While people measure things casually in daily life, research measurement is more precise and controlled. In measurement, one settles for measuring properties of the objects rather than the objects themselves. An event is measured in terms of its duration. What happened during it, who was involved, where it occurred, and so forth, are all properties of the event. To be more precise, what are measured are indicants of the properties. Thus, for duration, one measures the number of hours and minutes recorded. For what happened, one uses some system to classify types of activities that occurred. Measurement typically uses some sort of scale to classify or quantify the data collected.
There are four scale types. In increasing order of power, they are nominal, ordinal, interval, and ratio. Nominal scales classify without indicating order, distance, or unique origin. Ordinal data show magnitude relationships of more than and less than but have no distance or unique origin. Interval scales have both order and distance but no unique origin. Ratio scales possess classification, order, distance, and unique origin.
Instruments may yield incorrect readings of an indicant for many reasons. These may be classified according to error sources: (a) the respondent or participant, (b) situational factors, (c) the measurer, and (d) the instrument.
Sound measurement must meet the tests of validity, reliability, and practicality. Validity reveals the degree to which an instrument measures what it is supposed to measure to assist the researcher in solving the research problem. Three forms of validity are used to evaluate measurement scales. Content validity exists to the degree that a measure provides an adequate reflection of the topic under study. Its determination is primarily judgmental and intuitive. Criterion-related validity relates to our ability to predict some outcome or estimate the existence of some current condition. Construct validity is the most complex and abstract. A measure has construct validity to the degree that it conforms to predicted correlations of other theoretical propositions. A measure is reliable if it provides consistent results. Reliability is a partial contributor to validity, but a measurement tool may be reliable without being valid. Three forms of reliability are stability, equivalence, and internal consistency. A measure has practical value for the research if it is economical, convenient, and interpretable. . . .
