際際滷

際際滷Share a Scribd company logo

Measurement

Download as PPT, PDF
Amrita University
Amrita University

This document discusses different types of measurement used in research including direct measures like physiological and behavioral observations, self-reports like surveys and interviews, and indirect measures relying on observer perceptions. It defines nominal, ordinal, interval, and ratio levels of data and provides examples, noting that higher levels of precise measurement are preferred for research conclusions.

1 of 17
Downloaded 28 times
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 researchstudying 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 arent limited possibilities for statistical hierarchical, one category isnt 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 arent 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 doesnt mean Fred is twice as attractive as Barny no true zero point (a complete absence of the phenomenon being measured) example: A person cant 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.

More Related Content

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 researchstudying 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 arent limited possibilities for statistical hierarchical, one category isnt 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 arent 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 doesnt mean Fred is twice as attractive as Barny no true zero point (a complete absence of the phenomenon being measured) example: A person cant 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.