Essentials of SmartPLS 4: For Research and Data Analysis

Learn the basics of structural equation modeling by contrasting exploratory and confirmatory approaches, including EFA and CFA, and understand how PLS-SEM and CB-SEM model latent constructs.

Differentiate CB-SEM and PLS-SEM, clarifying covariance-based versus variance-based structural equation modeling. Show how PLS-SEM emphasizes prediction with small samples and soft modeling, while CB-SEM confirms theories with goodness-of-fit measures.

Learn the research process, download and install SmartPLS and SPSS, perform basic SPSS analyses, run structural equation modeling in SmartPLS, and report results in APA format.

Explore the steps of structural equation modeling using a ChatGPT intention study, defining constructs, testing measurement and structural models, and examining mediation and moderation in smartpls.

Develop and validate the outer and inner models in SmartPLS 4, using a basic reflective (and, when appropriate, formative) model for usefulness, enjoyment, ease of use, and intention to use chatgpt.

Practice data preparation and preliminary analysis in SPSS, determine sample size with the ten-times rule and online calculators, and compute latent variables for descriptive statistics in SPSS and SmartPLS.

Prepare data by computing descriptive statistics and latent variable scores, then assess multicollinearity among three exogenous constructs using SPSS regression with VIF and tolerance, confirming suitability for SmartPLS SEM.

Assess measurement model validity in SEM by evaluating reliability and the outer model. Establish convergent validity via factor loadings and AVE, and discriminant validity via Fornell-Larcker and HTMT.

Learn how to assess reliability and convergent validity with SmartPLS, using Cronbach's alpha, composite reliability, AVE, and outer loadings in a PLS-SEM framework.

Learn to report construct reliability and convergent validity in SmartPLS 4 by detailing Cronbach alpha, composite reliability, factor loadings above 0.708, and AVE above 0.50, with results and interpretation.

Explore discriminant validity using Fornell-Larcker and HTMT with constructs intention, usefulness, ease of use, and enjoyment, focusing on AVE, square roots, and between and within construct variance in SmartPLS 4.

Explore how to assess discriminant validity in SmartPLS using the Fornell-Larcker criterion and HTMT, interpreting AVE square roots, construct correlations, and reporting results.

Assess structural model validity in pls-sem by evaluating path coefficients, bootstrapping results, and multicollinearity, and by measuring predictive power with r square, f square, and q square.

Learn to run a structural model in SmartPLS 4 using bootstrapping, interpret path coefficients, and assess inner and outer model significance with t-statistics, p-values, and confidence intervals.

Explains structural model analysis with bootstrapping, reporting path coefficients, t statistics, and p values, and reviews SRMR and NFI model fit for predicting intention to use ChatGPT.

Assess structural model validity by examining r-square, adjusted r-square, f-square, and q-square predict to gauge explanatory power, effect size, and predictive relevance of three predictor constructs on the endogenous outcome.

Report SmartPLS results by examining R square, F square, and Q square predict, then present latent variable prediction summaries for intention, noting 0.766 predictive relevance and substantial explanatory power.

Learn mediation analysis and mediating effects between exogenous and endogenous constructs, and analyze direct and indirect effects in SmartPLS 4 using service quality, satisfaction, and loyalty.

Learn mediating effects analysis in SmartPLS 4, examining how perceived usefulness and perceived ease of use mediate enjoyment's impact on intention with bootstrapping and reporting direct and indirect effects.

Learn to report mediating effects from SmartPLS 4 outputs, interpret direct and indirect paths, and distinguish full versus partial mediation using confidence intervals.

Examine moderating analysis to show how a third variable changes relationships between variables, using perceived risk to affect usefulness and intention and ease of use in SmartPLS.

Explore moderating analysis in SmartPLS 4, testing perceived risk as a moderator on usefulness and intention, with the interaction effect significant and negative, while the other path remains insignificant.

Report moderating effects in SmartPLS by presenting direct and indirect effects, copying confidence intervals, and interpreting perceived risk as a moderator of usefulness and intention to use ChatGPT.

Perform slope analysis of a significant moderator in SmartPLS 4 to show how perceived usefulness influences intention to use ChatGPT under varying perceived risk, via an interaction chart.

Explore higher order constructs in PLS-SEM, including reflective and formative forms, their measurement via repeated indicator or two-stage approaches, and practical examples like halal marketing influencing intention via awareness.

Create a higher order construct in SmartPLS 4 using a two-stage disjoint approach, moving from first-order to a formative second-order construct, and assess reliability, validity, and collinearity.

Use bootstrapping to test model, confirming direct and indirect effects of halal marketing on awareness and intention, with halal awareness as a partial mediator in a two-stage higher order construct.

Learn to measure higher order constructs in SmartPLS using the repeated indicator approach, compare it with the two-stage disjoint approach, and assess R-squared and discriminant validity.

Evaluate higher order constructs in smartpls using two measurement models, assess reliability, validity, and discriminant validity, and report structural model results with path coefficients and indirect effects.

Examine a higher order halal marketing construct through a two-stage disjoint approach, testing its impact on halal awareness and intention with mediating effects and rigorous SEM analysis.

Understand how multi-group analysis compares path coefficients across groups in SmartPLS, using between-group constraints to identify significant gender differences.

Explore multi group analysis in SmartPLS 4 to compare structural models across groups, such as male and female, using bootstrapping to test path coefficients and mediating effects.

Interpret multi-group results in smartpls by evaluating path coefficients and p values, comparing male and female groups, and reporting insignificant differences in APA format.

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