constructing/transforming variables still preliminary to data analysis (statistics) would fit...
TRANSCRIPT
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Constructing/transforming Variables
• Still preliminary to data analysis (statistics)
Would fit comfortably under
Measurement
A bit more advanced is all
• All the earlier material about operationalization (importance of it, difficulty of doing well, need to think about and assess reliability and validity) apply
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How (and why) change variables?
• Collapse codes
• Reverse coding
• Combine items
• Standardize items
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Collapse codes
• Example: Party identification
1 = SD, 2 = WD, 3 = ID, 4 = Ind,
5 = IR, 6 = WR, 7 = SR
Collapse to 1 = Dem (1-2 above),
2 = Ind (3-5), 3 = Rep (6-7)
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Collapse codes (cont.)
• Why collapse codes?
For theoretical reasons
(In a given instance) we think only
direction of partisanship matters
To combine small categories
Another option is to delete those cases
To get a reasonable # of categories
Age 18, 19, 20…97 = 80 categories
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Reverse coding
• Examples: 1 = Disagree, 2 = Neutral, 3 = Agree Reverse so 1 = Agree, 2 = Neutral, 3 = Disagree
1 = Low…10 = High Reverse so 1 = High…10 = Low
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Reverse coding (cont.)
• Why reverse coding?• Questions are reversed in surveys Example: from homework, next slide Indicators have “opposite” meanings Example: unemployment (up is “bad”); increase in income (up is “good”)• With reversal: It’s often easier to interpret items Combining items make sense
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Tolerance questions
• Members of the [least-liked group] should be banned from being president of the U.S.
Disagree is the “tolerant” response.
• Members of the [least-liked group] should be allowed to teach in public schools.
Agree is the “tolerant” response.
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Combine items
• Examples can be complex (we’ll see later)
• Simple example:
Count the number of correct, or
tolerant, or liberal, or … responses
As is Knowledge and Tolerance scales
(in homework)
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Combine items (cont.)
• Why combine items?• Multi-variable items are usually more valid
Many concepts—e.g., type of election
system, tolerance, knowledge—are hard
to measure with a single question or indi-
cator (they contain multiple components).
In a combined measure, we can include items
that measure all of these components
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Combine items (cont.)
• Combined (multi-variable) items typically increase reliability as well
Random error that affects individual
items is averaged out
• Combining items also yield more refined measurement
Simply put, we get more categories
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Ex: Pro-business support
• Conceptual definition is simple: how favorable toward business are members of Congress?
• More specifically, rank U.S. House members by their favorability toward business.
• How might we do this?
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Standardize items• Why standardize items?
• To account for different numbers of base items.
• To compare or combine items measured on different scales altogether.
Save this for later.
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Standardize items
• Examples
Percentaging (e.g., 90% is “equivalent”
despite different length tests).
Making measures comparable by
deflating for changing bases (e.g.,
increased media coverage over time).
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Media coverage over time: More pages & more periodicals
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Deflating for total volume matters
• Downward trend in coverage of auto safety masked by increasing media volume.
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A reminder
• Composite (multi-variable) measures are not always better (more valid/reliable).
• Results can be artifact of how you construct your variables.
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Real world example of a composite (better?) measure
• Likelihood of voting (in election polls)
Need to estimate because many who are
interviewed will not vote
People won’t/can’t estimate own behavior• Pollsters use information about past voting,
whether registered, interest in the race, etc.
But: polls vary (in part) because different
pollsters use different sets of questions
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Why change variables?Additional reasons
• To change the nature of the variable
Example: log transformation (age often
done this way)
• Create new variables
“Just” combining items again, but it can
be very complex
Example: next slide
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Example: Clarity of responsibility for government decisions (Powell)
• Idea is that in some instances it is easy to assign credit or blame for what a government does; other times not
• Powell builds an index using measures of:
Presence of minority government
Whether there is bicameral opposition
Number of political parties in the legislature
Degree of cohesion among the parties
Strength of committee chairs in the legislature
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The text on combining/altering variables in SPSS
• Text talks about Recode and Compute operations for simple purposes
Collapsing Additive index (e.g., # of yes answers) Useful but far from all that you can do• Mentions transformations Refers to “a dizzying variety of complex transformations” possible” (text)
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• Comment/advice:
If you want to do it, you almost certainly
can do it in SPSS—and you can
probably do it quite easily.
Ex: Make individuals (in a survey) a 1 if
var 5 + abs. value of var 6 + 2(var17) +
log(var 19) is greater than 24.5 OR if var 3
equals 4; otherwise make them 0
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• Final note on SPSS
When you recode:
Save in a new variable almost always
Checking after each operation
• More on changing variables in the lab
sessions