BASIC MEASUREMENT AND SEALING TECHNIQUES NOTES
4.0 Introduction
In everyday usage, measurement occurs when an established yardstick
verifies the height, weight or another feature of a physical object. How
well you like a song, a painting, or the personality of a friend is also
a measurement. In a dictionary sense, to measure is to discover the
extent, dimensions quantity, or capacity of something, especially by comparison with a
standard. We measure casually in daily life, but in research the
requirements are rigorous.
Measurement in research consists of assigning numbers to empirical
events in compliance with a set of rules. This definition implies that
measurement is a three part process:
- Selecting observable empirical events.
- Using numbers or symbols to represent aspects of the events, and
- Applying a mapping rule to connect the observation to the symbol.
As already noted, measurement is the assignment of numerals to objects
or events according to some rules. A rule is a guide that directs you on
how to go about assigning numerals. A numeral is a symbol of the form 1,
2, 3, or I, II, III…… A numeral has no quantitative meaning unless we
give it such a meaning. It is simply a symbol of special kind. Numbers
are used because they facilitate communication
of the measurement procedures and the results from researcher to
researcher. In addition the use of numbers allows mathematical
manipulations of the measurement data.
A note of postulates
A postulate is an assumption that is an essential prerequisite to
carrying out some operations or some thinking. In this case it is an
assumption about the relations between the objects being measured.
There are three important postulates;
- Either (a=b) or (a≠b) but not both. This postulate is necessary for
classification in data analysis. - If (a=b) and (b=c), then (a=c). This postulate enables a measurement
to establish the equality of set numbers on a characteristic by
comparing objects. - (a b) and (b c) then (a c). This is an important postulate and most
measurements in marketing research depend on it.
4.1 Types of scales
A scale is a device for measuring magnitude or quantity of a variable.
Scales may be a series of steps, degrees, a scheme of graded amounts
from the highest to lowest, an indicator of relative size; scales may also designate appropriate
categories such as age, sex, etc;
There are four types of scales commonly used as levels of measurements.
- Nominal scale
In business and social research, nominal data are probably more widely
collected than any other. When you collect nominal data, you partition a
set into categories that are mutually exclusive and collectively
exhaustive. In this type of scale, the numbers serve only as labels or
tags for identifying objects, events or characteristics. For instance, a
person identity card number is a nominal scale. It only serves the
function of identifying the person. We can assign numbers to football
players, telephone subscribers or to products in
a storeroom. These numbers or codes have no mathematical implication,
and the only property conveyed by the number is identity. Arithmetic
operations cannot be performed on these numbers, as they would have no
meaning.
The only permissible mathematical operations in nominal scales are those
leased upon counting such as frequencies, modes and percentages. There
are three forms of nominal scales:
- label nominal scale
- category nominal scale
- mixture nominal scale
- Label nominal scale: This is the most elementary nominal scale. A
label nominal scale is simply a label assigned to an object in order to
identify and keep track of it. In this kind of scaling each label is
unique to one object and possesses no meaning in itself. - Category nominal scale: This is the most commonly used nominal scale
in marketing research. In category nominal scale, numbers are used to represent mutually exclusive and exhaustive categories of objects. Thus, one might classify the residents of a city according to their expressed religious preferences. Classification set A given in table 8.1 is not a sound category nominal scale because it is not collectively exhaustive. Set B meets the minimum requirements, although this classification may be more useful for some research purposes than others.
Thus each category must be assigned to one, and only one scale category,
and must possess the measured common characteristic. Other examples of
characteristics measured with category nominal scale include sex, tribe
and so forth. For instance, in a given study men may be coded ‘1’ and
women ‘2’ and this serves no other function apart from classification.
- Mixture nominal Scale: This is a nominal scale which is partially a
label. The numbers and labels assigned football players serve to
identify the individual players, and also to place players in a category.
Ordinal scales
This is a qualitative scale comprised of equal appearing intervals that
rank observations from large to small. This scale indicates rank order
only. It does not indicate the nature of the intervals between the
ranks. For example, if several soft drinks are scaled according to
sweetness, and number 1 represents the highest degree of sweetness, then
the drinks assigned number 3 would be sweeter than one assigned number 4
but less sweet than one assigned number 2.
