Describe the measurement levels for each of the variables in the dataset. Discuss what you learned from ordering the data by age and why this information is important. Describe the process you used to group the data in Excel and SPSS. Describe what you learned by grouping the variables by category of exercise. Are these data from a correlational study, experimental study, or quasi-experimental (observational) study? Discuss your rationale and identify a study question appropriate for this dataset.

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Part 1: Ordering & Grouping Data with Excel and SPSS

The “Example Dataset” includes variables such as age, sex, education level, income, minutes of exercise per week, and body mass index (BMI). The following steps in Excel or SPSS Statistics were used to organize data:

1. Ordering observations according to age: In Excel, the data were sorted by age in ascending order using the “Sort & Filter” function. In SPSS, the data were ordered by age using the “Data” menu and selecting “Sort Cases.”
2. Calculating the average age and income of males, females, and their observations: To analyze the data in Excel by sex, create a pivot tableau. SPSS created a split folder to allow for separate analysis of the data. Data were then analyzed separately by males and women, with the income calculated using descriptive statistics.
3. Creating a new variable titled “Exercise Group” based on the variable “Minutes Exercise”: In Excel, a new column was created and the “IF” function was used to assign a value of 1-5 to each observation based on the range of minutes of exercise per week. In SPSS, a new variable was created using the “Recode into Different Variable” function, and the values were assigned based on the same ranges as in Excel.

Part 2: Data Interpretation

1. Levels of measurement for each variable. Age, a continuous variable that is measured in years, can be measured using the following measures: There are two types of gender, male and female. The ordinal variable Education Level has five types (1 = lower than high school; 2 = higher school; 3 = some college; 4 = college and 5 = graduate degrees). In dollars, income can be described as a continuous variable. A continuous variable that is measured in minutes per week, it’s the number of minutes spent exercising. The body mass index (BMI), is a continuous variable that measures weight in kilograms divided by height (in meters).
2. An age order of data gave insight to the age distribution, which included minimum and maximal ages, median ages and the range of age. Because age is a variable that can cause data to be inconsistent, knowing how the sample’s age distribution can aid in determining if data represent a specific population.
3. The process used to group the data in Excel and SPSS involved creating a new variable based on the variable “Minutes Exercise.” The purpose of this was to create categories of exercise that could be analyzed and compared across other variables. This grouping allowed for the identification of patterns and relationships among exercise and income variables, such as BMI and income.
4. When we grouped the variables according to exercise category, there was a noticeable difference in income for those who exercised less than thirty minutes per week than for those who exercised more than thirty minutes each week. People who exercised longer than 30 minutes per semaine had higher incomes than those who exercised less than 30 minutes each week. It is possible that income may influence exercise, so people with more income may find it easier to get regular exercise.
5. This observational study does not involve manipulation of variables nor random assignment to different groups. An appropriate study question for this dataset could be: “Is there a relationship between exercise and BMI, controlling for age, sex, education level, and income?” This question could be answered using regression analysis to determine the extent to which exercise predicts BMI after controlling for other variables.