1. Exercise 1: Entering data into SPSS
Open SPSS and select ‘New Dataset’ from the options
Go to ‘Variable View’ and create the dataset template for inputting the 5 variables on the handout: ID, Colour, Maths, Time, Gender
Input the data, creating numeric codes for ‘Favourite colour’ and ‘Love_maths’. Input ‘Gender’ as a string variable
Use Automatic recode to create a new Gender variable with numeric codes rather than the string variable

2. Exercise 2: Titanic
Which variables could be used to investigate whether ‘wealthy’ people were more likely to survive?
Variable name
Variable label
Data type
pclass
Class
 Ordinal
survived
 Binary (Nominal)
Residence
Country of Residence
 Nominal
age
 Scale
sibsp
Number of siblings/ spouses
 Scale (Discrete)
parch
Number of parents/ children on board
 Scale (discrete)
fare
Price of ticket (£)
Sex

3. Exercise 3: Dangerous drivers
Ask the students
Is there a relationship between age, gender and accidents?
Could this data display be improved?

4. Exercise 4: Survival of the pushiest?
Are American’s more likely to survive when a boat sinks? Produce a suitable summary table and stacked barchart to investigate this

5. Exercise 5: Compare genders
The number of haircuts a year for a sample of people was summarised: On average who gets more haircuts a year and which gender is more spread out? Do the means and medians look similar for each gender?
Men
Women
Mean
11.5
3.12
Standard deviation
6.15
2.68
Median 10 3

6. Exercise 6: Use Explore to compare the cost of ticket by survival
Use Explore to get summary statistics and histograms of Cost of ticket by Survival status
Analyze  Descriptive Statistics  Explore

7. Exercise 6: Use Explore to compare the cost of ticket by survival
Which summary statistics should be used?
Interpret the output: how do the two groups (those who died and who survived) compare?
Use the histograms to decide which summary measures to use
Statistic
Died
Survived
Average:
Measure of spread:

8. Exercise 7: Birth weight
Open up the ‘Birthweight_reduced’ spreadsheet from the EXCEL file
Give the variables suitable labels, including labels for the different levels of ‘lobwt’ and ‘mage35’
Recode mnocig ‘Number of cigarettes smoked per day’ into smoker/non-smoker
Use Automatic recode to convert ‘lowbwt’ from String to Numeric
Calculate the mean birth weight and produce a histogram. Is birth weight normally distributed or skewed?

9. Exercise 8: Gestational age and birth weight
Describe the relationship between the gestational age of a baby and their weight at birth
Is there a difference between the babies of smokers and non-smokers?

10. Additional exercise Open the file ‘Housework_mini’
Use suitable summary statistics and charts to see if there is a difference between the amount of housework carried out by men and women each week.
Investigate the relationship between the amount of housework someone carries out per week and the hours they work using different markers for males and females.
Create a new binary variable from ‘Hours worked per week’ to indicate whether someone is full time or part time. Classify part time as under 30 hours.
Summarise the amount of housework carried out per week by working full/ part time using a table and a plot and interpret.

11. Additional exercise Which summary statistics/ charts could you use
to investigate the following research questions?
Question
Summary statistics/ charts
Do women do more housework than men?
Do hours of work influence the hours of housework someone does?

12. Additional exercise Interpret the output Gender Female Male
Gender
Female
Male
Hours per week on housework
Mean
16.6
5.7
Median
14.5 5
Count 14 12
Minimum 3
Maximum 30 18
Standard Deviation
8.7
4.72

13. Additional exercise
Is there a relationship between hours worked and amount of housework?

14. Exercise 2: Titanic
Which variables could be used to investigate whether ‘wealthy’ people were more likely to survive? Survival with class or price of ticket
Variable name
Variable label
Data type
pclass
Class
 Ordinal
survived
 Binary (Nominal)
Residence
Country of Residence
 Nominal
age
 Scale
sibsp
Number of siblings/ spouses
 Scale (Discrete)
parch
Number of parents/ children on board
 Scale (discrete)
fare
Price of ticket (£)
Sex

15. Exercise 3: Dangerous drivers
%’s fairer than frequencies
Categories are different widths, more middle aged drivers with higher annual mileage.

16. Exercise 3: Dangerous drivers
The bar chart below shows the % of drivers in each category having accidents in 2012
Men consistently have more for each age group

17. Exercise 4: Survival of the pushiest?
Americans were more likely to survive: 56% of Americans survived compared to only 32% of British passengers.

18. Exercise 4: Survival of the pushiest?

19. Exercise 5: Compare genders
The number of haircuts a year for a sample of people was summarised: On average who gets more haircuts a year and which gender is more spread out? On average men have 8 more haircuts a year than women and for both the mean and median are similar in value. There is more than twice as much variation for women than men
Men
Women
Mean
11.5
3.12
Standard deviation
6.15
2.68
Median 10 3

20. Exercise 6: Use Explore to compare the cost of ticket by survival

21. Exercise 6: Use Explore to compare the cost of ticket by survival
Statistic
Died
Survived
Average: Median
£10.50
£26
Measure of spread: Interquartile range
£18.15
£46.59
The data are very skewed so the median and quartiles should be used The median for those who survived is much bigger and the data is more spread out

22. Exercise 7: Birth weight
Mean birthweight is 3.31kgs
The histogram is approximately symmetrical indicating that it is reasonable to assume the data are normally distributed

23. Exercise 8: Gestational age and birth weight
There is a strong positive relationship between gestational age and birthweight
It appears that the weight of babies born to smokers is less than the weight of babies born to non-smokers

24. Additional exercise Which summary statistics/ charts could you use
to investigate the following research questions?
Question
Summary statistics/ charts
Do women do more housework than men?
Means/ medians/ standard deviation
Box-plots
Do hours of work influence the hours of housework someone does?
Scatterplot/ correlation

25. Additional exercise Interpret the output
Females have higher averages and are more spread out. The means/ medians are similar although females may be a little skewed
Gender
Female
Male
Hours per week on housework
Mean
16.6
5.7
Median
14.5 5
Count 14 12
Minimum 3
Maximum 30 18
Standard Deviation
8.7
4.72

26. Additional exercise
Is there a relationship between hours worked and amount of housework?
There doesn’t appear to be a strong relationship especially for males
Weak negative relationship for females

27. Additional exercise
Full time workers carry out a lot less housework on average (6.33 hours compared to hours) The standard deviation and interquartile range are larger for those who work part time suggesting a larger range of housework hours