Survey Instrument
Development in OB/HRM
Research
Prof. Jiing-Lih Larry Farh
HKUST
IACMR
Guangzhou workshop
July 2007
Construct and Measurement Related
Problems in Manuscripts
 Too many constructs
 Constructs are poorly defined
 Measures do not match constructs
 Unreliable/invalid measures
 Level of measurement does not match the level of the
theory
Fatal flaws for empirical papers!!!
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“The construction of the measuring devices is perhaps the
most important segment of any study. Many well-conceived
research studies have never seen the light of day because of
flawed measures.”
Schoenfeldt, 1984
“The point is not that adequate measurement is ‘nice’. It is
necessary, crucial, etc. Without it we have nothing.”
Korman, 1974, p. 194
“Validation is an unending process….Most psychological
measures need to be constantly evaluated and reevaluated to see
if they are behaving as they should.”
Nunnally & Bernstein, 1994, p. 84
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Empirical Research Model
Independent
Conceptual
X’
From Schwab (1999)
Dependent
(a)
Y’
(b1)
(b2)
(c)
Operational
1.
2.
3.
4.
X
(d)
Y
Independent and dependent variables are identified by X and Y, respectively.
The symbol prime, ’ is used to designate that a variable is specified at the conceptual level.
Arrows represent the direction of influence or cause.
a—conceptual relationship; d---empirical relationship; b1, b2—construct validity; c---internal validity
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Validity in Research



Construct validity is present when there is a high
correspondence between the scores obtained on a
measure and the mental definition of a construct it is
designed to represent.
Internal validity is present when variation in scores on a
measure of an independent variable is responsible for
variations in scores on a measure of a dependent variable.
External validity is present when generalizations of findings
obtained in a research study, other than statistical
generalization, are made appropriately.
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Construct Validation



Involves procedures researchers use to
develop measures and to make
inferences about a measure’s construct
validity
It is a continual process
No one method alone will give
confidence in the construct validity of
your measure
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Construct Validation Steps
From Schwab (1999)
Define the construct and develop
conceptual meaning for it
Develop/choose a measure consistent with
the definition
Perform logical analyses and empirical
tests to determine if observations obtained
on the measure conform to the conceptual
definition
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Content validity
Factor analysis
Reliability
Criterion-related/
Convergent/
Discriminant/
Nomological
validity
7
Survey Instrument Development
Why is it important?
How to do it?
What are some of the best
practices?
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Instrumentation in Perspective

Selection and application of a technique that
operationalizes the construct of interest




e.g., physics = colliders
e.g., MDs = MRI
e.g., OB = Job descriptive index
Instruments are devices with their own
advantages and disadvantages, some more precise
than others, and sophistication doesn’t guarantee
validity
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Survey Instruments

3 most common types of instrumentation in social
sciences




Observation
Interview
Survey instrumentation
Survey instrumentation


Most widely used across disciplines
Most abused technique---people designing instruments
who have little training in the area
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Why do we do surveys?


To describe the populations: What is going
on?
Theoretical reasons: Why is it going on?


Develop and test theory
Theory should always guide survey
development and data collection
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What construct does this scale measure? (1)
1.
2.
3.
4.
5.
6.
7.
8.
Have a job which leaves you sufficient time for your personal or family
life. (.86)
Have training opportunities (to improve your skills or learn new skills).
(-.82)
Have good physical working conditions (good ventilation and lighting,
adequate work space, etc.). (-.69)
Fully use your skills and abilities on the job. (-.63)
Have considerable freedom to adapt your own approach to the job.
(.49)
Have challenging work to do---work from which you can get a
personal sense of accomplishment. (.46)
Work with people who cooperate well with one another.(.20)
Have a good working relationship with your manager.(.20)
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Adapted from Heine et al. (2002)12
What construct does this scale measure? (2)











I would rather say “no” directly, than risk being misunderstood. (12)
Speaking up during a class is not a problem for me. (14)
Having a lively imagination is important to me. (12)
I am comfortable with being singled out for praise or rewards. (13)
I am the same person at home that I am at school. (13)
Being able to take care of myself is a primary concern for me. (12)
I act the same way no mater who I am with. (13)
I prefer to be direct and forthright when dealing with people I have
just met. (14)
I enjoy being unique and different from others in many respects. (13)
My personal identity, independent of others, is very important to me.
(14)
I value being in good health above everything. (8)
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Adapted from Heine et al. (2002)
13
Example: Computer
satisfaction
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Construct Definition

