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Chapter Eight

Chapter Eight. Measurement and Scaling Fundamentals. Chapter Outline. Measurement and Scaling 2) Primary Scales of Measurement Nominal Scale Ordinal Scale Interval Scale Ratio Scale. 1- Measurement and Scaling.

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Chapter Eight

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  1. Chapter Eight Measurement and Scaling Fundamentals

  2. Chapter Outline • Measurement and Scaling 2) Primary Scales of Measurement • Nominal Scale • Ordinal Scale • Interval Scale • Ratio Scale

  3. 1- Measurement and Scaling Measurement means assigning numbers or other symbols to characteristics of objects according to certain pre-specified rules. Scaling involves creating a continuum upon which measured objects are located. Example: consider a scale from 1 to 10 for located consumers according the characteristic “attitude toward department stores”. Each respondent is assigned a number from 1 to 10 indicating the degree of favorableness or un-favorableness, with 1= extremely unfavorable and 10= extremely favorable. Measurement is the actual assignment of a number from 1 to 10 to each respondent. Scaling is the process of placing the respondents on a continuum with respect to their attitude toward department stores.

  4. Nominal Numbers Assigned to Runners OrdinalRank Order of Winners IntervalPerformance Rating on a 0 to 10 Scale RatioTime to Finish in Seconds Finish 7 8 3 Finish Third place Second place First place 8.2 9.1 9.6 15.2 14.1 13.4 2- Primary Scales of Measurement

  5. Nominal Ordinal Ratio Scale Scale Scale Preference $ spent last No. Store Rankings 3 months 1. Parisian 2. Macy’s 3. Kmart 4. Kohl’s 5. J.C. Penney 6. Neiman Marcus 7. Marshalls 8. Saks Fifth Avenue 9. Sears 10.Wal-Mart IntervalScale Preference Ratings 1-7 Illustration of Scales of Measurement

  6. Nominal Scale • The numbers (or any other symbol) serve only as labels for identifying and classifying objects. • When used for identification, there is a strict one-to-one correspondence between the numbers and the objects – each number is assigned to only one object and each object has only one number assigned to it (e.g. numbers of foot ball players). • The numbers do not reflect the amount of the characteristic possessed by the objects. • The only permissible operation on the numbers in a nominal scale is counting (description). • Only a limited number of statistics, all of which are based on frequency counts, are permissible, e.g., percentages, mode, and chi-square.

  7. Nominal Scale • Example 1: Gender: Males= 1, Females= 2 • Example 2: Sales Zone: Riyadh= 1, Jeddah= 2, Alkhobar= 3 • Example 3: Drink: Pepsi= A, 7 up= B, Miranda= c • Example 4: Product category: Drinks= A, meat= B, Dairy products= C

  8. Ordinal Scale • A ranking scale in which numbers are assigned to objects to indicate the relative extent to which the objects possess some characteristic from lowest to highest. • Can determine whether an object has more or less of a characteristic than some other object, but not how much more or less. • In marketing research ordinal scales are used to measure relative attitudes, opinions, perceptions, and preferences such as brand ranking. • In addition to the counting operation allowable for nominal scale data, ordinal scales permit the use of statistics based on centiles, e.g., percentile, and median, and some inferential statistics such as rank-order correlation and Friedman ANOVA. • A problem with ordinal scales is that the difference between categories on the scale is hard to quantify, ie., exscellent is better than good, but how much is excellent better.

  9. Ordinal Scale • Example 1: rank your preferences of the following banking services, giving 1 to the first preference, 2 for the second, and 3 for the third: • ATM banking • Telebanking • Digital banking • Example 2: Rank the following Mobile brands according to your buying intention: • Samsung • Lenovo • iPhone

  10. Interval Scale • Numerically equal distances on the scale represent equal values in the characteristic being measured. • It permits comparison of the differences between objects. • The location of the zero point is not fixed. Both the zero point and the units of measurement are arbitrary. • In marketing research, attitudinal data obtained from rating scales are often treated as interval data. • It is not meaningful to take ratios of scale values. • Statistical techniques that may be used include all of those that can be applied to nominal and ordinal data, and in addition the arithmetic mean, standard deviation, and other statistics commonly used in marketing research.

  11. Interval Scale • Example 1: the food variety of the restaurant is: • Entirely important • Important • Neutral • Not important • Not entirely important • Example 2: I enjoy watching online ads • Entirely agree • Agree • Neutral • Disagree • Entirely disagree

  12. Ratio Scale • Possesses absolute rather than relative qualities. • It has an absolute zero point. • It is meaningful to compute ratios of scale values. • Possesses all the properties of the nominal, ordinal, and interval scales. Hence, All statistical techniques can be applied to ratio data. • It is the highest level of measurement. • examples: money, weight, distance, income, age.

  13. Ratio Scale • Example 1: No. of my children …….………. • Examples 2: My monthly salary is SR ……………… • Example 3: In the last 7 days, How many times did you go to the market? ……………. • Example 4: My age …………. Years • Example 5: How often do you go to the barber shop every 3 months? …………………

  14. Primary Scales of Measurement

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