{"id":6981,"date":"2021-12-02T02:06:33","date_gmt":"2021-12-02T02:06:33","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=6981"},"modified":"2021-12-02T03:50:18","modified_gmt":"2021-12-02T03:50:18","slug":"understanding-factor-loadings-in-structural-equation-modelling-sem","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/understanding-factor-loadings-in-structural-equation-modelling-sem\/","title":{"rendered":"Understanding Factor Loadings in Structural Equation Modelling (SEM)"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"6981\" class=\"elementor elementor-6981\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5n893dp elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5n893dp\" 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\"><b>Understanding Factor Loading and Heywood Cases<\/b><\/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-758cfb09 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"758cfb09\" 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-41c04b2e\" data-id=\"41c04b2e\" 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-5cce5a63 elementor-widget elementor-widget-image\" data-id=\"5cce5a63\" 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\/12\/Factor-Loadings-and-Heywood-Cases.png\" class=\"attachment-full size-full wp-image-6994\" alt=\"Factor Loadings and Heywood Cases\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/12\/Factor-Loadings-and-Heywood-Cases.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/12\/Factor-Loadings-and-Heywood-Cases-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/12\/Factor-Loadings-and-Heywood-Cases-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/12\/Factor-Loadings-and-Heywood-Cases-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-68d4c39b\" data-id=\"68d4c39b\" 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-13a15aba elementor-widget elementor-widget-heading\" data-id=\"13a15aba\" 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\">IBM SPSS AMOS Series<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d0572d5 elementor-widget-divider--separator-type-pattern elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"4d0572d5\" 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-7939bb3b elementor-widget elementor-widget-text-editor\" data-id=\"7939bb3b\" 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 focus of the session is on Factor Loading, When to delete an item based on low loading, and how to deal with Standardized loading greater than 1 normally referred to as Heywood Cases. <\/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-0ykkuha elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0ykkuha\" 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\">Factor Loadings<\/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>Factor Loadings are the correlations between the construct and each of its indicators (i.e., correlation weights), which become the indicator loadings.<\/li><li>Factor loading is a coefficient generated from the evaluation of confirmatory factor analysis for the measurement model.<\/li><li>Factor loading indicates that a certain factor represents a variable well.<\/li><li>Each factor loading is a measure of the importance of the variable in measuring each factor.<\/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-4cea2e5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4cea2e5\" 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-7b09ae1\" data-id=\"7b09ae1\" 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-fb50d16 elementor-widget elementor-widget-heading\" data-id=\"fb50d16\" 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\">If I Have a Weak Factor Loading, Should I Drop It?<\/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-6f1e203\" data-id=\"6f1e203\" 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-1b3f2be elementor-widget elementor-widget-text-editor\" data-id=\"1b3f2be\" 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 you have a factor loading that is near or below the .70 threshold, it does not mean you need to drop the indicator from the analysis. Complex or newly developed constructs will often have numerous indicators in an attempt to capture a comprehensive aspect of a construct.<\/li><li>If you have numerous indicators that are strongly loading on the unobserved construct and an AVE value that is still exceeding .50, then I would suggest keeping the indicator. The weaker indicator could very well be helping to capture a unique component of the construct.<\/li><li>That said, if the construct is nowhere near the threshold (&lt; .60), this item is contributing very little in understanding the unobservable construct.<\/li><li>With a factor loading lower than .60, you are barely explaining a third of the variance in the indicator. If this is the case, you should strongly consider dropping this indicator.<\/li><li>This poor-performing indicator can create more unexplained variance in your model and ultimately hurt your ability to achieve convergent and discriminant validity.<\/li><li>A word of caution should be given about deleting indicators in the measurement analysis. If you collect data on some phenomenon and in the analysis decide to start dropping indicators from your constructs, then you really need to have a second data collection to verify that your revised scales (without the dropped items) are valid.