{"id":8873,"date":"2022-06-05T09:48:16","date_gmt":"2022-06-05T09:48:16","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=8873"},"modified":"2022-06-05T10:01:11","modified_gmt":"2022-06-05T10:01:11","slug":"predictive-power-assessment-using-plspredict-in-smartpls3","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/predictive-power-assessment-using-plspredict-in-smartpls3\/","title":{"rendered":"Predictive Power Assessment using PLSPredict in SmartPLS3"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"8873\" class=\"elementor elementor-8873\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4p3uwuk elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4p3uwuk\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d4acc06\" data-id=\"d4acc06\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bee4950 elementor-widget elementor-widget-heading\" data-id=\"bee4950\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Predictive Power Assessment using PLSPredict in SmartPLS3<\/h1>\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-xkabg7q elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"xkabg7q\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-2b10226\" data-id=\"2b10226\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fc6092a elementor-widget elementor-widget-image\" data-id=\"fc6092a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1280\" height=\"720\" src=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/06\/How-to-assess-predictive-power-of-the-model-using-PLSPredict.png\" class=\"attachment-full size-full wp-image-8880\" alt=\"How to assess predictive power of the model using PLSPredict\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/06\/How-to-assess-predictive-power-of-the-model-using-PLSPredict.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/06\/How-to-assess-predictive-power-of-the-model-using-PLSPredict-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/06\/How-to-assess-predictive-power-of-the-model-using-PLSPredict-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/06\/How-to-assess-predictive-power-of-the-model-using-PLSPredict-768x432.png 768w\" sizes=\"(max-width: 1280px) 100vw, 1280px\" \/>\t\t\t\t\t\t\t\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<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-1e8e919\" data-id=\"1e8e919\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-755d715 elementor-widget elementor-widget-heading\" data-id=\"755d715\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Learn to assess Predictive Power in PLS-SEM<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da54622 elementor-widget-divider--separator-type-pattern elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"da54622\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\" style=\"--divider-pattern-url: url(&quot;data:image\/svg+xml,%3Csvg xmlns=&#039;http:\/\/www.w3.org\/2000\/svg&#039; preserveAspectRatio=&#039;none&#039; overflow=&#039;visible&#039; height=&#039;100%&#039; viewBox=&#039;0 0 24 24&#039; fill=&#039;black&#039; stroke=&#039;none&#039;%3E%3Cpolygon points=&#039;9.4,2 24,2 14.6,21.6 0,21.6&#039;\/%3E%3C\/svg%3E&quot;);\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf39eb3 elementor-widget elementor-widget-text-editor\" data-id=\"cf39eb3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The Concept and Process of Predictive Power Assessment using PLSPredict in SmartPLS3<\/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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-jvh5tdm elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"jvh5tdm\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-c927f23\" data-id=\"c927f23\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bf8264c elementor-widget elementor-widget-heading\" data-id=\"bf8264c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What is Predictive Power?<\/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<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-61ca281\" data-id=\"61ca281\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-338057a elementor-widget elementor-widget-text-editor\" data-id=\"338057a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Many researchers interpret the R-Sq statistic as a measure of their model\u2019s predictive power (Sarstedt &amp; Danks, 2021; Shmueli &amp; Koppius, 2011). This interpretation is not entirely correct, however, since the R-Sq only indicates the model\u2019s in-sample explanatory power.<\/li><li>&#8220;In sample&#8221; refers to the data that you have, and &#8220;out of sample&#8221; to the <b>data you don&#8217;t have<\/b> but want to forecast or estimate.<\/li><li>It says nothing about the model\u2019s <b>predictive power <\/b>(Chin et al., 2020; Hair &amp; Sarstedt, 2021), also referred to as <b>out-of-sample predictive power<\/b>, which indicates a model\u2019s ability to predict new or future observations.<\/li><li>Does the model has good predictive quality?<\/li><li>Addressing this concern, Shmueli, Ray, Estrada, and Chatla (2016) introduced <b>PLSpredict<\/b>, a procedure for out-of-sample prediction.<\/li><li>Execution of PLSpredict involves estimating the model on a <b>training sample <\/b>and evaluating its predictive performance on a <b>holdout sample <\/b>(Shmueli et al., 2019).<\/li><li>Note that the holdout sample is separated from the total sample before executing the initial analysis on the training sample data, so it includes data that were not used in the model estimation.<\/li><li>Researchers need to make sure that the training sample for each fold meets minimum sample size guidelines (e.g., by following the inverse square root method).<\/li><li>\u00a0<\/li><\/ul>\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8dd1876 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8dd1876\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-ec1738d\" data-id=\"ec1738d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0d0ecc0 elementor-widget elementor-widget-heading\" data-id=\"0d0ecc0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How to Assess Predictive Power?