ATENEO DE DAVAO UNIVERSITY
School of Business and Governance
Doctor of Business Administration
A SUMMARY OF MEASUREMENT IN RESEARCH
RESEARCH METHODS
ARVIN T. GALIGAO, MBM
Student
DR. FELICITAS D. CRUZ
Professor
August 4, 2018
To give understanding about a particular thing such as height, weight
or other feature of a physical object, measurement is a very essential tool
to use. We use measurement in our daily lives to make conclusions and
decisions. To measure, means, to discover the extent, dimensions,
quantity, or capacity of something, especially by compassion with a
standard. In research, measurement is very important because it gives
researchers the understanding about the variables as to its extent by
assigning numbers to empirical events, objects or properties, or activities in
compliance with the set of rules.
Measurement scales has four (4) classifications: nominal, ordinal,
interval, and ratio. Nominal scale is used when nominal data are being
measured. These data are variables that naturally or by design can be
grouped into two or more categories that are mutually exclusive and
collectively exhaustive. It is use to determine equality. For example, gender
(male, female). However, nominal scales are the least powerful of the four
data types because they suggest no order or distance relationship and
have no arithmetic origin. Although nominal data are statistically weak, they
are still useful. Nominal measures are especially valuable in exploratory
work where the objective is to uncover relationships rather than secure
precise measurements. This type of scale is also useful in survey and other
research when data are classified by major subgroups of the population.
Ordinal scales show extent relationships of more than or less than
but have no distance or unique origin. Thus, ordinal data can be useful in
terms of ranking. Examples of nominal data include attitude and preference
scales. However, the numbers used with nominal scales have only a rank
meaning, the appropriate measure of central tendency is the median. The
median is the midpoint of a distribution and a percentile or quartile reveals
the dispersion. Correlation analysis is being used in nominal data but it is
only limited to various ordinal techniques.
Interval scales have the power of nominal and ordinal data plus one
additional strength. They incorporate the concept of equality of interval like
the scaled distance between 1 and 2 equals the distance between 2 and 3.
These scales have both order and distance but no unique origin.
Centigrade and Fahrenheit temperature scales are other examples of
classical interval scales. Arithmetic mean is used to measure central
tendency when interval data are relatively symmetric with one mode is
being used. The product-moment correlation, t-test, F-test, and other
parametric tests are the statistical procedures of choice for interval data.
And, ratio scales incorporate all the powers of the previous scales
plus the provision for absolute zero or origin. It possesses classification,
order, distance , and unique origin. Ratio data represent the actual
amounts of a variable. Measures of physical dimensions such as weight,
height, distance, and area are example. In business research, we find ratio
scales in many areas such as money values, population counts, distances,
return rates, productivity rates, and amount of time. A lot of statistical tool
can be used with ratio scales.
Measurements can be used to control the variables to come up with
an ideal study. However, complete control is unattainable. Thus, an error
does occur. There are classifications of errors according to sources: the
respondents or participant, situational factors, the measurer, and the
instrument.
Moreover, a good measurement test need to test based on validity,
reliability, and practicability. Validity is the extent to which a test measures
what it supposed to measure. There are also three (3) forms of validity:
content validity, criterion-related validity, and construct validity. Content
validity is used when determining degree to which the content of the items
adequately represents the universe of all the relevant items under study.
Criterion-related validity relates to our capability to predict some outcome
or estimate the existence of some current condition or situation. While
construct validity is used to measure the degree that conforms to predicted
relationships of other theoretical intentions.
A good measurement tool needs also to test based on reliability. It is
reliable if it provides consistent results. Reliability is necessary contributor
to validity but is not a sufficient condition for validity. There are three (3)
forms of reliability: stability, equivalence, and internal consistency. A
measure also has practical value for the research of it is economical,
convenient, and interpretable.