Constructing/transforming Variables

1
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

2
How (and why) change variables?

• Collapse codes
• Reverse coding
• Combine items
• Standardize items

3
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)

4
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, 2097 80 categories

5
Reverse coding

• Examples
• 1 Disagree, 2 Neutral, 3 Agree
• Reverse so 1 Agree, 2 Neutral,
• 3 Disagree
• 1 Low10 High
• Reverse so 1 High10 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
• Its often easier to interpret items
• Combining items make sense

7
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.

8
Combine items

• Examples can be complex (well see later)
• Simple example
• Count the number of correct, or
• tolerant, or liberal, or responses
• As is Knowledge and Tolerance scales
• (in homework)

9
Combine items (cont.)

• Why combine items?
• Multi-variable items are usually more valid
• Many conceptse.g., type of election
• system, tolerance, knowledgeare
  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

11
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?

12
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.

13
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).

14
Media coverage over time More pages more
periodicals
15
Deflating for total volume matters

• Downward trend in coverage of auto safety masked
  by increasing media volume.

16
A reminder

• Composite (multi-variable) measures are not
  always better (more valid/reliable).
• Results can be artifact of how you construct your
  variables.

17
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 wont/cant 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

20
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 SPSSand 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