MEASUREMENT OFVARIABLES: OPERATIONAL

DEFINITION AND SCALES

THE RESEARCH DESIGNDETAILS OF STUDY MEASURMENT

Purpose of thestudy

ExplorationDescriptionHypothesis testing

Types ofInvestigation

Establishing:-Casual relationships-Correlations-Group differences,

Extent of researcherInterference

Minimum: Studying eventsas they normally occurModerate: Minimumamount of interferenceMaximum: High degreeof control and artificialsettings

Study setting

Contrived

Noncontrived

Measurementand measures

Operationaldefinition

items (measure)ScalingCategorizingCoding

Unit of analysis(Population to bestudied)

IndividualsDyadsGroupsOrganizationsMachinesetc.

Sampling design

Probability/nonprobability

SampleSize (n)

Time horizon

One-Shot(cross-sectional)

Multishot(longitudinal)

Data-Collectionmethod

ObservationInterview

Questionnaire

Physicalmeasurement

Unobtrusive

1. Feel fordata

2. Goodnessof data

3. Hypothesestesting

PRO

BLE

MST

ATEM

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DATAANALYSIS

OPERATIONAL DEFINITION

Reduction of abstract concepts to render themmeasurable in tangible way is called operationalizing theconcepts. It is done by looking at the behavioraldimensions, facts, or properties denoted by the concept.

EXAMPLE OF DIMENTIONS AND ELEMENTS The concept of thirst is abstract: we cannot see thirst. However, we would

expect a thirsty person to drink plenty of fluid. If several people say they

are thirsty, then we may determine the thirst level of each of these

individuals by the measure of the quantity of fluid that they drink to

quench their thirst. We will thus be able to measure their level of thirst,

even though the concept of thirst itself is abstract and nebulous (unclear).

In the above example the thirst is the concept, the drinking of plenty offluid is the dimension, and the measuring of the quantity of fluid that

they drink to quench their thirst is the element.

The nature of measurement

Measurement occurs when an established index verifies the height, weight,or other feature of a physical object.

To measure is to discover the extent, dimension, quantity, or capacity ofsomething, especially by comparison with a standard.

Measurement in research consists of assigning numbers to empirical events,objects or properties, or activities in compliance with a set of rules.

This definition implies that measurement is a three-part process

The nature of measurement

Select observable empirical events. Developing a set of mapping rules Applying the mapping rule(s) to each observation of that event.

Gender Attendees Styling Characteristics Attendees

Gender

AssignM if maleF if female

(M, F)

A B C D E Desirability of auto styling

Assign5 if very desirable4 if desirable3 if neither2 if undesirable1 if very undesirable

(1 through 5)

A B C D E

M F1 2 3 54

Sampleelements

EmpiricalObservations

Mapping Rules

Symbol

Methods of scale

A scale is a tool or mechanism by which individuals are distinguished asto how they differ from one another on the variable of interest to ourstudy.

There are four basic methods of scales: nominal, ordinal, interval, andratio. The degree of sophistication to which the scales are fine-tunedincreases progressively as we move from nominal to the ratio scale.

Nominal Scale

The lowest measurement level you can use, from a statistical point of view,is a nominal scale, as the name implies, is simple some placing of data intocategories, without any order or structure.

Example of a nominal scale SEX 1. Male 2. Female Area 2. Rural 2. Urban

Numbers assigned to represent the categories cannot meaningfully beadded, subtracted, or divided.

A mean or a median cannot be calculated for nominal data. A mode and a chi-square statistical test can be used.

Ordinal Scale Ordinal scale include the characteristics of the nominal scale plus an

indication of order. Ordinal data require conformity to a logical postulate,which states: If a is greater than b and b is greater than c, then a is greaterthan c.

The use of an ordinal scale implies a statement of greater than or lesser than(an equality statement is also acceptable) without stating how much greateror less. Example of a ordinal scale: Brand Preference for purchasing a Television

Onida : Samsung : LG : Sony : Sharpe :

Median and mode, rank order correlation statistical test can be used

INTERVAL SCALE An interval scale measure the distance between any two points on the

scale. This help us to compute the means and the standard deviations of the

responses on the variables.

