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. ratio interval nominal ordinal Levels of data • Nominal • Ordinal • Interval (Scale in SPSS) • Ratio (Scale in SPSS)

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 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 Nominal data-continued

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.) 1st 2nd 3rd

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 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 More about ratio data TRUE

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.