Should the voting age be lowered to 16? Example using the Citizenship Survey,

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1 Should the voting age be lowered to 16? Example using the Citizenship Survey, 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 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. 1

2 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 2

3 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. 3

4 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. 4

5 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. 5

6 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 6

7 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. 7

8 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 :- 8

9 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. 9

10 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. 10

11 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:- 11

12 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= 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. 12

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14 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. 14

15 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. 15

16 Where it says Percentages click the little box next to Column. Then click Continue and then OK. 16

17 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 years olds and 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. 17