1.
Definition of Paradigm
2.
a philosophical and theoretical framework of a scientific school or
discipline within which theories, laws, and generalizations and the experiments
performed in support of them are formulated; broadly : a philosophical or
theoretical framework of any kind
3.
A typical example or pattern of something; a model.
4.
A worldview underlying the theories and methodology of a particular
scientific subject.
5.
Definition
of approach
1.
A way of dealing with a situation or problem
2.
Start to deal with (a situation or problem) in a
certain way
3.
Measurement Scales
Scaling defined:
Procedures for
assigning numbers (or other symbols) to properties of an object in order to
impart some numerical characteristics to the properties in question.
4.
Scaling
Approaches:
1.
Unidimensional:
Measures only
one attribute of a concept, respondent, or object.
2.
Multidimensional:
Measures
several dimensions of a concept, respondent, or object.
3.
Types of
Scales:
1.
Noncomparative Scale:
Scales in which
judgment is made without reference to another object, concept, or person.
2.
Comparative Scale:
Scales in which
one object, concept, or person is compared with another on a scale.
3.
Churchill’s
paradigms
Here is Figure from "A Paradigm for Developing Better Measures
for Marketing Constructs". Gilbert A. Churchill, Jr., Journal of Marketing
Research, 16:1. (Feb., 1979)
(Churchill,
1979)(A Paradigm for Developing Better Measures of marketing Constructs)
1.
Introduction
1.
Measurements are “rules for assigning
numbers to objects to represent qualities of attributes”.
2.
What is measured? ATTRIBUTES of objects.
NOT objects themselves.
3.
What is the goal? To have measures that
are RELIABLE and VALID
4.
Construct
1.
Construct, e.g. customer satisfaction
2.
True level of satisfaction (True score)
denoted Xt
3.
Observed score X0, rarely similar to Xt
due to differences in stable characteristics, transient personal factors, situational factors etc.
4.
Validity and Reliability
X0 = Xt + Xs + Xr, where
1.
Xs – systematic source of error
2.
Xr – random source of error
Validity: X0 = Xt
Perfect reliability: Xr = 0
1.
Validity => Reliability
2.
Reliability is necessary but not
sufficient for Validity
3.
Validity and Reliability (2)
1.
Objective: find X0 that approximate Xt
2.
Measures are inferences, their “goodness”
is supported by the evidence, that is based on reliability or validity index
3.
Reliability forms: split-half,
test-retest etc.
4.
Validity forms: face, content,
predictive, concurrent, pragmatic, construct, convergent, discriminant.
5.
Specify domain of the construct
1.
Exactly defining what is included in the
definition and what is excluded
2.
Consulting the literature
3.
Widely varying definitions should be
avoided
4.
Example: to measure customer satisfaction
1.
Measure both expectations at the time of
purchase and reactions at some time after the purchase
2.
Expectations: cost, durability, quality,
operating performance, aesthetic features, sales assistance, advertising,
availability of competitor’s alternatives,
3.
Generate sample of items
1.
Literature searches
2.
Experience surveys
3.
Insight-stimulating examples
4.
Critical incidents and focus groups
5.
Purify the measure
1.
Domain sampling model: purpose of any
particular measurement is to estimate the score that would be obtained if all
the items in the domain were used
2.
In practice use of SAMPLE of items
3.
Measurement error due to inadequate
sampling
4.
Correlation matrix of the items in the
domain
1.
Average correlation in the matrix
2.
Dispersion of the correlation about the
average
1.
Assumption: all items, “if they belong to
the domain of the concept, have an equal amount of common core”
2.
Coefficient Alpha
1.
Measure of internal consistency of a set
of items
2.
Low coefficient alpha indicates that the
sample of items badly describes the construct which motivated the measure
3.
Procedure by low alpha: some items should
be eliminated.
1.
Calculate correlation of each item with
total score
2.
Plot the correlations by decreasing order
of magnitude
3.
Items with correlations near zero should
be eliminated
4.
Items of substantial drop in the
item-to-total correlations also deleted
1.
Mistake to do split-half reliability
2.
Purify the measure (2)
1.
Desirable outcome: high coefficient
alpha, dimensions agree with the conceptualized. Then, additional testing with a new sample of data.
2.
Second outcome: Factor analysis suggests
the overlapping dimensions. Items with pure loadings on the new factor are
retained, new alpha calculated.
3.
Non-desirable outcome: alpha coefficient
is low and restructuring of items forming each dimension is unproductive. Loop
back to 1. and 2.
4.
Assess Reliability with new Data
1.
Source of error within a test or measure
is the sampling of items.
