30 interview questions with answers on SmartPLS

 Here are 30 interview questions with answers on SmartPLS used in research, categorized for better understanding:

🔹 A. Basics of SmartPLS and PLS-SEM (1–10)

  1. What is SmartPLS?
    SmartPLS is a software tool used for Partial Least Squares Structural Equation Modeling (PLS-SEM), allowing researchers to analyze complex cause-effect relationship models with latent variables.

  2. What is PLS-SEM?
    Partial Least Squares Structural Equation Modeling is a variance-based SEM technique ideal for predictive modeling and handling small sample sizes and non-normal data.

  3. What type of data is suitable for PLS-SEM?
    PLS-SEM works well with non-normal data, small to medium sample sizes, and exploratory research.

  4. When should one use SmartPLS instead of CB-SEM?
    Use SmartPLS when the objective is prediction, the model is complex, the sample size is small, or the data is not normally distributed.

  5. What are the key components of a PLS-SEM model?

    • Measurement model (outer model)

    • Structural model (inner model)

  6. What is the difference between reflective and formative constructs?

    • Reflective constructs: indicators reflect the latent variable.

    • Formative constructs: indicators form or cause the latent variable.

  7. Can SmartPLS handle higher-order constructs (HOC)?
    Yes, SmartPLS supports second-order constructs using methods like repeated indicator, two-stage, or hybrid approach.

  8. What kind of research questions can SmartPLS help answer?
    Questions related to relationships between latent constructs, mediation, moderation, and prediction of outcomes.

  9. What are latent variables in SmartPLS?
    Variables that are not directly observed but inferred from measured indicators.

  10. What is the file format used in SmartPLS?
    Data files are typically .csv or .xlsx format.

🔹 B. Measurement Model Evaluation (11–20)

  1. What is indicator reliability?
    It measures how much of the variance in an observed variable is explained by the latent variable (should be > 0.70 ideally).

  2. What is composite reliability (CR)?
    CR assesses the internal consistency of a construct; values should be > 0.70.

  3. What is Average Variance Extracted (AVE)?
    AVE measures the convergent validity of a construct. Should be > 0.50 to be acceptable.

  4. How do you assess discriminant validity in SmartPLS?
    Using:

  • Fornell-Larcker criterion

  • Cross-loadings

  • HTMT ratio (Heterotrait-Monotrait Ratio)

  1. What is HTMT ratio?
    A modern criterion for assessing discriminant validity. HTMT < 0.85 (or 0.90) indicates good discriminant validity.

  2. What is outer loading?
    Outer loading is the correlation between an observed variable and its latent variable. Values above 0.70 are acceptable.

  3. What happens if an indicator’s loading is below 0.70?
    It may be removed if it improves the model's reliability and validity, but theoretical justification is needed.

  4. How do you test multicollinearity in SmartPLS?
    Using VIF (Variance Inflation Factor); acceptable VIF < 5 (preferably < 3.3).

  5. What is cross-loading?
    It helps to identify if an indicator loads higher on its own construct than on other constructs.

  6. What are model fit indices in SmartPLS?

  • SRMR (Standardized Root Mean Square Residual): < 0.08

  • NFI (Normed Fit Index): > 0.90 (optional)

🔹 C. Structural Model Evaluation (21–30)

  1. What is the structural model in SmartPLS?
    It shows the relationships (paths) between latent constructs.

  2. What is the R² value in SmartPLS?
    It indicates the explained variance in the endogenous construct. Values:

  • 0.25 = weak

  • 0.50 = moderate

  • 0.75 = substantial

  1. What is path coefficient?
    It shows the strength and direction of the relationship between constructs. Should be significant and positive/negative as hypothesized.

  2. How is significance tested in SmartPLS?
    Using bootstrapping (e.g., 5000 subsamples), which provides t-values, p-values, and confidence intervals.

  3. What is effect size (f²)?
    Measures the impact of an exogenous construct on an endogenous construct.

  • 0.02 = small

  • 0.15 = medium

  • 0.35 = large

  1. What is predictive relevance (Q²)?
    Indicates model’s predictive capability, assessed via blindfolding. Q² > 0 indicates predictive relevance.

  2. What is mediation analysis in SmartPLS?
    Tests whether an indirect effect exists between two constructs via a third (mediator). Significance is assessed through bootstrapping.

  3. What is moderation in SmartPLS?
    Examines if the relationship between two constructs changes based on a third variable (moderator). Can be tested via interaction terms.

  4. What is multigroup analysis (MGA)?
    MGA in SmartPLS allows testing if path coefficients are significantly different across groups, e.g., male vs. female.

  5. How do you report SmartPLS results?
    Include:

  • Measurement model evaluation (CR, AVE, HTMT, loadings)

  • Structural model evaluation (R², path coefficients, t-values, f², Q²)

  • Hypotheses testing with significance values and confidence intervals.


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