research method for business chapter 7

Scaling is a procedure for the assignment of numbers (or other symbols) to a property of objects in order to import some of the characteristics of numbers to properties in question

Scaling

Nominal Scales

Ordinal Scales

Interval Scales

Ratio Scales

Four Scales of Measurement;

4

Measurement and Scaling

A scale is a mechanism by which individuals are

distinguished as to how they differ from one another on the

variables of interest.

A scale is a continuous series of categories and has been

defined as any series of items that are arranged

progressively according to value or magnitude, into which

an item can be placed according to its quantification

Four popular scales in business research are:

1. Nominal scales

2. Ordinal scales

3. Interval scales

4. Ratio scales

SCALES

Nominal Scales: splits data into groups, e.g., men,

women

Ordinal Scales: ranks data in some order, e.g.,

exercising for 20 minutes is good, for 30 minutes is

better, for 40 minutes is best

Interval Scales: sets data on a continuum, e.g.

1 2 3 4 5very low very high

Ratio Scales: starts with absolute zero and indicates

proportion, e.g.

0 5 10 ten is twice as big as five

6

A Nominal Scale is the simplest of the four

scale types and in which the numbers or letters

assigned to objects serve as labels for

identification or classification

Example: variable of gender

Males = 1, Females = 2

Sales Zone A = Islamabad, Sales Zone B = Rawalpindi

Drink A = Pepsi Cola, Drink B = 7-Up, Drink C = Miranda


7

An Ordinal Scale is one that arranges objects or

alternatives according to their magnitude

Examples:

Career Opportunities = Moderate, Good, Excellent

Investment Climate = Bad, inadequate, fair, good, very

good

Merit = A grade, B grade, C grade, D grade

A problem with ordinal scales is that the difference

between categories on the scale is hard to quantify, i.e..,

excellent is better than good but how much is excellent

better?


8

An Interval Scale allows us to perform certain arithmetical

operations on the data collected from respondents. This scale

measure the distance between any two points on the scale

It taps the differences and the magnitudes of the differences in

the variable----Example:


9

A Ratio Scale is a scale that possesses absolute rather than relative qualities and has an absolute zero point.

Examples: Money

Weight

Distance

Temperature on the Kelvin Scale

Interval scales allow comparisons of the differences of magnitude (e.g. of attitudes) as well as determinations of the actual strength of the magnitude


10

Type of ScaleNumerical

Operation

Descriptive

Statistics

Nominal Counting

Frequency in each

category, percentage in

each category, mode

Ordinal Rank OrderingMedian, range,

percentile ranking

Interval

Arithmetic Operations on

Intervals between

numbers

Mean, standard

deviation, variance

RatioArithmetic Operations on

actual quantities

Geometric mean,

coefficient of variation


Nominal Numbers

Assigned

to Runners

OrdinalRank Order of

Winners

Interval Performance

Rating on a

0 to 10 Scale

Ratio Time to

Finish, in

Seconds

7 38

Third

place

Second

place

First

place

Finish

Finish

8.2 9.1 9.6

15.2 14.1 13.4


Classification of Scaling Techniques;

Scales

Nominal Ordinal

Fixed sum

Graphic rating

Interval Ratio


Scales

Nominal Ordinal Interval

Likert

Semantic differential

Numerical

Itemized rating

Staple

Ratio


Scales

Nominal

Dichotomous

Category

Ordinal

Fixed sum

Graphic rating

Interval

Likert

Semantic differential

Numerical

Itemized rating

Staple

Ratio

Nominal scales focus on only requiring arespondent to provide some type ofdescriptor as the raw response

Example.

Please indicate your current martial status.

__Married __ Single __ Single, never married __ Widowed


Ordinal scales allow the respondent to express “relative magnitude” between the raw responses to a question

Example.

Which one statement best describes your opinion of an Intel PC processor?

__ Higher than AMD’s PC processor

__ About the same as AMD’s PC processor

__ Lower than AMD’s PC processor


Interval scales demonstrate the absolute

differences between each scale point

Example.

How likely are you to recommend the new phone to a friend?

Definitely will not Definitely will

1 2 3 4 5 6 7


Ratio scales allow for the identification of absolute differences between each scale point, and absolute comparisons between raw responses

Example 1.

Please circle the number of children under 18 years of age

currently living in your household.

0 1 2 3 4 5 6 7 (if more than 7, please specify ___.)


Chapter 7

MEASUREMENT:

SCALING, RELIABILITY,

VALIDITY

Rating scales

Have several response categories and are used to obtain responses with regard to the object, event, or person studied.

Ranking scales

Make comparisons between or among objects, events, persons and extract the preferred choices and ranking among them.

Methods of Scaling;

Measurement scales that allow a respondent to register the degree (or amount) of a characteristic or attribute possessed by an object directly on the scale.

