MEASUREMENT

Chitrasen

Introduction

Measurement is ubiquitous in management and an indispensable concept in research. We will discuss the concept in the perspective of Business Research.

Problem – Defining what is to be measured, and how to measure it accurately and reliably.

In business research the things (or concepts) which are inherently abstract in their nature (e.g. job satisfaction, employee morale, brand loyalty of consumers) pose some degrees of difficulty for measurement while concepts which can be assigned numerical values (e.g. sales volume) are more or less easier.

Definitions

Some formal definitions of measurement as proposed by experts are reproduced below for giving the discussion an academic tone. Measurement is defined as:-

“The assignment of numerals to objects or events according to rules”-Steven

“The assignment of numbers to represent properties”- Campbell

“The assignment of numbers to objects to represent amounts or degrees of a property possessed by all of the objects- Torgerson.

Concept of measurement

In research it is necessary to distinguish between “objects” and “properties”. Most often their properties and not the objects themselves are the concerns for measurement. While physical properties may be directly observed, psychological properties are inferred.

In order for a concept to have the quality of being measurable, it must first be made operational. An operational definition gives meaning to the concept by specifying the activities or operations which are necessary and in turn these can be measured. For example – a satisfied consumer will make at least five purchases of Product A from Shop T over a three-month period of time is the operational definition of consumer satisfaction.

However, depending on the context of the research study, it may be difficult to make operational definitions with ease.

Scale

A scale is basically a concept, device, or procedure used in arranging, measuring, or quantifying events, objects, or phenomenon in any sequence. Scales may be broadly classified as:

(1) Category scale: Sequence of numbers or words which only serve to identify certain entities or observations and have no quantitative significance; for example, a numbered or named list, or numbers on the uniforms of the members of a team. Also called nominal scale or qualitative scale.

(2) Interval scale: Sequence of numbers in a fixed order representing amount, interval, or numeric values; for example, a distance or temperature scale. Also called quantitative scale.

(3) Sequence scale: Ranked entities or items with a uniform spacing but no quantitative significance; for example, hardness scale (Moh's scale), pH Scale, or Richter scale. Also called ordinal scale.

(4) Ratio scale: When a scale consists not only of equidistant points but also has a meaningful zero point, then we refer to it as a ratio scale. If we ask respondents their ages, the difference between any two years would always be the same, and ‘zero’ signifies the absence of age or birth. Hence, a 100-year old person is indeed twice as old as a 50-year old one.

Levels of measurement

Based on the characteristics of order, distance and origin of the scale used measurement may be classified in to four different levels as under:

(1) Nominal measurement

(2) Ordinal measurement

(3) Interval measurement

(4) Ratio measurement

Nominal measurement

A nominal measurement is the simplest of the four and in which the numbers or letters assigned to objects serve as labels for identification or classification.

Example:

Males = 1, Females = 2

Sales Zone A = Islamabad, Sales Zone B = Rawalpindi

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

Ordinal measurement

An ordinal measurement 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?

Interval measurement

An interval level of measurement is one that not only arranges objects or alternatives according to their respective magnitudes, but also distinguishes this ordered arrangement in units of equal intervals. Interval scales indicate order (as in ordinal scales) and also the distance in the order.

Examples:

Consumer Price Index

Temperature Scale in Fahrenheit

Interval scales allow comparisons of the differences of magnitude (e.g. of attitudes) but do not allow determinations of the actual strength of the magnitude. The criteria lacking is the origin or zero point.

Ratio measurement

A ratio level measurement is one that possesses absolute rather than relative qualities and has an absolute zero.

Examples:

Money

Weight

Distance

Temperature on the Kelvin Scale

Ratio scales are the most sophisticated of scales, since it incorporates all the characteristics of nominal (definition of nominal scale), ordinal (definition of ordinal scale) and interval scales (definition of interval scale). As a result, a large number of descriptive calculations are applicable.

Characteristics of measurement levels

Level of measurement

Characteristics Descriptive Statistics

Nominal No, Order, Distance or Origin

Frequency in each category, percentage in each category, mode

Ordinal Order but not distance or origin

Median, range, percentile ranking

Interval Both order and distance but no origin

Mean, standard deviation, variance

Ratio Order, Distance and Origin

Geometric mean, coefficient of variation

Characteristics of good measurement

Uni-dimensionality: Measurement should not measure more than one characteristic at a time. Example: scale should not measure length and temperature at the same time.

Linearity: A good measurement should follow the straight line model.

Validity: The measurement scale should measure what it is supposed to measure.

Reliability: This refers to consistency. The measurement should give consistent result.

Accuracy and precision: It should give an accurate and precise measure of what is being measured.

Simplicity : Measurement tool should not be very complicated or over elaborate.

Practicability: The tool should be easy to understand administer.

Validity

Validity is an important criteria for a measurement and required to be established in research. A scale may be considered valid if it effectively measures a specific property or characteristic that it intends to measure. Validity is not a problem while measuring physical characteristics like length, weight and height etc. but for abstract characteristics such as attitude and motivation which are measured indirectly. In such measurement evidence of validity are required.

Validity is classified in to following three types and degree of validity of each type can be determined using logic, statistical technique or both.

Content validity

Predictive validity

Construct validity

Content Validity

Content validity is also of two types- (1) Face validity (2) Sampling validity.

Face validity is determined through a subjective evaluation of a measuring scale.

Example: a researcher may develop a scale to measure consumer attitude towards a brand and pre test the scale among a few experts. If the experts are satisfied with the scale the researcher may conclude face validity of the scale.

Sample validity refers to how representative the content of measuring instrument is? Measuring instrument’s content should be representative of content universe of the characteristic being measured.

