Now that we have seen how to operationally define variables, it is important to make sure that the instrument that we develop to measure a particular concept is indeed accurately measuring the variable, and 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. The scales developed could often be imperfect and errors are prone to occur in the measurement of attitudinal variables. The use of better instruments will ensure more accuracy in results, which in turn, will enhance the scientific quality of the research. Hence, in some way, we need to assess the “goodness” of the measure developed.

research methods business mathematics statistics  CRITERIA FOR GOOD MEASUREMENT

What should be the characteristics of a good measurement? An intuitive answer to this question is that the tool should be an accurate indicator of what we are interested in measuring. In addition, it should be easy and efficient to use. There are three major criteria for evaluating a measurement tool: validity, reliability, and sensitivity.


Validity is the ability of an instrument (for example measuring an attitude) to measure what it is supposed to measure. That is, when we ask a set of questions (i.e. develop a measuring instrument) with the hope that we are tapping the concept, how can we be reasonably certain that we are indeed measuring the concept we set out to do and not something else? There is no quick answer.

Researchers have attempted to assess validity in different ways, including asking questions such as “Is there consensus among my colleagues that my attitude scale measures what it is supposed to measure?” and “Does my measure correlate with others’ measures of the ‘same’ concept?” and “Does the behavior expected from my measure predict the actual observed behavior?” Researchers expect the answers to provide some evidence of a measure’s validity.

What is relevant depends on the nature of the research problem and the researcher’s judgment. One way to approach this question is to organize the answer according to measure-relevant types of validity. One widely accepted classification consists of three major types of validity: (1) content validity, (2) criterion-related validity, and (3) construct validity.

(1) Content Validity

The content validity of a measuring instrument (the composite of measurement scales) is the extent to which it provides adequate coverage of the investigative questions guiding the study. If the instrument contains a representative sample of the universe of subject matter of interest, then the content validity is good. To evaluate the content validity of an instrument, one must first agree on what dimensions and elements constitute adequate coverage. To put it differently, content validity is a function of how well the dimensions and elements of a concept have been delineated. Look at the concept of feminism which implies a person’s commitment to a set of beliefs creating full equality between men and women in areas of the arts, intellectual pursuits, family, work, politics, and authority relations. Does this definition provide adequate coverage of the different dimensions of the concept? Then we have the following two questions to measure feminism:

1. Should men and women get equal pay for equal work?

2. Should men and women share household tasks? These two questions do not provide coverage to all the dimensions delineated earlier. It definitely falls short of adequate content validity for measuring feminism.

A panel of persons to judge how well the instrument meets the standard can attest to the content validity of the instrument. A panel independently assesses the test items for a performance test. It judges each item to be essential, useful but not essential, or not necessary in assessing performance of a relevant behavior.

Face validity is considered as a basic and 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. For example a few people would accept a measure of college student math ability using a question that asked students: 2 + 2 = ? This is not a valid measure of college-level math ability on the face of it. Nevertheless, it is a subjective agreement among professionals that a scale logically appears to reflect accurately what it is supposed to measure. When it appears evident to experts that the measure provides adequate coverage of the concept, a measure has face validity.

(2) Criterion-Related Validity

Criterion validity uses some standard or criterion to indicate a construct accurately. The validity of an indicator is verified by comparing it with another measure of the same construct in which research has confidence. There are two subtypes of this kind of validity.

Concurrent validity: To have concurrent validity, an indicator must be associated with a preexisting indicator that is judged to be valid. For example we create a new test to measure intelligence. For it to be concurrently valid, it should be highly associated with existing IQ tests (assuming the same definition of intelligence is used). It means that most people who score high on the old measure should also score high on the new one, and vice versa. The two measures may not be perfectly associated, but if they measure the same or a similar construct, it is logical for them to yield similar results.

Predictive validity:

Criterion validity whereby an indicator predicts future events that are logically related to a construct is called a predictive validity. It cannot be used for all measures. The measure and the action predicted must be distinct from but indicate the same construct. Predictive measurement validity should not be confused with prediction in hypothesis testing, where one variable predicts a different variable in future. Look at the scholastic assessment tests being given to candidates seeking admission in different subjects. These are supposed to measure the scholastic aptitude of the candidates – the ability to perform in institution as well as in the subject. If this test has high predictive validity, then candidates who get high test score will subsequently do well in their subjects. If students with high scores perform the same as students with average or low score, then the test has low predictive validity.