Personal computer satisfaction is an emotional response
resulting from an evaluation of the speed, durability, and
initial price, but not the appearance of a personal
computer. This evaluation is expected to depend on
variation in the actual characteristics of the computer (e.g.,
speed) and on the expectations a participant has about
those characteristics. When characteristics meet or exceed
expectations, the evaluation is expected to be positive
(satisfaction). When characteristics do not come up to
expectations, the evaluation is expected to be negative
(dissatisfaction).
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From Schwab (1999)
15
Hypothetical Computer Satisfaction Questionnaire

Decide how satisfied or dissatisfied you are with each characteristic of your
personal computer using the scale below. Circle the number that best
describes your feelings for each statement.
Very
Dissatisfied
Dissatisfied
1
Neither Satisfied nor
Dissatisfied
Satisfied
Very
Satisfied
3
4
5
2
My satisfaction with:
1.
Initial price of the computer
1
2
3
4
5
2.
What I paid for the computer
1
2
3
4
5
3.
How quickly the computer performs calculations
1
2
3
4
5
4.
How fast the computer runs programs
1
2
3
4
5
5.
Helpfulness of the salesperson
1
2
3
4
5
1
2
3
4
5
6.
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How I was treated
LarryFarh
when I bought the computer
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Construct Validity Challenges
Construct
Variance
Deficiency
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Systematic
Variance
Construct
Valid
Variance
From Schwab (1999)
Observed Score
Variance
Reliable
Contamination
Unreliability
17
Scale Development Process
From Hinkin (1998)


Step 2: Questionnaire Administration



Step1: Item Generation
Step 3: Initial Item Reduction
Step 4: Confirmatory Factor Analysis
Step 5: Convergent/Discriminant Validity
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
Step 6: Replication
18
Step 1:
Item Generation -Deductive Approach
It requires:
(a) an understanding of the phenomenon
to be investigated;
(b) thorough review of the literature to
develop the theoretical definition of the
construct under examination
From Hinkin (1998)
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Step 1:
Item Generation-Deductive Approach


Advantages: through adequate construct
definitions, items should capture the domain of
interest, thus to assure content validity in the final
scale
Disadvantages: requires the researchers to
possess working knowledge of the phenomena;
may not be appropriate for exploratory studies
From Hinkin (1998)
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Step 1:
Item Generation - Inductive Approach



Appropriate when the conceptual basis may not result in
easily identifiable dimensions for which items can then be
generated
Frequently researchers develop scales inductively by asking
a sample of respondents to provide descriptions of their
feelings about their organizations or to describe some
aspects of behavior
Responses classified into a number of categories by
content analysis based on key words or themes or using a
sorting process
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Step 1:
Item Generation - Inductive Approach


Advantages: effective in exploratory research
Disadvantages:



Without a definition of construct under examination,
it is difficult to develop items that will be conceptually
consistent.
Requires expertise on content analyses
Rely on factor analysis which does not guarantee
items which load on the same factors share the same
theoretical construct
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Characteristics of Good Items






As simple and short as possible
Language should be familiar to target audience
Keep items consistent in terms of perspectives
(e.g., assess behaviors vs. affective response)
Item should address one single issue (no doublebarreled items)
Leading questions should be avoided
Negatively worded questions should be carefully
constructed and placed in the survey
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What about these items?




I would never drink and drive for fear of that I
might be stopped by the police (yes or no)
I am always furious (yes or no)
I often lose my temper (never to always)
滿招損，謙受益
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Content Validity Assessment


Basically a judgment call
But can be supplemented statistically



Proportion of substantive agreement (Anderson
& Gerbing, 1991) (see next slide)
Item re-translation (Schriesheim et al. 1990)
Content adequacy (Schriesheim et al. 1993)
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Content Validation Ratio
CVR =
2ne
- 1
N
n e is the number of Subject Matter Experts (SMEs) rating the selection tool
or skills being assessed is essential to the job, i.e., having good coverage
of the KSAs required for the job.
N
is the total number of experts
CVR = 1 when all judges believe the tool/item is essential;
CVR = -1 when none of the judge believes the tool/skill is essential; CVR = 0
means only half of the judges believe that the tool/item is essential.
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How many items per construct?

