Introduction to structural equation modeling

1
Introduction to structural equation modeling

• Ned Kock

2
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.

3
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.

4
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.

Partial regression (standardized) of Y and X,
controlling for Z.
X
Y
Mathematical formula
Z
Partial regression (standardized) of Y on Z,
controlling for X.
Diagrammatic representation
5
Path coefficient example
Note Hypothetical situation
R
0.10
W
Mathematical formula
E
0.36
Note This is a simple linear regression model,
where R, W and E are manifest variables.
Diagrammatic representation
6
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.

7
SEM model components
Exogenous construct (a.k.a. independent construct)
Construct (a.k.a. latent variable)
Indicator
Path
Path coefficient
Endogenous construct (a.k.a. dependent construct)
Interaction effect construct (a.k.a. moderating
effect construct)
Source Chin (2001)
8
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).

9
Reflective measurement example

• Construct
• New product development team effectiveness
• Indicators (question-statements answered on a
  Likert-type scale)
• The product met or exceeded volume expectations.
• The product met or exceeded sales dollar
  expectations.
• The product overall met or exceeded sales
  expectations.

10
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.

11
Formative measurement example

• Construct
• Team electronic communication use
• Indicators (question-statements answered on a
  Likert-type scale)
• 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.
• The team used file transfer protocols (FTP) to
  attach documents to e-mails and Web pages.
• The team used a Web page dedicated to this
  project.
• The team used a Web page for this project that
  contained project specs, market research
  information, and test results.
• The team used voice messaging.
• The team used teleconferencing.
• The team used video conferencing
• The team used desktop video conferencing
• The team used attached audio files to electronic
  documents.
• The team used attached video files to electronic
  documents.

12
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.

13
SEM techniques usage
Source Gefen, Straub, Boudreau (2000)

• Notes
• Information Management (IM), 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.

14
Techniques comparison
Source Gefen, Straub, Boudreau (2000)
15
Techniques capabilities
Source Gefen, Straub, Boudreau (2000)
16
Acknowledgements
Adapted text, illustrations, and ideas from the
following sources were used in the preparation of
the preceding set of slides

1. 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.
2. 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.
3. PLS-Graph Users Guide, by W.W. Chin. Publisher
   Soft Modeling Inc. (2001).

Final slide