Survey Instrument Development in OB/HRM Research Prof. Jiing-Lih Larry Farh HKUST IACMR Guangzhou...
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Transcript of Survey Instrument Development in OB/HRM Research Prof. Jiing-Lih Larry Farh HKUST IACMR Guangzhou...
Survey Instrument Development in OB/HRM
Research
Prof. Jiing-Lih Larry FarhHKUST
IACMRGuangzhou workshop
July 2007
2HKUST Business SchoolLarryFarh
Too many constructsConstructs are poorly definedMeasures do not match constructsUnreliable/invalid measuresLevel of measurement does not match the level of the
theory
Fatal flaws for empirical papers!!!
Construct and Measurement Related Problems in Manuscripts
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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
X’ Y’
YX
Independent
Operational
Conceptual
Dependent(a)
(b1)
(d)
(c)
(b2)
1. Independent and dependent variables are identified by X and Y, respectively.2. The symbol prime, ’ is used to designate that a variable is specified at the conceptual
level.3. Arrows represent the direction of influence or cause.4. a—conceptual relationship; d---empirical relationship; b1, b2—construct validity; c---
internal validity
From Schwab (1999)
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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 StepsDefine 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
From Schwab (1999)
Content validityFactor analysisReliabilityCriterion-related/Convergent/Discriminant/Nomologicalvalidity
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Why is it important?
How to do it?
What are some of the best practices?
Survey Instrument Development
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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. Have a job which leaves you sufficient time for your personal or family life. (.86)
2. Have training opportunities (to improve your skills or learn new skills). (-.82)
3. Have good physical working conditions (good ventilation and lighting, adequate work space, etc.). (-.69)
4. Fully use your skills and abilities on the job. (-.63)5. Have considerable freedom to adapt your own approach to
the job. (.49)6. Have challenging work to do---work from which you can get a
personal sense of accomplishment. (.46)7. Work with people who cooperate well with one another.(.20)8. Have a good working relationship with your manager.(.20)
Adapted from Heine et al. (2002)
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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)
Adapted from Heine et al. (2002)
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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).
From Schwab (1999)
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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.
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
6. How I was treated when I bought the computer 1 2 3 4 5
My satisfaction with:
Very Dissatisfied Dissatisfied
Neither Satisfied nor Dissatisfied Satisfied
Very Satisfied
1 2 3 4 5
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Construct Validity Challenges
Construct Variance
Observed Score Variance
Systematic Variance
Unreliability
Reliable Contaminatio
n
Construct Valid
VarianceDeficiency
From Schwab (1999)
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Scale Development Process
Step1: Item Generation
Step 2: Questionnaire Administration
Step 3: Initial Item Reduction
Step 4: Confirmatory Factor Analysis
Step 5: Convergent/Discriminant Validity
Step 6: Replication
From Hinkin (1998)
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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
double-barreled 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 RatioContent Validation Ratio
CVR =2 n e - 1
N
ne
N
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.
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-to-response 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 alphaThe average of all possible split halve reliabilities.
n is the number of items for each applicantt is the total of all items for an applicant is the variance across all applicants 2
)(1 2
1
22
t
n
iit
nn
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An example of coefficient Subject
Item A B C Variance1 6 5 4 1.002 6 4 5 1.003 5 3 3 1.334 4 4 4 .005 4 5 4 .33
3.67Total 25 21 20 7.00
Variance of total = 7.0 ; Total of variance = 3.67
2t
22
5
1
2
ii
60.)0.7
67.30.7(
45
)(1 2
1
22
t
n
iit
nn
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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 ValidityConstruct
Measure B
Measure A
From Schwab (1999)
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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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A sample MTMM matrix
Heterotrait-monomethod
Monotrait-monomethodMonotrait-
heteromethod
Heterotrait-heteromethod
Adapted from http://www.socialresearchmethods.net/kb/mtmmmat.htm
Note: SE: self esteem; SD: self disclosure; LC: Locus of control
(Paper & Pencil self test)
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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. Try to institute new methods that are more effective
2. Try to introduce new structure, technologies, or approach to improve efficiency
3. Try to change how his/her job is executed in order to more effective
4. 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
Felt responsibility
Expert power
Taking charge
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Deductive Example: Org. Justice (Colquitt, 2001, JAP)
The Dimensionality of Organizational Justice
Organizational Justice
Distributive Justice
Procedural Justice
Interactive Justice
Informational Justice
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Sample Items: Org. Justice (Colquitt, 2001, JAP) Distributive Justice
“Does your outcome reflect the effort you have input into your work” (Leventhal, 1976)
Procedural justice “Have you been able to express your views and feelings during those
procedures” (Thibaut & Walker, 1975) Interactive justice
“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
Procedural Justice
Interactive Justice
Informational Justice
Outcome Satisfaction
Rule Compliance
Leader Evaluation
Collective Self-esteem
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Four Types of Scale Development Approaches in Chinese Management Research