Note that with ordinal scale the only permitted statements are of
greater than or less than nature; we cannot make statements about how
much less of characteristic one object posses relative to another.
Ordinal measures commonly have only three to five categories, i.e, good,
better, best or:
In dealing with ordinal scale, statistical description to positional
measures such as median, quartile, percetile or other summary
characteristics which deal with order among quantities.
Interval scales
Interval scale has the power of nominal and ordinal scale plus one
additional strength; it incorporates the concept of equality in interval
(the distance between 1 and 2 equals the distance between 2 and 3). The
intervals are known and equal. They can be added, subtracted and their
summaries can be subjected to statistical tests. The interval scale does
not have an absolute zero. The zero point of this scale is arbitrary,
but it permits inferences to be made.
One common example of the interval scaling is the Fahrenheit and
centigrade scales used to measure temperature. An arbitrary zero is
assigned to each scale, and equal temperature differences are found by
scaling equal volumes of expansion in the liquid used in the
thermometer.
Interval scales permit inferences to be made about the differences
between the entities to be measured (warmness); but we cannot say that
any value on a specific interval scale is a multiple of another. Thus a
temperature of 50°F is not twice as hot as a temperature of 25°F. Also,
the elapsed time between 3 and 6 a.m equals the time between 4 and 7
a.m., but one cannot say 6 a.m. is twice as late as 3 a.m.
When a scale is interval, you use the arithmetic mean as the measure of
central tendency. You can compute the average time of first arrival of
trucks at a warehouse. The standard deviation is the measure of
dispersion for arrival time. Product moment correlation, t-tests, and
F-tests and other parametric tests are the statistical procedures of choice.
Ratio Scale
This is the highest level of measurement among scales. It incorporates
all the powers of the previous scales plus the provision for absolute
zero or origin. Ratio scale represents the actual amounts of a variable.
Measures of physical dimensions such as weight, height, distance, and
are examples. In business research, we find ratio scales in many areas.
These include money values, population counts, distances, return rates,
productivity rates.
4.2 Sources of measurement differences
The ideal study should be designed and controlled for precise and
unambiguous measurement of the variables. Since attainment of this ideal
is unlikely, we must recognize the sources of potential error and try to
eliminate, neutralize or otherwise deal with them. Much potential error
is systematic (results from a bias) while the remainder is random
(occurs erratically). Seltiz C etal (1976) has pointed out several
sources from which measured differences can come.
- The respondent as an error source
A respondent may be reluctant to express strong negative feelings or
may have little knowledge about a personality i.e the president, but
be reluctant to admit ignorance. This reluctance can lead to an
interview of ‘guesses’. Respondents may also suffer from temporary
factors like fatigue, boredom, anxiety, or other distractions; these
limit the ability to respond accurately and fully. Hunger,
impatience, or general variations in mood may also have an impact. - Situational Factors. Any condition that places a strain on the
interview can have serious effects on the interviewer – respondent
rapport. If another person is present, that person can distort
responses by joining in, by distracting, or merely by being present.
If the respondents believe anonymity is no ensured, they may be
reluctant to express certain feelings. - The measure as an error source
The interviewer can distort responses by rewording, paraphrasing, or
reordering questions. Stereotypes in appearance and action introduce
bias. Inflections of voice and conscious or unconscious prompting
with smiles, nods, and so forth may
encourage or discourage certain replies: careless mechanical
processing – checking of the wrong response or failure to record
full replies – will obviously distort feelings.
In the data analysis stage, incorrect coding and careless
tabulation, and faulty
statistical calculation may introduce further errors.
Instrument as an error source
A defective instrument can cause distortion in two major ways. First, it
can be too confusing and ambiguous. The use of complex words and syntax
beyond respondent comprehension is typical. Leading questions, ambiguous
meanings, mechanical defects (inadequate space for replies, response
choice omissions, and poor printing), and multiple questions suggest the
range of problems.
A more elusive type of instrument deficiency is poor sampling of the
universe of content items. Seldom does the instrument explore all the
potentially important issues.
