Tests of Sound Measurement

While evaluating a measurement tool, three major considerations must be taken into account: validity, reliability and practicality. A sound measurement should fulfill all of these tests.

Test of Validity

It is the most important criterion. It indicates the degree to which an instrument measures what it is supposed to measure. There are three types of validity: Content validity, Criterion-related validity, and Construct validity.

Content validity refers to the extent to which a measuring instrument adequately covers the topic under study. Its determination is mainly judgmental and intuitive. It cannot be expressed in numerical terms. It can also be determined by a panel of persons who judge the extent of the measuring instruments standards.

Criterion-related validity refers to our ability to predict or estimate the existence of a current condition. It reflects the success of measures used for empirical estimating purposes. Criterion-related validity is expressed as the coefficient of correlation between the test scores. Here, the concerned criterion must possess the following characteristics:

  • Relevance: When a criterion is defined in terms judged to be the proper measures, it is known to be relevant.
  • Unbiased: When the criterion provides each subject an equal opportunity to score, it is unbiased.
  • Reliability: When a criterion is stable or reproducible, it is considered as reliable.
  • Availability: The information specified by the criterion should be easily available.

Construct validity is most complex and abstract. It is the extent up to which the scores can be accounted for by the explanatory constructs of a sound theory. Its determination requires association of a set of other propositions with the results received from using the measurement instrument. If the measurements correlate with the other propositions as per our predictions, it can be concluded that there is some degree of construct validity.

If the above criteria are met, we may conclude that our measuring instrument is valid and provides correct measurement; if not, we may have to look for more information and/or depend on judgment.


Sources of Error in Measurement

Measurement should be precise and unambiguous in an ideal research study. However, this objective is often not met with in entirety. As such, the researcher must be aware about the sources of error in measurement. Following are listed the possible sources of error in measurement.

a) Respondent: At times the respondent may be reluctant to express strong negative feelings or it is just possible that he may have very little knowledge, but may not admit his ignorance. All this reluctance is likely to result in an interview of ‘guesses.’ Transient factors like fatigue, boredom, anxiety, etc. may limit the ability of the respondent to respond accurately and fully.

b) Situation: Situational factors may also come in the way of correct measurement. Any condition which places a strain on interview can have serious effects on the interviewer-respondent rapport. E.g., if someone else is present, he can distort responses by joining in or merely by being present. If the respondent feels that anonymity is not assured, he may be reluctant to express certain feelings.

c) Measurer: The interviewer can distort responses by rewording or reordering questions. His behavior, style and looks may encourage or discourage certain replies from respondents. Careless mechanical processing may distort the findings. Errors may also creep in because of incorrect coding, faulty tabulation and/or statistical calculations, particularly in the data-analysis stage.

d) Instrument: Error may arise because of the defective measuring instrument. The use of complex words, beyond the comprehension of the respondent, ambiguous meanings, poor printing, inadequate space for replies, response choice omissions, etc. are a few things that make the measuring instrument defective and may result in measurement errors.

Hence, researcher must know that correct measurement depends on successfully meeting all of the issues mentioned above. He must, as far as possible, try to eliminate, neutralize or otherwise deal with all the possible sources of error so that the final results may not be contaminated.


Measurement Scales

The most commonly used  measurement scales are: (i) Nominal scale; (ii) Ordinal scale; (iii) Interval scale; and (iv) Ratio scale.

(i) Nominal scale: In this scale, symbols, events or attributes are numbered in order to identify them. The number order is symbolic and not quantitative  it is just convenient labels. Nominal scales are convenient ways to track people, objects and events. Although the nominal scale is the least powerful measurement level, yet it is very useful and is used in routinely in surveys and ex-post-facto researches for classification of major sub-groups of the population.

(ii) Ordinal scale: The ordinal scale measures degrees of separation between an event, object or emotion rather than quantitative measurement. The scale measures qualitative phenomena, and rank from highest to lowest. Ordinal measures have absolute values, and the real differences between adjacent ranks may not be equal. The usage of an ordinal scale implies ‘greater than’ or ‘less than’ without our being able to state how much greater or less. Measures of statistical significance are restricted to non-parametric methods.

(iii) Interval scale: In interval scale, the intervals in the scale are not fixed by zero but are adjusted as assumptions. Interval scales have an arbitrary zero. The Fahrenheit scale and time can be examples of an interval scale.

(iv) Ratio scale: Ratio scales are those scales of measurement which have an absolute or true zero of measurement. The various examples of ratio scale are Mass, length, duration,energy etc. A ratio scale has equal distances and a true zero.


Measurement in Research

The core of any research is measurement. It can be defined as the method of assigning numbers to things. It is essential in research as everything has to be reduced to numbers.

Assigning numbers to properties of things is easy. However, it is quite difficult in other cases. Measuring social conformity or intelligence is much complex than measuring weight, age or financial assets, which can be directly measured directly with some standard unit of measurement. Measurement tools of abstract/qualitative concepts are not standardized, and the results are not very accurate.

A clear understanding of the level of measurement of variables is important in research because it is the level, which determines what type of statistical analysis has to be conducted. The collected data can be classified into distinct categories. If there are limited categories, then they are known as discrete variables. If there are unlimited categories, they are known as continuous variables. The nominal level of measurement describes these categorical variables. Nominal variables include demographic properties like sex, race, religion, etc. This is considered as the most basic level of measurement. No ranking or hierarchy is present in this level.

The variables that can be sequenced in some order of importance can be described by the ordinal level. Opinions and attitude scales or indexes in the social sciences are ordinal in nature. Ex.: Upper, middle, and lower class. In this case, the order is known; however, the interval between the values is not meaningful.

Variables that have more or less equal intervals are described by the interval level of measurement. Crime rates come under this measurement level. Temperature is also an interval variable. Here, the interval between variables can be interpreted; but, ratios are not meaningful.

Ratio level describes variables that have equal intervals and a reference point. Measurement of physical dimension such as weight, height, distance, etc. falls under this level.