<\/li><li>Having a single sample and dropping indicators sets you up for criticism that you are capitalizing on chance. If you cannot verify that changes you made in the scales are stable and not based on the unique aspects of that specific data collection, then criticism could ensue in regard to the validity of your results.<\/li><li>That is why pretesting a survey or scales is so important, especially with an indicator that is being adapted into a new context or even if a relatively new construct is being measured.<\/li><li>The pretest should be where indicators are dropped, and your final data collection should verify the structure and measurement of each construct established at the end of the pretest.<\/li><\/ul><h3><b>Another Perspective!<\/b><\/h3><ul><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.<\/li><li>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><\/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-517f2a9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"517f2a9\" 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-23f3488\" data-id=\"23f3488\" 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-027608d elementor-widget elementor-widget-heading\" data-id=\"027608d\" 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 I Have a Standardized Factor Loading Greater Than 1?<\/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-98a867f\" data-id=\"98a867f\" 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-284043d elementor-widget elementor-widget-text-editor\" data-id=\"284043d\" 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 standardized factor loading greater than 1 is stating that you are explaining more than 100% of the variance in an indicator.<\/li><li>In this instance, you will also see a negative number in your error term, which is often called a \u201cHeywood case\u201d.<\/li><li>The causes of a Heywood case are often the result of an outlier, multicollinearity between indicators, or a mis-specified model.<\/li><li>You will see Heywood cases more often when a construct has only two indicators.<\/li><li>Possible solutions are to remove a covariance between indicator error terms, deleting the problematic indicator, dropping outliers, adding another indicator to the unobserved variable, or dropping the maximum likelihood estimation in favor of GLS (generalized least squares\u2014this can be done the Analysis Properties window).<\/li><li>You can also move the constraint (Parameter -&gt; Regression Weight) to another indicator<\/li><li>If you are only concerned with the standardized factor loadings (and not the unstandardized), I have seen Heywood cases addressed by constraining the unobserved variable\u2019s variance to \u201c1\u201d and then labelling all the paths from the unobservable construct to all the indicators the same term (Like an \u201cA\u201d).<\/li><li>By labeling all the paths to the indicators the same name, it will constrain all the paths to be equal. So, the unstandardized estimates will all be the same using this technique, but the standardized estimates will reflect the difference in the indicators.<\/li><li><b>This is not an ideal method to address a Heywood case, but it is an option if all else fails.<\/b><\/li><li>Note: you can have unstandardized loadings greater than 1 and it is perfectly acceptable.<\/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-98240d8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"98240d8\" 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-0a115ae\" data-id=\"0a115ae\" 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-ac21772 elementor-widget elementor-widget-heading\" data-id=\"ac21772\" 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-17f04b0\" data-id=\"17f04b0\" 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-04d8e3c elementor-widget elementor-widget-text-editor\" data-id=\"04d8e3c\" 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<div class=\"gs_citr\" tabindex=\"0\">Collier, J. E. (2020). <i>Applied structural equation modeling using AMOS: Basic to advanced techniques<\/i>. Routledge.<\/div>\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-a27b2fb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a27b2fb\" 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-8c575d2\" data-id=\"8c575d2\" 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-d4ba5bf elementor-widget elementor-widget-heading\" data-id=\"d4ba5bf\" 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 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-33 elementor-top-column elementor-element elementor-element-5d1a03f\" data-id=\"5d1a03f\" 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-3dee978 elementor-widget elementor-widget-video\" data-id=\"3dee978\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/E560GtWsFPw&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<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-1d6a771\" data-id=\"1d6a771\" 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-9874550 elementor-widget elementor-widget-video\" data-id=\"9874550\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/tK9u0EnJe_I&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-168dbf9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"168dbf9\" 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-d4a21c5\" data-id=\"d4a21c5\" 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-0db2425 elementor-widget elementor-widget-heading\" data-id=\"0db2425\" 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 SPSS AMOS 