{"id":6538,"date":"2021-11-09T11:48:06","date_gmt":"2021-11-09T11:48:06","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=6538"},"modified":"2021-11-10T01:47:00","modified_gmt":"2021-11-10T01:47:00","slug":"ibm-spss-amos-series-4-introduction-to-amos","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-4-introduction-to-amos\/","title":{"rendered":"IBM SPSS AMOS Series &#8211; 4 &#8211; Introduction to AMOS"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"6538\" class=\"elementor elementor-6538\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-s8r1u4o elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"s8r1u4o\" 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 AMOS<\/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-izrrqtf elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"izrrqtf\" 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\">IBM SPSS AMOS Series - Introduction to AMOS.<\/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 discusses<\/p><ul><li>What is AMOS?<\/li><li>Understanding Diagram Symbols<\/li><li>AMOS Terminologies<\/li><li>Sample Model in AMOS and What it Means?<\/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-pis3kdk elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"pis3kdk\" 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\/IBM-SPSS-AMOS-SERIES-Introduction-to-AMOS.png\" class=\"attachment-full size-full wp-image-6539\" alt=\"IBM SPSS AMOS SERIES - Introduction to AMOS\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/IBM-SPSS-AMOS-SERIES-Introduction-to-AMOS.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/IBM-SPSS-AMOS-SERIES-Introduction-to-AMOS-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/IBM-SPSS-AMOS-SERIES-Introduction-to-AMOS-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/IBM-SPSS-AMOS-SERIES-Introduction-to-AMOS-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\">Introduction to AMOS<\/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>The session discusses the very basics of AMOS including the different concepts, terminologies, and a sample AMOS model<\/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-gr26s5b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"gr26s5b\" 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\">What is AMOS?<\/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>AMOS stands for \u201cAnalysis of Moment Structures\u201d. Moment structures refer to means, variance, and covariance.<\/li><li>AMOS is a program tied to SPSS that uses a graphical interface for input.<\/li><li>AMOS software could be utilized to explore statistical relationships among the items of each construct and also between constructs.<\/li><li>Using AMOS, the researcher can specify, estimate, assess, and present the model in a causal path diagram to show the hypothesized relationships among constructs of interest.<\/li><li>The empirical model can be tested against the hypothesized model for goodness of fit.<\/li><li>If the researchers found any path that does not fit with the original model, they could either modify the path to improve the fitness of the model or remove that particular path completely from the hypothesized model.