4 - 6 items for most constructs. For initial item
generation, twice as many items should be
generated
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Item Scaling



Scale used should generate sufficient variance among
respondents for subsequent statistical analyses
Likert-type scales are the most frequently used in survey
questionnaire. Likert developed the scale to be composed
of five equal appearing intervals with a neutral midpoint
Coefficient alpha reliability with Likert scales has been
shown to increase up to the use of five points, but then it
levels off
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Step 2:
Questionnaire Administration

Sample size: Recommendations for item-toresponse ratios range from 1:4 to 1:10 for each
set of scales to be factor analyzed

e.g., if 30 items were retained to develop three
measures, a sample size of 150 observations should be
sufficient in exploratory factor analyses. For
confirmatory factor analysis, a minimum sample size of
200 has been recommended.
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Step 3: Initial Item Reduction



Interitem correlations of the variables to be conducted
first. Corrected item-total correlations smaller than 0.4 can
be eliminated
Exploratory factor analysis. An appropriate loadings
greater than 0.40 and /or a loading twice as strong on an
appropriate factor than on any other factor. Eigenvalues of
greater than 1 and a scree test of the percentage of
variance explained should also be examined
Be aware of construct deficiency problems in deleting
items
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Step 3:
Internal Consistency Assessment


Reliability is the accuracy or precision of a
measuring instrument and is a necessary condition
for validity
Use Cronbach’s alpha to measure internal
consistency. 0.70 should be served as minimum for
newly developed measures.
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Coefficient alpha
The average of all possible split halve reliabilities.
n
  
n

(
n 1
2
t

i 1
2
t
2
i
)
n is the number of items for each applicant
t is the total of all items for an applicant
 2 is the variance across all applicants
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An example of coefficient 
Item
1
2
3
4
5
Total
Subject
B
C
5
4
4
5
3
3
4
4
5
4
A
6
6
5
4
4
25
21
20
Variance
1.00
1.00
1.33
.00
.33
3.67
7.00

2
2
5

i 1
2
i
t
2
Variance of total = 7.0 ; Total of variance = 3.67

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n
   i2
n
(
n 1
2
t

i 1
2
t
5 7.0  3.67
) (
)  .60
4
7.0
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How High Cronbach Alpha Needs to be?



In exploratory research where hypothesized
measures are developed for new constructs, the
Alphas need to exceed .70
In basic research where you use well-established
instruments for constructs, the Alphas need to
exceed .80.
In applied research where you need to make
decisions based on the measurement outcomes,
the Alphas need to exceed .90.
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Step 4:
Confirmatory Factor Analysis (CFA)



Items that load clearly in an exploratory factor analysis
may demonstrate a lack of fit in a multiple-indicator
measurement model due to lack of external consistency
It is recommended that a Confirmatory Factor Analysis be
conducted using the item variance-covariance matrix
computed from data collected from an independent
sample.
Then assess the goodness of fit index, t-value, and chi
square
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Step 5:
Convergent/Discriminant Validity


Convergent validity—when there is a high
correspondence between scores from two or more different
measures of the same construct.
Discriminat validity---when scores from measures of
different constructs do not converge.


Multitrait-Multimethod Matrix (MTMM)
Nomological networks---relationships between a
construct under measurement consideration and other
constructs.

Criterion-related validity
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Convergent Validity
From Schwab (1999)
Construct
Measure A
Measure B
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Step 6: Replication


Find an independent sample to collect more
data using the measure.
The replication should include confirmatory factor
analysis, assessment of internal consistency, and
convergent, discriminant, and criterion-related
validity assessment
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Elements of a MTMM matrix
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A sample MTMM matrix
(Paper & Pencil self test)
Heterotraitmonomethod
Monotraitheteromethod
Monotraitmonomethod
Heterotraitheteromethod
Note: SE: self esteem; SD: self disclosure; LC: Locus of control
Adapted from http://www.socialresearchmethods.net/kb/mtmmmat.htm
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Interpreting MTMM


Reliability (monotrait-monomethod) should be the highest
Monotrait-heteromethod (convergent validity) must be >0 and
high

Monotrait-heteromethod (convergent
validity) > heterotrait-
monomethod (discriminant validity)> heterotrait-heteromethod
(i.e., convergent validity should be higher than discriminant validity)
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Inductive Example: Taking Charge
(Morrison & Phelps, 1999, AMJ)





