King Fahd University of Petroleum & Minerals Department of Management and Marketing MKT 345 Marketing Research Dr. Alhassan G. Abdul-Muhmin Introduction to Measurement Reference: Zikmund, Chapter 13 Learning Objectives At the end of this discussion you should be able to: 1. define measurement and explain its importance in marketing research 2. list and explain the requirements for effective measurement in marketing research 3. list and explain the different types of measurement scales 4. know how to form an index or composite measure 5. list and explain the criteria used to evaluate the quality of index measures 6. Perform basic assessment of scale reliability and validity THE NATURE OF MEASUREMENT 1. The process of assigning numbers or scores to attributes of people or objects. 2. The process of describing some property of a phenomenon of interest by assigning numbers in a reliable and valid way Precise measurement requires: a) Careful conceptual definition – i.e. careful definition of the concept (e.g. loyalty) to be measured b) Operational definition of the concept c) Assignment rules by which numbers or scores are assigned to different levels of the concept that an individual (or object) possesses. 1. Conceptual Definition Concept - A generalized idea about a class of objects, attributes, occurrences, or processes. Examples: Gender, Age, Education, brand loyalty, satisfaction, attitude, market orientation Construct - A concept that is measured with multiple variables. Examples: Brand loyalty, satisfaction, attitude, market orientation, socio-economic status Variable - Anything that varies or changes from one instance to another; can exhibit differences in value, usually in magnitude or strength, or in direction. 1. Conceptual Definition Concepts must be precisely defined for effective measurement. E.g. consider the following definitions of “brand loyalty”: 1. “The degree to which a consumer consistently purchases the same brand within a product class.” (Peter & Olson) 2. “A favorable attitude toward, and consistent purchases of, a particular brand”. (Wilkie, p.276) The two definitions have different implications for measurement – they imply different operationalizations of the concept of brand loyalty 2. Operational Definition/Operationalization Operational definition - A definition that gives meaning to a concept by specifying what the researcher must do (i.e. activities or operations that should be performed) in order to measure the concept under investigation. Operationalization - The process of identifying scales that correspond to variance in a concept. For example: Conceptual definition # 1 for brand loyalty in the previous slide implies that in order to measure loyalty for brand A (operational definition), you will need to: 1) Observe consumers’ brand purchases over a period of time, and 2) Compute the percent of purchases going to brand A For conceptual definition # 2 you will need to: 1) Observe consumers’ brand purchases over a period of time, 2) Compute the percent of purchases going to brand A, and 3) Ask consumers questions to determine their attitudes toward brand A 3. Rules of Measurement Guidelines established by the researcher for assigning numbers or scores to different levels of the concept (or attribute) that different individuals (or objects) possess The process is facilitated by the operational definition. For example, if you operationalized brand loyalty as “purchase sequences” (conceptual definition # 1), then you may establish the following rules for assigning scores: If consumer purchased brand A: 90% or more –> loyalty for brand A = 1 (Extremely loyal) 80 - 89% –> loyalty for brand A = 2 (Very loyal) 70 - 79% –> loyalty for brand A = 3 (Loyal) Etc. In this case, we have assigned the numbers 1, 2, 3 to different levels of loyalty toward brand A. We have measured loyalty for brand A. MEASUREMENT SCALES To effectively carry out any measurement (whether in the physical or social sciences) we need to use some form of a scale. A scale is any series of items (numbers) arranged along a continuous spectrum of values for the purpose of quantification (i.e. for the purpose of placing objects based on how much of an attribute they possess) E.g. the thermometer consists of numbers arranged in a continuous spectrum to indicate the magnitude of “heat” possessed by an object. Three Meanings of “Scale” in Marketing Research There are three ways in which the word “scale” is used in marketing research 1) The level at which a variable is measured (Level of scale measurement) – the arithmetical properties implied by the numbers assigned to levels of an attribute possessed by an object (i.e. the unit of analysis) – Discussed in this chapter 2) An index, or composite measure of a construct – Multiple statements used to measure a construct (also called a multi- item measure of the construct) – Discussed in this chapter 3) The response categories provided for a close-ended question in a questionnaire, e.g. – Subjects expressed their agreement / disagreement on a 5-point category scale or on a 5-point semantic differential scale. – Will be discussed in chapter 14 (1) LEVELS OF SCALE MEASUREMENT Numbers assigned in measurement can take on different levels of meaning depending on one of four mapping characteristics possessed by the numbers: 1. Classification - The numbers are used only to group or sort responses. No order exists 2. Order - The numbers are ordered. One number is greater than, less than, or equal to another 3. Distance - Differences between the numbers are ordered. The difference between any pair of numbers is greater than, less than, or equal to the difference between any other pair of numbers 4. Origin - The number series has a unique origin indicated by the number zero The type of mapping characteristic assumed depends on the properties of the attribute (or construct) being measured The Four Characteristics of Mapping Rules 1. Classification – The numbers are used only to group or sort responses. No order exists 2. Order – The numbers are ordered. One number is greater than, less than, or equal to another 3. Distance – Differences between the numbers are ordered. The difference between any pair of numbers is greater than, less than, or equal to the difference between any other pair of numbers 4. Origin – The number series has a unique origin indicated by the number zero The Four Levels of Scale Measurement Four levels of scale measurement result from this mapping 1. Nominal Scale: a scale in which the numbers or letters assigned to an object serve only as labels for identification or classification, e.g. Gender (Male=1, Female=2) 2. Ordinal Scale: a scale that arranges objects or