(3) Construct Validity

Construct validity is for measures with multiple indicators. It addresses the question: If the measure is valid, do the various indicators operate in consistent manner? It requires a definition with clearly specified conceptual boundaries. In order to evaluate construct validity, we consider both theory and the measuring instrument being used. This is assessed through convergent validity and discriminant validity.

Convergent Validity: This kind of validity applies when multiple indicators converge or are associated with one another. Convergent validity means that multiple measures of the same construct hang together or operate in similar ways. For example, we construct “education” by asking people how much education they have completed, looking at their institutional records, and asking people to complete a test of school level knowledge. If the measures do not converge (i.e. people who claim to have college degree but have no record of attending college, or those with college degree perform no better than high school dropouts on the test), then our test has weak convergent validity and we should not combine all three indicators into one measure.

Discriminant Validity: Also called divergent validity, discriminant validity is the opposite of convergent validity. It means that the indicators of one construct hang together or converge, but also diverge or are negatively associated with opposing constructs. It says that if two constructs A and B are very different, then measures of A and B should not be associated. For example, we have 10 items that measure political conservatism. People answer all 10 in similar ways. But we have also put 5 questions in the same questionnaire that measure political liberalism. Our measure of conservatism has discriminant validity if the 10 conservatism items hang together and are negatively associated with 5 liberalism ones.


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 measure.

Stability of Measures

The ability of the 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.

(1) Test-retest Reliability: Test-retest method of determining reliability involves administering the same scale to the same respondents at two separate times to test for stability. If the measure is stable over time, the test, administered under the same conditions each time, should obtain similar results. For example, suppose a researcher measures job satisfaction and finds that 64 percent of the population is satisfied with their jobs. If the study is repeated a few weeks later under similar conditions, and the researcher again finds that 64 percent of the population is satisfied with their jobs, it appears that the measure has repeatability. The high stability correlation or consistency between the two measures at time 1 and at time 2 indicates high degree of reliability. This was at the aggregate level; the same exercise can be applied at the individual level. When the measuring instrument produces unpredictable results from one testing to the next, the results are said to be unreliable because of error in measurement.

There are two problems with measures of test-retest reliability that are common to all longitudinal studies. Firstly, the first measure may sensitize the respondents to their participation in a research project and subsequently influence the results of the second measure. Further if the time between the measures is long, there may be attitude change or other maturation of the subjects. Thus it is possible for a reliable measure to indicate low or moderate correlation between the first and the second administration, but this low correlation may be due an attitude change over time rather than to lack of reliability.

(2) Parallel-Form Reliability: When responses on two comparable sets of measures tapping the same construct are highly correlated, we have parallel-form reliability. It is also called equivalent-form reliability. Both forms have similar items and same response format, the only changes being the wording 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, we may be fairly certain that the measures are reasonably reliable, with minimal error variance caused by wording, ordering, or other factors.

Internal Consistency of Measures

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. This can be seen by examining if the items and the subsets of items in the measuring instrument are highly correlated. Consistency can be examined through the inter-item consistency reliability and split-half reliability.

(1) Inter-item Consistency reliability: This is a test of 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.

(2) Split-Half reliability: Split half reliability reflects the correlations between two halves of an instrument. The estimates could vary depending on how the items in the measure are split into two halves. The technique of splitting halves is the most basic method for checking internal consistency when measures contain a large number of items. In the split-half method the researcher may take the results obtained from one half of the scale items (e.g. odd-numbered items) and check them against the results from the other half of the items (e.g. even numbered items). The high correlation tells us there is similarity (or homogeneity) among its items.

It is important to note that reliability is a necessary but not sufficient condition of the test of goodness of a measure. For example, one could reliably measure a concept establishing high stability and consistency, but it may not be the concept that one had set out to measure. Validity ensures the ability of a scale to measure the intended concept.


The sensitivity of a scale is an important measurement concept, particularly when changes in attitudes or other hypothetical constructs are under investigation. Sensitivity refers to an instrument’s ability to accurately measure variability in stimuli or responses. A dichotomous response category, such as “agree or disagree,” does not allow the recording of subtle attitude changes. A more sensitive measure, with numerous items on the scale, may be needed. For example adding “strongly agree,” “mildly agree,” “neither agree nor disagree,” “mildly disagree,” and “strongly disagree” as categories increases a scale’s sensitivity.

The sensitivity of a scale based on a single question or single item can also be increased by adding additional questions or items. In other words, because index measures allow for greater range of possible scores, they are more sensitive than single item.

Practicality: The scientific requirements of a project call for the measurement process to be reliable and valid, while the operational requirements call for it to be practical. Practicality has been defined as economy, convenience, and interpretability.

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