Using the SmartPLS Software Assessment of Measurement Models Joe F. Hair, Jr. Founder & Senior Scholar

Reflective Measurement Models Stage 5a All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Corporate Reputation Extended Model All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Extended Reputation Model Constructs Outcome Reputation Constructs (endogenous) CUSL = loyalty (3 items) COMP = competence (3 items) CUSA = satisfaction (1 item) LIKE = likability (3 items) Driver Constructs (exogenous) QUAL = quality of a company’s products/services and customer orientation (8 items) PERF = economic and managerial performance (5 items) CSOR = corporate social responsibility (5 items) ATTR = attractiveness (3 items) All rights reserved ©. Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, Sage, SmartPLS, and session presenters.

Reflective Measurement Models To evaluate reflectively measured models, we examine the below: outer loadings composite reliability average variance extracted (AVE = convergent validity) discriminant validity All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

When you select the Default Report this is the screen you will get. Run the PLS algorithm to obtain information to evaluate Reflective Measurement Models To access the information to evaluate reflective models select one of the reports under this tab. All outer loadings of the reflective constructs COMP, CUSL, and LIKE are well above the minimum threshold value of .708. The loadings range from a low of 0.7985 to a high of 0.9173. When you select the Default Report this is the screen you will get. To eliminate the unnecessary options on the navigation tree click on the minus sign on the left side. You will get the simplified screen on the next slide. All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

The loadings range from a low of 0.7985 to a high of 0.9173. Outer Loadings All outer loadings of the reflective constructs COMP, CUSL, and LIKE are well above the minimum threshold value of .708. The loadings range from a low of 0.7985 to a high of 0.9173. The “Toggle Zeros” button in the task bar (top left of screen) was used to improve the readability of the results table above. This button suppresses the zeros in the table. All rights reserved ©. Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Composite Reliability vs. Cronbach Alpha? Reliability results for the Reputation model are in the Default Report under Quality Criteria and Overview All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Composite Reliability All three reflective constructs have high levels of internal consistency reliability, as demonstrated by the above composite reliability values. To obtain the above table that shows the AVE, Composite reliability, Communality, Redundancy, etc., left click on the Overview tab under the Quality Criteria. All rights reserved ©. Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

What is Convergent Validity and Discriminant Validity?

Discriminant validity is not present in the above constructs Discriminant validity is not present in the above constructs. Correlation squared (variance shared between constructs = 64%) is larger than the AVE of Y1 (only 0.55 – variance shared within construct = 55%).

Average Variance Extracted = AVE The AVE values (convergent validity) are well above the minimum required level of .50, thus demonstrating convergent validity for all three constructs. To obtain the above table that shows the AVE, left click on the Overview tab under the Quality Criteria. All rights reserved ©. Cannot be reproduced or distributed without express written permission from Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Discriminant Validity The off-diagonal values in the above matrix are the correlations between the latent constructs. To obtain the shared values between the constructs you must square these correlations. See next slide where this calculation is shown. The results on the next slide indicate there is discriminant validity between all the constructs. To obtain the above table that includes information to determine the Fornell-Larcker criterion for discriminant validity, left click on the Latent Variable Correlations tab under the Quality Criteria. All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Discriminant Validity – Fornell-Larcker Criterion Interconstruct Correlations COMP CUSA CUSL LIKE 1 0.4356 0.4496 0.6892 0.6452 0.5284 0.6146 Squared Interconstruct Correlations 0.6806 0.1897 Single-Item Construct 0.0000 0.2021 0.4750 0.7484 0.4163 0.2792 0.3777 0.7471 Note: diagonal = AVEs All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Discriminant Validity – Cross Loadings Criterion Comparing the loadings across the columns in the above matrix indicates that an indicator’s loadings on its own construct are in all cases higher than all of its cross loadings with other constructs. The results indicate there is discriminant validity between all the constructs based on the cross loadings criterion. To obtain the above table that shows the cross loadings to assess discriminant validity, left click on the Latent Cross Loadings tab under the Quality Criteria. All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Formative Measurement Models Stage 5b All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Evaluating Formative Measurement Models Empirical assessment of formative measurement models is not the same as with reflective measurement models. This is because the indicators theoretically represent the construct’s independent causes and thus do not necessarily correlate highly. As a result, internal consistency reliability measures such as Cronbach Alpha are not appropriate. Instead, researchers should focus on establishing content validity before empirically evaluating formatively measured constructs. This process requires ensuring that the formative indicators capture all (or at least major) facets of the construct. All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Corporate Reputation Extended Model All rights reserved ©. Cannot be reproduced or distributed without express written permission from Sage, Prentice-Hall, McGraw-Hill, SmartPLS, and session presenters.

Corporate Reputation Extended Model The extended corporate reputation model has three main conceptual/theoretical components: (1) the target constructs of interest (i.e., CUSA and CUSL); (2) the two corporate reputation dimensions, COMP and LIKE, that represent key determinants of the target constructs; and (3) the four exogenous driver constructs (i.e., ATTR, CSOR, PERF, and QUAL) of the two corporate reputation dimensions.

Indicators for SEM Model Exogenous Constructs – Assessing Content Validity –