<\/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-dcb3d0f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dcb3d0f\" 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-49e0d28\" data-id=\"49e0d28\" 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-e3c6f2f elementor-widget elementor-widget-heading\" data-id=\"e3c6f2f\" 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\">Understanding the Diagram Symbols<\/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-56262b3\" data-id=\"56262b3\" 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-0e6eabd elementor-widget elementor-widget-text-editor\" data-id=\"0e6eabd\" 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>SEM uses diagrams to denote relationships to be tested.<\/li><li>It is important that you understand what these diagram symbols mean because AMOS is going to make you draw out your conceptual model.<\/li><li>One of the frustrating aspects of SEM is that there are often multiple terms that mean the exact same thing.<\/li><li>You can read three different books, and all three can use different terms to mean the same thing. Hence, all the overlapping terms here to add greater clarity to the different nomenclature.<\/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-3c9109d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3c9109d\" 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-5ac86bd\" data-id=\"5ac86bd\" 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-d04c191 elementor-widget elementor-widget-heading\" data-id=\"d04c191\" 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\">Terminologies and Symbols<\/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-2593044\" data-id=\"2593044\" 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-dd96a85 elementor-widget elementor-widget-text-editor\" data-id=\"dd96a85\" 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><b>Latent Variable\/Construct\u2014<\/b>A latent variable is also referred to as \u201cunobservable\u201d.<\/li><li>This is a concept that cannot be directly observed. It would be nice to just look at someone and tell his\/her level of anxiety, but the fact is some people are great at hiding their true feelings.<\/li><li>In these instances, we cannot simply observe a person and determine levels of anxiety. Thus, concepts such as anxiety are an \u201cunobservable\u201d and require the researcher to find a way to capture the concept by other means, such as asking survey questions.<\/li><li>You will also see the term \u201cFactors\u201d used when referring to latent\/unobservable constructs. Examples of unobservable constructs in psychology are Anxiety, Motivation, and Trust.<\/li><li>These unobservable constructs are often measured by \u201cindicators\u201d, or \u201cmeasurement items\u201d, which often take the form of survey questions. For example, you might ask survey questions (indicators) to measure a consumer\u2019s level of trust with a company (unobservable).<\/li><\/ul><div><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/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-1d63c52 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1d63c52\" 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-e7a84a0\" data-id=\"e7a84a0\" 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\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-512e148\" data-id=\"512e148\" 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-01fbead elementor-widget elementor-widget-text-editor\" data-id=\"01fbead\" 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><b>Observed Variable\/Indicator\u2014<\/b>As the name denotes, measures are taken to capture an unobservable concept through observable means.