<\/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<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-c9d8e7c\" data-id=\"c9d8e7c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c180e07 elementor-widget elementor-widget-text-editor\" data-id=\"c180e07\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>To assess a model\u2019s predictive power, researchers can draw on several <b>prediction statistics <\/b>that quantify the amount of <b>prediction error <\/b>in the indicators of a particular endogenous construct.<\/li><li>Error is not an error (as in a mistake). It is a residual, the lower the better, this is the difference between actual values and the predicted values.<\/li><li>The most popular metric to quantify the degree of prediction error is the <b>root-mean-square error (RMSE)<\/b>.<\/li><li>Another popular metric is the <b>mean absolute error (MAE)<\/b>.<\/li><li>In most instances, researchers should use the RMSE to examine a model\u2019s predictive power.<\/li><li>But if the prediction error distribution is highly nonsymmetric, as evidenced in a long left or right tail in the distribution of prediction errors (Danks &amp; Ray, 2018), the MAE is the more appropriate prediction statistic (Shmueli et al., 2019).<\/li><li>To assess the degree of prediction error, use the RMSE unless the prediction error distribution is highly non-symmetric. In this case, the MAE is the more appropriate prediction statistic<\/li><li>To interpret these metrics, researchers need to compare each indicator\u2019s RMSE (or MAE) values with a na\u00efve <b>linear regression model (LM) benchmark<\/b>.<\/li><li>The LM benchmark values are obtained by running a linear regression of each of the dependent construct\u2019s indicators on the indicators of the exogenous constructs in the PLS path model (Danks &amp; Ray, 2018). In comparing the RMSE (or MAE) values with the LM values, the following guidelines apply (Shmueli et al., 2019):<ul><li><p>If <i>all <\/i>indicators in the PLS-SEM analysis have lower RMSE (or MAE) value compared to the na\u00efve LM benchmark, the model has high predictive power.<\/p><\/li><li><p>If the <i>majority <\/i>(or the same number) of indicators in the PLS-SEM analysis yields smaller prediction errors compared to the LM, this indicates a medium predictive power.<\/p><\/li><li><p>If a <i>minority <\/i>of the dependent construct\u2019s indicators produce lower PLS-SEM prediction errors compared to the na\u00efve LM benchmark, this indicates the model has low predictive power.<\/p><\/li><li><p>If the PLS-SEM analysis (compared to the LM) yields lower prediction errors in terms of the RMSE (or the MAE) for <i>none <\/i>of the indicators, this indicates the model lacks predictive power.<\/p><\/li><\/ul><\/li><\/ul>\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-87b38bf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"87b38bf\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ff9eeb8\" data-id=\"ff9eeb8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7da4d62 elementor-widget elementor-widget-heading\" data-id=\"7da4d62\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">In comparing the RMSE (or MAE) values with the LM values, the following guidelines apply <\/h4>\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-xiy6oqh elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"xiy6oqh\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-591f7d0\" data-id=\"591f7d0\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-90393fd elementor-view-stacked elementor-position-inline-start elementor-shape-circle elementor-mobile-position-block-start elementor-widget elementor-widget-icon-box\" data-id=\"90393fd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-crown\"><\/i>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tAll Indicators\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tIf all indicators in the PLS-SEM analysis have lower RMSE (or MAE) value compared to the na\u00efve LM benchmark, the model has high predictive power.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\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<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-0199beb\" data-id=\"0199beb\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-dbb9845 elementor-view-stacked elementor-position-inline-start elementor-shape-circle elementor-mobile-position-block-start elementor-widget elementor-widget-icon-box\" data-id=\"dbb9845\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-greater-than\"><\/i>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tMajority\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tIf the majority (or the same number) of indicators in the PLS-SEM analysis yields smaller prediction errors compared to the LM, this indicates a medium predictive power.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\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<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-6a06582\" data-id=\"6a06582\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-db0e74f elementor-view-stacked elementor-position-inline-start elementor-shape-circle elementor-mobile-position-block-start elementor-widget elementor-widget-icon-box\" data-id=\"db0e74f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-less-than\"><\/i>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tMinority\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tIf a minority of the dependent construct\u2019s indicators produce lower PLS-SEM prediction errors compared to the na\u00efve LM benchmark, this indicates the model has low predictive power.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\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<div class=\"elementor-column elementor-col-25 elementor-top-column elementor-element elementor-element-d56b535\" data-id=\"d56b535\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4240644 elementor-view-stacked elementor-position-inline-start elementor-shape-circle elementor-mobile-position-block-start elementor-widget elementor-widget-icon-box\" data-id=\"4240644\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"remixicon ri-battery-low-line\"><\/i>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tNone\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tIf the PLS-SEM analysis (compared to the LM) yields lower prediction errors in terms of the RMSE (or the MAE) for none of the indicators, this indicates the model lacks predictive power.