{"id":8950,"date":"2022-06-19T02:11:05","date_gmt":"2022-06-19T02:11:05","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=8950"},"modified":"2023-10-09T09:02:54","modified_gmt":"2023-10-09T09:02:54","slug":"steps-in-data-analsis","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/smartpls4-tutorial-series\/steps-in-data-analsis\/","title":{"rendered":"Steps in Data Analysis"},"content":{"rendered":"\t\t
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Steps in Data Analysis - SmartPLS4 Series<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Learn the steps in Data Analysis when using Structural Equation Modelling.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The Process of Data Analysis<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The focus of the session is to help scholars learn the basic and advance techniques in Data Analysis when using SmartPLS4.\u00a0<\/p>

Click\u00a0Here<\/a>\u00a0to visit the Complete Playlist<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Steps in Data Analysis<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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The first stage in a data analysis process using SmartPLS is data cleaning, focused on handling missing values, outliers, and data quality concerns through respondent misconduct to ensure a valid dataset. The next step is Measurement Model Assessment, which involves assessing the quality of the\u00a0 measurement indicators in capturing the underlying constructs. Examining factor loadings, reliability using metrics such as Cronbach’s alpha and composite reliability, and validating the constructs using convergent and discriminant validity tests.\u00a0\u00a0<\/div>
The Structural Model Assessment is the next step following the measurement model assessment.\u00a0 In this step, the links between latent constructs are assessed. The relationship is assessed via bootstrapping, to determine path coefficients, t-statistics, and p-values. A t-value more than 1.96 and a p-value less than 0.05 usually imply statistically significant results. Finally, you assess your structural model’s explanatory strength (R-square) and predictive ability (PLS-Predict) for making out-of-sample predictions.<\/div>
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Following are the steps to follow for data analysis using SmartPLS4\u00a0<\/span><\/div><\/div><\/div>