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-09f41be\" data-id=\"09f41be\" 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-8451688 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"8451688\" 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-6829 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/\" class=\"menu-link\">Assessing Construct Reliability and Convergent Validity in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7107 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/basic-first-structural-model-in-spss-amos\/\" class=\"menu-link\">Basic\/First Structural Model in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6713 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/building-a-basic-model-in-spss-amos\/\" class=\"menu-link\">Building a Basic Model in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7029 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/common-method-bias-in-spss-amos\/\" class=\"menu-link\">Common Method Bias in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7064 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/common-method-bias-using-latent-common-method-factor\/\" class=\"menu-link\">Common Method Bias using Latent Common Method Factor<\/a><\/li>\n<li class=\"page_item page-item-6757 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/confirmatory-factor-analysis-and-analyzing-spss-amos-output\/\" class=\"menu-link\">Confirmatory Factor Analysis and Analyzing SPSS AMOS Output<\/a><\/li>\n<li class=\"page_item page-item-6687 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/first-measurement-model-in-amos\/\" class=\"menu-link\">First Measurement Model in AMOS<\/a><\/li>\n<li class=\"page_item page-item-7247 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/full-structural-model-analysis\/\" class=\"menu-link\">Full Structural Model Analysis<\/a><\/li>\n<li class=\"page_item page-item-6909 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/how-to-assess-discriminant-validity-in-spss-amos\/\" class=\"menu-link\">How to Assess Discriminant Validity in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6415 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-lecture-series-basics\/\" class=\"menu-link\">IBM SPSS AMOS Lecture Series &#8211; Basics<\/a><\/li>\n<li class=\"page_item page-item-6443 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-1-what-is-structural-equation-modelling\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; 2 &#8211; What is Structural Equation Modelling<\/a><\/li>\n<li class=\"page_item page-item-6538 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-4-introduction-to-amos\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; 4 &#8211; Introduction to AMOS<\/a><\/li>\n<li class=\"page_item page-item-6638 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-factor-loadings-and-fit-statistics\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; Factor Loadings and Fit Statistics<\/a><\/li>\n<li class=\"page_item page-item-6561 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/introduction-to-confirmatory-factor-analysis-cfa\/\" class=\"menu-link\">Introduction to Confirmatory Factor Analysis (CFA)<\/a><\/li>\n<li class=\"page_item page-item-7330 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/mediation-analysis-with-multiple-mediators\/\" class=\"menu-link\">Mediation Analysis with Multiple Mediators<\/a><\/li>\n<li class=\"page_item page-item-7820 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/moderation-analysis-with-categorical-moderator-in-spss-amos\/\" class=\"menu-link\">Moderation Analysis with Categorical Moderator in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7370 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/moderation-anlaysis-in-spss-amos\/\" class=\"menu-link\">Moderation Anlaysis in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6944 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/reporting-measurement-model-fit-indices-reliability-and-validity\/\" class=\"menu-link\">Reporting Measurement Model &#8211; Fit Indices, Reliability and Validity<\/a><\/li>\n<li class=\"page_item page-item-7354 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/serial-mediation-analysis-in-spss-amos\/\" class=\"menu-link\">Serial Mediation Analysis in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7080 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/spss-amos-assessing-normality-of-data\/\" class=\"menu-link\">SPSS AMOS Assessing Normality of Data<\/a><\/li>\n<li class=\"page_item page-item-7300 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/spss-amos-mediation-analysis\/\" class=\"menu-link\">SPSS AMOS Mediation Analysis<\/a><\/li>\n<li class=\"page_item page-item-6782 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/understanding-assessing-and-improving-model-fit-in-spss-amos\/\" class=\"menu-link\">Understanding, Assessing, and Improving Model fit in SPSS AMOS<\/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 Factor Loading and Heywood Cases IBM SPSS AMOS Series The focus of the session is on Factor Loading, When to delete an item based on low loading, and how to deal with Standardized loading greater than 1 normally referred to as Heywood Cases. Factor Loadings Factor Loadings are the correlations between the construct and [&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-6981","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - 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