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Panel: Using Structural Equation Modeling (SEM)

Using Partial Least Squares (SmartPLS)

Presenters: Dr. Faizan Ali, Assistant Professor

Dr. Cihan Cobanoglu, McKibbon Endowed Chair ProfessorUniversity of South Florida Sarasota-Manatee

Housekeeping

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Raise Hand

Webinar will be recorded and sent to all attendees

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Sponsors

http://www.iibaconference.org

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http://conference.anahei.org

ANAHEI Journals

http://www.anahei.org

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Webinar Outline

• Introduction to SEM•Usage of SEM•Various methodological issues

• Formative vs. reflective constructs

•Modelling using PLS

• Evaluation of measurement model

• Evaluation of structural model

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Statistical Methods

• With first‐generation statistical methods, the general assumption is that the data are error free.

‐ Multiple regression 

‐ Logistic regression 

‐ Analysis of variance

‐ Cluster analysis 

‐ Exploratory factor analysis

‐ Multidimensional scaling

• With second‐generation statistical methods, the measurement model stage attempts to identify theerror component of the data.

‐ SEM 

‐ CB ‐ SEM

‐ PLS ‐ SEM

• Regression Based Approaches

• Only accounts for observed variables

• Simultaneous model evaluation;

• Accounts for observed and 

unobserved variables

Measurement error

• Measurement error is the difference between true value of variable and value obtained by using scale.

• Types of measurement error

• Random error can affect the reliability of construct

• Systematic error can affect the validity of construct (Hair et al. 2014)

• Source of error• Poorly worded questions in survey• Incorrect application of statistical methods

• Misunderstanding of scaling approach

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Structural equation modeling (SEM)

• An advanced statistical tool used to assess complex models with many relationships,perform confirmatory factor analysis, and incorporate both unobserved and observedvariables.

• It combines characteristics of factor analysis and multiple regressions to simultaneouslyexamine both direct and indirect effects of independent and dependent variables.

• Largely used in various academic disciplines over the last decade

• There are two approaches to estimate the relationships in a structural equation model(SEM):

• Covariance‐based SEM (CB‐SEM) 

• Variance‐Based ‐ VB‐SEM/ PLS‐SEM.

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CB‐SEM PLS‐SEMAMOS SmartPLS

LISREL PLS‐Graph

MPLUS PLS‐GUI

EQS SPADPLS

SAS LVPLS 

R WarpPLS

SEPATH PLS‐PM

CALIS semPLS

LISCOMP Visual PLS

Lavaan PLSPath

COSAN XLSTAT

SEM Software / Applications

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Usage of SEM in Hospitality Research

Main usages of SEM in hospitality research are;

• Aspects related to causality (71%).

• Testing complex models having mediating and moderating effects (45%).

• Scale development (13%).

• 22.3% articles published in four top 

hospitality journals from 2008 to 2010 

used SEM (Line & Runyan, 2014).

• 10.2% articles published in four top 

hospitality journals from 2000 to 2009 

used SEM (Yoo et al., 2011).

• 7.51% articles published in the Cornell 

Hospitality Quarterly from 2008 to 2011 

used SEM (Law et al., 2012).

64

6

129

1613

24

29

36

51

44

5351

0 0 1 0 0 0 0 0 1 2 2 1

811

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 1: Usage of SEM is Hospitality Research

SEM CB‐SEM SEM PLS‐SEM

Of the articles, 379 utilized CB‐SEM and 45 PLS‐SEM.

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Methodological Issues Ignored

•Sample Size

•Model Complexity

•Prediction‐Based Modelling

•Data Normality

•Formative and Single Item Constructs

•Weak Theoretical Support.

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PLS‐SEM

Partial Least Squares (PLS) focuses on the prediction of a specific set ofhypothesized relationships that maximizes the explained variance in thedependent variables (Hair, Ringle, & Sarstedt, 2011).

SmartPLS (Ringle et al., 2005)

Google Scholar's Page

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http://www.journals.elsevier.com/long‐range‐planning/most‐cited‐articles

Presence of PLS‐SEM

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Justification for usage of PLS‐SEM

• Primary objective of study is prediction and explanation of target constructs.

• Smaller sample sizes

• Complex models

• No assumptions about the underlying data (Normality assumptions)

• Support reflective and formative measurement models as well as single itemconstruct.

• Weaker theoretical support/ Integration of multiple theories.

• Works with ordinal and binary scaled questions.

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Missing Data Treatment and Data Normality• In general, missing data occurs when respondents intentionally or unintentionally fail to answer at least one question in a survey.

• If > 25% missing, throw out the questionnaire.• Less than 5% values missing per indicator, use; 

• Use the midpoint of the scale• Mean of the respondent• Expectation maximization (EM)

• Construct missing; If respondent did not answer all items related to one construct, delete it.

• Suspicious Respond Patterns; Remove responses

• Data Normality – Report Skewness and Kurtosis

PLS‐SEM

A PLS path model consists of two elements: 

The structural model displays the relationships (paths) between the constructs.

Themeasurement models display the relationships between the constructs and the indicator variables (rectangles).

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Reflective vs. Formative

The decision of whether to measure a construct reflectively orformatively is not clear‐cut (Hair et al., 2014).