4.3 The Characteristics of Sound Measurement
What are the characteristics of a good measurement tool? An intuitive
answer to this question is that the tool should be an accurate counter
or indicator of what we are interested in measuring. In addition, it
should be easy and efficient to use. There are three (3) major criteria
for evaluating a measurement tool:
- Validity: This refers to the extent to which a test measures what we
actually wish to measure. - Reliability: has to do with the accuracy and precision of a
measurement procedure. - Practicality: is concerned with a wide range of factors of economy,
convenience, and interpretability.
Validity in Research
Validity in research is achieved through the internal and external
validity of the study.
Internal validity: This refers to the outcome of the study as based on
the function of the program, a study has internal validity if the
outcome of the study is a function of the approach being tested rather
than results of the causes not systematically dealt with.
Internal validity is justified by the conclusions we have as researchers
when we have been able to control the threats of other variables (i.e
intervening variables, or moderating variables or extraneous variables).
The more you reduce the nuisances (other variables) affecting the study,
the more you attain the internal validity. There are three widely
accepted classification of internal validity:
- content validity
- criterion – related validity
- construct validity
1. Content validity
The content validity of a measuring instrument is the extent to which it
provides adequate coverage of the topic under study. If the instrument
contains a representative sample of the universe of subject matter of
interest, then content validity is good. To evaluate the content
validity of an instrument, one must first agree on what elements
constitute adequate coverage of the problem.
2. Criterion-Related Validity
Criterion-related validity reflects the success of measures used for
prediction or estimation. You may want to predict an outcome or estimate
the existence of a current behaviour or condition. These are predictive
and concurrent validity, respectively. They differ only in a time
perspective. An opinion questionnaire that correctly forecasts the
outcome of a union election has predictive validity. An observational
method that correctly categorizes families by current income class has
concurrent validity. While these examples appear to have simple and
ambiguous validity criteria, there are difficulties in estimating
validity. Consider the problem of estimating family income. There
clearly is a knowable true income for every family. However, we may find
it difficult to secure this figure. Thus, while the criterion is
conceptually clear, it may be unavailable.
3.Construct Validity
One may also wish to measure or infer the presence of abstract
characteristics for which no empirical validation seems possible.
Attitude scales and aptitude and personality tests generally concern
concepts that fall in this category. Although this situation is much
more difficult, some assurance is still needed that the measurement has
an acceptable degree of validity. In attempting to evaluate construct
validity, we consider both the theory and the measuring instrument being
used. If we were interested in measuring the effect of ceremony on
organizational culture, the way in which ceremony was operationally
defined would have to correspond to an empirically grounded theory. Once
assured that the construct was meaningful in a theoretical sense, we
would next investigate the adequacy of the instrument. If a known
measure of ceremony in organizational culture was available, we might
correlate the results
obtained using this measure with those derived from our new instrument.
Such an approach would provide us with preliminary indications of
convergent validity.
Reliability
Reliability means many things to many people, but in most contexts the
notion of consistency emerges. A measure is reliable to the degree that
it supplies consistent results. Reliability is a contributor to validity
and is a necessary but not sufficient condition for validity. The
relationship between reliability and validity can be simply illustrated
with the use of a bathroom scale. If the scale measures your weight
correctly (using a concurrent criterion such as a scale known to be
accurate), then it is both reliable and valid. If it consistently
overweighs you by six pounds, then the scale is reliable but not valid.
If the scale measures erratically from time to
time, then it is not reliable and therefore cannot be valid.
Reliability is concerned with estimates of the degree to which a
measurement is free of random or unstable error. It is not as valuable
as validity determination, but it is much easier to assess. Reliable
instruments can be used with confidence that transient and situational
factors are not interfering. Reliable instruments are robust; they work
well at different times under different
conditions.
4.4 Factors affecting the interval validity of a study
Among the many threats to internal validity, we consider the following:
- History
During the time that a research is taking place, some events may
occur that confuse the relationship being studied. These are events
may either increase or decrease the expected outcomes of the
project. These are events which are not part of the project and they
are not planned for. They may just happen in the process of the
research and have tremendous effects on the results of the study. - Testing
The process of taking a test can affect the scores of a second test.
