{"id":6872,"date":"2021-11-21T07:12:26","date_gmt":"2021-11-21T07:12:26","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=6872"},"modified":"2021-11-21T07:25:41","modified_gmt":"2021-11-21T07:25:41","slug":"analyzing-formative-formative-higher-order-construct-in-smartpls","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/analyzing-formative-formative-higher-order-construct-in-smartpls\/","title":{"rendered":"Analyzing Formative-Formative Higher-Order Construct in SmartPLS"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"6872\" class=\"elementor elementor-6872\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-zmvdrn7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"zmvdrn7\" 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\">Formative-Formative Higher Order Construct Analysis using SmartPLS<\/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-37339403 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"37339403\" 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-56c316d1\" data-id=\"56c316d1\" 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-79b4ed4d elementor-widget elementor-widget-image\" data-id=\"79b4ed4d\" 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\/2021\/11\/Formative-Formative-Higher-Order-Construct-using-SmartPLS.png\" class=\"attachment-full size-full wp-image-6873\" alt=\"\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Formative-Formative-Higher-Order-Construct-using-SmartPLS.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Formative-Formative-Higher-Order-Construct-using-SmartPLS-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Formative-Formative-Higher-Order-Construct-using-SmartPLS-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Formative-Formative-Higher-Order-Construct-using-SmartPLS-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-7e0cbcec\" data-id=\"7e0cbcec\" 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-338c4f9d elementor-widget elementor-widget-heading\" data-id=\"338c4f9d\" 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\">Formative-Formative Higher Order Construct Analysis using SmartPLS<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4f34c002 elementor-widget-divider--separator-type-pattern 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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>In CB-SEM, Loadings, Model Fit, and Modification Indices are critical to model identification. This post will discuss in detail all these concept. <\/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-hp7b60p elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"hp7b60p\" 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\">Analyzing Formative-Formative Higher Order Constrcut in SmartPLS<\/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<p>A Formative-Formative model is one where the first order has formative indicators while in the second-order, the dimensions are also formative. Here is how it looks<\/p><p><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/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-9f2fb10 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9f2fb10\" 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-247f6fa\" data-id=\"247f6fa\" 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-06a3d6b elementor-widget elementor-widget-heading\" data-id=\"06a3d6b\" 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\">Two Stage Approach<\/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-0e28847\" data-id=\"0e28847\" 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-6f99aac elementor-widget elementor-widget-text-editor\" data-id=\"6f99aac\" 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>In order to analyze the formative-formative indicators, this tutorial will use the disjoint two stage approach. To know more about the approaches, Click <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\/\">here <\/a><\/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-9fa43dc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9fa43dc\" 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-1ee2f12\" data-id=\"1ee2f12\" 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-9d4fdd7 elementor-widget elementor-widget-heading\" data-id=\"9d4fdd7\" 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\">Disjoint Two Stage Approach<\/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-cf424ba\" data-id=\"cf424ba\" 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-0b79a6b elementor-widget elementor-widget-text-editor\" data-id=\"0b79a6b\" 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>The disjoint two-stage approach considers only the lower-order components of the higher-order construct (i.e., without the higher-order component) in the path model at Stage 1.<\/li><li>These are directly linked to all other constructs that the higher-order construct is theoretically related to.<\/li><li>To execute the disjoint two-stage approach, researchers then need to save the construct scores (LVS), but only those of the lower order components.<\/li><li>In stage two, these scores are then used to measure the higher-order construct.<\/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-d336614 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d336614\" 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-95a3697\" data-id=\"95a3697\" 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-1950995 elementor-widget elementor-widget-heading\" data-id=\"1950995\" 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\">Step 1: Validation of LOCs<\/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-ca19516\" data-id=\"ca19516\" 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-59428ea elementor-widget elementor-widget-text-editor\" data-id=\"59428ea\" 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>The Step 1 is the validation of the LO formative constructs. The procedure used is the same as we use for a Formative construct as explained in the tutorial <a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/validating-formative-indicators-using-smart-pls\/\">here<\/a>.<\/li><li>Note: If there are other constructs in the study that are reflective, they will be validated as per the guidelines of reflective constructs (Construct Reliability, Convergent Validity, and Discriminant Validity)<\/li><li>The next step after validation is to generate Latent Variable Scores (LVS).<\/li><li>Save the LVS score in your datasheet, and import the datasheet again into the SmartPLS.<\/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-c399938 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c399938\" 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-2a1dc1b\" data-id=\"2a1dc1b\" 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-a73a77f elementor-widget elementor-widget-heading\" data-id=\"a73a77f\" 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\">Step 2: Validation of HOC<\/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-e694625\" data-id=\"e694625\" 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-47cf306 elementor-widget elementor-widget-text-editor\" data-id=\"47cf306\" 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>Once the new datasheet is imported, add LOCs as indicators to the HO formative construct.<\/li><li>Add other LOCs or HOCs in the model as required.<\/li><li>Validation of the HO formative construct, is the same as we did in step 1. Further steps are explained in the tutorial <a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/validating-formative-indicators-using-smart-pls\/\">here<\/a>.<\/li><li>Next, after validation, assess the model for significance of relationships.<\/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-f92bf60 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f92bf60\" 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-f6a50f1\" data-id=\"f6a50f1\" 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-13afcdd elementor-widget elementor-widget-heading\" data-id=\"13afcdd\" 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-b20b8c2\" data-id=\"b20b8c2\" 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-3694433 elementor-widget elementor-widget-video\" data-id=\"3694433\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/tPJnDaTVU18&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-18cf91e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"18cf91e\" 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-d46ed0a\" data-id=\"d46ed0a\" 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-bfecf83 elementor-widget elementor-widget-heading\" data-id=\"bfecf83\" 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 Tutorials<\/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-16e1d66\" data-id=\"16e1d66\" 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-550b817 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"550b817\" 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-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>Formative-Formative Higher Order Construct Analysis using SmartPLS Formative-Formative Higher Order Construct Analysis using SmartPLS In CB-SEM, Loadings, Model Fit, and Modification Indices are critical to model identification. This post will discuss in detail all these concept. Analyzing Formative-Formative Higher Order Constrcut in SmartPLS A Formative-Formative model is one where the first order has formative indicators [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":2939,"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":"","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-6872","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Analyzing Formative-Formative Higher-Order Construct in SmartPLS - ResearchWithFawad<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/analyzing-formative-formative-higher-order-construct-in-smartpls\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analyzing Formative-Formative Higher-Order Construct in SmartPLS - ResearchWithFawad\" \/>\n<meta property=\"og:description\" content=\"Formative-Formative Higher Order Construct Analysis using SmartPLS Formative-Formative Higher Order Construct Analysis using SmartPLS In CB-SEM, Loadings, Model Fit, and Modification Indices are critical to model identification. 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