In other words, the interval scale not only groups, it also measures themagnitude of the differences in the preferences among the individuals.

It is more powerful scale than the nominal and ordinal scale, and has forits measure of central tendency the arithmetic mean. Its measure of

dispersion are the range, the standard deviation, and the variance.

EXAMPLE OF INTERVAL SCALE

Indicate the extent to which you agree with the following statements asthey related to your job, by circling the appropriate number against each,using the scale given below.

StronglyDisagree

1

Disagree

2

Neither AgreeNor Disagree

3

Agree

4

StronglyAgree

5The following opportunities offered by the job are very important to me:

a. Interacting with others. 1 2 3 4 5b. Using a number of different skills. 1 2 3 4 5

c. Completing a task from beginning to end. 1 2 3 4 5d. Serving others. 1 2 3 4 5

e. Working independently. 1 2 3 4 5

RATIO SCALE The ratio scale overcomes the disadvantage of the arbitrary origin point of the

interval scale, in that it has an absolute zero point, which is a meaningful

measurement point. Thus the ratio scale not only measures the magnitude of the

differences between points on the scale but also tapes the propositions in the

differences. It is most powerful of the four scales because it has a unique zero

origin (not an arbitrary origin) and subsumes all the properties of the other three

scales.

The measurement of central tendency of the ratio scale could be either thearithmetic or the geometric mean and the measure of dispersion could be either

the standard deviation, or variance, or the coefficient of variation.

EXAMPLE OF RATIO SCALE1. How many other organization did you work for before joining this system? ____2. Indicate the number of children you have in each of the following categories:

____below three years of age

____between three to six years

____over six years but under twelve years

____twelve years and over.

3. How many retail outlets do you operate? ____.

The responses to the questions could range from 0 to any reasonable figure.

SCALE

Assigning numbers or symbols to elicit the attitudinal responses of subjectstoward object, event, or persons is called scale.

TYPES OF SCALES

There are two main categories of attitudinal scales.

1.Rating scale: Rating scales have several response categories and are used toelicit responses or behavioral concept with regard to the object, events,

or person studied.

2.Ranking scale: Ranking scales make comparison between or amongobjects, events, or persons and elicit the preferred choices and ranking

among them.

Rating Scale

Simple Attitude Scale Likert Scale Semantic Differential Scales Numerical/Multiple Rating List Scale STAPEL Scales Constant-Sum Scales Graphic Rating Scales

Simple Attitude Scale

The simple category scale (also called a dichotomous scales) offers twomutually exclusive response choice.

This response strategy is particularly useful for demographic questions orwhere a dichotomous response is adequate.

Examples of Simple Attitude Scale Do you plan to purchase a laptop in the next 3 Months

Yes No

What newspaper do you read most often for financial newsThe Economic times the financial express business standard Others

Check any of the sources you consulted when designing your new homeOnline Magazines Designer Architect others

The internet is superior to traditional libraries for comprehensive searchesStrongly Agree Agree Neutral Disagree Strongly Disagree

Likert Scale

The Likert scale, developed by Rensis Liker (pronounced Lick-ert), is themost frequently used variation of the summated scale. Summated rating

Scale consist of statements that express either a favourable or an

unfavourable attitude towards the object of interest.

Typically, each scale item will have 5 categories, with scale values rangingfrom -2 to 2 with 0 as neutral response.

Examples of Likert Scale

Strongly Agree Agree Neutral DisagreeStronglyDisagree

Quality of theFood

2 1 0 -1 -2

Cleanness ofthe Hostel

2 1 0 -1 -2

Amenitiesprovided by the

management5 4 3 2 1

Training timeintervals

1 2 3 4 5

Satisfactionwith thepresent

Appraisalsystem

5 4 3 2 1

Semantic Differential Scales

This type of scale makes extensive use of words rather than numbers.Respondents describe their feelings about the products or brands on scales

with semantic labels. When bipolar adjective are used at the end points of

the scales, these are termed Semantic Differential Scales.

