{"id":3102,"date":"2021-08-25T11:45:28","date_gmt":"2021-08-25T11:45:28","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=3102"},"modified":"2022-06-04T01:16:37","modified_gmt":"2022-06-04T01:16:37","slug":"understanding-r-square-f-square-and-q-square-using-smart-pls","status":"publish","type":"page","link":"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\/","title":{"rendered":"Understanding R Square, F Square, and Q Square using SMART-PLS"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"3102\" class=\"elementor elementor-3102\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-lrjuz3e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"lrjuz3e\" 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-80293b9\" data-id=\"80293b9\" 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-de8583b elementor-widget elementor-widget-heading\" data-id=\"de8583b\" 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\">Understanding R Square, F Square, and Q Square using SMART-PLS<\/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-4blf6fi elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4blf6fi\" 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-85b149e\" data-id=\"85b149e\" 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-f1cecd6 elementor-widget elementor-widget-heading\" data-id=\"f1cecd6\" 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\">R-Square\n<\/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-39c4fce\" data-id=\"39c4fce\" 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-dba6bdb elementor-widget elementor-widget-text-editor\" data-id=\"dba6bdb\" 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>R Square statistics explains the variance in the endogenous variable explained by the exogenous variable(s).<\/li><li>For example, a variable Y influenced by X1, X2, and X3 has a R-Square value of 0.623. This would mean that 62.3% change in Y can be explained by X1, X2, X3.<\/li><li>In order to make it easier to interpret, look for the arrows that are pointing towards the dependent (endogenous) variable.<\/li><li><b>Falk and Miller (1992)<\/b> recommended that R2 values should be <b>equal to or greater than 0.10<\/b> in order for the variance explained of a particular endogenous construct to be deemed adequate.<\/li><li><b>Cohen (1988)<\/b> suggested R2 values for endogenous latent variables are assessed as follows: <b>0.26 (substantial), 0.13 (moderate), 0.02 (weak).<\/b><\/li><li><b>Chin (1998)<\/b> recommended R2 values for endogenous latent variables based on: <b>0.67 (substantial), 0.33 (moderate), 0.19 (weak).<\/b><\/li><li><b>Hair et al. (2011) &amp; Hair et al. (2013)<\/b> suggested in scholarly research that focuses on marketing issues, R2 values of <b>0.75, 0.50, or 0.25<\/b> for endogenous latent variables can, as a rough rule of thumb, be respectively described as <b>substantial, moderate or weak.<\/b><\/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-cddeaa8 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cddeaa8\" 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-a35bd82\" data-id=\"a35bd82\" 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-12dd8a3 elementor-widget elementor-widget-heading\" data-id=\"12dd8a3\" 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\">F-Square\n<\/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-7bb847f\" data-id=\"7bb847f\" 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-555962e elementor-widget elementor-widget-text-editor\" data-id=\"555962e\" 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>A variable in a structural model may be affected\/influenced by a number of different variables.<\/li><li>Removing an exogenous variable can affect the dependent variable.<\/li><li>F-Square is the change in R-Square when an exogenous variable is removed from the model.<\/li><li>f-square is effect size (&gt;=0.02 is small; &gt;= 0.15 is medium;&gt;= 0.35 is large) (Cohen, 1988).<\/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-03d3af2 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"03d3af2\" 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-45496cf\" data-id=\"45496cf\" 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-b05096b elementor-widget elementor-widget-heading\" data-id=\"b05096b\" 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\">Q-Square\n<\/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-f55eec0\" data-id=\"f55eec0\" 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-ec717de elementor-widget elementor-widget-text-editor\" data-id=\"ec717de\" 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>Q-square is predictive relevance, measures whether a model has predictive relevance or not (&gt; 0 is good).<\/li><li>Further, Q2 establishes the predictive relevance of the endogenous constructs.<\/li><li>Q-square values above zero indicate that your values are well reconstructed and that the model has predictive relevance.<\/li><li>A Q2 above 0 shows that the model has predictive relevance.<\/li><li>In order to find out the Q Square value, Run Blindfolding procedure in SMART-PLS.<\/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-42eef6a elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"42eef6a\" 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-362e3ae\" data-id=\"362e3ae\" 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-313e0f6 elementor-widget elementor-widget-heading\" data-id=\"313e0f6\" 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\">References<\/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-5f478e6\" data-id=\"5f478e6\" 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-7b4f348 elementor-widget elementor-widget-text-editor\" data-id=\"7b4f348\" 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>Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). New York: Routledge.<\/li><li>Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.<\/li><li>Falk, R. F., &amp; Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.<\/li><li>Hair, J. F., Ringle, C. M., &amp; Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.<\/li><li>Hair, J. F., Ringle, C. M., &amp; Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. <i>Long range planning<\/i>, <i>46<\/i>(1-2), 1-12.<\/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-42122c2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"42122c2\" 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-100 elementor-top-column elementor-element elementor-element-e667ba2\" data-id=\"e667ba2\" 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-ce76b93 elementor-widget elementor-widget-heading\" data-id=\"ce76b93\" 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\">Watch the Video Tutorial for further Detail<\/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-9ad56e3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9ad56e3\" 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-100 elementor-top-column elementor-element elementor-element-cbcec12\" data-id=\"cbcec12\" 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-2b3895c elementor-widget elementor-widget-video\" data-id=\"2b3895c\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/Q6YGY5ITBUk&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-ae6232c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ae6232c\" 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-100 elementor-top-column elementor-element elementor-element-4144bfe0\" data-id=\"4144bfe0\" 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-722d106c elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"722d106c\" 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<h5>Other SmartPLS Tutorials<\/h5><ul><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-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-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-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-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-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-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-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-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-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<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-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-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-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-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<\/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>Understanding R Square, F Square, and Q Square using SMART-PLS R-Square R Square statistics explains the variance in the endogenous variable explained by the exogenous variable(s). For example, a variable Y influenced by X1, X2, and X3 has a R-Square value of 0.623. This would mean that 62.3% change in Y can be explained by [&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":"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-3102","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - 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