<\/li><li>This can be done through survey questions, manipulations, or behavioral tracking.<\/li><li>This concept is also referred to as Manifest Variables or Reference Variables. You will often see the term \u201citems\u201d and \u201cindicators\u201d used to represent observed variables.<\/li><li>In essence, observed variables\/indicators are the raw data captured that will be used to explain concepts in a SEM model. These observed variables\/indicators can be categorical, ordinal, or continuous.<\/li><\/ul><div><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/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-48c2b8d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"48c2b8d\" 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-f2e3bbd\" data-id=\"f2e3bbd\" 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\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-4c9874a\" data-id=\"4c9874a\" 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-05e6ff8 elementor-widget elementor-widget-text-editor\" data-id=\"05e6ff8\" 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><b>Measurement Error\/Residual Term\u2014<\/b>Measurement error represents the unexplained variance by an indicator measuring its respective latent construct.<\/li><li>In trying to capture an unobservable construct with a measurement indicator, the unexplained variance in the measurement is error, or \u201cmeasurement error\u201d. Along with the indicator, an error term is also present on a dependent latent construct.<\/li><li>This is the unexplained variance on the construct level as a result of the relationships from the independent variables.<\/li><li>Error terms for latent variables are also called residual terms or disturbance terms. Since measurement error and residual terms represent unexplained variance, AMOS treats these terms like unobserved variables.<\/li><li>Thus, the symbol for these error terms are a circle and a one-way arrow.<\/li><\/ul><div><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/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-b91a3a5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b91a3a5\" 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-6b006fe\" data-id=\"6b006fe\" 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\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-feed7b9\" data-id=\"feed7b9\" 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-956a010 elementor-widget elementor-widget-text-editor\" data-id=\"956a010\" 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><b>Direct Path Effect\u2014<\/b>Hypothesized directional effects of one variable on another.