Measurement in social science research including all the technique and level and other measurements

1. MEASUREMENT TECHNIQUES IN
SOCIAL SCIENCES RESEARCH
SONDARVA YAGNESH M
MSc Agricultural Extension Education
BACA AAU, Anand

2. Measurement Scales
Four kinds of scale of measurement are
important for quantifying variables in the
behavioral sciences:
1. Nominal Scale
2. Ordinal Scale
3. Interval Scale
4. Ratio Scale

3. Nominal Scale
○ This type of scale allows a researcher to classify
characteristics of the persons, places or objects into
categories.
○ It is simply a system of assigning of number
symbols to events in order to label them.
○ Example: Assignment of numbers to basket ball
players to identify them and as such , the numbers
have no quantitative value.
○ Sometimes variables measured on nominal scales
are called categorical or qualitative.
Examples:
Group membership (1 = Experimental, 2=Placebo )
A person’s gender (0 = Female, 1 = Male)
Blood type, marital status, religion

4. Nominal Scale (contd.)
○ The weakest or least powerful level of
measurement
○ Indicates no order or distance relationship and
has no arithmetic origin
○ Simply describes differences between things by
assigning them into categories
○ Counting of numbers in each group is the only
possible arithmetic operation
○ Mode as the measure of central tendency can only
be used
○ Chi-square test of statistical significance can be
utilized
○ Contingency coefficient for measures of
correlation can be worked out.

5. Ordinal Scale
○ In this case, the characteristics can be put into categories and
the categories also can be ordered in some meaningful way.
The distance between the categories, however, is unknown.
○ A student’s rank in his class involves use of this scale.
○ Permits the ranking of items from highest to lowest but the
real difference between adjacent ranks may not be equal.
○ Implies a statement of ‘greater than’ or ‘less than’ without our
being able to state how much greater or less.
○ Median can be used as the measure of central tendency.
○ Percentile or quartile measure is used for measuring
dispersion
○ Correlations are restricted to various rank order methods
○ Measures of statistical significance are restricted to non-
parametric methods.

6. Ordinal Scale, Continued
○ Examples:
Socioeconomic Status
1 = Low
2 = Middle
3 = High
Health Status
1 = Poor
2 = Fair
3 = Good
4 = Excellent

7. 3. Interval Scale
❖ Numbers are assigned to objects or events which
can be categorized, ordered and assumed to
have an equal distance between scale values.
❖ It has an arbitrary zero, but it lacks true zero or
absolute zero.
❖ It dose not have the capacity to measure the
complete absence of a trait or characteristic.
❖ Example: Fahrenheit or centigrade scale of
temperature
❖ Addition and subtraction are permissible, but not
multiplication and division

8. 3. Interval Scale, Continued
❖ More powerful measurement than ordinal
scale as it involves the concept of equality of
interval.
❖ Mean-appropriate measure of central
tendency, std. deviation most widely used
measure of dispersion
❖ Product moment correlation technique
❖ ‘t’ test and ‘z’ test for statistical test of
significance

9. 4. Ratio Scale
○ The most precise level of measurement consists of
meaningfully ordered characteristics with equal
intervals between them and the presence of a zero
point that is not arbitrary but determined by nature.
○ For example, the zero point on a centimeter scale
indicates complete absence of length or height, but
absolute zero of temperature is theoretically
unobtainable.
○ Represents the actual amount of variables
○ Ratio is possible, e.g. it can be said that 40 kg. is
four times more than 10 kg.
○ Examples: weight, height, income, distance etc.
○ All statistical techniques are usable.

10. Examples of Appropriate
comparison statements
A is equal to (not equal to) B = (≠)
A is greater than (less than) B > (<)
A is three more than (less than) B + (–)
A is twice (half) as large as B × (/)
Relevant level of measurement
NominalOrdinal Interval Ratio
√ √ √ √
√ √ √
√ √
√
The Types of Comparisons That Can
Be Made With Different Levels of
Measurement
© Pine Forge Press, an imprint of Sage Publications, 2004

12. Sources of error in measurement
a. Respondent:
● Reluctance
● Fatigue, boredom, anxiety etc.
b. Situation:
c. Measurer:
● Behaviour, style or look may
encourage/discourage certain replies from
respondents
● Incorrect coding
● Careless mechanical processing of data
● Faulty tabulation and/or statistical calculation etc.
d. Instrument:
● complex words, ambiguous meaning, poor
printing, inadequate space for replies etc.

