{"id":8417,"date":"2022-03-18T04:08:53","date_gmt":"2022-03-18T04:08:53","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=8417"},"modified":"2022-03-18T07:57:38","modified_gmt":"2022-03-18T07:57:38","slug":"seminr-package-how-to-solve-convergent-and-discriminant-validity-problems","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-how-to-solve-convergent-and-discriminant-validity-problems\/","title":{"rendered":"SEMinR Package: How to Solve Convergent and Discriminant Validity Problems"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"8417\" class=\"elementor elementor-8417\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fk2xtvx elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fk2xtvx\" 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\">How to Solve Convergent and Discriminant Validity Issues in SEMinR<\/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-3356c192 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3356c192\" 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-d79799c\" data-id=\"d79799c\" 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-196fd403 elementor-widget elementor-widget-image\" data-id=\"196fd403\" 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\/03\/SEMinR-Lecture-Series-Convergent-and-Discriminant-Validity-Issues.png\" class=\"attachment-full size-full wp-image-8439\" alt=\"SEMinR Lecture Series - Convergent and Discriminant Validity Issues\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/03\/SEMinR-Lecture-Series-Convergent-and-Discriminant-Validity-Issues.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/03\/SEMinR-Lecture-Series-Convergent-and-Discriminant-Validity-Issues-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/03\/SEMinR-Lecture-Series-Convergent-and-Discriminant-Validity-Issues-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2022\/03\/SEMinR-Lecture-Series-Convergent-and-Discriminant-Validity-Issues-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-25405857\" data-id=\"25405857\" 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-6d875749 elementor-widget elementor-widget-heading\" data-id=\"6d875749\" 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\">SEMinR Lecture Series<br><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1648ff07 elementor-widget-divider--separator-type-pattern elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"1648ff07\" 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-62b53f7a elementor-widget elementor-widget-text-editor\" data-id=\"62b53f7a\" 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>This series of lectures on SEMinR Package will focus on how to solve Convergent and Discriminant Validity issues using SEMinR package in R. <\/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-3a227ee elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3a227ee\" 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-8fe1f84\" data-id=\"8fe1f84\" 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-c5d71e5 elementor-widget elementor-widget-heading\" data-id=\"c5d71e5\" 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\">Before collecting the data<\/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-1a2a2f4\" data-id=\"1a2a2f4\" 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-76e480e elementor-widget elementor-widget-text-editor\" data-id=\"76e480e\" 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>Make sure to have adequate No. of Items in each scale\/construct.<\/li><li>There should be at least 4-6 items, since, in SEM items are deleted if they fail to load or due to cross-loading.<\/li><li>Make sure that the Items\/Statements are easy to understand.<\/li><li>Make sure there is no overlap in the statements of different constructs.<\/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-52d1fbf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"52d1fbf\" 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-e7d48bb\" data-id=\"e7d48bb\" 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-966420c elementor-widget elementor-widget-heading\" data-id=\"966420c\" 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\">Recommendations<\/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-c93a32f\" data-id=\"c93a32f\" 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-5e4bfbc elementor-widget elementor-widget-text-editor\" data-id=\"5e4bfbc\" 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>Check for item factor loading, if item loading is too low and removing the items can substantially improve the convergent validity, remove the item.<\/li><li>Although factor loading over 0.7 is desirable (Vinzi, Chin, Henseler, &amp; Wang, 2010), researchers frequently obtain weaker outer loadings (&lt;0.70) in social science studies. Rather than automatically eliminating indicators, the effects of the removal of the item on composite reliability, content, and convergent validity shall be examined.