Measurement scales


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Measurement scales

  1. 1. Types of Data & Measurement Scales: Nominal, Ordinal, Interval and Ratio Research Writing
  2. 2. 4 Measurement Scales (or types of data) • nominal, ordinal, interval and ratio • They are simply ways to categorize different types of variables
  3. 3. Levels of Measurement
  4. 4. Survey Question Types • • • • • Open-ended text questions Multiple choice questions Ordinal scale questions Interval scale questions Ratio scale questions The type of data generated by the survey question constrains the type of analysis you can perform.  
  5. 5. 1) Nominal Scale (Names/Labels) 0.gif
  6. 6. • These scales are mutually exclusive (no overlap) and none of them have any numerical significance)
  7. 7. Nominal Data Example • Dichotomous: a nominal scale with only two variables
  8. 8. Gender Distribution
  9. 9. 2) Ordinal Data: Rank Order Scale create an ordinal scale of preference • Rank order scaling questions allow a certain set of variables to be ranked based upon a specific attribute or characteristic.
  10. 10. Ranking questions are best to use when all the choices listed should be ranked Ranking questions are best to use when all the choices listed should be ranked according to a level of specification (e.g. level of importance). according to a level of specification (e.g. level of importance). If you have a question in which you need the respondents to indicate what items If you have a question in which you need the respondents to indicate what items are the “most important” to “least important,” then you can set up a ranking are the “most important” to “least important,” then you can set up a ranking question (Waddington 2000). question (Waddington 2000).
  11. 11. Rating (Lickert* ) Scale: ordinal? (non-numeric concepts like satisfaction, happiness, etc.) ( • the order of the  In each case, we know that a #4 is better than  In each case, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot a #3 or #2, but we don’t know–and cannot quantify–how much better ititis.  quantify–how much better is.  * pronounced 'lick-urt' with a short "i" sound values is what’s important and significant, but the differences between each one is not really known
  12. 12. surveying the frequency of something like behavior or attitude. It is best to present the rating scale in a logical or consistent order. Therefore, it makes sense to order the ranking or rating choices from low to high (e.g. Strongly Disagree to Strongly Agree going from left to right). • Rating type questions are used when
  13. 13. Interval Scale • It is an interval scale because it is assumed to have equidistant (equal distance) points between each of the scale elements. This means that we can interpret differences in the distance along the scale. We contrast this to an ordinal scale where we can only talk about differences in order, not differences in the degree of order .
  14. 14. Interval Scale • When you are asked to rate your satisfaction with a piece of software on a 7 point scale, from Dissatisfied to Satisfied, you are using an interval scale • it is important that the space between each option, whether it's a number range or a feeling range, are equal. • scales asking about agreement strength, likelihood or satisfaction (i.e. very unsatisfied, unsatisfied, neither satisfied nor unsatisfied, satisfied, very satisfied). 
  15. 15. • …Similar to ordinal, but the intervals between • • the values of the response options are evenly spaced …Can be used for any quantitative variable …Measures variables that fall into logical ranges • Example: What was your undergraduate GPA upon graduation? a) 3.5-4.0 b) 3.0-3.49 c) 2.5-2.99 d) 2.0-2.49
  16. 16. According to James Dean Brown (University of Hawai‘i at Manoa) • Interval scales show the order of things, but with • • equal intervals between the points on the scale. Thus, the distance between scores of 50, 51, 52, 53 and so forth are all assumed to be the same all along the scale. Test scores are usually treated as interval scales in language research. Scales based on Likert items are also commonly treated as interval scales in our (education/social research) field.
  17. 17. Ordinal & Interval Example
  18. 18. Ratio Scale • Ratio scales differ from interval scales in that they have a zero value and points along the scale make sense as ratios. For example, a scale like age can be zero, and it makes sense to think of four years as twice as old as two years
  19. 19. • When respondents are asked to tell us some physical measure, such as income, years of education, or how long their phone call was on hold, these are ratio scale questions.  The data they provide have a true zero.  (On an interval scale, a zero response option is simply arbitrary.  Zero income, for example, is real.) • Frequently, we solicit ratio data with what appears to be an ordinal • scale with response options presented in ranges, such as if we were to ask for the number of years of education the person had achieved, asking the respondent to check one of the following options: 1) 1 to 12 years, 2) high school degree, 3) associates degree, 4) bachelors degree, 5) graduate degree.  While the question looks ordinal, we could treat the data as ratio in our analysis.   Why present ranges?   – First, it's faster for the respondent to answer the question, lowering respondent burden. – Second, it's less invasive to ask someone to check an income range, for example, then to ask them their annual income.  Would you tell a stranger your income level?  Probably not, but you might be willing to check a box that says your income is $50,000 to $75,000 per year.
  20. 20. Measurement Scales
  21. 21. • Nominal: mode crosstabulation - with chi-square, etc. • Ordinal: use non-parametric statistics, i.e. Median and mode, rank order • Measurement correlation, non-parametric analysis of variance – Modelling techniques can also be used with ordinal data. Interval: Interval scale data would use parametric statistical techniques: – Mean and standard deviation Correlation - r Regression Analysis of variance Factor analysis – Cronbach analysis – Plus a whole range of advanced multivariate and modelling techniques • Ratio:  variables which are ratio scaled include weights, lengths and times. virtually all statistical operations can be performed on ratio scales
  22. 22. Scoring the Data