Open-end survey to 148 MBA to list 152 individuals
efforts, and collected 445 statements.
Reduce the list to 180 by eliminating redundant and ambiguous ones,
and sort the statements into 19 groups based on similarity.
Write a general statement to reflect each group, compare the content
of the statements with the construct, and result in 10 prototypical
activities to reflect the construct.
Pretest it to 20 MBA students, to check for clarity and suggestion for
wording improvements
Pretest the measure with a sample of 152 working MBAs, to assess
internal consistency of the items and check whether the 10 specific
behaviors were extra-role activities. 77% checked six or more.
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Open-ended Survey: Taking Charge
(Morrison & Phelps, 1999, AMJ)
• To think of individuals with whom they had worked who
have actively tried to bring about improvement within their
organization. These change efforts could be aimed at any
aspect of the org, including the person’s job, how work was
performed within their dept, and org’al policies or
procedures.
• To focus on efforts that went beyond the person’s formal
role or, efforts that were not required or formally expected.
• To list specific behaviors that reflected or exemplified the
person’s change effort.
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Sample Items: Taking Charge
(Morrison & Phelps, 1999, AMJ)
1.
2.
3.
4.
Try to institute new methods that are more
effective
Try to introduce new structure, technologies, or
approach to improve efficiency
Try to change how his/her job is executed in
order to more effective
Try to bring about improved procedures for the
work unit or department
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Theoretical Model: Taking Charge
(Morrison & Phelps, 1999, AMJ)
Top management
openness
Group norms
Self-efficacy
Taking charge
Felt responsibility
Expert power
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Deductive Example: Org. Justice
(Colquitt, 2001, JAP)
Organizational Justice
Distributive
Justice
Procedural
Justice
Interactive
Justice
Informational
Justice
The Dimensionality of Organizational Justice
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Sample Items: Org. Justice
(Colquitt, 2001, JAP)

Distributive Justice


Procedural justice


“Have you been able to express your views and feelings during
those procedures” (Thibaut & Walker, 1975)
Interactive justice


“Does your outcome reflect the effort you have input into your
work” (Leventhal, 1976)
“Has he/she treated you in a polite manner” (Bies & Moag, 1986)
Informational justice

“Has he/she communicated details in timely manner” (Shapiro, et
al., 1994)
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Theoretical Model: Org. Justice
(Colquitt, 2001, JAP)
Distributive Justice
Outcome Satisfaction
Procedural Justice
Rule Compliance
Interactive Justice
Leader Evaluation
Informational Justice
Collective Self-esteem
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Research in
Chinese context
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Four Types of Scale Development Approaches in Chinese
Management Research
Farh, Cannella, & Lee (2006, MOR)
Expectations about Cultural Specificity
Etic Orientation
Source of the
scale
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Emic Orientation
Use or Modify an
Existing Scale
Translation
Adaptation
Develop a New
Scale
De-contextualization
Contextualization
50
Four Types of Scale Development Approaches in Chinese
Management Research
Scale
Development
Approaches
Translation
approach
Adaptation
approach
Key
Assumptions
Target construct is equivalent
across cultures in terms of overall
definition, content domain, and
empirical representations of the
content domain
Availability of high quality
culturally unbiased Western scales
for target construct

Target construct is equivalent
between cultures in terms of
overall definition and content
domain
Availability of high quality
Western scales for target construct

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Major
Strengths
Low developmental time
and costs
Preserve the possibility of
a high level of equivalence
Allow for direct crosscultural comparison of
research findings

Low to moderate
developmental time and
costs
Ease of scholarly
exchanges of research
findings with the Western
literature

Major
Limitations
Difficulty in achieving
semantic equivalence
between the Chinese and
Western scales
Culturally unbiased
Western scales are hard to
come by

Difficulty in conducting
cross-cultural research
Drastic adaptation may
create new scale that
requires extensive
validation in the Chinese
context

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Four Types of Scale Development Approaches in
Chinese Management Research:
Scale
Development
Approaches
Decontextualization
approach
Contextualization
approach
Key
Assumptions
Target construct is etic or
universal or culturally invariant
High quality scale for the
target construct is unavailable
in the literature

Target construct is emic or
culture specific
High quality emic scale for
target construct unavailable in
the literature

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Major
Strengths
Opportunity to develop
universal measure for target
construct
Ease of scholarly exchange
of research findings with the
Western literature