<\/li><li>Generally, items with outer loadings from 0.40 to 0.70 shall be considered for removal only if deletion results in an increase of composite reliability or average variance extracted (AVE) over the recommended value (Hair et al., 2016).<\/li><li>Check for Standard Deviation in the responses and remove responses with Standard Deviation less than 0.25.<\/li><li>Check for Cross-loading, if an item is cross-loading, and the difference is less than .10, REMOVE the item(s).<\/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-4e2c80b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4e2c80b\" 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-d329b91\" data-id=\"d329b91\" 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-2011b7d elementor-widget elementor-widget-heading\" data-id=\"2011b7d\" 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 if the Problem Persists?<\/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-812e6bc\" data-id=\"812e6bc\" 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-760ac11 elementor-widget elementor-widget-text-editor\" data-id=\"760ac11\" 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>If discriminant validity issues persist, no option may exist but to combine constructs into one overall measure, where correlations in the region of 0.8 to 0.9 are regularly reported in the literature between dimensions that are theoretically distinct.<\/li><li>In such cases, researchers can collapse measures into a single construct, rather than conduct dimension-by-dimension analysis.<\/li><li>If none of the methods presented address the issue, a researcher may have to collect additional data to determine if discriminant validity or multicollinearity issues are a result of sampling flukes.<\/li><li>If problems still persist, dropping one (or more) independent variables (i.e., collinear variables that demonstrate insufficient discriminant validity) from the model may also help.<\/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-a839bb9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a839bb9\" 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-e6baadd\" data-id=\"e6baadd\" 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-f122875 elementor-widget elementor-widget-heading\" data-id=\"f122875\" 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\">Reference<\/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-39b2245\" data-id=\"39b2245\" 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-1e7dc4b elementor-widget elementor-widget-text-editor\" data-id=\"1e7dc4b\" 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>Farrell, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). <i>Journal of Business Research<\/i>, <i>63<\/i>(3), 324-327.<\/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-0e774a4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0e774a4\" 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-50 elementor-top-column elementor-element elementor-element-bf030ad\" data-id=\"bf030ad\" 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-2884ec7 elementor-widget elementor-widget-heading\" data-id=\"2884ec7\" 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 (Coming Soon)<\/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-50 elementor-top-column elementor-element elementor-element-f2154ec\" data-id=\"f2154ec\" 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-7165922 elementor-widget elementor-widget-video\" data-id=\"7165922\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/WBAwK7o-N_Q&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-71b12dd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"71b12dd\" 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-050dc11\" data-id=\"050dc11\" 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-10e2105 elementor-widget elementor-widget-heading\" data-id=\"10e2105\" 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 SEMinR 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-a7600f7\" data-id=\"a7600f7\" 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-a1a3231 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"a1a3231\" 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-7498 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/an-introduction-to-r-and-r-studio\/\" class=\"menu-link\">An Introduction to R and R Studio<\/a><\/li>\n<li class=\"page_item page-item-7539 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/an-introduction-to-seminr-package\/\" class=\"menu-link\">An Introduction to SEMinR Package<\/a><\/li>\n<li class=\"page_item page-item-7645 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/create-project-load-and-inspect-the-data\/\" class=\"menu-link\">Create Project, Load, and Inspect the Data<\/a><\/li>\n<li class=\"page_item page-item-7938 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-an-introduction-to-evaluating-formative-measurement-model\/\" class=\"menu-link\">SEMinR Package: An Introduction to Evaluating Formative Measurement Model<\/a><\/li>\n<li class=\"page_item page-item-8491 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-categorical-predictors\/\" class=\"menu-link\">SEMinR