SPSS (I) Practical Session

SPSS (I) – Practical Session Examples

A: Patient recovery after MI

A study was conducted in investigate the progress of patients after an MI. Part of the data collected relating to 15 patients is shown below. The variables given relate to each patient’s ID, gender and age. The cholesterol levels two days and four days after the MI are also given.

1. Enter the data into an SPSS spreadsheet. Create variables for the ID number of the patient, the gender, age and cholesterol levels two days and four days after the event. Attach labels to the variables

2. Create a new variable to identify elderly patients ( 70 or more years old). This variable will take the value 1 if the patient is 70 or more years old or the value 2 if they are younger (hint: use the Recode option) Label this variable and label the values.

3. Compute a new variable that is equal to the average of the two cholesterol levels.

Patient Cholesterol Level Gender Age no. Day 2 Day 4 1 M 50 270 218 2 M 49 236 234 3 M 62 210 214 4 F 72 142 116 5 M 61 280 200 6 F 57 272 276 7 F -99 160 146 8 F 62 220 182 9 F 48 226 238 10 F 77 242 288 11 M 83 186 190 12 M 61 266 236 13 F 59 270 245 14 M 64 250 228 15 F 62 268 248 * -99 indicates missing age value

Last printed 4/29/2018 Page 1 of 2 B: Asthma Database

The file asthma.sav contains data on 403 asthma patients

Data recorded include: patient id gender weight height bmi age presence of hayfever presence of eczema inhaled steroids family history of asthma smoking status no. of cigarettes asmal pft value fev1 value

1. Calculate appropriate summary statistics for each of the following variables (a) Qualitative variables: smoking status, presence of eczema (b) Quantitative variables: age, fev1, no. of cigarettes.

2. Draw a bar chart for smoking status and a histogram for fev1.

3. Look at the association between each of the following pairs of categorical variables (a) smoking status and presence of hayfever (b) smoking status and presence of eczema.

4. Compare fev1 values between smokers, ex-smokers and non-smokers

5. Look at the association between each of the following pairs of quantitative variables (a) fev1 and age (b) fev1 and no. of cigarettes.

6. Compare the BMI values for men and women. (hint: check the distribution of BMI first).

Last printed 4/29/2018 Page 2 of 2