51.8%)
Female	53 (48.2%)
Race/ethnicity
Education level
BMI
Stress
Yoga
Sleep

Footnote: SD=standard deviation
 

3. (2 points) Using Excel, draw 2 bar graphs, one to show the frequency distribution of yoga and another the frequency distribution of sleep. Format each of your graph so that it has the appropriate x-axis labels (not just 0, 1, 2, etc.) as well as an appropriate title.

Hint #1: The Excel function to use is “Insert > PivotChart”.
Hint #2: Your graphs should look like the sample below:
 
Paste your two bar graphs below. Make sure to give each graph an appropriate title, and label all your x-axis categories.
 
 
Part 3: Let’s run some inferential statistics
Problem 1 (2 points) – Say you want to know whether there is a statistically significant difference between males and females when it comes to their BMI levels.

1. Which of the following test would you use to prove whether or not there is a significant difference in mean BMI levels between the genders in this study? (highlight your answer)
2. Independent samples t-test
3. Paired samples/repeated measures t-test
4. Chi-square test
5. Correlation test

 

2. Assuming you chose the correct test above, the results from your analysis can be presented in a Table 2. The template below has the data/results but no labels. Complete this table by filling in the row and column labels (by replacing the ???). Hint #1: Men on average had higher BMI than women. Hint #2: One of these labels should say “p-value”.

 
Table 2. BMI levels of study participants by gender.

???	???	???
???	27.7 (4.3)	0.810
???	27.5 (4.7)

Footnote: SD = standard deviation
 
 
Problem 2 (2 points) – Now we come to the thick of the hypothesis. You want to know whether doing yoga makes any difference to participants’ stress level, and if so, does the amount of yoga practices, measured as the number of times per week make a difference?

1. Which of the following test would you use to prove whether or not there is a significant association between the frequency of doing yoga and stress level in this study? (highlight your answer)
2. Independent samples t-test
3. Paired samples/repeated measures t-test
4. Chi-square test
5. Correlation test

 

2. Assuming you chose the correct test above, the results from your analysis can now be presented in a Table 3. Again, the template below has the data but no labels. Complete this table by filling in the row and column labels (by replacing the ???). Hint #1: We usually present categories from low to high. Hint #2: One of these labels should say “p-value”.

 
Table 3. Association between frequency of doing yoga and stress level.

	???	???	???
Frequency of doing yoga			<0.000
???	5 (8.3%)	17 (34.0%)
???	0 (0.0%)	20 (40.0%)
???	13 (21.7%)	2 (4.0%)
???	30 (50.0%)	6 (12.0%)
???	12 (20.0%)	5 (10.0%)