1. variance- & covariance-based sem 2. testing for common method

SEM OVERVIEW

1. VARIANCE- & COVARIANCE-BASED SEM

2. TESTING FOR COMMON METHOD BIAS IN SEM

3. NESTED MODELS AND MULTI-GOUP SEM

4. ADVANCES TO WATCH IN SEM

Jagdip Singh and Mark Leach 2013

VARIANCE- & COVARIANCE-BASED SEM

Four Questions: 1. When is it appropriate to use VBSEM (PLS)?

2. What is the state-of-art in PLS analysis?

3. What questions will likely arise in the review process?

4. What are some key references?


VARIANCE- & COVARIANCE-BASED SEM

VB-SEM

Causal/formative/composite

Multidimensional Items (complete set)

Unidentified + 2 reflective measures = Identified

Measures-error-free

No Measurement Invariance

CB-SEM

Effect/reflective

Unidimensional item (useful redundancy)

> 3 measures = Identified

Measures-error-prone

Yes Measurement Invariance Jagdip Singh and Mark Leach 2013

SmartPlS

Source: http://www.smartpls.de/


VARIANCE- & COVARIANCE-BASED SEM Hair, J.F./ Sarstedt, M./ Ringle, C.M./ Mena, J.A.: An assessment of the use of partial least squares structural equation modeling in marketing research, in: Journal of the Academy of Marketing Science (JAMS), Volume 40 (2012), Issue 3, pp. 414-433. Lara Lobschat, Markus A. Zinnbauer, Florian Pallas and Erich Joachimsthaler: Why Social Currency Becomes a Key Driver of a Firm’s Brand Equity: Insights from the Automotive Industry, Long Range Planning, Volume 46 (2013), pp. 125-148. Sarstedt, M./ Henseler, J./ Ringle, C.M.: Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results, in: Advances in International Marketing (AIM), Vol. 22, Bingley 2011, pp. 195-218. Edwards, Jeffery (2011), “The Fallacy of Formative Measurement,” Organizational Research Methods, 14 (2): 370-388. Hardin, Andrew and George Marcoulides (2011), “A Commentary on the Use of Formative Measurement,” Educational Psychological Measurement, 71 (5): 753-764. Treiblmaier, Horst, Peter Bentler and Patrick Mair (2011), “Formative Constructs Implemented via Common Factors,” Structural Equations Modeling, 18:1, 1-17.


“In fact, our evidence suggests that even simple summed scales provide better reliability than PLS… In addition, using a model-based weighting system as used in PLS will guarantee problems with interpretational confounding.”

Ronkko and Evermann (2013), “A Critical Examination of Common Beliefs about Partial Least Squares Path Modeling,” ORM, online March 7, 2013.


“The authors [Hardin and Marcoulides 2011. p. 753] suggest that to avoid further confusing the consumers of this research, the prudent course of action may be to consider temporarily suspending the use of formative measurement.”

They further contend that the debate on formative measurement should be restricted primarily to premier methods journals where experts can ultimately develop a theoretical perspective that supports or rejects its implementation.”


SEM IN RECENT SALES PUBLICATIONS

JPSSM 2012-13

SEM

nonSEM

JAMS January 2013

SEM

nonSEM


COMMON METHOD BIAS

Three questions

1. How is CMB evaluated in SEM?

2. What questions will arise in the review process?



COMMON METHOD BIAS

Marker Variable

Method Factor

Harmon

What is most appropriate and when?

Which is most robust?


COMMON METHOD BIAS

Lindell, Michael K., and David J. Whitney (2001), “Accounting for Common Method Variance in Cross-Sectional Research Designs,” Journal of Applied Psychology, 86 (1), 114–121.

Podsakoff, Philip M., Scott B. MacKenzie, Jeong-Yeon Lee, and Nathan P. Podsakoff (2003), “Common Method Bias in Behavioral Research: A Critical Review of the Literature and Recommended Remedies,” Journal of Applied Psychology, 88 (October), 879–903.


NESTED MODELS

Four Questions

1. How are nested models used in SEM?

2. What are their strengths and pitfalls?

3. What questions will arise in the review process?



NESTED MODELS

Measurement

• Measurement vs. Structural Models

• Lower vs. Higher order Models

• Common method bias

Hypotheses Testing

• Moderation and group differences


MULTI-GROUP SEM IN RECENT SALES PUBLICATIONS

4

5

0 2 4 6

Multi goup

One group

JPSSM 2012-13

4

0

Multi group

one group

0 2 4 6

JAMS January 2013


NESTED MODELS

MacKenzie, Scott B. and R. A. Spreng (1992), “How Does Motivation Moderate the Impact of Central and Peripheral Processing on Brand Attitudes and Intentions?” Journal of Consumer Research, 18 (March), 519-29.

