structural equation modeling made easy a tutorial based on a behavioral study of communication in...

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

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Note: Also testable through correlation analysis.

Predictor or independent variable

Criterion or dependent variable

Multiple ANOVAs

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Note: The test would be MANOVA if the impact of “commor” on the three dependent variables as a group was being assessed.

Independent variable

Dependent variables

Multiple regression

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Dependent variable

Independent variables

Path analysis

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Dependent variable

Independent variables

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Intervening variable

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.

Mathematical formula

X

Y

Z

Diagrammatic representation

Partial regression (standardized) of Y and X, controlling for Z.

Partial regression (standardized) of Y on Z, controlling for X.

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 componentsConstruct (a.k.a. latent variable)

Exogenous construct (a.k.a. independent construct)

Indicator

Path

Interaction effect construct (a.k.a. moderating effect construct)

Source: Chin (2001)

Endogenous construct (a.k.a. dependent construct)

Path coefficient

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!