13. Tests of sound measurement
Tests of sound measurement must meet
the tests of:
❖ Validity
❖ Reliability and
❖ Practicability

14. ○ Measurement is said to be reliable when it
give consistent results. i.e. when repeated
measurements of same things give constant
results.
○ Reliability is the extent to which the same
finding will be obtained if the research is
repeated at another time by another
researcher. If the same finding can be
obtained again, the instrument is
consistent or reliable.
○ Reliability refers to the consistency of
scores obtained by the same individuals
when reexamined with test on different
occasions, or with different sets of
equivalent items, or under variable
examining conditions.
Reliability

16. Methods of estimating reliability
coefficient
❖ Test-retest method:
➢ Single form of test is administered
twice on the same sample with a
reasonable time gap.
➢ It yields two independent sets of scores
and the correlation between them gives
the value of reliability coefficient which
is also known as temporal stability
coefficient.

17. Methods of estimating reliability
coefficient
❖ Split-half method:
➢ It indicates homogeneity of the test.
➢ Test is divided into two halves, say, one set
contains odd numbered items and another contains
even numbered items.
➢ A single administration of the two sets of items to a
sample of respondents yields two sets of scores. A
positive and significant correlation indicates that
the test is reliable.
➢ The advantage is that data necessary for
computation of the reliability coefficient are
obtained in a single administration of the test, and
hence variability produced by two administrations
is automatically eliminated.

18. Validity of measurement
○ Validity of the measuring instrument is the degree
or the extent to which it measures what it is
supposed to measure.
○ The term validity means truth or fidelity. It can be
defined as the accuracy with which it measures
that which is intended to measure.
○ Validity is epitomized by the question: ‘Are we
measuring what we think we are measuring?’ This
is very difficult to assess. The following questions
are typical of those asked to assess validity
issues:
➢ Has the researcher gained the full access to the
knowledge and meanings of informants?
➢ Would experienced researcher use the same
questions or methods?

19. ○ A good measure must not only be reliable, but
also valid.
○ A valid measure measures what it is intended to
measure.
○ Validity is not a property of a measure, but an
indication of the extent to which an assessment
measures a particular construct in a particular
context—thus a measure may be valid for one
purpose but not another.
○ A measure cannot be valid unless it is reliable,
but a reliable measure may not be valid

20. Content validity
○ When the content of items individually
and as a whole are relevant to the test, it
represents content validity.
○ It requires both:
● Item validity: concerned with whether the
test items represent measurement in the
contended area, and
● Sampling validity: concerned with the extent
to which the test samples the total content
area.

21. Concurrent validity
○ In this method, a test is correlated with a
criterion which is available at present time.
○ It means how well performance on a test
estimates current performance on some
valued measure (criterion).
○ e.g. test of dictionary skills can estimate
students’ current skills in the actual use of
dictionary – observation.
○ e.g. the Scholastic Aptitude Test (SAT) is
valid to the extent that it distinguishes
between students that do well in college
versus those that do not.

22. Predictive validity
○ It is the degree to which a measure predicts a
second future measure.
○ A test is correlated against the criterion to be
made available sometimes in future.
○ Predictive Criterion Validity = how well
performance on a test predicts future
performance on some valued measure
(criterion)?
○ e.g. reading readiness test might be used to
predict students’ achievement in reading.
○ Predictive validity is needed for tests which
include long range forecast of academic
achievement, industrial management etc.

23. Construct validity
○ It is the extent to which the test may be
said to measure a theoretical construct or
trait.
○ A construct is a non-observable trait such
as intelligence, motivation etc.
○ Construct validation is a more complex
and difficult process than content
validation and criterion validation.
○ Construct validity is computed only when
the scope for investigating criterion
related validity or content validity is
bleak.

24. Practicability
○ From the operational point of view,
the measuring instrument ought
have:
❖ Economy,
❖ Convenience and
❖ Interpretability