{"id":6561,"date":"2021-11-10T11:29:30","date_gmt":"2021-11-10T11:29:30","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=6561"},"modified":"2021-11-10T11:47:16","modified_gmt":"2021-11-10T11:47:16","slug":"introduction-to-confirmatory-factor-analysis-cfa","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/introduction-to-confirmatory-factor-analysis-cfa\/","title":{"rendered":"Introduction to Confirmatory Factor Analysis (CFA)"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"6561\" class=\"elementor elementor-6561\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-xtvbsv3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"xtvbsv3\" 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\">Introduction to Confirmatory Factor Analysis<\/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-gi6ld22 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"gi6ld22\" 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-885cb99\" data-id=\"885cb99\" 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-50ddc50 elementor-widget elementor-widget-heading\" data-id=\"50ddc50\" 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\">Confirmatory Factor Analysis<\/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-105f188\" data-id=\"105f188\" 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-4918b41 elementor-widget elementor-widget-text-editor\" data-id=\"4918b41\" 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 session will discuss<\/p><ul><li>Confirmatory Factor Analysis (CFA)<\/li><li>How CFA is Different from EFA<\/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-ha2ca13 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ha2ca13\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-2b10226\" data-id=\"2b10226\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fc6092a elementor-widget elementor-widget-image\" data-id=\"fc6092a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1280\" height=\"720\" src=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Introduction-to-Confirmatory-Factor-Analysis-CFA.png\" class=\"attachment-full size-full wp-image-6563\" alt=\"Introduction to Confirmatory Factor Analysis (CFA)\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Introduction-to-Confirmatory-Factor-Analysis-CFA.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Introduction-to-Confirmatory-Factor-Analysis-CFA-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Introduction-to-Confirmatory-Factor-Analysis-CFA-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Introduction-to-Confirmatory-Factor-Analysis-CFA-768x432.png 768w\" sizes=\"(max-width: 1280px) 100vw, 1280px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-1e8e919\" data-id=\"1e8e919\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-755d715 elementor-widget elementor-widget-heading\" data-id=\"755d715\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">IBM SPSS AMOS Lecture Series<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-da54622 elementor-widget-divider--separator-type-pattern elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"da54622\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\" style=\"--divider-pattern-url: url(&quot;data:image\/svg+xml,%3Csvg xmlns=&#039;http:\/\/www.w3.org\/2000\/svg&#039; preserveAspectRatio=&#039;none&#039; overflow=&#039;visible&#039; height=&#039;100%&#039; viewBox=&#039;0 0 24 24&#039; fill=&#039;black&#039; stroke=&#039;none&#039;%3E%3Cpolygon points=&#039;9.4,2 24,2 14.6,21.6 0,21.6&#039;\/%3E%3C\/svg%3E&quot;);\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf39eb3 elementor-widget elementor-widget-text-editor\" data-id=\"cf39eb3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Lecture 5 of the IBM SPSS AMOS tutorial series. <\/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-wrhoh8m elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"wrhoh8m\" 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\">Confirmatory Factor Analysis (CFA)<\/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>Confirmatory factor analysis (CFA) is a statistical technique that analyzes how well your indicators measure your unobserved constructs and if your unobserved constructs are uniquely different from one another.<\/li><li>In a CFA, an unobservable construct is often referred to as a \u201cfactor\u201d.<\/li><li>So, when we use the term \u201cfactor\u201d, it represents an unobservable construct we are trying to measure.<\/li><li>In a diagram format, an unobserved variable is represented by a circle or oval.<\/li><li>The indicators that measure the unobserved variable will have single headed arrows coming from the unobserved construct to each of the indicators.<\/li><li>Each indicator is represented by a square or a rectangle.<\/li><li>The single arrow line directly from the factor, or unobservable construct, to the indicator represents the influence that is reflected from the unobservable construct to its indicators.<\/li><li>Statistical estimates of these direct effects are called \u201cfactor loadings\u201d and are interpreted as regression coefficients that may be in unstandardized or standardized form.<\/li><li>Indicators that \u201creflect\u201d an unobservable construct or factor are called simply reflective indicators.<\/li><li>With most indicators, a level of error is present. Each measurement error of an indicator represents \u201cunique variance\u201d, or variance not explained by the factor level.<\/li><li>Measurement errors are represented by two types of unique variance: random error and systematic error.<\/li><li>Random error occurs when the measurement of a concept is unpredictably fluctuating due to unforeseen or random influences. This type of error is considered \u201cnoise\u201d because there is often no consistency to the measurement error.<\/li><li>Systematic error is different in that the error is systematic in nature. The results of a measurement can be consistently high or low due to a systematic influence in the measurement of a concept.