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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
• the dependent variable(s) must be
  measured in any quantitative study.
• the more precise, sensitive the method of
  measurement, the better.
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
• 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
• 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
• 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
•   Nominal
•   Ordinal
•   Interval (Scale in SPSS)
•   Ratio (Scale in SPSS)                          ratio

                                        interval

                              ordinal

                    nominal
Nominal data
•   a more “crude” form of data:            •   nominal categories aren’t
    limited possibilities for statistical       hierarchical, one category isn’t
    analysis                                    “better” or “higher” than another
•   categories, classifications, or         •   assignment of numbers to the
    groupings                                   categories has no mathematical
      • “pigeon-holing” or labeling             meaning
•   merely measures the presence or         •   nominal categories should be
    absence of something                        mutually exclusive and
      • gender: male or female                  exhaustive
      • immigration status;
        documented, undocumented
      • zip codes, 90210, 92634,
        91784
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
Ordinal data
•   more sensitive than nominal data,    •   examples:
    but still lacking in precision            • 1st, 2nd, 3rd places finishes
•   exists in a rank order, hierarchy,          in a horse race
    or sequence
                                              • top 10 movie box office
     • highest to lowest, best to
        worst, first to last                    successes of 2006
•   allows for comparisons along              • bestselling books (#1, #2, #3
    some dimension                              bestseller, etc.)
     • example: Mona is prettier
        than Fifi, Rex is taller than            1st         2nd          3rd
        Niles
More about ordinal data
•   no assumption of “equidistance” of         •   •Top 10 Retirement Spots, according
    numbers                                        to USN&WR Sept. 20, 2007
     • increments or gradations aren’t         •   Boseman, Montana
        necessarily uniform                    •   Concord, New Hampshire
•   researchers do sometimes treat             •   Fayetteville Arkansas
    ordinal data as if it were interval data   •   Hillsboro, Oregon
•   there are limited statistical tests        •
    available with ordinal data                    Lawrence, Kansas
                                               •   Peachtree City, Georgia
                                               •   Prescott, Arizona
                                               •   San Francisco, California
                                               •   Smyrna, Tennessee
                                               •   Venice, Florida
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
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
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
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
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.)
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.

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Measurement

  • 1. Measurement quantifying the dependent variable
  • 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) ratio interval ordinal nominal
  • 8. Nominal data • a more “crude” form of data: • nominal categories aren’t limited possibilities for statistical hierarchical, one category isn’t analysis “better” or “higher” than another • categories, classifications, or • assignment of numbers to the groupings categories has no mathematical • “pigeon-holing” or labeling meaning • merely measures the presence or • nominal categories should be absence of something mutually exclusive and • gender: male or female exhaustive • immigration status; documented, undocumented • zip codes, 90210, 92634, 91784
  • 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, • examples: but still lacking in precision • 1st, 2nd, 3rd places finishes • exists in a rank order, hierarchy, in a horse race or sequence • top 10 movie box office • highest to lowest, best to worst, first to last successes of 2006 • allows for comparisons along • bestselling books (#1, #2, #3 some dimension bestseller, etc.) • example: Mona is prettier than Fifi, Rex is taller than 1st 2nd 3rd Niles
  • 11. More about ordinal data • no assumption of “equidistance” of • •Top 10 Retirement Spots, according numbers to USN&WR Sept. 20, 2007 • increments or gradations aren’t • Boseman, Montana necessarily uniform • Concord, New Hampshire • researchers do sometimes treat • Fayetteville Arkansas ordinal data as if it were interval data • Hillsboro, Oregon • there are limited statistical tests • available with ordinal data 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.