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

How (and why) change variables?

• Collapse codes

• Reverse coding

• Combine items

• Standardize items

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)

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

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

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

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.

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)

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

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

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?

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.

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).

Media coverage over time: More pages & more periodicals

Deflating for total volume matters

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

A reminder

• Composite (multi-variable) measures are not always better (more valid/reliable).

• Results can be artifact of how you construct your variables.

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

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

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

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)

• 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

• 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