Opportunity to develop
scales highly relevant for the
Chinese context
Opportunity to contribute
context-specific knowledge
to Chinese management

Major
Limitations
Long developmental time
and high developmental
costs
Items tend to be phrased
at a more abstract level,
which may limit its
informational and practical
value

Long developmental time
and high developmental
costs
Limited generalizability of
the new scale
Hard to communicate
research findings with the
Western literature

52
Should you use well-established scales from
the (western) literature or develop local scales?
Align your measure with your theoretical orientation
 When you take an etic (universal or cultural invariant) perspective
to a research topic, you assume that the Chinese context is largely
irrelevant. Here your study is based on general theories, and you
should use well-established measures in the literature.
 When you take an emic (cultural specific) perspective to a research
topic, you assume that the phenomenon is Chinese context specific.
Here your study is based on context embedded theories, and you
should consider using measures appropriate for the Chinese context.
 When you do cross-cultural research, you try to study phenomena
common across societies. You model culture explicitly in your
theories (either as a main or a moderating effect) and should apply
measures that work in multiple cultural contexts.
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A Close Look at Item
Generation Using
Inductive Approach
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Item Generation Process
high
Key issues
Content domain clarity?
low
sampling/method
Collect behavioral incidents
classification/panel
test
Classify into categories
Empirical/
conceptual
Creativity &
insight
content validation
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Form dimensions from categories
Domain definition
Item development &
refinement
Empirical testing
55
Research project in focus
Investigate the
construct domain of
moral leadership in the
PRC…
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Generate Behavioral Descriptions



這份調查表是想了解您對領導道德行為的看法，在您回答之
前，請先回想您在工作上曾經遇到過的一兩位道德行為表現
良好的主管，及一兩位道德行為表現不佳的主管，并想想他
們具有哪些行為表現。
然后,請根據您所回想的結果，在下列表格中,寫出您認為一
個有道德的企業主管應該表現哪些行為,請寫出最重要的六項。
在列出六項行為后，再依據這六項行為的相對重要性，由1
至6加以排序，1代表最重要，6代表最不重要，將排序的數
字填在后面的括號中。
有道德的企業主管應具备的行為排序
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Sample Items
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
NID:
111
112
113
114
121
122
123
124
125
126
131
132
133
134
142
144
145.
146.
151.
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為人坦誠
處理工作的態度比較公正
容易和員工接近
有相關的知識，工作技巧
無畏，具有挑戰中上級的勇氣，堅持做正確的事情
關心，栽培下屬．有人情味
坦白．鼓勵團結內部充份的信息流通
認真做事
公平對待下屬，以業績表現評價下屬
獎勵，肯定下屬優秀表現
公私分明
言行一
一視同仁
光明正大
有責任
不推卸
公私分明
尊重別人
誠實正直
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Some 44 Categories
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#1 胸怀宽广
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#3 敬业
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不疾贤妒能，具有包容性，不斤斤计较，能够宽容别人的错误。
工作热情主动，积极进取，敬业奉献，勤奮
#8 诚实坦诚
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不隐瞒，不误导，不欺骗，能够使员工得到真实的信息
How do you define moral leadership to
begin with?
How do you consolidate into dimensions?
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Content domain clarity
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Must do an exhaustive literature review
How do you define your construct?
How does it differ from others?
What about its content domains? Its state? Its
level? Its structure?
The more you are able to define your construct
clearly before you proceed, the greater your
chance of success!!!
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Collect Behavioral Incidents
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Sampling is crucial
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Try to sample the entire content domain
Diverse sampling
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“Adequate” sample size
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Sampling across attributes (e.g., age, gender, education)
Sampling across contexts (e.g., position levels, job types, organizations, industries)
Sample until saturation (no new information yielded by additional sampling)
If you plan to do item level analysis, you need at least 200+ clear incidents
Mode of data collection should match the complexity of the
phenomenon
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Simple listing of events
Description of complete scenarios or events
In-depth personal interview
Focus group
Participant observation
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Classify Incidents into Categories
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Classification system
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Based on content similarity/dissimilarity
Aim at an “all inclusive” and “mutually exclusive” system
May need to provide sorters with more guidance
Must have clear category definition
Must have clear classification rules
Panel testing
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Use subject matter experts as panel members if possible
Train the judges well
Check interrater reliability
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From categories to concepts
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Empirical approach
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Factor analysis (Kipnis et al. 1980)
Q sort followed by cluster analysis (Coleman & Borman,
2000)
Conceptual approach
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Rely on theoretical insights (Farh et al., 2004)
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Construct re-definition
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Re-define your “constructs” while taking into account of
the results of content analysis
Constructs should be more abstract and broader than
categories
Clear construct definition is a
developing valid measures
A challenging but key task!!!
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Item development and refinement
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Content validation
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Write items based on your construct definition (be
aware of contamination & deficiency!!!)
Be sure to review items of extant scales
Incident descriptions may not make good survey items
(may be too specific, too ambiguous)
Schriesheim et al. (1990) method is useful for
multidimensional constructs
Judgments from a few content experts will do (e.g.,
MacKenzie et al. 1991)
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Summary: Best practices