Rating Scales;

1. Dichotomous scale

2. Category scale

3. Likert scale

4. Numerical scales

5. Semantic differential scale

6. Itemized rating scale

7. Constant sum scale

8. Stapel scale

9. Graphic scale

10. Consensus scale

Types of rating scales Formats:

Dichotomous scale

Is used to obtain a Yes or No answer.

Nominal scale

Do you own a car?

Yes

No

Rating Scales Formats;

Category scale

Uses multiple items to elicit a single response.

Nominal scale


A Category rating scale which the response options provided for a closed-ended question are labeled with specific verbal descriptions.

Example:

Please rate car model A on each of the following dimensions:

Poor Fair Good V. good Excellent

a) Durability [ ] [ ] [ ] [ ] [ ]

b) Fuel consumption [ ] [ ] [ ] [ ] [ ]


A simple category scale with only two response categories

(or scale points) both of which are labeled.

Example:

Please rate brand A on each of the following dimensions:

poor excellent

a) Durability [ ] [ ]

b) Fuel consumption [ ] [ ]


Likert scale

Is designed to examine how strongly subjects

agree or disagree with statements on a

5-point scale.

Interval scale


The Likert Scale (Summated Ratings Scale)

A multiple item rating scale in which the degree of an attribute

possessed by an object is determined by asking respondents to

agree or disagree with a series of positive and/or negative

statements describing the object.

Example:

Totally

disagree Disagree Neutral Agree

Totally

agree

a) Shopping takes much longer on the Internet [ ] [ ] [ ] [ ] [ ]

b) It is a good thing that Saudi consumers have

the opportunity to buy products through the [ ] [ ] [ ] [ ] [ ]

c) Buying products over the Internet is not a

sensible thing to do [ ] [ ] [ ] [ ] [ ]

Attitude toward buying from the Internet


Likert scaleMy work is very interesting

Strongly disagree

Disagree

Neither agree nor disagree

Agree

Strongly agree


Semantic differential scale

Several bipolar attributes are identified at the

extremes of the scale, and respondents are asked to

indicate their attitudes.

Interval scale


A Semantic Differential rating scale in which bipolar adjectives

are placed at both ends (or poles) of the scale, and response

options are expressed as “semantic” space.

Example:

Please rate car model A on each of the following dimensions:

Durable ---:-X-:---:---:---:---:--- Not durable

Low fuel consumption ---:---:---:---:---:-X-:--- High fuel consumption


Numerical scale

Similar to the semantic differential scale, with the difference

that numbers on a 5-point or 7-point scale are provided, with

bipolar adjectives at both ends.

Interval scale

Poor Excellent

Durability 1 2 3 4 5 6 7

Durable Not Durable

Durability 1 2 3 4 5 6 7


Itemized rating scale

A 5-point or 7-point scale with anchors, as needed, is

provided for each item and the respondent states the

appropriate number on the side of each item, or circles the

relevant number against each item.

Interval scale

I will be changing my job within the next 12 months

1 2 3 4 5

Very Unlikely Unlikely Neither Unlikely Likely Very Likely

Nor Likely


Fixed or constant sum scale The respondents are here asked to distribute a given number

of points across various items.

Ordinal scale


A Constant-Sum rating scale in which respondents divide a

constant sum among different attributes of an object (usually to

indicate the relative importance of each attribute).

Assumed to have ratio level properties.

Example: Divide 100 points among the following dimensions to

indicate their level of importance to you when you purchase a

car:

Durability

Fuel Consumption

Total 100


Stapel scale

This scale simultaneously measure both the direction and

intensity of the attitude toward the items under study.

A simplified version of the semantic differential scale in which

a single adjective or descriptive phrase is used instead of

bipolar adjectives.

Interval data

Model A

-3 -2 -1 Durable Car 1 2 3

-3 -2 -1 Good Fuel Conaumption 1 2 3


The Stapel scale is a unipolar rating scale with ten categories

numbered from -5 to +5, without a neutral point (zero). This scale

is usually presented vertically.

SEARS

+5 +5

+4 +4

+3 +3

+2 +2X

+1 +1

HIGH QUALITY POOR SERVICE

-1 -1

-2 -2

-3 -3

-4X -4

-5 -5

The data obtained by using a Stapel scale can be analyzed in the

same way as semantic differential data.


Graphic rating scale

A graphical representation helps the respondents to indicate

on this scale their answers to particular question by placing a

mark at the appropriate point on the line.

Rating scales in which respondents rate an object on a

graphic continuum, usually a straight line.

Modified versions are the ladder scale and happy face scale.

Ordinal scale


Graphic Rating Scales


Paired Comparison

Used when, among a small number of objects, respondents are

asked to choose between two objects at a time.