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REFLECTIVE MEASUREMENT MODEL 

The goal of reflective measurement model assessment is toensure the reliability and validity of the construct measures andtherefore provide support for the suitability of their inclusion inthe path model.

• Reliability is the extent to which an assessment tool produces stable and consistent results.

• Validity refers to the extent to which the construct measures what it is supposed to measure.

REFLECTIVE MEASUREMENT MODEL EVALUATION

Internal Consistency Reliability Composite Reliability (CR> 0.70 ‐ in exploratory research  0.60 to 0.70is acceptable).

Cronbach’s alpha (α> 0.7 or 0.6)

Indicator reliability (> 0.708) Squared Loading ‐ the proportion of indicator variance that is explained by the latent variable

Convergent validity  Average Variance Extracted (AVE>0.5) 

Discriminant validity Fornell‐Larcker criterion Cross Loadings HTMT Criteria (New Tool).

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Convergent validity

•An established rule of thumb is that a latent variableshould explain a substantial part of each indicator'svariance, usually at least 50%.

• This means that an indicator's outer loading should beabove 0.708 since that number squared (0.7082) equals0.50.

Discriminant validity• Cross‐Loadings: An indicator's outer loadings on a construct should behigher than all its cross loadings with other constructs.

• Fornell‐Larcker criterion: The square root of the AVE of each constructshould be higher than its highest correlation with any other construct.• For example, with respect to construct  , 0.60, 0.70, and 0.90 squared are 0.36, 0.49, and 0.81. The sum of these three numbers is 1.66 and the average value is therefore 0.55 (i.e., 1.66/3).

. =0.64 . =0.64

.=0.80

AVE=0.55 AVE=0.65

0.600.700.90

0.700.800.90

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Discriminate Validity

LIKECUSLCUSACOMP

COMP

Single-item Construct

0.4356CUSA

0.68920.4496CUSL

Formative0.61460.52840.6452LIKE

Discriminant validity

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FORMATIVE MEASUREMENT MODEL EVALUATION

• The statistical evaluation criteria for reflective measurement scales cannot be directly transferred to formative measurement models where indicators are likely to represent the construct's independent causes and thus do not necessarily correlate highly.

Formative Measurement Model

Assess Collinearity Among Indicators (VIF < 5)

Assess the Significance and relevance of outer weights (T‐Value > 1.645).

The estimated values of outer weights in formative measurement models are frequently smaller than the of reflective indicators

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Interpretation:

When an indicator's weight is significant, there is empirical support to retain theindicator.

When an indicator's weight is not significant but the corresponding item loading isrelatively high (> 0.50), the indicator should generally be retained.

If both the outer weight and outer loading are nonsignificant, there is no empiricalsupport to retain the indicator and it should be removed from the model.

PLS‐SEM Structural Model Evaluation

PLS‐SEM relies on a nonparametric bootstrap procedure to test coefficients for theirsignificance.

• In bootstrapping, a large number of subsamples (i.e., bootstrap samples)are drawn from the original sample with replacement (random from thesampling population).

If the measurement characteristics of constructs are acceptable, continue with theassessment of the structural model results. Path estimates should be statisticallysignificant and meaningful.

Moreover, endogenous constructs in the structural model should have high levels ofexplained variance—R² (coefficients of determination).

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Coefficient of Determination (R²)

• The coefficient represents the exogenous latent variables' combinedeffects on the endogenous latent variable.

• It also represents the amount of variance in the endogenousconstructs explained by all of the exogenous constructs linked to it.

• The R² value ranges from 0 to 1.

• In scholarly research as a rough rule of thumb• 0.75 is substantial • 0.50 is moderate

• 0.25 is weak  

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Additional Statistics to Report

• Effect Size (ƒ²)

• Predictive Relevance (Q²)

• Goodness of Fit (GoF) Statistic

Mediator

• A mediating effect is created when a third variable or constructintervenes between two other related constructs.

• The role of the mediator variable then is to clarify or explain therelationship between the two original constructs.

• Indirect effects are those relationships that involve a sequence of relationships with at least one intervening construct involved.

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Mediator

• Baron & Kenny (1986) has formulated the steps and conditions to ascertain whether full or partial mediating effects are present in a model.

Reputation

Satisfaction

Loyalty

X

M

YP12 P23

P13

Mediation

• When testing mediating effects, researchers should rather followPreacher and Hayes (2004,2008) and bootstrap the samplingdistribution of the indirect effect, which works for simple andmultiple mediator models.

• Run Bootstrapping, and then Indirect Effects + Confidence IntervalBias Corrected.

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Higher‐Order Models ‐ Hierarchical Component Models (HCM)

• Higher‐order models or HCM most often involve testing second‐order structures that contain two layers of components.

• Complex phenomena.

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Thank you!

Dr. Faizan Ali, Assistant ProfessorCollege of Hospitality and Tourism LeadershipUniversity of South Florida Sarasota‐Manateefaizanali@usf.edu

Dr. Cihan Cobanoglu, McKibbon Endowed Chair Professor

College of Hospitality and Tourism LeadershipUniversity of South Florida Sarasota‐Manateecihan@usf.edu

http://conference.anahei.org/webinar