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Measurement - PowerPoint PPT Presentation

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Measurement. quantifying the dependent variable. 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

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quantifying the

dependent variable

Importance of measurement
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.

Direct measures
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

Self reports or paper pencil measures
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

Indirect measures
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.

Miscellaneous measures
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

Levels of data





Levels of data

  • Nominal

  • Ordinal

  • Interval (Scale in SPSS)

  • Ratio (Scale in SPSS)

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

Nominal data

Nominal data continued
Nominal data-continued statistical analysis

  • 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

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


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

top 10 movie box office successes of 2006

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

Ordinal data




More about ordinal data

no assumption of “equidistance” of numbers precision

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

More about ordinal data

Interval data scale data
Interval data (scale data) precision

  • 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

More about interval data
More about interval data precision

  • 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

Ratio data
Ratio data precision

  • 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 about ratio data
More about ratio data precision

  • 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


Examples of levels of data
Examples of levels of data precision

  • 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.)

Application to experimental design
Application to experimental design precision

  • 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.