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ce8624f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ce8624f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-9472c98\" data-id=\"9472c98\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-414e4b9 elementor-widget elementor-widget-heading\" data-id=\"414e4b9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Video Tutorial<\/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<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-68c46bc\" data-id=\"68c46bc\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c480de5 elementor-widget elementor-widget-video\" data-id=\"c480de5\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/3KlD-SwR56E&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2d94ced1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2d94ced1\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-7da485b6\" data-id=\"7da485b6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-472c4626 elementor-widget elementor-widget-heading\" data-id=\"472c4626\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Additional SmartPLS Resources<\/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<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-6268b407\" data-id=\"6268b407\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-544091e8 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"544091e8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-ccchildpages_widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<ul><li class=\"page_item page-item-6872 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/analyzing-formative-formative-higher-order-construct-in-smartpls\/\" class=\"menu-link\">Analyzing Formative-Formative Higher-Order Construct in SmartPLS<\/a><\/li>\n<li class=\"page_item page-item-3267 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/categorical-predictor-variable-using-smart-pls\/\" class=\"menu-link\">Categorical Predictor Variable using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-5446 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/complex-higher-order-model-using-smartpls\/\" class=\"menu-link\">Complex Higher-Order Model using SmartPLS<\/a><\/li>\n<li class=\"page_item page-item-3309 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/concept-of-higher-order-constructs-in-pls-sem\/\" class=\"menu-link\">Concept of Higher-Order Constructs in PLS-SEM<\/a><\/li>\n<li class=\"page_item page-item-5116 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/how-to-solve-issues-in-convergent-and-discriminant-validity\/\" class=\"menu-link\">How to Solve Convergent and Discriminant Validity Issues<\/a><\/li>\n<li class=\"page_item page-item-2953 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/how-to-start-data-analysis-using-smart-pls\/\" class=\"menu-link\">How to Start Data Analysis using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3515 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/how-to-structure-format-and-report-smart-pls-sem-results\/\" class=\"menu-link\">How to Structure, Format, and Report SMART PLS-SEM Results<\/a><\/li>\n<li class=\"page_item page-item-3205 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/mediation-analysis-interpretation-and-reporting-using-smart-pls\/\" class=\"menu-link\">Mediation Analysis, Interpretation, and Reporting using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3152 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/moderation-analysis-with-categorical-variables-using-smart-pls\/\" class=\"menu-link\">Moderation Analysis with Categorical Variables using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3128 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/moderation-analysis-interpretation-and-reporting-using-smart-pls\/\" class=\"menu-link\">Moderation Analysis, Interpretation, and Reporting using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3356 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/reflective-vs-formative-indicators-the-concept-and-differences\/\" class=\"menu-link\">Reflective Vs Formative Indicators: The Concept and Differences<\/a><\/li>\n<li class=\"page_item page-item-3423 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/reflective-formative-higher-order-construct-using-smart-pls\/\" class=\"menu-link\">Reflective-Formative Higher-Order Construct using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3458 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/reflective-reflective-higher-order-construct-using-smart-pls\/\" class=\"menu-link\">Reflective-Reflective Higher-Order Construct using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3068 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/reporting-measurement-and-structural-model-in-smart-pls\/\" class=\"menu-link\">Reporting Measurement and Structural Model in SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3025 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/understanding-convergent-and-discriminant-validity-using-smart-pls\/\" class=\"menu-link\">Understanding Convergent and Discriminant Validity using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3102 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/understanding-r-square-f-square-and-q-square-using-smart-pls\/\" class=\"menu-link\">Understanding R Square, F Square, and Q Square using SMART-PLS<\/a><\/li>\n<li class=\"page_item page-item-3397 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/validating-formative-indicators-using-smart-pls\/\" class=\"menu-link\">Validating Formative Indicators using SMART-PLS<\/a><\/li>\n<\/ul>\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<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Predictive Power Assessment using PLSPredict in SmartPLS3 Learn to assess Predictive Power in PLS-SEM The Concept and Process of Predictive Power Assessment using PLSPredict in SmartPLS3 What is Predictive Power? Many researchers interpret the R-Sq statistic as a measure of their model\u2019s predictive power (Sarstedt &amp; Danks, 2021; Shmueli &amp; Koppius, 2011). This interpretation is [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"enabled","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-8873","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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