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POLI 399 – Research Methods
Week TwoRecoding and Creating Variables, Creating Indices and Reliability Scores with SPSS
Today’s Agenda
Questions on assignment Recoding variables Creating variables (“IF”) Creating an index Reliability Scores
Recoding Variables in SPSS
Why recode variables?– There are too few cases in one or more
categories of a variable to allow for analysis.– We need fewer categories in the variable.– We need to build an index to measure a
concept more completely, and we need each variable to have the same categories.
– We need to move from an interval variable to a nominal or ordinal level variable.
Examples of Recoding
Age– Turn the interval variable “age” or “year of birth” into
categories.
Support for Prime Minister– Turn four categories (support, somewhat support, somewhat
oppose, oppose)– Into two categories (support and oppose)
Province of Residence and Region– Turn province of residence (10 provinces, 3 territories)– Into five regions (Atlantic, Quebec, Ontario, West and North)
How to Recode?
Transform menu → Recode → Into different variables Pick the variable you want to recode → enter name and label
for new variable → click “Change” → click “Old and New Values”
Enter the value or range of values for the value of the new category (For example, for region, NS, NB, NL and PEI are valued 10-13, then enter it into the range pane)
Enter the new value for the new combined category. Do this for all the values in the old variable. Click continue and then OK in the next menu. Go to the “Variable View” – the new variable will be at the
bottom of the list. Label the values for the new variable accordingly.
In Class Examples
Province– From 12 categories to 5 regional categories– Atlantic = NF (10) + PEI (11) + NS (12) + NB (13)– Quebec = Quebec (24)– Ontario = Ontario (35)– Prairies = MB (46) + SK (47) + AB(48)– BC/North = BC(59) + YK (60) + NWT(61)
Regional Recode - Output
Regions
341 10.9 10.9 10.9
657 20.9 20.9 31.8
1004 32.0 32.0 63.8
668 21.3 21.3 85.1
468 14.9 14.9 100.0
3138 100.0 100.0
Atlantic
Quebec
Ontario
Prairies
BC/North
Total
Valid
Frequency Percent Valid Percent
Cumulative
Percent
province of interview
76 2.4 2.4 2.4
90 2.9 2.9 5.3
89 2.8 2.8 8.1
86 2.7 2.7 10.9
657 20.9 20.9 31.8
1004 32.0 32.0 63.8
150 4.8 4.8 68.6
169 5.4 5.4 74.0
349 11.1 11.1 85.1
468 14.9 14.9 100.0
3138 100.0 100.0
nfld
pei
ns
nb
quebec
ontario
manitoba
sask
alberta
bc
Total
Valid
Frequency Percent Valid PercentCumulative
Percent
Before recode After recode
In Class Examples
How do you feel about Paul Martin? (pes_f2)– Measured on a thermometer scale of 0 (really
dislike) to 100 (really like)– I want to turn this into five categories– Dislike a lot = 0 to 20– Dislike somewhat = 21 to 40– Neutral = 41 to 60– Like somewhat = 61 to 80– Like a lot = 81 to 100
Like Paul Martin Recode - Output
How do you feel about Paul Martin? (Recoded)
473 15.1 15.9 15.9
447 14.2 15.0 30.8
1012 32.2 33.9 64.8
852 27.2 28.6 93.3
199 6.3 6.7 100.0
2983 95.1 100.0
155 4.9
3138 100.0
dislike a lot
dislike somewhat
neutral
like somewhat
like a lot
Total
Valid
SystemMissing
Total
Frequency Percent Valid Percent
Cumulative
Percent
Before recode
After recode
how do you feel about paul martin?
191 6.1 6.4 6.4
8 .3 .3 6.7
17 .5 .6 7.2
4 .1 .1 7.4
4 .1 .1 7.5
34 1.1 1.1 8.6
5 .2 .2 8.8
6 .2 .2 9.0
7 .2 .2 9.3
86 2.7 2.9 12.1
1 .0 .0 12.2
12 .4 .4 12.6
1 .0 .0 12.6
97 3.1 3.3 15.9
1 .0 .0 15.9
1 .0 .0 15.9
1 .0 .0 16.0
64 2.0 2.1 18.1
128 4.1 4.3 22.4
1 .0 .0 22.4
21 .7 .7 23.1
1 .0 .0 23.2
1 .0 .0 23.2
228 7.3 7.6 30.8
1 .0 .0 30.9
37 1.2 1.2 32.1
1 .0 .0 32.1
1 .0 .0 32.2
565 18.0 18.9 51.1
2 .1 .1 51.2
3 .1 .1 51.3
2 .1 .1 51.4
1 .0 .0 51.4
36 1.1 1.2 52.6
363 11.6 12.2 64.8
2 .1 .1 64.8
2 .1 .1 64.9
83 2.6 2.8 67.7
2 .1 .1 67.8
1 .0 .0 67.8
291 9.3 9.8 77.5
2 .1 .1 77.6
218 6.9 7.3 84.9
2 .1 .1 85.0
6 .2 .2 85.2
243 7.7 8.1 93.3
1 .0 .0 93.4
52 1.7 1.7 95.1
1 .0 .0 95.1
77 2.5 2.6 97.7
13 .4 .4 98.2
1 .0 .0 98.2
1 .0 .0 98.2
2 .1 .1 98.3
1 .0 .0 98.3
50 1.6 1.7 100.0
2983 95.1 100.0
17 .5
23 .7
56 1.8
26 .8
33 1.1
155 4.9
3138 100.0
really dislike him
1
2
3
4
5
6
7
8
10
12
15
19
20
21
22
24
25
30
32
35
36
39
40
44
45
47
49
50
51
52
53
54
55
60
61
62
65
66
69
70
71
75
77
78
80
83
85
89
90
95
96
97
98
99
really like him
Total
Valid
don't know any of the leaders
don't know who he is
don't know/don't know how to
rate him
refused
System
Total
Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
Recoding Recap
When we recode a variable, we change the variable’s original code to something we want it to be.