2.
Coefficient alpha is the basic statistic
for determining the reliability of a measure based on internal consistency, but
it does not estimate errors external to the instrument.
3.
Collect additional data to rule our the
chance possibility of previous findings
4.
Do not use test-retest reliability
5.
Assess Construct Validity
1.
Face or content valid measure has an
appropriate sample
2.
To establish construct validity
1.
Determine the extent to which the measure
correlates with other measures designed to measure the same thing
2.
Determine whether the measure behaves as
expected
3.
Correlations with Other Measures
1.
Any construct or trait should be measurable
by at least two different methods
2.
Convergent validity – extent to which it
correlates highly with other methods designed to measure the same construct
3.
Discriminant validity – the extent to
which a measure a novel
4.
Multitrait-multimethod matrix: methods
and traits generating it should be as
independent as possible
5.
Multitrait-multimethod matrix 
6.
Does the measure behave as expected?
1.
Internal consistency is insufficient
condition for construct validity
2.
Assess whether scale correctly predicts
criterion measure (criterion validity)
1.
The constructs job satisfaction (A) and
likelihood of quitting the job (B) are related.
2.
The scale X provides a measure of A.
3.
Y provides a measure of B.
4.
X and Y correlate positively.
1.
Establish the validity by relating the measure
to a number of other constructs and not
only one
2.
Developing Norms
1.
Assessing the position of the individual
on the characteristic is to compare the person’s score with the scores achieved
by other people
2.
Norm quality depends on both the number
of cases on which the average is based and their representativeness
3.
Anderson (1977)
and Garbing’s (1988) paradigms
The purpose of measurement in
theory testing and development research is to provide an empirical estimate of
each theoretical construct of interest. Because of the limitations inherent in
single-item measures (cf. Churchill 1979), respondents usually are administered
two or more measures, often referred to as a scale, that are intended to be
altemative indicators of the same underlying construct. A composite score
defined by the respondent's scores on these measures, generally calculated as
an unweighted sum, provides an estimate of the corresponding construct. Our
central thesis is that the computation of this composite score is meaningful
only if each of the measures is acceptably unidimensional. Unidimensionality
refers to the existence of a single trait or construct underlying a set of
measures (Hattie 1985; McDonald 1981).' The importance of unidimensionality has
been stated succinctly by Hattie (1985 p. 49): "That a set of items
forming an instrument all measure just one thing in common is a most critical
and basic assumption of measurement theory."
Because the meaning of a measure
intended by the researcher may not be the same as the meaning imputed to it by
the respondents, the scale development process must include an assessment of
whether the multiple measures that defme a scale can be acceptably regarded as
altemative indicators of the same construct. Building on the earlier work of
Churchill (1979) and Peter (1979, 1981), we outline an updated paradigm for
scale development that incorporates confirmatory factor analysis (cf. Bentler
1985; Joreskog and Sorbom 1984) for the evaluation of unidimensionality. The key aspect
of this updated paradigm is that confimiatory factor analysis affords a
stricter interpretation of unidimensionality than can be provided by more
traditional methods such as coefficient alpha, item-total correlations, and exploratory factor
analysis and thus generally will provide different conclusions about the
acceptability of a scale.
Contributing to
the tradition of articles by Churchill (1979) and Peter (1979,1981). We outline
an updated Paradigm for scale development that incorporates a more recent
methodological development: confirmatory factor analysis. In doing so, we
attempt to provide a better understanding of the concept of unidimensional
measurement and the ways in which it can be assessed and, in particular, to
demonstrate that an explicit evaluation of unidimensionaiity is accomplished
with a confirmatory factor analysis of the individual measures as specified by
a multiple-indicator measurement model. Coefficient alpha is important in the
assessment of reliability, but it does not assess dimensionality. Though
item-total correlations and exploratory factor analysis can provide useful
preliminary analyses, particularly in the absence of sufficiently detailed
theory, they do not directly assess unidimensionality. There as on is that a
confirmatory factor analysis makes possible an assessment of the intemal
consistency and extemal consistency criteria of unidimensionality implied by
the multiple indicator measurement model.
Following the
paradigm of scale development outlined here, after the unidimensionality of a
set of scales has been acceptably established, one would assess its
reliability. Even a perfectly unidimensional scale will not be useful in
practice if the resultant scale score has unacceptably low reliability. Because
most measures in marketing are administered at a single point in time,
coefficient alpha or some other coefficient of equivalence reliability would
probably be used for this assessment.
