2. Importance of measurement
• 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.
3. Direct measures
• 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
4. Self reports or “paper pencil”
measures
• 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
5. Indirect measures
• 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.
6. Miscellaneous measures
• 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
7. Levels of data
• Nominal
• Ordinal
• Interval (Scale in SPSS)
• Ratio (Scale in SPSS)
nominal
ordinal
interval
ratio
8. Nominal data
• 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
9. Nominal data-continued
• 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
10. Ordinal data
• 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 3rd
1st
11. More about ordinal data
• 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
12. Interval data (scale data)
• 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
13. More about interval data
• 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
14. Ratio data
• 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
15. More about ratio data
• 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
16. Examples of levels of data
• 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.)
17. Application to experimental design
• 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.