We must have a theoretical justification for operationalizing variables (recoding).
We will be recoding variables for the rest of the semester, so it should become easy (or at least easier) with time and practice.
Recoding Using “if” Statement
New variables can be created using “IF” If the result of a conditional expression is true,
the case is included in the selected subset. If the result of a conditional expression is false
or missing, the case is not included in the selected subset.
How to create a variable using “IF”
Follow the same steps as you would for recoding a variable into a new variable. Use “if” statement when you want specific conditions to be met:
In SPSS dataset, click on Transform, Recode Into Different Variables
In the recode dialog box, click IF. Select Include if case satisfies condition. Enter the conditional expression (=, <, > etc) Click on continue, ok.
Creating an Index
What is an index?– It is a new variable created by adding together a number of
other variables with similar attributes. Why create an index?
– To turn a number of similar variables into one variable. – Simplifies analysis.– To create a single indicator to fully capture a complex
concept . Things to think about before creating an index
– Do the variables to be used go together?– Is there a theoretical basis for putting them together?– Do they measure the same thing?– Are the variables measured in the same direction?
Creating an Index cont…
Examples of indices– Attitudes towards government.– Political participation.– Attitudes towards different policies.
In Class Example
You are writing your research report on the activities of political parties during an election campaign.
In the CES 2004 survey, there are three questions that ask if you have been contacted by a political party during the campaign
– pes_co_a (contacted by phone)– pes_co_b (contacted in person)– pes_co_c (contacted by mail)
You want to create an index of political communication to measure the frequency by which voters were contacted by political parties.
In Class Example
Index Creation Check List1. Is there theoretically justification for creating your
index?2. Are all the variables you want to include the index
measured in the same way and in the same direction?
3. Have you removed the “don’t know”, “r volunteers: have not been contacted at all” and “refused” respondents from the variable? (missing values)
4. Have you run a reliability analysis to test whether the variables go together?
How to Create an Index
Choose the variables to combine Make sure the variables are measured in the same
direction and have similar values assigned to the different categories.
If they do not, recode the variables to make them all consistent.
Make note of the different values assigned to the variables once recoded.
If needed, remove those who answer don’t know or those who refused to answer the questions (use missing values).
Creation of the Index
Recode:
pes_co_a (contact by phone)pes_co_b (contact by person)pes_co_c (contact by mail)
1 = contacted by party 0 = not contacted by party Recode ‘don’t know’ or ‘refused’ responses ‘missing’
Creation of the Index
Compute a new variable.
SPSS: transform menu → compute → enter the name of the new variable in the “target variable” box
→ find newly created variables to add to index in list
→ move them over to the box on the right (“numeric expression”)
→ add three new variables together
→ press OK. (Run frequency table to confirm the index ranges from 0 to 3.)
Reliability Scores
The reliability analysis evaluates what items are to go into the index.
This function uses the internal consistency approach to reliability. (Cronbach’s Alpha)
Calculates the number of items going into the index
Calculates the average inter-item correlation among the variables.
Cronbach’s Alpha
Ranges from 0 to 1(1 indicates perfect internal consistency and 0 indicates no internal consistency)
Alpha increases with the number of variables in the index and as the relationships between the component variables become stronger
Although there are no hard and fast rules, in this class we will consider .40 an acceptable level.
How to run reliability scores
In SPSS, click on Analyze, Scale, Reliability Select two or more variables as potential
components of an additive scale (the index). (In our example, pes_co_a, pes_co_b, pes_co_c)
Ensure in the model pane that ALPHA is selected.
Click on ok.
For next time…
We will develop and diagram causal models Read Chapter 12, Section B in Jackson and
Verberg