alternatives according to their magnitude in an ordered relationship, e.g. Academic status (Sophomore=1, Freshman=2, Junior=3, etc 3. Interval Scale: a scale that both arranges objects according to their magnitude, distinguishes this ordered arrangement in units of equal intervals, but does not have a natural zero representing absence of the given attribute, e.g. the temperature scale (40oC is not twice as hot as 20oC) 4. Ratio Scale: a scale that has absolute rather than relative quantities and an absolute (natural) zero where there is an absence of a given attribute, e.g. income, age. Nominal, Ordinal, Interval, and Ratio Scales Provide Different Information Characteristics of Different Levels of Scale Measurement Type of Data Numerical Descriptive Examples Scale Characteristics Operation Statistics Nominal Classification but no Counting Frequency in each Gender (1=Male, order, distance, or category 2=Female) origin Percent in each category Mode Ordinal Classification and Rank ordering Median Academic status order but no Range (1=Freshman, distance or unique Percentile ranking 2=Sophomore, origin 3=Junior, 4=Senior) Interval Classification, order, Arithmetic Mean Temperature in and distance but no operations that Standard deviation degrees unique origin preserve order and Variance Satisfaction on magnitude semantic differential scale Ratio Classification, order, Arithmetic Geometric mean Age in years distance and unique operations on Coefficient of Income in Saudi origin actual quantities variation riyals Note: All statistics appropriate for lower-order scales (nominal being lowest) are appropriate for higher-order scales (ratio being the highest) (2) INDEX OR COMPOSITE MEASURES • Both index and composite measures use combinations (or collection) of several variables to measure a single construct (or concept); they are multi-item measures of constructs. • However, for index measures, the variables need not be strongly correlated with each other, whilst for composite measures, the variables are typically strongly correlated as they are all assumed to be measuring the construct in the same way Example 1: Index Measure Construct: Social class Measures: Linear combination (index) of occupation, education, income. Social class = β1Education + β2Occupation + β2Occupation Example 2: Composite Measure Construct: Attitude Toward Brand A Measures: Extent of agreement/disagreement with multiple statements: a) “I like Brand A very much” b) “Brand A is the best in the market” c) “I always buy Brand A” • Statements a), b), c), constitute a “scale” to measure attitudes toward brand A Computing Scale Values for Composite Scales • Summated Scale – A scale created by simply summing (adding together) the response to each item making up the composite measure. • Reverse Coding – Means that the value assigned for a response is treated oppositely from the other items. CRITERIA FOR GOOD MEASUREMENT Three criteria are commonly used to assess the quality of measurement scales in marketing research: 1. Reliability 2. Validity 3. Sensitivity RELIABILITY The degree to which a measure is free from random error and therefore gives consistent results. An indicator of the measure’s internal consistency Test-Retest Stability (Repeatability) Reliability Splitting halves Internal Consistency Equivalent forms Assessing Stability (Repeatability) • Stability the extent to which results obtained with the measure can be reproduced. 1. Test-Retest Method • Administering the same scale or measure to the same respondents at two separate points in time to test for stability. 2. Test-Retest Reliability Problems • The pre-measure, or first measure, may sensitize the respondents and subsequently influence the results of the second measure. • Time effects that produce changes in attitude or other maturation of the subjects. Assessing Internal Consistency • Internal Consistency: the degree of homogeneity among the items in a scale or measure 1. Split-half Method • Assessing internal consistency by checking the results of one- half of a set of scaled items against the results from the other half. • Coefficient alpha (α) – The most commonly applied estimate of a multiple item scale’s reliability. – Represents the average of all possible split-half reliabilities for a construct. 2. Equivalent forms • Assessing internal consistency by using two scales designed to be as equivalent as possible. VALIDITY • The accuracy of a measure or the extent to which a score truthfully represents a concept. • The ability of a measure (scale) to measure what it is intended measure. • Establishing validity involves answers to the ff: – Is there a consensus that the scale measures what it is supposed to measure? – Does the measure correlate with other measures of the same concept? – Does the behavior expected from the measure predict actual observed behavior? Validity Face or Criterion Construct Content Validity Validity Concurrent Predictive ASSESSING VALIDITY 1. Face or content validity: The subjective agreement among professionals that a scale logically appears to measure what it is intended to measure. 2. Criterion Validity: the degree of correlation of a measure with other standard measures of the same construct. • Concurrent Validity: the new measure/scale is taken at same time as criterion measure. • Predictive Validity: new measure is able to predict a future event / measure (the criterion measure). 3. Construct Validity: degree to which a measure/scale confirms a network of related hypotheses generated from theory based on the concepts. • Convergent Validity. • Discriminant Validity. Relationship Between Reliability & Validity 1. A measure that is not reliable cannot be valid, i.e. for a measure to be valid, it must be reliable Thus, reliability is a necessary condition for validity 2. A measure that is reliable is not necessarily valid; indeed a measure can be but not valid Thus, reliability is not a sufficient condition for validity 3. Therefore, reliability is a necessary but not sufficient condition for Validity. Reliability and Validity on Target SENSITIVITY • The ability of a measure/scale to accurately measure variability in stimuli or responses; • The ability of a measure/scale to make fine distinctions among respondents with/objects with different levels of the attribute (construct). – Example - A typical bathroom scale is not sensitive enough to be used to measure the weight of jewelry; it cannot make fine distinctions among objects with very small weights. • Composite measures allow for a greater range of possible scores, they are more sensitive than single-item scales. • Sensitivity is generally increased by adding more response points or adding scale items.