• Ping, Robert A. (1994), “Does Satisfaction Moderate the Association between Alternative Attractiveness and Exit Intention in a Marketing Channel?”, Journal of the Academy of Marketing Science, 22 (Fall), 364-71.

Hair, Joseph F., William C. Black, Barry J. Babin, and Rolph E. Anderson (2009), Multivariate Data Analysis, 7th ed. Upper Saddle River, NJ: Prentice Hall.


MEDIATION, MODERATION, AND MULTIDATA: THE THREE MS OF SEM

SALES CONSORTIUM: 2013


X (independent

variable)

Y (dependent

variable)

MEDIATION BASICS

Byx = significant?

X (independent

variable)

Y (dependent

variable)

M (mediating variable)

Yes

Byx ~ 0

Bmx = sig Bym = sig

A significant relationship between X and Y…

vanishes with the inclusion of a third variable (M), which explains

why X and Y are related


18

X (independent

variable)

Y (dependent

variable)

MEDIATION BASICS

Byx = nonsignificant

X (independent

variable)

Y (dependent

variable)

M (mediating variable)

Yes

Byx = significant

Bmx = sig Bym = sig

A nonsignificant relationship between X and Y…

becomes significant with the inclusion of a third variable (M), which separates the positive and

negative effects of X on Y


19

19

Role Stress

Performance

MEDIATION Example


Role Stress

Performance Burnout

Yes

Byx = positive

Bmx = + Bym = -

A nonsignificant relationship between role stress and

performance…

is separated into a positive (eustress) and negative (distress)

effect on performance


20

20

20

Change Performance

MEDIATION Example


Change Performance Detachment

Yes

Byx = positive

Bmx = + Bym = -

A nonsignificant relationship between change and

performance…

is separated into a positive ( functional) and negative (dysfunctional) effect on

performance


21

21

21

21

MODERATED MEDIATION Example


Bmx|1 = + Bym|1 = -

A significant mediated relationship between change

and performance…

is turned off or on by a third variable that makes one or both mediated paths nonsignificant


Bym|2 = - Bmx|2 = 0

Participation


General Markov process (linear)

Stable process b1 = b2 = b3

Y1 Y2 Y3 Y4

e11

e21

e31

b1 b2 b3

MIULTI-PERIOD Example


y1

x11

e1

x12

e2

x13

e3

y2

x21

e4

x22

e5

x23

e6

y3

x31

e7

x32

e8

x33

e9

d1 d2

Constrain same loading to be equal over time

General Markov process with Factorial Invariance


A series of chi-square difference tests enables selection of parsimonious model, for example, c1 = c2 = c3, or d1 = d2 = d3 = 0.

Y1 Y2 Y3 Y4

e1 1

e2 1

e3 1

a1 a2 a3

X1 X2 X3 X4

e4 e5 e6

1 1 1

b1 b2 b3

d1 d2 d3

c1 c2 c3

Cross-lagged Panel Data Model


mem1

x11

e1

x12

e2

x13

e3

mem2

x21

e4

x22

e5

x23

e6

mem3

x31

e7

x32

e8

x33

e9

..71 .92

trust1 trust2 trust3.56 .91

.04*

.1* .05*

.05*

d3

d1 d2

d4

Cross-lagged Panel Data Model with Correlated Errors


Y1 Y2 Y3 Y4

e1 1

e2 1

e3 1

X1 X2 X3 X4

e4 e5 e6

1 1 1

Z

Cross-lagged Panel Data Model with Covariate Z


Y1 Y2 Y3 Y4

e1 1

e2 1

e3 1

X1 X2 X3 X4

e4 e5 e6

1 1 1

Z2 Z3 Z4

Cross-lagged Panel Data Model with Time-dependent Covariate Z


Longitudinal SEM models can include:

• Multiple group analysis

• Interaction effects

• Different models for different racial/ethnic groups

• Multiple indicators at each wave of measurement

• Allows estimation of reliability and appropriate path coefficient adjustment for unreliability

• Psychometric assessment of measurement invariance

• Multiple Covariates

• Time invariant covariates, gender, or personal characteristics

• Time varying covariates, household income.

• Complex error structures


X1 X2 X3

Z2 Z3

Y1

1

1 1 1

Y2

1

1 1 1

Y3

1

1 1 1

GROUP 1

GROUP 2

X1 X2 X3

Z2 Z3

Y1

1

1 1 1

Y2

1

1 1 1

Y3

1

1 1 1


UNCONDITIONAL RANDOM COEFFICIENTS GROWTH CURVE MODEL: BASIC IDEA

Intercept

y1

1

1

y2

1

1

y3

1

1

Slope

y4 y5 y6

4

1

5

1

6

1

1

1

13

1

0


1. variance- & covariance-based sem 2. testing for common method

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