Example; Choose any combination

Package -A 512 kbps 8 GB Rs: 750

Package -B 1 Mbps 8 GB Rs: 850

Package -C 512 Kbps 12 GB Rs: 900

Package -D 1 Mbps 12 GB Rs: 1000


Paired Comparison







Package -C 1 Mbps 12 GB Rs: 1000


Paired Comparison







Package -D 1 Mbps 12 GB Rs: 1000


Ranking Scales Formats;

Forced Choice

Enable respondents to rank objects relative to one another,

among the alternatives provided.


Forced Choice


Comparative Scale Provides a benchmark or a point of reference to assess

attitudes toward the current object, event, or situation under

study.


Comparative Scale


Hard to attach a verbal

explanation to response

Visual impact, easy for

poor readers

Choose a visual picture 8. Graphic scale-picture

response

No standard answers Visual impact, unlimited

scale points

Choose a point on a

continuum

7. Graphic scale

Endpoints are

numerical, not verbal.

Easier to construct than

semantic differential

Choose point on scale

with 1 center adjective

6. Stapel scale

Difficult for respondents

with low education

levels

Scale approximates an

interval measure

Divide a construct sum

among response

alternatives

5. Constant sum scale

Bipolar adjectives must

be found, data may be

ordinal, not interval

Easy to construct, norms

exist for comparison, e.g.

profile analysis

Choose points between

bipolar adjectives on

relative dimensions

4. Semantic differential

and numerical scales

Hard to judge what a

single score means

Easiest scale to

construct

Evaluate statements on

a 5-point scale

3. Likert scale

Ambiguous items, few

categories, only gross

distinction.

Flexible, easy to respond Indicate a response

category

2.Category scale

Disadvantages Advantages Subject must:Rating Scale

Characteristics Different Types of Rating Scales

Goodness of Measures

Goodness of Measures;

Understanding Validity and Reliability

Situation 2 Situation 3Situation 1

Neither Reliable

nor Valid

Highly Reliable

nor Not Valid

Highly Reliable

and Valid

Illustrations of Possible Reliability and Validity Situations in

MeasurementFigure 8.1

Reliability (accuracy in

measurement)

Validity (are we

measuring the right thing?)

Goodness of data

Test-retest reliability

Parallel-form reliability

Interitem consistency reliability

Split-half reliability

Stability

Consistency

Face validity

Logical validity (content)

Congruent validity

(construct)

Convergent Discriminant

Criterion-related validity

Predictive Concurrent

Testing Goodness of Measures: Forms of Reliability and Validity.

Goodness of Measures It is important to make sure that the instrument that we develop to

measure a particular concept is indeed accurately measuring the

variable, and that in fact, we are actually measuring the concept

that we set out to measure.

This ensures that in operationally defining perceptual and

attitudinal variables, we have not overlooked some important

dimensions and elements or included some irrelevant ones.


Item Analysis Item analysis is done to see if the items in the instrument belong

there or not.

Each item is examined for its ability to discriminate between those

subjects whose total scores are high, and those will low scores.

In item analysis, the means between the high-score group and the

low-score group are tested to detect significant differences

through the t-values.

The items with a high t-value (test which is able to identify the

highly discriminating items in the instrument) are then included in

the instrument.


Reliability

The reliability of a measure indicates the extent to which it

is without bias (error free) and hence ensures consistent

measurement across time and across the various items in

the instrument.

In other words, the reliability of a measure is an indication

of the stability and consistency with which the instrument

measures the concept and helps to assess the “goodness”

of a measure.


Stability of Measures The ability of a measure to remain the same over time —despite

uncontrollable testing conditions or the state of the respondents

themselves—is indicative of its stability and low vulnerability to

changes in the situation.

This attests to its “goodness” because the concept is stably

measured, no matter when it is done. Two tests of stability are

test-retest reliability and parallel-form reliability.



measurement)

Validity (are we


Goodness of data





Stability

Consistency

Face validity


Congruent validity

(construct)





Test-Retest Reliability The reliability coefficient obtained with a repetition of the same

measure on a second occasion is called test-retest reliability.

That is, when a questionnaire is administered to a set of

respondents now, and again to the same respondents, says

several weeks to 6 months later, then the correlation between

the scores obtained at the two different times from one and the

same set of respondents is called the test-retest coefficient.

The higher it is, the better the test-retest reliability, and

consequently, the stability of the measure across time.


Parallel-Form Reliability

When responses on two comparable sets of measures tapping

the same construct are highly correlated, we have parallel-form

reliability.

Both forms have similar items and the same response format, the

only changes being the wordings and the order or sequence of

the questions.

What we try to establish here is the error variability resulting

from wording and ordering of the questions.

