quantifying thedependent variable

research conclusions are only as good as the data on which they are based

observations must be quantifiable in order to subject them to statistical analysis

the dependent variable(s) must be measured in any quantitative study.

the more precise, sensitive the method of measurement, the better.

physiological measures• heart rate, blood pressure,

galvanic skin response, eye movement, magnetic resonance imaging, etc.

behavioral/observational measures• naturalistic settings.

example: videotaping leave-taking behavior (how people say goodbye) at an airport.

• laboratory settings example: videotaping married

couples’ interactions in a simulated environment

oral interviews• either in person or by phone

surveys and questionnaires• self-administered, or other

administered

• on-line surveys standardized scales and

instruments• examples: ethnocentrism

scale, dyadic adjustment scale, self monitoring scale

relying on observers’ estimates or perceptions• indirect questioning

example: asking executives at advertising firms if they think their competitors use subliminal messages

example: asking subordinates, rather than managers, what managerial style they perceive their supervisors employ.

unobtrusive measures• measures of accretion, erosion, etc.

example: “garbology” research—studying discarded trash for clues about lifestyles, eating habits, consumer purchases, etc.

archived data• example: court records of spouse

abuse• example: number of emails sent

to/from students to instructors retrospective data

• example: family history of stuttering

• example: employee absenteeism or turn-over rates in an organization

Nominal Ordinal Interval (Scale in SPSS) Ratio (Scale in SPSS)

nominal

ordinal

interval

ratio

a more “crude” form of data: limited possibilities for statistical analysis

categories, classifications, or groupings

• “pigeon-holing” or labeling

merely measures the presence or absence of something

• gender: male or female

• immigration status; documented, undocumented

• zip codes, 90210, 92634, 91784

nominal categories aren’t hierarchical, one category isn’t “better” or “higher” than another

assignment of numbers to the categories has no mathematical meaning

nominal categories should be mutually exclusive and exhaustive

nominal data is usually represented “descriptively”

graphic representations include tables, bar graphs, pie charts.

there are limited statistical tests that can be performed on nominal data

if nominal data can be converted to averages, advanced statistical analysis is possible

more sensitive than nominal data, but still lacking in precision

exists in a rank order, hierarchy, or sequence

• highest to lowest, best to worst, first to last

allows for comparisons along some dimension

• example: Mona is prettier than Fifi, Rex is taller than Niles

examples:

• 1st, 2nd, 3rd places finishes in a horse race

• top 10 movie box office successes of 2006

• bestselling books (#1, #2, #3 bestseller, etc.)

2nd 3rd1st

no assumption of “equidistance” of numbers

• increments or gradations aren’t necessarily uniform

researchers do sometimes treat ordinal data as if it were interval data

there are limited statistical tests available with ordinal data

Rank City Violent crimes per 1000 people

1. Flint, MI 23.4

2. Detroit, MI 21.4

3. St. Louis, MO 18.6

4. Oakland, CA 16.8

5. Memphis, TN 15.8

6. Little Rock, Ark. 14.9

7. Birmingham, AL 14.8

8. Atlanta, GA 14.3

9. Baltimore, MD 14.2

10. Stockton, CA 14.1

represents a more sensitive type of data or sophisticated form of measurement

assumption of “equidistance” applies to data or numbers gathered• gradations, increments, or units of

measure are uniform, constant examples:

• Scale data: Likert scales, Semantic Differential scales

• Stanford Binet I.Q. test

scores can be compared to one another, but in relative, rather than absolute terms.• example: If Fred is rated a “6” on

attractiveness, and Barney a “3,” it doesn’t mean Fred is twice as attractive as Barny

no true zero point (a complete absence of the phenomenon being measured)• example: A person can’t have zero intelligence

or zero self esteem scale data is usually aggregated or

converted to averages amenable to advanced statistical analysis

the most sensitive, powerful type of data• ratio measures contain the most

precise information about each observation that is made

examples: • time as a unit of measure• distance as a unit of measure (setting

an odometer to zero before beginning a trip)

• weight and height as units of measure

more prevalent in the natural sciences, less common in social science research

includes a true zero point (complete absence of the phenomenon being measured)

allows for absolute comparisons• If Fred can lift 200 lbs and

Barney can lift 100 lbs, Fred can lift twice as much as Barney, e.g., a 2:1 ratio

nominal: number of males versus females who are HCOM majors

ordinal: “small,” “medium,” and “large” size drinks at a movie theater.

interval: scores on a “self-esteem” scale of Hispanic and Anglo managers

ratio: runners’ individual times in the L.A. marathon (e.g., 2:15, 2: 21, 2:33, etc.)

As far as the dependent variable is concerned:• always employ the highest level of

measurement available, e.g., interval or ratio, if possible

• rely on nominal or ordinal measurement only if other forms of data are unavailable, impractical, etc.

• try to find established, valid, reliable measures, rather than inventing your own “home-made” measures.