Smart PLS

Question 1

Structural Equation Modeling (SEM)

Answer 1

a family of statistical models that seek to explain the relationships among multiple variables

Question 2

What can SEM be used for?

Answer 2

regression analysis, path analysis and factor analysis.

Question 3

Research goals:
CB-SEM
PLS-SEM

Answer 3

Parameter-oriented
Prediction-oriented

Question 4

Method:
CB-SEM
PLS-SEM

Answer 4

Covariance-based
Variance-based

Question 5

Data assumption:
CB-SEM
PLS-SEM

Answer 5

Normal distribution
None

Question 6

Good reasons to use PLS-SEM

Answer 6

• Estimation of complex models
• Integration of formatively measured constructs
• Working with small sample sizes
• Focus is on prediction
• Focus is on exploring new relationships

Question 7

Not so good reasons to use PLS-SEM

Answer 7

• Focus is on exploring new relationships without having a hypothesized model.
• Working with small sample sizes (when the population is large)

Question 8

Path model

Answer 8

a diagram that connects variables/constructs based on theory and logic to visually display the hypotheses that will be tested.

Question 9

Correlation

Answer 9

linear relationship between two variables
range from -1 to +1

Question 10

Covariance

Answer 10

• unstandardised form of correlation
• positive number leads to positive relationship

Question 11

Reflective scale

Answer 11

changes in the latent variable directly cause changes in the assigned indicators

Question 12

Formative scale

Answer 12

changes in one or more of the indicators cause changes in the latent variable

Question 13

Stage 1:

Answer 13

Evaluation of the Measurement Model

Question 14

Reliability models

Answer 14

• Indicator Reliability (Loading)
• Composite Reliability (CR) and Cronbach Alpha values

Question 15

Indicator Reliability (Loading) and Composite Reliability (CR) and Cronbach Alpha values

Answer 15

Each indicator’s loading should be above 0.7

Question 16

Convergent Validity

Answer 16

Average Variance Extracted (AVE) should be above 0.5, meaning that more than 50% of the variance is explained by the construct.

Question 17

Discriminant Validity

Answer 17

Each construct should be distinct from others, often assessed using the Fornell-Larcker criterion or the Heterotrait-Monotrait ratio (HTMT).

Question 18

Fornell-Larcker Criterion:

Answer 18

The square root of AVE of each construct should be higher than its highest correlation with any other construct.

Question 19

HTMT:

Answer 19

Should be below 0.9 (or 0.85 for stricter threshold).

Question 20

Reliability

Answer 20

the consistency or stability of a measurement instrument. A reliable instrument yields the same results under consistent conditions.

Question 21

Internal Consistency:

Answer 21

Assesses the consistency of results across items within a test.
- Commonly used metrics:
Cronbach’s Alpha and
Composite Reliability (CR)

Question 22

Indicator reliability

Answer 22

the degree to which an individual indicator (or observed variable) consistently and accurately measures the construct it is intended to measure. PLS-SEM= examining the loadings

Question 23

loadings

Answer 23

measure of how strongly it correlates with its latent variable.

Question 24

Validity

Answer 24

how well a test or measurement tool actually measures what it is intended to measure. It’s about accuracy.
- Convergent and Discriminant Validity

Question 25

Convergent validity

Answer 25

how similar they are

Question 26

Covergent Validity uses

Answer 26

Average Variance Extracted

Question 27

AVE should be

Answer 27

greater than 0.5 = 50% of the variance in the indicators is explained by the construct

Question 28

Discriminant validity

Answer 28

how different they are

Question 29

Discriminant validity uses

Answer 29

Fornell-Larcker Criterion and HTMT

Question 30

Fornell- Larcker

Answer 30

The square root of the AVE for each construct should be higher than its highest correlation with any other construct.

Question 31

HTMT

Answer 31

The ratio should be below 0.9 (or 0.85 for stricter criteria).

Question 32

Stage 2:

Answer 32

Check to see if there are any multicollinearity issues

Question 33

Inner Collinearity

Answer 33

the correlation between the predictor constructs in the structural model. - Assess for multicollinearity to ensure reliable path coefficient estimates.

Question 34

Variance Inflation Factor (VIF)

Answer 34

measures the extent to which the variance of a regression coefficient is inflated due to multicollinearity with other predictors. -It will show you which item has a problem

Question 35

VIF should be

Answer 35

less than 5

Question 36

Predictive relevance (R squared)

Answer 36

the proportion of variance in the dependent variables (endogenous constructs) that is explained by the independent variables (exogenous constructs). - Evaluate how well the model explains and predicts the endogenous variables.

Question 37

Model Fit

Answer 37

how well the model reproduces the observed data. - Measure how well the model reproduces the observed data.

Question 38

Standardized Root Mean Square Residual (SRMR)

Answer 38

SRMR is a measure of the average discrepancy between the observed and predicted correlations.

Question 39

Significance and Path Coefficients:

Answer 39

Path coefficients represent the strength and direction of the relationships between constructs in the structural model. - Check the statistical significance and practical relevance of the relationships between constructs.

Question 40

Strong path coefficient

Answer 40

close to -1 or 1