Examples of Semantic Differential Scales

Extremely Quite Slightly Neither Slightly Quite Extremely

Good Bad

Important unimportant

High Low

Strong Week

Active Passive

Semantic Scales

Semantic DifferentialScales

Numerical/Multiple Rating List Scale

The Numerical/Multiple Rating List Scale is commonly used variation of thelinear, numeric scale but there is an important distinction. With the linear,

numeric scale the respondents has to pick a number from the scale and then

write the number beside the item.

Examples of Numerical/Multiple Rating ListScale

Extremelysafe

ExtremelyUnsafe

SavingsAccount

1 2 3 4 5 6 7

Loansaving

account1 2 3 4 5 6 7

Certificateof deposit

1 2 3 4 5 6 7

Corporatecommon

stocks1 2 3 4 5 6 7

Preciousmetals

1 2 3 4 5 6 7

STAPAL (BASIC) SCALE

This scale simultaneously measure both the direction and intensity of theattitude toward the item under study. The characteristic of interest to the

study is placed at the center and a numerical scale ranging, say from +3 to -3

or +5 to -5, on either side of the item. This gives the idea of how closer or

distant the individual response to the stimulus. Since this does not an

absolute zero point, this is an interval scale.

Example of STAPAL (BASIC) SCALE

FIXED OR CONSTANT SUM SCALE

The respondents are here asked to distribute a given number of pointsacross various items. This is an ordinal scale

Example of Fixed or Constant Sum Scale

GRAPHIC RATING SCALE

A graphical scale representation helps the respondents to indicate on thisscale their answers to a particular question by placing a mark at the

appropriate point in the line. This is an ordinal scale. The faces scale, which

shows faces ranging from smiling to sad is also a graphic scale, used to

obtain responses regarding peoples feelings.

EXAMPLE GRAPHIC RATING SCALE

On a scale of 1 to 10 how would you rate your supervisor?

1 5 10

Ranking Scale

In ranking scales, the participants directly compares two or more objectsand makes choices among the.

Ranking scales used in organization1. PAIRED COMPARISON.2. FORCED CHOICE.3. COMPARATIVE SCALE.

PAIRED COMPARISON

It is used when, among a small number of objects, respondents are asked tochoose between two objects at a time.

As the number of objects to be compared increases, so does the number ofpaired comparisons. The paired choices for n objects will be n (n-1) / 2.

Examples of Paired Comparison

FORCED CHOICE

It enables respondents to rank objects relative to one another, among thealternatives provided. This is easier for the respondents, practically if the

number of choices to be ranked is limited in number.

Example of Forced Ranking Scale

COMPARATIVE SCALE

It provides a benchmark or a point of reference to assess attitudes towardthe current object, event, or situation under study.

EXAMPLE OF COMPARATIVE SCALE

In a volatile (evaporation) financial environment, compared to stocks,how wise or useful is it to invest in Treasury bonds? Circle theappropriate response.More Useful About the

SameLess Useful

1 2 3 4 5

GOODNESS OF DATA 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 perceptual and attitudinal

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 carried out to see if the items in the instrument belong thereor not. Each item is examined for its ability to discriminate between thosesubjects whose total scores are high, and those with low scores.

Thereafter, test for the reliability of the instrument are carried out and thevalidity of the measure is established.

Reliability is a test of how consistently a measuring instrument measureswhatever concept it is measuring.

Validity is a test of how well an instrument that is developed measures theparticular concept it is intended to measure. In other words, validity isconcerned with whether we measure the right concept, and reliability withstability and consistency of measurement.

TESTING GOODNESS OF MEASURES

Goodnessof data

Reliability(accuracy

InMeasure-

ment)

Validity(we are

MeasuringThe right

Thing)

Stability

Consistency

Test-retest reliability

Interitem consistency reliability

Parallel-form reliability

Split-half reliability

Logical validity(content)

Criterion-relatedvalidity

Congruent validity(construct)

Face validity ConvergentPredictive Concurrent Discriminant

Types of validity tests

1. Content validity.2. Criterion-related validity.3. Construct validity

CONTENT VALIDITY It ensures that the measure includes an adequate and representative set

of items that tap the concept. The more the scale items represent thedomain (circle of affection) or universe of the concept being measured, thegreater the content validity.

