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Should the voting age be lowered to 16? Example using the

Citizenship Survey, 2010-2011

Open IBM SPSS 20 and open the data file. Do this by going to:

File> Open > Data from SPSS once it is open and selecting the correct dataset.

The data is called ‘q2eda.sav’. It is a cut-down version of the full 2010-2011 Citizenship

Survey and contains the following variables. They can be viewed in more detail by clicking

on the ‘Variable View’ window at the bottom left of the SPSS screen.

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Frequency Tables

Our four main outcome variables of interest for this task are:

1) EResp07 ‘Responsibilities of everyone in the UK - To vote’

2) EShoul04 ‘Rights you SHOULD have - To have free elections’

3) PAffLoc ‘Can you influence decisions affecting local area’

4) PInfl ‘How important is it for you personally to feel that you can influence decisions in

your local area?

And our main exploratory variable of interest is of course age.

These are all questions about Civic Engagement and are relevant to our research question

to assess whether 16 and 17 year olds have different views to 18 and 19 year olds who do

have a right to vote.

To assess these variables overall, firstly frequency tables should be run on them. This can

be done by clicking ‘Analyse’> hovering over ‘Descriptive Statistics’ and then clicking

‘Frequencies’

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Once you have clicked ‘Frequencies’ this box will appear:-

Select the variable you wish to run a frequency table on and click the arrow in the middle of

the box to ‘push’ it across into the box on the right. Click ‘OK’.

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The frequency table will appear in the Output window as follows.

Continue for each of the four outcome variables of interest.

Look at the tables in the output window once they have been run and look at the overall

patterns for each variable. Perhaps write a couple of sentences interpreting each frequency

table.

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Graphs

To produce a histogram or bar chart for a single variable in SPSS just follow the same

process as for a frequency table (above) but before you click ‘OK’, click on the ‘Charts’

button, select ‘Histogram’ of the variable is continuous (such as Age) or Bar Chart if the

data is categorical (such as our outcome variables of interest) and then ‘Continue’. Then

click ‘OK’.

To begin with run a histogram on the ‘xRage (age)’ variable.

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In the output window you will get a frequency table (which will be very long for the

continuous Age variable!) and also a histogram:

It can be seen from the histogram that the age variable here is reasonably normally

distributed and there are no problems with missing data.

Now do bar charts of all of the outcome variables of interest to assess visually how they are

distributed.

Remember: Analyse>Descriptive Statistics>Frequencies>Charts>Bar Chart

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Weights

Data producers calculate weights to make the data better represent the population it is

designed to represent. A weighting variable assigns a value to each case in the dataset to

indicate how much they should be represented in the analysis.

To add weights click on the little scales icon at the top of the screen:-

When the ‘Weight Cases’ box appears, click on ‘Weight cases by’ and then select the

Weighting variable, here called ‘WtFInds. Click ‘OK’.

You will be able to tell easily if the weights are on as it should now say ‘Weights On’ at the

bottom right of the SPSS screen.

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Now, re-run all the frequency tables for our four outcome variables of interest.

How have the weights affected the results in the tables? This is a good way of demonstrating

the importance of weighting the data.

Recoding

To assess whether under 18’s should be given the right to vote, firstly we need to separate

the data for under 18’s. This can then be compared to the data for 18 and 19 year olds to

see if there is a difference in the Civic Engagement variables between the two age groups.

You can recode data (that is, create new categories from an existing variable and save it as

a new variable with the new, more workable or relevant categories) by clicking on

‘Transform’ and then ‘Recode into Different Variables’:-

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A ‘Recode into Different Variables’ box will appear:-

Find the ‘xRage’ variable from the list of variables and click on the arrow in the middle of

the box so that it appears in the big white box in the middle. Where it says ‘Output Variable’

on the left, type in what you want the new age variable to be called. Choose something such

as ‘AgeRecode’ so it will be obvious what the new variable is. Type it into both the Name

and the Label box (with no spaces in the Name box) then press ‘Change’.

Then press the ‘Old and New Values’ box.

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A ‘Recode into Different Variables: Old and New Values’ box will appear:

As we wish to compare 16 and 16 year olds to 18 and 19 year olds, we need a category

which is just 16 and 17 year olds, just 18 and 19 year olds and then everyone else.

This can be done by clicking on ‘Range’ on the left hand side of the Recode box and then

adding 16 through 17 (see above in the screen cap). On the ‘New Value’ right hand side of

the screen where it says ‘Value’ type a ‘1’ into the box, then click ‘Add’.

This will create a new category (category ‘1’ until re-labelled) just for 16 and 17 year olds’.

Repeat this but put 18 through 19 into the Range on the left hand side and a 2 into the Value

box on the right and then click ‘Add’ again.

Finally to put everyone else into a separate category click in the ‘Range, value through

HIGHEST’ (see arrow, above) button and put 20 into that box and then on the right hand

side under New Value put a 3 and then click ‘Add’. This creates a new category (Category

‘3’) or everyone above 20. I.e. everyone from 20 to the HIGHEST age in the dataset.

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When you have done this the ‘Old>New’ box should have the following in it:-

Click ‘Continue’.

Then go into the variable view of SPSS and you will see the new variable at the bottom.

When you have checked it is there, click in the little blue box in the values column for the

new variable:-

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When you click on the little blue box a ‘Value Labels’ box will appear. Add in the labels from

the re-code.

There were 1=‘16-17’, 2=‘19-20’ and 3=‘20+’. Add the 1 into the value box and the label (16-

17 etc.) into label box and click add, until all the labels are added. Then click ‘OK’.

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Once this new variable has been created we can run a frequency on it to check the recode

worked:-

We can see that there are no missing values and the categories are what we would expect,

so the re-code was successful.

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Cross-tabulations

Now we have our new Age variable, we can run cross-tabulations on our four outcome

variables of interest by the new Age variable to see whether there is any difference in views

towards Civic Engagement between 16 and 17 year olds’ and 18 and 19 year olds’.

This can be done by again clicking on Analyse > Descriptive Statistics > and the clicking

Cross-tabulations. This box will appear:-

Select one of our four outcome variables of interest from the box on the left and click the top

arrow to ‘push’ it across into the Row(s) box. Then select our recoded age variable and

‘push’ it across into the Column(s) box. Before you click ‘OK’, however, click on the ‘’ button

on the right-hand side of the box.

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Where it says ‘Percentages’ click the little box next to ‘Column’. Then click ‘Continue’ and

then ‘OK’.

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The output from your Crosstab analysis will be in your Output screen.

What are does the crosstab analysis tell us? How would we interpret this?

Now run cross-tabulations for each of the other outcome variables of interest by our recoded

Age variable.

Are there large differences in attitudes to Civic Engagement between 16-17 years olds’ and

18-19 year olds’?

Are there differences for some forms of Civic Engagement and not others?

What are the arguments for and against lowering the voting age based on the evidence here?

If you have time, you can produce other analyses using the other variables in the dataset.

Perhaps you would like to look at political engagement by gender? Or look at how many

people trust Parliament? Produce some bar charts and tables.