The goal of
most research projects is not just to develop unidimensional and reliable
measurement scales, but to build and test theory. Essential to this undertaking
is the assessment of construct validity. A construct achieves its meaning in
two ways (Anderson1987; CronbachandMeehl1955): (1) through observed indicators
for which it is posited to be causally antecedent (and through observed
measures for which it is not) and (2) through the set of relationships of the
construct with other constructs as specified by some theory (the nomoiogical
network). Unidimensionality, then, is necessary but not sufficient for
construct validity. Not only should all the indicators that define a scale
provide estimates of exactly one factor, but the meaning of the underlying
factors should correspond to the construct of interest.
The nomological
network can be explored with in the
Context of the
full structural equation model. One means for accomplishing this is the
approach developed byAnderson and Gerbing(1988) that allows an assessment of
nomological validity that is asymptotically independent of the assessment of
the measurement model. It is called a "two-step" approach because the
measurement model first is developed and evaluated separately from the full
structural equation model that simultaneously models measurement and structural
relations. The measurement model in conjunction with the structural model makes
possible a comprehensive confirmatory assessment of construct validity
(Bentler1978). Hence, the assessment of unidimensionality provided by a
confirmatory factor analysis represents but a first step in the establishment
of meaning for the estimated factors.
(Gerbing &
Anderson, 1988)
4.
Loewenthal (1996) Approach
1.
Features of
Good Psychological Measures
1.
A statement of what the scale measure;
2.
Justification for the scale___ its uses and advantages over
existing measures
3.
A description of how the pool of items was drawn up
4.
A description of the sample used for testing
5.
An indication of the population
(kind of people) for whom the measure would be appropriate
6.
Descriptive statistics (norms) means, standard deviations, ranges,
different sub scales
7.
Reliability statistics
8.
Validity statistics
9.
The scale itself (introduction, items or examples of items)
10.
Writing
1.
Defining what you want to measure
The
first and very important step is to work out and then write down exactly what
you want to measure
2.
Collecting items
3.
Producing the preliminary questions or test
4.
Testing
1.
Deciding on a sample and reducing sample bias
2.
Reducing methods
3.
Testing
4.
Data and preliminary analysis
1.
Coding, scoring and data entry
2.
Selecting reliable items
3.
Descriptive statistics (norms) for final scale
4.
Steps for data entry and reliability
5.
Factor and principal factor analysis
6.
The final scale and its validation
1.
Descriptive statistics (norms)
2.
Validity
3.
Presenting the scale
9. Eclectic Approach
1. Definition
Selecting or
choosing from various systems, methodologies, etc.; not following any one
system.
Made up of
elements selected from various sources: an
eclectic philosophy.
2. Eclecticism is a conceptual approach that does not hold rigidly to a single paradigm or set of
assumptions, but instead draws upon multiple theories, styles, or ideas to gain
complementary insights into a subject, or applies different theories in
particular cases. It can sometimes seem inelegant or lacking in simplicity, and
eclectics are sometimes criticized for lack of consistency in their thinking.
It is, however, common in many fields of study.
The theory of
internalization itself is based on the transaction cost theory. This theory
says that transactions are made within an institution if the transaction costs
on the free market are higher than the internal costs. This process is called internalization.
4.
Theory
The idea behind the Eclectic Paradigm is to merge several isolated
theories of international economics in one approach.
5.
Mixed-Method Design for Scale Development
Scale
development guidelines by Churchill (1979) and DeVellis (2003) clearly involve two phases, the exploratory
(quantitative and qualitative data gathering)
structure, followed by a confirmatory phase (quantitative) involving
purification and confirmation of the
scale. The mixed-method approach is a suitable research design to be applied in scale development research
especially when the objective is to discover
in-depth knowledge of a complex phenomenon in a social context
(Creswell, 2008; Bryman, 2008) and test
hypotheses (Creswell, 2008). In
exploratory MMR, there are two distinct types of qualitative methods being
employed.
i. The first
are studies that review the literature togather dimensions or indicators of service quality and present it to focus
groups to revise the existing
instruments.
ii. The second
is the use of qualitative data gathering through focus group interviews to discover new indicators or
dimensions of service quality.
1.
References
1.
Churchill, G. A., Jr., (1979). A paradigm for developing better
measures of marketing constructs. Journal of Marketing Research, 16(February),
64-73.
2.
Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm
for scale development incorporating unidimensionality and its assessment.
Journal of Marketing Research, 25(2), 186-192.
3.
Loewenthal, K. M. (2001).An introduction
of psychological tests and scales._2nd ed. psychology
press Ltd. 27 Church Road, Hove, East Sussex, BN3 2FA, ISBN 1-84169-106-2 (hbk)
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