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Study the literature & the phenomenon to come
up with a broad definition of the construct
Collect good behavioral incidents (quantity &
quality)
Build a sound classification system
Conduct panel test to verify your results
Use inductive and deductive approaches
alternately in the dev. process
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Take Away Lessons

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Good survey measures must be grounded on
sound theory and conceptual definitions
Developing good survey measures takes much
time, resources, experiences, and commitment,
but the payoff can be immense!!
Avoid convenience measurement at all time!!!
If there is a good, published measure available,
Use it!!! Not to reinvent the vehicle!!!
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Questions and
Answers
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References :
1.
2.
3.
3.
4.
5.
6.
7.
8.
Anderson, J. C. and Gerbing, D. W. (1991). Predicting performance of measures in a
confirmatory factor analysis with a pretest assessment of their substantive validities. Journal
of Applied Psychology, 76, 732- 740.
Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation
of a measure. Journal of Applied Psychology, 86, 386-400.
Coleman, V. I. & Borman, W. C. (2000). Investigating the underlying structure of the
citizenship performance domain. Human Resource Management Review, 10, 25-44.
Farh, J. L., Cannella, A. A. Jr., & Lee, C. (2006). Approaches to scale development in Chinese
management research. Management and Organization Review, 2, 301-308.
Farh J. L., Zhong, C. B. and Organ, D.W. (2004). Organizational citizenship behavior in the
People's Republic of China. Organization Science, 15, 241-253.
Heine, S. J., Lehman, D. R., Peng, K. 2002. What’s wrong with cross-cultural comparisons of
subjective Likert scales?: The reference-group effect. Journal of Personality and Social
Psychology, 82, 903–918.
Hinkin, T.K. (1998). A brief tutorial on the development of measures for use in survey
questionnaires. Organizational Research Methods, 1, 104-121.
Kipnis, D., Schmidt, S. M., & Wilkinson, I. (1980). Intraorganizational influence tactics:
Explorations in getting one's way. Journal of Applied Psychology, 65, 440-452.
Korman, A. K. (1974). Contingency approaches to leadership: An overview. In J.G. Hunt
and L.L. Larson (Eds.), Contingency Approaches to Leadership (pp. 189-198). Southern Illinois
University
HKUSTPress.
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References:
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MacKenzie, S. B., Podsakoff, P. M. and Fetter, R. (1991). Organizational citizenship
behaviors and objective productivity as determinants of managerial evaluations of
salespersons’ performance. Organizational Behavior and Human Decision Processes, 50, 123-150.
Morrison, E. W., & Phelps, C. C. (1999). Taking charge at work: Extrarole efforts to initiate
workplace change. Academy of Management Journal, 42, 403–419.
Nunnally J. C. and Bernstein, I. H. (1994). Psychometric Theory (3rded.). McGraw Hill.
Schwab, D. P. (1999). Research methods for organizational studies, Mahwah, NJ: Lawrence
Erlbaum.
Schriesheim, C. A. and Hinkin, T. R. (1990). Influence tactics used by subordinates: A
theoretical and empirical analysis and refinement of the Kipnis, Schmidt, and Wilkinson
subscales. Journal of Applied Psychology, 75, 246-257.
Schriesheim, C. A., Powers, K. J., Scandura, T. A., Gardiner, C. C. and Lankau, M. J. (1993).
Improving construct measurement in management research: Comments and a quantitative
approach to assessing the theoretical content adequacy of paper-and-pencil survey-type
instruments. Journal of Management, 19, 385-417.
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