Expectations about Cultural Specificity
Etic Orientation Emic Orientation
Source of the scale
Use or Modify an Existing Scale
Translation Adaptation
Develop a New Scale
De-contextualization
Contextualization
Farh, Cannella, & Lee (2006, MOR)
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Four Types of Scale Development Approaches in Chinese
Management Research
ScaleDevelopmentApproaches
KeyAssumptions
MajorStrengths
MajorLimitations
Translation approach
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
Low developmental time and costsPreserve the possibility of a high level of equivalenceAllow for direct cross-cultural comparison of research findings
Difficulty in achieving semantic equivalence between the Chinese and Western scalesCulturally unbiased Western scales are hard to come by
Adaptation approach
Target construct is equivalent between cultures in terms of overall definition and content domainAvailability of high quality Western scales for target construct
Low to moderate developmental time and costsEase of scholarly exchanges of research findings with the Western literature
Difficulty in conducting cross-cultural researchDrastic 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:
ScaleDevelopmentApproaches
KeyAssumptions
MajorStrengths
MajorLimitations
De-contextualization approach
Target construct is etic or universal or culturally invariantHigh quality scale for the target construct is unavailable in the literature
Opportunity to develop universal measure for target constructEase of scholarly exchange of research findings with the Western literature
Long developmental time and high developmental costsItems tend to be phrased at a more abstract level, which may limit its informational and practical value
Contextualization approach
Target construct is emic or culture specificHigh quality emic scale for target construct unavailable in the literature
Opportunity to develop scales highly relevant for the Chinese context Opportunity to contribute context-specific knowledge to Chinese management
Long developmental time and high developmental costsLimited generalizability of the new scaleHard to communicate research findings with the Western literature
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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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Item Generation Process
Content domain clarity?
low
Collect behavioral incidents
Classify into categories
Form dimensions from categories
Domain definition
Item development & refinement
Empirical testing
Key issueshigh
sampling/method
classification/panel test
Empirical/ conceptual
Creativity & insight
content validation
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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 ItemsNID: 111 為人坦誠 NID: 112 處理工作的態度比較公正NID: 113 容易和員工接近 NID: 114 有相關的知識,工作技巧 NID: 121 無畏,具有挑戰中上級的勇氣,堅持做正確的事情NID: 122 關心,栽培下屬.有人情味 NID: 123 坦白.鼓勵團結內部充份的信息流通 NID: 124 認真做事 NID: 125 公平對待下屬,以業績表現評價下屬NID: 126 獎勵,肯定下屬優秀表現NID: 131 公私分明NID: 132 言行一NID: 133 一視同仁 NID: 134 光明正大NID: 142 有責任NID: 144 不推卸NID: 145. 公私分明 NID: 146. 尊重別人NID: 151. 誠實正直
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Some 44 Categories #1 胸怀宽广
不疾贤妒能,具有包容性,不斤斤计较,能够宽容别人的错误。 #3 敬业
工作热情主动,积极进取,敬业奉献,勤奮 #8 诚实坦诚
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 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 Sampling is crucial
Try to sample the entire content domain Diverse sampling
Sampling across attributes (e.g., age, gender, education) Sampling across contexts (e.g., position levels, job types, organizations,
industries) “Adequate” sample size
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
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
Classification system 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 Use subject matter experts as panel members if possible Train the judges well Check interrater reliability
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From categories to concepts
Empirical approach Factor analysis (Kipnis et al. 1980) Q sort followed by cluster analysis (Coleman &
Borman, 2000) Conceptual approach
Rely on theoretical insights (Farh et al., 2004)
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Construct re-definition
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 must before developing valid measures
A challenging but key task!!!
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Item development and refinement Content validation
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 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 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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References :1. 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.
2. Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86, 386-400.
3. Coleman, V. I. & Borman, W. C. (2000). Investigating the underlying structure of the citizenship performance domain. Human Resource Management Review, 10, 25-44.
3. 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.
4. 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.
5. 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.
6. Hinkin, T.K. (1998). A brief tutorial on the development of measures for use in survey
questionnaires. Organizational Research Methods, 1, 104-121.7. Kipnis, D., Schmidt, S. M., & Wilkinson, I. (1980). Intraorganizational influence
tactics: Explorations in getting one's way. Journal of Applied Psychology, 65, 440-452.8. 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 Press.
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References:9. MacKenzie, S. B., Podsakoff, P. M. and Fetter, R. (1991). Organizational
citizenshipbehaviors and objective productivity as determinants of managerial evaluations of
salespersons’ performance. Organizational Behavior and Human Decision Processes, 50, 123-150.
10. Morrison, E. W., & Phelps, C. C. (1999). Taking charge at work: Extrarole efforts to initiate
workplace change. Academy of Management Journal, 42, 403–419.11. Nunnally J. C. and Bernstein, I. H. (1994). Psychometric Theory (3rded.). McGraw
Hill. 12. Schwab, D. P. (1999). Research methods for organizational studies, Mahwah,
NJ: Lawrence Erlbaum. 13. 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.
14. 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.