quantifying the dependent variable. research conclusions are only as good as the data on which they...
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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 measures• in a naturalistic setting.
example: videotaping leave-taking behavior (how people say goodbye) at an airport.
• in a laboratory setting 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
•Top 10 Retirement Spots, according to USN&WR Sept. 20, 2007
Boseman, Montana Concord, New Hampshire Fayetteville Arkansas Hillsboro, Oregon Lawrence, Kansas Peachtree City, Georgia Prescott, Arizona San Francisco, California Smyrna, Tennessee Venice, Florida
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• most standardized scales or
diagnostic instruments yield numerical scores
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.