If two such comparable forms are highly correlated the measures

are reasonably reliable.


Inter item Consistency Reliability

This is a test of the consistency of respondents’ answers to all

the items in a measure.

To the degree that items are independent measures of the same

concept, they will be correlated with one another.

The most popular test of inter item consistency reliability is the

Cronbach’s coefficient alpha (Cronbach’s alpha; Cronbach,

1946), which is used for multipoint-scaled items, and the Kuder-

Richardson formulas (Kuder & Richardson, 1937), used for

dichotomous items.

The higher the coefficients, the better the measuring instrument.


Split-Half Reliability

Split-half reliability reflects the correlations between two halves

of an instrument.

The estimates would vary depending on how the items in the

measure are split into two halves.

Split-half reliabilities could be higher than Cronbach’s alpha only

in the circumstance of there being more than one underlying

response dimension tapped by the measure and when certain

other conditions are met as well.

Hence, in almost all cases, Cronbach’s alpha can be considered

a perfectly adequate index of the interitem consistency reliability.


Understanding Validity and Reliability

5. ValiditySeveral types of validity tests are used to test the goodness of measures and

writers use different terms to denote them. For the sake of clarity, we may

group validity tests under three broad headings: content validity,

criterion-related validity, and construct validity.

5.1 Content ValidityContent validity ensures that the measure includes an adequate and

representative set of items that tap the concept. The more the scale items

represent the domain or universe of the concept being measured, the greater

the content validity. To put it differently, content validity is a function of how

well the dimensions and elements of a concept have been delineated.

Face validity is considered by some as a basic and a very minimum index of

content validity. Face validity indicates that the items that are intended to

measure a concept, do on the face of it look like they measure the concept.


Criterion-Related Validity

Criterion-related validity is established when the measure differentiates

individuals on a criterion it is expected to predict. This can be done by

establishing con-current validity or predictive validity, as explained below.

Concurrent validity is established when the scale discriminates individuals

who are known to be different; that is, they should score differently on the

instrument as in the example that follows.


5.3 Construct ValidityConstruct validity testifies to how well the results obtained from the use of the

measure fit the theories around which the test is designed. This is assessed

through convergent and discriminant validity, which are explained below.

Convergent validity is established when the scores obtained with two different

instruments measuring the same concept are highly correlated.

Discriminant Validity is established when, based on theory, two variables are

predicted to be uncorrelated, and the scores obtained by measuring them are

indeed empirically found to be so.


Thanks

Chapter 9:

Measurement: Scaling, Reliability, Validity

Table 9.1 Types of Validity

Validity Description

Content validity Does the measure adequately measure the concept?

Face validity Do “experts” validate that the instrument measures what its

name suggests it measure?

Criterion-related validity Does the measure differentiate in a manner that helps to

predict a criterion variable?

Concurrent validity Does the measure differentiate in a manner that helps to

predict a criterion variable currently?

Predictive validity Does the measure differentiate individuals in a manner as to

help predict a future criterion?

Construct validity Does the instrument tap the concept as theorized?

Convergent validity Does the measure have low correlation with a variable

That is supposed to be unrelated to this variable?

Reliability Indicates the extent to which it is without bias (error

free) and hence ensures consistent measurement across time and across the various items in the instrument.

Goodness of Measures

Stability of measures:



Correlation

Internal consistency of measures:


Cronbach’s alpha


Correlation

Goodness of Measures-Reliability

Validity

Ensures the ability of a scale to measure the intended concept.

Content validity

Criterion related validity

Construct validity

Goodness of Measures-Validity

Content validity Ensures that the measure includes an adequate and

representative set of items that tap the concept.

A panel of judges


Criterion related validity

Is established when the measure differentiates individuals on a

criterion it is expected to predict

Concurrent validity: established when the scale differentiates

individuals who are known to be different

Predictive validity: indicates the ability of measuring

instrument to differentiate among individuals with reference

to future criterion

Correlation


Construct validity

Testifies to how well the results obtained from the use of the

measure fit the theories around which the test is designed.

Convergent validity: established when the scores obtained

with two different instrument measuring the same concept are

highly correlated

Discriminant validity: established when, based on theory, two

variables are predicted to be uncorrelated, and the scores

obtained by measuring them are indeed empirically found to

be so

Correlation, factor analysis, convergent-discriminant

techniques, multitrait-multimethod analysis


Situation 2 Situation 3Situation 1

Neither Reliable

nor Valid

Highly Reliable

nor Not Valid

Highly Reliable

and Valid

Illustrations of Possible Reliability and Validity Situations in

MeasurementFigure 8.1


measurement)

Validity (are we


Goodness of data





Stability

Consistency

Face validity


Congruent validity

(construct)




Diagram 9.1


research method for business chapter 7

Education