Package: Analyzing Categorical Predictor Variables<\/a><\/li>\n<li class=\"page_item page-item-7773 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-bootstrapping-pls-model\/\" class=\"menu-link\">SEMinR Package: Bootstrapping PLS Model<\/a><\/li>\n<li class=\"page_item page-item-7952 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-evaluating-formative-measurement-model-convergent-validity-and-collinearity\/\" class=\"menu-link\">SEMinR Package: Evaluating Formative Measurement Model &#8211; Convergent Validity and Collinearity<\/a><\/li>\n<li class=\"page_item page-item-7971 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-evaluating-indicator-weights\/\" class=\"menu-link\">SEMinR Package: Evaluating Formative Measurement Model &#8211; Step 3- Indicator Weights<\/a><\/li>\n<li class=\"page_item page-item-8007 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-evaluating-formative-measurement-model-when-to-delete-formative-indicators\/\" class=\"menu-link\">SEMinR Package: Evaluating Formative Measurement Model &#8211; When to Delete Formative Indicators<\/a><\/li>\n<li class=\"page_item page-item-7850 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-evaluating-reflective-measurement-model\/\" class=\"menu-link\">SEMinR Package: Evaluating Reflective Measurement Model<\/a><\/li>\n<li class=\"page_item page-item-8024 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-evaluating-structural-model\/\" class=\"menu-link\">SEMinR Package: Evaluating Structural Model<\/a><\/li>\n<li class=\"page_item page-item-8071 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-evaluating-structural-model-step-4-predictive-power-plspredict\/\" class=\"menu-link\">SEMinR Package: Evaluating Structural Model &#8211; Step 4: Predictive Power (PLSPredict)<\/a><\/li>\n<li class=\"page_item page-item-8176 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-higher-order-analysis-ref-for\/\" class=\"menu-link\">SEMinR Package: Higher Order Analysis &#8211; REF-FOR<\/a><\/li>\n<li class=\"page_item page-item-8145 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-higher-order-analysis-ref-ref\/\" class=\"menu-link\">SEMinR Package: Higher Order Analysis &#8211; REF-REF<\/a><\/li>\n<li class=\"page_item page-item-8097 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-mediation-analysis\/\" class=\"menu-link\">SEMinR Package: Mediation Analysis<\/a><\/li>\n<li class=\"page_item page-item-8124 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-moderation-analysis\/\" class=\"menu-link\">SEMinR Package: Moderation Analysis<\/a><\/li>\n<li class=\"page_item page-item-7727 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-pls-estimation\/\" class=\"menu-link\">SEMinR Package: PLS Estimation<\/a><\/li>\n<li class=\"page_item page-item-7601 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-plotting-printing-exporting-results\/\" class=\"menu-link\">SEMinR Package: Print, Export and Plot Results<\/a><\/li>\n<li class=\"page_item page-item-7873 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-reflective-measurement-model-step-2-consistency-and-step-3-convergent-validity\/\" class=\"menu-link\">SEMinR Package: Reflective Measurement Model Step 2: Consistency and Step 3: Convergent Validity<\/a><\/li>\n<li class=\"page_item page-item-7902 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-reflective-model-discriminant-validity\/\" class=\"menu-link\">SEMinR Package: Reflective Measurement Model Step 4: Discriminant Validity<\/a><\/li>\n<li class=\"page_item page-item-7836 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-single-item-smartpls-comparison-and-summary-of-seminr\/\" class=\"menu-link\">SEMinR Package: Single Item, SmartPLS Comparison and Summary of SEMinR<\/a><\/li>\n<li class=\"page_item page-item-7669 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-specifying-measurement-model\/\" class=\"menu-link\">SEMinR Package: Specifying Measurement Model<\/a><\/li>\n<li class=\"page_item page-item-7691 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/seminr-lecture-series\/seminr-package-specifying-the-structural-model\/\" class=\"menu-link\">SEMinR Package: Specifying the Structural Model<\/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>How to Solve Convergent and Discriminant Validity Issues in SEMinR SEMinR Lecture Series This series of lectures on SEMinR Package will focus on how to solve Convergent and Discriminant Validity issues using SEMinR package in R. Before collecting the data Make sure to have adequate No. of Items in each scale\/construct. There should be at [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":7476,"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-8417","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>SEMinR Package: How to Solve Convergent and Discriminant Validity Problems - ResearchWithFawad<\/title>\n<meta name=\"description\" content=\"SEM tools for R. 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