<\/li><li>For instance, taking a survey online as compared to a paper and pencil survey may systematically influence the way respondents answer questions about a construct. With systematic error, this is often classified as a \u201cbias\u201d compared to the \u201cnoise\u201d of random error. Note that if an unobserved variable has only one indicator, then the error term for the single indicator variable is constrained to have a mean of 0 and a variance of 0. (You must assume the indicator has no measurement error\u2014which is often a very flawed assumption.)<\/li><li>In a CFA, you also want to account for \u201cunmeasured covariance\u201d. To do this, a two-headed arrow is drawn between all independent unobservable variables.<\/li><li>In a CFA, the analysis will treat all unobserved variables as exogenous or independent variables.<\/li><li>Thus, you will draw a covariance double headed arrow between each unobservable construct in a CFA.<\/li><li>If you don\u2019t do this, AMOS will give you an error message stating that a covariance should be drawn between all unobserved constructs in your CFA.<\/li><\/ul><div><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/div><p>Source: Collier, J. E. (2020). <i>Applied structural equation modeling using AMOS: Basic to advanced techniques<\/i>. Routledge.<\/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-0c1b14a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0c1b14a\" 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-bb6d2fe\" data-id=\"bb6d2fe\" 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-08598f6 elementor-widget elementor-widget-heading\" data-id=\"08598f6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How CFA is different from EFA?<\/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-6ae2d72\" data-id=\"6ae2d72\" 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-f96b1ba elementor-widget elementor-widget-text-editor\" data-id=\"f96b1ba\" 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>An exploratory factor analysis (EFA) is useful in data reduction of a large number of indicators and can be quite helpful in seeing if indicators are measuring more than one construct.<\/li><li>EFAs are typically the first step in determining if an indicator is measuring a construct. In an EFA, the researcher is not denoting which indicators are measuring a construct.<\/li><li>The analysis simply tries to let every indicator load on every construct. This is where \u201ccross loading\u201d can occur, where an indicator is actually loading strongly on more than one construct. This is a concern that your indicator is not a strong measure of the specified construct.<\/li><li>In contrast, a CFA does not allow an indicator to load on more than one construct. The researcher prior to the analysis will specify what the indicators are for each construct, and those indicators can load only on that specific construct.<\/li><li>Other ways that an EFA and CFA are different is an EFA is typically conducted with correlation matrices, which can be problematic in comparing parameters across samples.<\/li><li>CFA uses a covariance matrix and is more adept at handling comparisons across samples.<\/li><li>An EFA will also consider data rotation that is often done to achieve a better loading of indicators on a construct or sometimes a reduction of cross loading with other constructs.<\/li><li>A CFA does not worry about rotation because you are denoting the specific items that are loading on a 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-544e632 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"544e632\" 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-87c23dd\" data-id=\"87c23dd\" 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-c6dc081 elementor-widget elementor-widget-heading\" data-id=\"c6dc081\" 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-573a93a\" data-id=\"573a93a\" 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-e1a266b elementor-widget elementor-widget-text-editor\" data-id=\"e1a266b\" 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>Collier, J. E. (2020). <i>Applied structural equation modeling using AMOS: Basic to advanced techniques<\/i>. Routledge.<\/li><li>Awang, Z. (2015). <i>A Handbook on SEM<\/i> (2nd Edition). Malaysia<\/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-15874a7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"15874a7\" 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-afd8f54\" data-id=\"afd8f54\" 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-512f77f elementor-widget elementor-widget-heading\" data-id=\"512f77f\" 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-50 elementor-top-column elementor-element elementor-element-db6c774\" data-id=\"db6c774\" 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-aff5778 elementor-widget elementor-widget-video\" data-id=\"aff5778\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/-bTk602AlPE&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-5ce8860 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5ce8860\" 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-6d21359\" data-id=\"6d21359\" 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-28951a8 elementor-widget elementor-widget-heading\" data-id=\"28951a8\" 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 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-50 elementor-top-column elementor-element elementor-element-2b40f68\" data-id=\"2b40f68\" 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-bdda701 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"bdda701\" 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-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>Introduction to Confirmatory Factor Analysis Confirmatory Factor Analysis The session will discuss Confirmatory Factor Analysis (CFA) How CFA is Different from EFA IBM SPSS AMOS Lecture Series Lecture 5 of the IBM SPSS AMOS tutorial series. Confirmatory Factor Analysis (CFA) Confirmatory factor analysis (CFA) is a statistical technique that analyzes how well your indicators measure [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":6468,"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-6561","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - 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