Data Manipulation and Basic Statistics in SPSS


This homework requires you to:

  • download, merge, and save data files;
  • identify variables and assign value labels;
  • define missing values;
  • select cases;
  • compute variables;
  • recode variables;
  • run frequency and descriptive statistics; and
  • interpret output.

To complete the Practice and Graded Homework assignments, you will need to complete the following steps before you can begin. 

Note: If you plan to use your merged dataset from the Practice Homework, start at Step 3 in the following section.

  1. Download the 2015-2016 Demographic Data File from the NHANES website (DEMO_I.xpt) and open it in SPSS.
  2. Download the 2015-2016 Blood Pressure and Cholesterol Questionnaire Data from the NHANES website (BPQ_I.xpt) and open it in SPSS.
  3. Bring the demographic dataset to the front of your screen and merge the demographic data and the blood pressure data files. Remember to choose add variables (not add cases).
  4. Save your merged files as an SPSS dataset.
  5. Keep the merged file open and close the other dataset(s).
  6. Download the 2015-2016 Physical Activity Questionnaire Data from the NHANES website (PAQ_I.xpt) and open it in SPSS.
  7. Bring the merged data set to the front of your screen then proceed to merge the physical activity data set. Data menu, Merge Data, Add Variables, and select the dataset with the Physical Activity Questionnaire (PAQ-I) Data. Please note that, unless you saved the PAQ_I dataset as an SPPSS file with a new name after opening it, it will be called something like [Dataset2] by SPSS.
  8. Complete the merge.
  9. Save the newly merged data set and close the Physical Activity dataset.
  10. Assign value labels to the codes of the variables listed below. Remember, you need to use the NHANES documentation files to find the correct labels for the codes.  Specifically, you need to use the documentation for the 2015-16 Demographic data, Blood Pressure Questionnaire data, and Physical Activity Questionnaire data.
      • RIAGENDR: Gender
      • DMDMARTL: Marital status
      • DMQMILIZ: Served active duty in the US Armed Forces
      • BPQ020: Ever told you had high blood pressure
      • BPQ040A: Taking prescription for hypertension
      • BPQ050A: Now taking prescribed medicine for HBP
      • PAD660: Minutes of vigorous recreational activities
      • PAD675: Minutes of moderate recreational activities
  11. As you answer questions within the homework, remember to check each variable in the question for “don’t know” and/or “refused” codes and set those values as missing data in the Variable View before running the analysis, as needed.
  12. Consider saving your final edited dataset with a new name. You may use this dataset and add to it for other course assignments.
These questions can be answered in a question-and-answer format.
 Example: “question 1. Answer”
Question 11.5 pts
Run a frequency analysis for the variable RIAGENDR. What is the valid percentage of females?
Group of answer choices



Flag question: Question 2

Question 21.5 pts
What graph would be the best choice to display marital status?
Group of answer choices

Paneled Histogram

Simple Bar Chart


These are all good choices

Flag question: Question 3

Question 31.5 pts
What is the average household size for 2015-1016?
Group of answer choices





Flag question: Question 4

Question 41.5 pts
What is the standard deviation for family size?
Group of answer choices





Flag question: Question 5

Question 51.5 pts
Compute a new variable for minutes of recreational activity on a typical day called ‘RECACTMINS‘ by summing the variables PAD660 and PAD675. What is the average length of time spent on all recreational activities?
Group of answer choices





Flag question: Question 6

Question 61.5 pts

For this question, we’ll do some data manipulation to create a new variable about people’s living arrangements. We’re interested in people who live with a partner, whether they are married or not and we want to group all the other people into one other group that we’ll label as live separate.

We need to Recode the variable DMDMARTL into a different variable named LIVETOGTHR.

  • People who are married and those who live with a partner should have the code 1.
  • All the other marital status options should be recoded to 2.
  • Don’t forget to address missing values by making 77 and 99 missing.

Now, complete these steps:

  1. Find your new variable, LIVETOGTHR, in the variable list.
  2. Define the value labels for LIVETOGTHR.
  3. Run a frequency for LIVETOGTHR.
  4. Go to the Analyze menu, choose Descriptive Statistics, choose Crosstabs.
  5. In Crosstabs, put the new variable LIVETOGTHR in the Rows box and put the variable RIAGENDR (gender) in the Columns box.
  6. Check the box in the lower-left corner to Display clustered bar charts.
  7. Hit OK.

Which statement is accurate regarding the chart you obtained in your output?

Group of answer choices

Fewer males live separately than females

There is no difference in the number of males living with a partner versus living separately

More females than males live with a partner

More females live separately than with a partner

Flag question: Question 7

Question 71.5 pts

What is the average time for RECACTMINS for males?


Group of answer choices





Flag question: Question 8

Question 81.5 pts
Run a frequency analysis for the variable DMQMILIZ. What is the percentage of missing responses?
Group of answer choices





Flag question: Question 9

Question 91.5 pts
Run a frequency analysis for the variable age told had hypertension.What percentage of respondents were less than 60 years of age?
Group of answer choices





Flag question: Question 10

Question 101.5 pts
What percentage of those who served in the military served in a foreign country?
Group of answer choices





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