Face validity considered by some as a basic and a very minimum index of contentvalidity. Face validity indicates that the items that are intended to measure a concept,so on the face of it look like they measure the concept.

the simplest and least scientific definition of validity it is demonstrated when a measure superficially appears to measure what it claims

to measure

Based on subjective judgment and difficult to quantify e.g. intelligence and reasoning questions on the IQ test Problem - participants can use the face validity to change their answers

CRITERION-RELATED VALIDITY

It is established when the measure differentiates individuals on a criterion itis expected to predict. This can be done by establishing concurrent ( withconsensus) validity or predictive validity

CONCURRENT VALIDITY: It is established when the scale discriminates individualswho are known to the different; that is they should score differently on the instrument

e.g. new IQ test correlates with an older IQ test PREDICTIVE VALIDITY: It indicates the ability of the measuring instrument to

differentiate among individuals with reference to a future criterion.

e.g. high scores on need for achievement test predict competitive behavior in children(ring toss game)

CONSTRUCT VALIDITY

It testified to how well the results obtained from the use of the measure fitthe theories around which the test is designed. This is assessed throughconvergent and discriminant validity.

CONVERGENT VALIDITY: It is established when the scores obtained with twodifferent instrument measuring the same concept are highly correlated

e.g. an experimenter observing aggressive behavior in children correlated withteachers ratings of their behavior

DISCRIMINANT VALIDITY: It is established when, based on the theory, two variablesare predicted to be correlated, and the scores obtained by measuring them are indeedempirically found to be so.

e.g. aggressive behavior and general activity level in children

Convergent validity, divergent validity and constructvalidity

By demonstrating strong convergent validity for two different constructs and then showingdivergent validity between the two constructs, you obtain strong construct validity of the twoconstructs

Aggressivebehavior

Teachers ratings Experimentersobservation

Active behavior

Teachers ratings Experimentersobservation

High convergentvalidity

Related scoresHigh Divergent Vali

dityUnrelated

scores

High convergentvalidity

Related scores

High Divergent Vali

dityUnrelated

scores

TYPES OF VALIDITYValidity Description Method

Content Validity Does the measure adequately measure the concept?JudgmentalPanel evaluation with content validity ratio

Face Validity Do experts validate that the instrument measureswhat its name suggests it measures?

Criterion-related Validity Does the measure differentiate in a manner thathelps to predict a criterion variable. Correlation

Concurrent Validity Does the measure differentiate in a manner thathelps to predict a criterion variable currently? Correlation

Predictive Validity Does the measure differentiate individuals in asmanner as to help predict a future criterion? Correlation

Construct Validity Does the instrument tap the concept as theorized?JudgmentalCorrelation of proposed test with established oneConvergent discriminant techniquesFactor analysisMultitrait multimethods analysis

Convergent Validity Do two instruments measuring the concept correlatehighly?

Discriminant ValidityDoes the measure have a low correlation with avariable that is supposed to be unrelated to thisvariable?

RELIBILITY

The reliability of a measure indicate 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

measures is an indication of the stability and consistency with which the

instrument measures the concept and helps to assess the goodness of a

measure.

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 containing some items that are supposed to measure a

concept is administered to a set of respondents now, and again to the same

respondents, say several weeks to 6 months later, the correlation between

the scores obtained at the two different times from one and the same set of

respondents is called the test-retest reliability.

PARALLEL-FORM RELIABILITY

When responses on two comparable sets of measure tapping the sameconstruct 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.

INTERNAL CONSISTENCY OF MEASUREThe internal consistency of measures is indicative of the homogeneity of the

items in the measure that tap the construct. In other words, the items

should hang together as a set and be capable of independently

measuring the same concept so that the respondents attach the same

overall meaning to each of the items.

Consistency can be examined through :

1.Inter-item Consistency Reliability.2.Split- Half Reliability.

INTER-ITEM CONSISTENCY RELIABILITY

This is a test of the consistency of respondents answer to all the items in ameasure. To the degree that items are independent measures of the same

concept, they will be correlated with one another.

SPLIT-HALF RELIABILITY

Split-half reliability reflects the correlations between two halves of aninstrument. The estimates would vary depending on how the items in the

measure are split into two halves.

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