Example: if attitude is the characteristic being measured, its content universe may comprise statements and questions indicating all aspects of attitude and the sampling validity can be determined by comparing these with the content of the measuring instrument.

Criterion related Validity

Criterion-related validity usually includes any validity strategies that focus on the correlation of the test being validated with some well-respected outside measure(s) of the same objectives or specifications. Example: if a group of testers were trying to develop a test for business English to be administered primarily in Japan and Korea, they might decide to administer their new test and the TOEIC® (Test of English for International Communication )to a fairly large group of students and then calculate the degree of correlation between the two tests. If the correlation coefficient between the new test and the TOEIC turned out to be high, that would indicate that the new test was arranging the students along a continuum of proficiency levels very much like the TOEIC does. The result could be used to support the validity of the new test.

Criterion-related validity of this sort is sometimes called concurrent validity (because both tests are administered at about the same time). Another version of criterion-related validity is called predictive validity. Predictive validity is the degree of correlation between the scores on a test and some other measure that the test is designed to predict. Example: a number of studies have been conducted to examine the degree of relationship between students' Graduate Record Examination® (GRE) scores and their grade point averages (GPA) after two years of graduate study. The correlation between these two variables represents the degree to which the GRE predicts academic achievement as measured by two years of GPA in graduate school.

Construct Validity

A construct, or psychological construct is an attribute, proficiency, ability, or skill of the human brain and is defined by established theories. For example, "overall English language proficiency" is a construct. It exists in theory and has been observed to exist in practice.

Construct validity has traditionally been defined as the experimental demonstration that a test is measuring the construct it claims to be measuring.

Example: in a differential-groups study the performances on the test are compared for two groups (1) one that has the construct and (2)that does not have the construct. If the group with the construct performs better than the group without the construct it may provide evidence of the construct validity of the test.

Alternatively in an intervention study, wherein a group that is weak in the construct is measured using the test, then taught the construct, and measured again. If a significant difference is found between the pretest and posttest, it can support the construct validity of the test.

The concept of construct validity is very well accepted. A contemporary view is that all three types of validity (content, criterion-related, and construct validity) are different facets of a single unified form of construct validity.

Reliability

Reliability refers to the ability of the scale to provide consistent and accurate results. The two dimension of reliability are stability and equivalence(non-variability). Stability refers to consistency of results with repeated measurements. Example: an weighing machine may be said to be reliable if the same reading is given every time the same object is weighed. Equivalence refers to consistency at a given point of time among different investigators and samples of items.

Reliability can be improved in three ways:-

(1) By reducing the external source of variation

(2) By making the instrument more consistent internally

(3) By adding more number of item to the measuring instrument.

Standardizing the conditions under which measurement takes place, employing trained investigators, providing standard instructions, analyzing the items in the instrument for consistency and adding more number of items will increase the probability of accurate measurement.

Reliability vs validity

Reliability and validity are closely interlinked.

A measurement instrument that is valid is always reliable but the reverse may not true. That is, an instrument that is reliable is not always valid.

However an instrument that is not valid may or may not be reliable but an instrument that is not reliable is never valid.

Complex measurement

When a concept is simple, it can be measured easily usually with one question or observation.

Example: To what extent do consumers of Product X like the product’s packaging material? (very much,

somewhat, not at all).

However, when the concept to be measured is complex and abstract, two or more questions or

observations may be required in order to get accurate data. Example: The level of a salesperson’s

motivation depends on (1) job satisfaction (2) workplace environment (3) family life. Its measurement will

require multiple questions or observations. Indexes (or composite measures) are meant to deal with the

issue of multi-dimensionality (e.g. an index of social class may have the variables residence, occupation

and education).

Measurement techniques

Common techniques used in business research include rating, ranking, sorting and the choice

technique.

Rating is frequently employed in business research and many scales have been developed

for this purpose like: Simple Attitude Scales, Category Scales, Likert Scale, Semantic

Differential, Numerical Scales, Constant-Sum Scale, Stapel Scale and Graphic Scales. Brief account

on important few are as under:-

In attitude scaling, individuals are typically asked whether they agree or disagree with a question (or questions). Simple

attitude scales have the properties of a nominal scale and they do not permit fine distinctions in the respondents’ answers.

A category scale consists of several response categories to provide the respondent with alternative ratings. Category

scales are more sensitive than rating scales.

A likert Scale is a measure of attitudes designed to allow respondents to indicate how strongly they agree or disagree

with carefully constructed statements that range from very positive to very negative towards an object or subject. A Likert Scale may

include a number of question items, each covering some aspect of the respondent’s attitude, and these items collectively form an

index.

The semantic differential is an attitude measuring technique that which consists of a series of seven bi-polar rating scales

which allow response to a concept (e.g. organization, product, service, job). An advantage of the semantic differential is its versatility,

on the other hand, it uses extremes which may influence respondents’ answers.

The Behavioural Differential: This is an instrument for measuring the behavioural intentions of subjects towards an

object or category of objects.

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Conclusion

Measurement is an important concept in research and perhaps the most difficult part. It facilitates verification of hypotheses, helps to quantify variables, makes data suitable for statitical analysis and enables comparision of objects in terms of specific characteristics. What can be measured can be solved- a saying on problem solving shows the importance of the concept in management as a whole.

The classical concept of quantity can be traced back to John Wallis and Isaac Newton, and was foreshadowed in Euclid's Elements. However among the representational theories contribution of psychologist Stanley Smith Stevens ‘s ‘The theory of scale of measurement’ is note worthy from perspective of research in social sciences and business. However concept is still developing and more robust scales and analysis techniques are being added with time.