Application of Structural Equation Modeling in PACS research

Application of Partial Least Squares SEM in PACS research

Utrecht, October 13, 2010

A conceptual introduction

Rogier van de Wetering

Overview of presentation

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Goal: Providing a non-technical overview of PLS SEM• Application oriented

• No prior knowledge of SEM required

Agenda:

Part 1:• Short summary SEM

• What is Partial Least Squares?

Part 2:

• Introduction PACS research

• Application of PLS to PACS research

• DEMO PLS using SMART PLS

© 2010 Deloitte Touche Tohmatsu

Part 1: Partial Least Squares

Short recap:

Basically a Structural Equation Model involves three primary components

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Manifest variables, i.e.,

observable measures Latent constructs

Path relationship(s)

MV1

MV3

MV2

MV1

SEM family can be divided into Covariance based SEM and Partial Least

Squares

SEM allows stating theory more exactly, testing theory more precisely, and yielding a more thorough

modeling/understanding of empirical data about complex phenomena and relationships

Related concepts:

• Path analysis

• Causal Models

• Confirmatory factor analysis

• Covariance structural analysis and

• Latent variable SEM

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Exploratory Factor

Analysis

Lawley (1940)

Factor Analysis

(PCA) through

iterated OLS

Wold (1966)

PLS-SEM through

iterated OLS

Wold (1978)

Path Analysis –

without latent

constructs - Wright

(1921)

Confirmatory Factor

Analysis

Jöreskog (1969)

LISREL-SEM

Jöreskog (1969)Model confirmation

Prediction oriented

Partial least squares is a powerful and effective means to test multivariate

structural models with latent constructs

Herman Wold („66) developed the PLS

LISREL “hard modeling” vs PLS “soft modeling”

Appealing alternative for researchers:

1. Small samples,

2. Theory in the early stages of development,

3. Data that violate traditional statistical assumptions,

4. modern easy-to-use PLS software with graphical user-interfaces.

PLS is generally recommended for predictive research models (it is a regression method that predicts

Y from X)

PLS application has encountered increased popularity across many scientific disciplines (e.g., MIS,

marketing, chemistry and chemometrics)

PLS represents a powerful and effective means to test multivariate structural models

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When to use Partial Least Squares SEM?

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small data

sample

non-normal

data

distribution

Formative /

reflective

measures

All ratio

measures

Emphasis on

theory

development

Many manifest

variables

Usage of Partial Least Squares (PLS) SEM

Sample characteristics

© 2010 Deloitte Touche Tohmatsu

Part 2: PACS research

MRQ: “How can hospital enterprises systematically mature their Picture Archiving and

Communication System?”

The core of stage IV is to investigate the relationship between PACS

alignment and PACS performance

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• Workflow

assessment

‒ Holistic perspective

‒ PACS impact on

workflow

• PACS maturity

‒ Meta-analysis

‒ Maturity model

• Development of strategic

situational paths

‒ Extending Maturity model

‒ Introducing strategic

alignment

• Explain PACS

performance

‒ Development

of higher order

model

‒ Application of

SEM

Stage I

Stage II

Stage III

Stage IV

Using SEM higher order constructs can be created

• SEM can be applied to test hierarchical constructs, or multidimensional constructs

• Higher order latent constructs can be created by specifying a latent variable that represents all the

manifest variables of the underlying lower-order latent variable

• In essence, two models of higher-order constructs can be distinguished:

‒ Reflective construct model

‒ Composite model

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S&P

M&C

O&P

IT

P&C

PACS

alignment

OP3

OP5

OP4

MV7

MV8

δ7

δ8

MV9

MV10

δ9

δ10

MV11

MV12

δ11

δ12

ζ

ζ

ζ

λ7

λ8

λ9

λ10

λ11

λ12

γ4

γ5

γ6

1st

order 2nd

order 3rd

order

“Alignment of PACS (represented by

the multi factorial nature of five

organizational domains and their

related maturity levels) has a positive

relationship on PACS performance

(represented by the multi factorial

nature in terms of hospital efficiency

and clinical effectiveness and their

related items.”

Hypothesis

This modelling of PACS alignment is statistically appropriately captured by a

pattern of co-variation

Our main is to develop an

integrative model to assess

maturity and organizational

alignment of PACS, and their

impact on PACS performance

Alignment coincides

covariation (Venkatraman,

„89) as an operationalized

statistical scheme within SEM

Covariation defined as the

pattern of internal consistency

among underlying constructs

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Integrative PACS performance model

Alignment Performanceβ = 0.6, t = 4.01; p < 0.0001

R2= .37

Short DEMO using PLS

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Further reading on PLS SEM

Chin, W. W. (1998). Issues and Opinion on Structural Equation Modeling. MIS Quarterly, 22(1), vii-xvi.

Marcoulides, G. A., & Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly 30(2), iv-viii.

Marcoulides, G. A., Chin, W. W., & Saunders, C. (2009). A critical look at partial least squares modeling.

MIS Quartery, 33(1), 171-175.

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