The mere experience of taking the first test can have a learning
effect that influences the results of the second test. Subjects who
are given a pretest are likely to remember some of the questions or
some of the errors they made when they are taking the posttest. They
are also likely to do somewhat better on the posttest than they did
on the pretest. - Instrumentation
This threat to internal validity results from changes between
observations, in measuring instruments or in observer. Using
different questions at each measurement is an obvious source of
potential trouble, but using different observers or interviewers
also threatens validity. Observer experience, boredom, fatigue, and
anticipation of results can all distort the results of
separate observations. For example, an experienced interviewer may
obtain more complete information from a correspondent than an
inexperienced interviewer. The additional information may be due to
the fact that the interviewer has become more skilled in asking
questions or observing events and not due to the effect of the
program or observing the effects of the treatment. - Maturation:
Changes may also occur within the subject that is a function of the
passage of time and not specific to any particular event. These are
of special concern when the study covers a long time, but they may
also be factors in tests that are as short as an hour or two. A
subject can become hungry, bored or tired in a short time, and this
condition can affect response results. - Selection
An important threat to internal validity is the differential
selection of subjects for experimental and control groups. Validity
considerations require that groups be equivalent in every respect.
If subjects are randomly assigned to experimental and control
groups, this selection problem can be largely overcome.
Additionally, matching the members of the groups on key factors can
enhance the equivalence of the groups. Validity considerations
require that the groups be largely overcome. Additionally, matching
the members of the groups on key factors can enhance the equivalence
of the groups. - Experiment Mortality
This occurs when the composition of the study groups changes during
the test. Attrition is especially likely in the experimental group,
and with each dropout, the group changes. Because members of the
control group are not affected by the testing situation, they are
less likely to withdraw. In a compensation incentive study, some
employees might not like the change in compensation method and
withdraw from the test group; this action could distort the
comparison with the control group that has continued working under
the established system, perhaps without knowing a test is under way.
4.5 Factors Affecting the External validity of the study
Internal validity factors cause confusion about whether the experimental
treatment (x) or extraneous factors are the source of observation
differences. In contrast, external validity is concerned with the
interactions of the experimental treatment with other factors and the
resulting impact on abilities to generalize to times, settings or persons.
- Reactive effects of testing:
If pre-testing has been used and which sensitizes the experimental
subjects to the particular treatment, then the effect of the
treatment may be partially the result of the sensitization of the
pre-test. - Interaction effects of selection bias
If the samples draw from the study is not representative of the
larger population, then it would be difficult to generalize findings
from the samples to the population, and this may arise when the
samples are not drawn randomly from the population. Consider a study
in which you a cross-section of a population to participate in an
experiment, but a substantial number refuse. If you do the
experiment only with those who agree to participate, can the results
be generalized to the total population? - Other reactive factors
The experimental settings themselves may have a biasing effect on a
subject’s response to the treatment. An artificial setting can
obviously give results that are not representative of large
populations. Suppose workers who are given an incentive pay are
moved to a different work area to separate them from the control
group. These new conditions alone could create a strong reaction
condition. If subjects know they are participating in an experiment,
there may be a tendency to roleplay in a way that distorts the
effect of the experimental treatment.
4.6 Common Effects related to the research process
There are other situations in which the internal and external validity
of the study may both be threatened simultaneously. This is brought
about by what we call research effects, which have nothing to do with
the treatment.
- Hawthorne Effect
This refers to a situation where subject awareness of being in an
experimental group motivates them to perform better. Therefore the most
influential factor on the subjects is not the independent variable but
their awareness of being in a special group. - The placebo Effect
This is common to medical studies. Researchers observe that a drug
administered to any group of parties has two effects.
- Chemical effect
- Psychological effect
To counteract this effect, researchers use a placebo and this is an
inactive substance which has the same colour and tests as the active
drug; Half of the subjects (experimental group) are given the active
drug and the other alf (control group) are given the placebo inactive
drug. If there is a significance difference between those two groups,
the drug may be said to have a significance effect.
