Transcript Fostering Interdepartmental Knowledge Communication

Introduction to
structural equation modeling
Ned Kock
SEM techniques
• Structural equation modeling (SEM) techniques
can be:
– Covariance-based – e.g., those employed by the
statistical software analysis tool called LISREL.
– Variance-based – e.g., those employed in partial least
squares (PLS) analysis.
• SEM techniques are known as second generation
data analysis techniques.
• SEM allows for the modeling and testing of
relationships among multiple independent and
dependent constructs, all at once.
Constructs, indicators and paths
• Construct
– This is a theoretical concept that is not directly
measurable. Also known as latent variable.
• Indicator
– Is a measurable variable used to represent a
construct (e.g., item on a questionnaire). Also
referred to as manifest variable, item, and indicant.
• Path
– Is the link between constructs, or from construct to
indicator. Also known as link, and often measured
through a path coefficient.
Path coefficient
Path coefficient between Y and X = standardized
partial regression of Y on X controlling for the
effect of one (e.g., Z) or more variables.
X
Partial regression
(standardized) of Y
and X, controlling
for Z.
Y
Mathematical formula
Z
Partial regression
(standardized) of Y
on Z, controlling
for X.
Diagrammatic representation
Path coefficient example
Wealth - W
Race - R
Education - E
W
1
0.25
0.45
R
E
1
0.31
1
Correlation matrix
Path coefficient between W and R controlling for E = 0.10
Note: Hypothetical situation
R
0.10
W
Mathematical formula
E
Note: This is a simple linear
regression model, where R, W and
E are manifest variables.
0.36
Diagrammatic representation
Endogenous vs. exogenous
• Exogenous construct
– This is a construct that is independent of any other
constructs.
– No other constructs point at it in an SEM diagram.
– Also known as exogenous latent variable.
• Endogenous construct
– This is a construct that depends on one or more
other constructs.
– Is pointed at by one or more constructs in an SEM
diagram.
– Also known as endogenous latent variable.
SEM model components
Exogenous
construct (a.k.a.
independent
construct)
Indicator
Construct
(a.k.a. latent
variable)
Path
Path coefficient
Endogenous
construct (a.k.a.
dependent
construct)
Interaction effect
construct (a.k.a.
moderating effect
construct)
Source: Chin (2001)
Reflective measurement
• In this form of construct measurement, paths
connecting construct to indicators are directed
towards the indicators.
• The indicators are supposed to load strongly on
the construct.
• Such constructs are often designated as latent
constructs (or reflective latent constructs).
Reflective measurement example
•
Construct
–
•
New product development team effectiveness
Indicators (question-statements answered on a
Likert-type scale)
1. The product met or exceeded volume expectations.
2. The product met or exceeded sales dollar
expectations.
3. The product overall met or exceeded sales
expectations.
Formative measurement
• In this form of construct measurement, paths
connecting construct to indicators are directed
towards the construct.
• The indicators are not assumed to have to load
strongly on the construct.
• Such constructs are often designated as formative
latent constructs.
• Only variance-based SEM techniques (e.g., PLS)
can deal with formative latent constructs.
Formative measurement example
•
Construct
–
•
Team electronic communication use
Indicators (question-statements answered on a Likert-type scale)
1.
2.
3.
4.
5.
6.
7.
The team used e-mail to fellow team members (1 to 1).
The team used e-mail to team distribution lists (1 to many).
The team used team messaging boards or team discussion forums.
The team used shared electronic files.
The team used Lotus notes to facilitate sharing information among team members.
The team used electronic newsletters that covered project information.
The team used auto routing of documents for team member and management
approval.
8. The team used file transfer protocols (FTP) to attach documents to e-mails and
Web pages.
9. The team used a Web page dedicated to this project.
10. The team used a Web page for this project that contained project specs, market
research information, and test results.
11. The team used voice messaging.
12. The team used teleconferencing.
13. The team used video conferencing
14. The team used desktop video conferencing
15. The team used attached audio files to electronic documents.
16. The team used attached video files to electronic documents.
The SEM advantage
• The ability to test multiple relationships at once
differentiates SEM techniques from several first
generation regression techniques such as:
–
–
–
–
ANOVA.
MANOVA.
LOGIT.
Linear regression.
• Generally, first generation techniques allow for
the analysis of a significantly more limited
number of relationships between independent and
dependent variables at once.
SEM techniques usage
Notes:
•Information & Management (I&M), Information Systems Research (ISR), and
Management Information Systems Quarterly (MISQ) are top-tier journals in
the field of information systems.
•Wynne Chin, one of the developers of PLS-Graph (a widely used PLS-based
SEM analysis tool), is an information systems researcher.
•SEM tools are also widely used in other disciplines, mostly behavioral science
research-based or related disciplines, for the causal modeling of multivariate
datasets where complex webs of relationships between variables are tested.
Techniques comparison
Techniques capabilities
Acknowledgements
Adapted text, illustrations, and ideas from the
following sources were used in the preparation of the
preceding set of slides:
1.
2.
3.
4.
Gefen, D., Straub, D. W., & Boudreau, M-C. (2000). Structural
equation modeling and regression: Guidelines for research
practice. Communications of the AIS, 4(7), 1-76.
Kock, N. and Lynn, G. (2005), The E-collaboration Paradox: A
Study of 290 New Product Development Teams, Proceedings of
the 16th Information Resources Management International
Conference, Khosrowpour, M. (Ed), Idea Group Publishing,
Hershey, PA, pp. 444-448.
Kock, N. (2015). WarpPLS 5.0 User Manual. Laredo, TX:
ScriptWarp Systems.
PLS-Graph User’s Guide, by W.W. Chin. Publisher: Soft Modeling
Inc. (2001).
Final slide