Structural Equation Modeling Made Easy
A Tutorial Based on a Behavioral Study of Communication
in Virtual Teams Using WarpPLS
Ned Kock
Outline of tutorial
• 5-minute video clip (available from YouTube via
WarpPLS.com).
• Basic overview of structural equation modeling
(SEM).
• Study of communication in virtual teams using
WarpPLS.
• Overview of web resources on WarpPLS, including
download site and blog.
Note: Questions will be answered at any time during
the tutorial.
5-minute video clip (available from
YouTube via WarpPLS.com)
Basic overview of structural
equation modeling (SEM)
Comparison of means model
Criterion or
dependent variable
Predictor or
independent
variable
commor
easgen
Note: Also testable through correlation analysis.
Multiple ANOVAs
Independent
variable
easund
commor
Dependent
variables
easgen
comple
Note: The test would be MANOVA if the impact of
“commor” on the three dependent variables as a group
was being assessed.
Multiple regression
Dependent
variable
easund
commor
easgen
comple
Independent
variables
Path analysis
Intervening
variable
easund
Independent
variables
Dependent
variable
easuse
commor
easgen
comple
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
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).
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.
Study of communication in
virtual teams using WarpPLS
Participants
• Contact persons in a variety of companies in the
Northeastern USA were selected to participate in
the study.
• To be included in this study, each company must
have developed a product that had been launched
into the marketplace and commercialized for at
least six months.
• Data from 290 new product development (NPD)
projects in 66 companies were obtained.
Research instrument
• A questionnaire developed based on previous
research on NPD teams was used.
• All constructs in the study were measured
using multiple-item scales, which in turn
were Likert-type scales (0 = “Strongly
Disagree” to 10 = “Strongly Agree”).
Constructs and measures
Constructs and measures (cont.)
Step 1: Create project file
Step 2: Read raw data
Step 3: Pre-process data
Step 4: Define model
Step 5: Perform analysis
Overview of web resources on
WarpPLS
WarpPLS.com
WarpPLS blog
WarpPLS on YouTube
Thank you!