<\/p><p><img decoding=\"async\" class=\"aligncenter\" 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MWqgRmNO9sR+Oodu05BEjsdBqFdUxO1atWoVduA6pW\/Q2\/UKmUouPxHf3741obbijtCz26mXV0jtNSrUbWaGlqmXTkYWFQp8mP8GGfVBPNGHWnVsBYqKuVp2G0Mu\/+Uz7ne2zwWzX81YvVWN\/q1MUNDtSZ9Fx6lgCzcnYZQ16A21apURVVNgxbDVvJC2VsVsHN0c36qYcmEsXYMs+2CrqoWViPm47ZuCUP696Vl\/Vr8qt6QtefLXg8gTXmMc6\/maGpooFpDB5PWA\/AOka+iyH9yBmNDDRp1H8bcmdMZNagzNWtZsPTIA+XRedilTbQ0NUa7RjWqVVFD09QGr8Cib89rTidUVFT4sVIlqlbXwtDIiCbd7bn9Mq9YGR4xd6AVhqaN6dC5K43qmdB71j5SyxqHlLygd9Nq6E3dq7wr\/NxGGutrU716DbR1DekzYwfJZX3tsnBG1KiOVb+lrFswirq1a1HL1IZjIWmAjDtHlmNpZESzFp2w7tAUI8teeN0t\/GwJTG1fGxWVf1OpUiWqadXGyNCQjpPWEVUsVVxyGYSKTidct66lZyNj1NR1GLPyHAWAJPY2k2yaYmDenE7W1piZ1Of3Fd7KUJZw9whd69dGU60GWrr6NBy2hAhlwJVyfe886hvVoVWrznRq2wijZoM4FVLUGZ1Z0IuKP+gwcKQTztMmYmNhQJ1WE\/BX1IXQi1tpol0ZFZV\/8+MvlahUrR7rr7yQP7kgnu3T+2NkWI\/WHW1o3cCElkOLvz+kPDjFoKZGaKjXoIqqBoZ6WlT4Vx1Wno0qY6PDqYV9+P6HatS3bIiueg0qV1bFvON07sXLyxV5YRm\/qlRh1rHCfTePHeObo6LalZvxpa+ek6U+wnFgWwx0a1GlcjXUaugwbNnJdwaDN0OBNPwkauqVGOGlaBilEYzQVKOaUWfGz5nNlFG\/YailS5+FhyiWYwi\/so3WJnWwbNKerp1bYWTano2XIsp878K6o6Ouqqw7nsXqzquLa1FX+ZEWgydgP8OJYd1aoG5oxY5bxcN+PhfdnKlbU4saaqqo1zRAu8bP\/Nh2BI\/LyMRR13fRuWEdtDVUqVJVFQ299uy8Ufb3lh\/rz5gO5mioa1BdUxcL6zFcj1bsrUk+tP53Nfov81E+3mteV1Qq6TJq4nScpk+kY0ND6nWy40FiYcWWEnBgIfVM6mHV0Qq9it\/w38p1GD5nIyFpgKRkKJDE+DO5WzMMzZrT0doac5N6\/L7Cu8zvOS\/qJhNsGqGvXZNfq1SjumYd7LfeKAoG0kj6NdRBd\/B8Ns8dRT1DLdT1u3DkYSqQyVxbYwxtp7PZxRFLQ23UdZuz6UoMIMPv4BKaGhqgp61JlWqa6Onr0+q3+YQkyfflnBcXaPRrZXq7XFGW58Kyvqio\/MjCC4q6RiqLbUxQazyN6Dwg5Q5tGmhh1K4\/s2c4MG54T7S1jLHfUqzMSAnwXEyDOkZYtrHGuk1DjJv34\/iDJOV3NalTU\/R1tfi1ajVqqBswZfPV14JF+pMrDG1WD00NdapWUcdAT5vyP1XDwTuszP3gf\/H\/HgrS7u3HrFpl+i31Jl0iA5KY26k+Rh0WkaSooNGX12Ggac6aS4oNnhXCqObm9F5wusy5rnv7HPn5Oy2c99wkSyajQJLA3IHmqA1arAwFN1xGUVNtIIGZADLuXTzOgROB8g2dfpdehjWwnl20KOayy3A0jftzO74ASMV1ZE96Oh9QJrZH7o5oflOZUauP8iI1k9iHAdwJiYWCVxzZeQC\/cPkkStjRuVSvbMKOO4WTKgV4TO2Aap3unHucgAyIOLOcyirl6Wy\/m5c5BZAfg0MHQzSsF1LWMUBG0B70VX5mkru8EXzyMICQWPkhhiTiNHqaVengsI8kiQzIZN3gxmi2tSNK0TYHXz3M7gtB8m2bcZceNarTeepRZWg4MNOG2tYOREtAJi0g+sFZeprUpMnILYrtKuHYH\/3RrD8Iv5fyLZMc6I6ZniWrrpTRSEmiGd7ZEMtZXsq7Doxtww+VbbiTCWRFc\/tuMGlljQLLIpleX52a5oM49eAZGamvuONzi9gcGblRf9JJz4BhroWVOZP1v1th1nOZYl9LYGoXc4zG7kAKyApyCL64CWM1TUasu6b8\/Dc2TeCbCro4uJ0nPj2diPsB3A15BWSxZXxHdFpOIlRxcseLC6sx1G\/FvvvyUHZzw3C++9WSExESIIUAvyASsuTVOvPxcVrUMmKSct4xiUW9m9FsmJtyjcWfLoOopGrOlmvPkAJ5z0\/TtFJNxrrdlm\/CvFx8106gZuVe+L4xX3fPYyaaWq3wvC\/fe2TxvtjUN2XMNj\/5A3IimWxVm2oNhnMzIhmZVEqS\/25Myxmz6B0jBcdnd6eqbnsOB8UgkUqJu3+UFjUq0Wqml+JMn2QW2hij3mYWSYAs5gJNqqjxWxnz\/HnRd9nhcZhwRWN83Lkblav2IqCUwbviSoSCp+cwqqvLlGMh8jtkz5lkpI1em+kEJ8h3plubx\/K9eltOK9YWSVODGGxuhPWMQ4rOqYBDM3ph2HIaEW89UUpWat3pNPmIsp1KuLoZ\/X+rMtrtT7KlgCSWKVaG6A9wVY5UJNzagf5Pv9Jr1kFe5cqQFuRx8A\/bd0wfyAi7ehwvbx\/5UW1uOP0tNakzfleZR91xF1egplKNReejAQmP7gUQkaDojpP96FbVkFGufsrHH1vWj\/\/UaIKH3wukQFboMSxraDJ+z135A7JDGdysLp1nHSYfyAw5Tue2PdgVoFjkmf3m9EE2Wyd0fq3ORF9cg6F+K\/bee\/sXnRx2E0\/3o0TnAOThNqoZ5ZtO4JkyX8YwzsqIH4364e3\/hMysePyu+xObLgUyWDm8ARX0O7DrSghZmYnc9b9JRLKUpAB3zCrXoO\/iw8RnFyCTSYjw88JKqxqW47eRLgOQcmKuLdWMhvA4D8h+xFBLDf5drhyWwzaSC+QGH8BUtTazTigW6CbcprmxGvWHrCUqqwAo4KBDV6rUG8Q9xar3nKdnaFXbgDGbC0NYKisHt8bit9WkAdmR\/uzefgr5wFo+20a3QL3RGJ4UpifJKxb2NOVnw35cDolHJpOSGXwUI0MtJhz6B40UpNzdQyOtBuy8U3SK3PmFAzBqMIowReN\/fEZ3qmo144+1m1i3di0b3dbRv4kG37Wexsu3ziJksKKLBXU7FC0qhALWjG\/x2kLDN0OB8tlxz7h50YPuFlq0ddivvP\/EDFv0rOcoh5NOONig3XGmspN+uMsOne8tOf70jaooLWwWZLwIvcuR9dPQqmnMumuFs7ASdo\/viGW3hSi3RNx5On5Xm4XHijrSc7MGoKkzmtAyaro0O5LVv7dDs6Yls7eco\/jgbH7oCWrXNWTu5RjlfTe3jqacZS\/uvDE6mvHyOTcve9Czpiadhu9TNi4HZtpQu4sTccW2feDWieho9eR2GiCLYWx7fTTaDMVt00bWrnXFzXU2pt9XpLPjqbcXvJRQEHNzP50NtDDrMJYTD95jllz2jCmGGrQb617iT6Fec1H9VpexC1zZsG4t6za44TigGRVV23M1ESCJqV3MqTNu12vP2zW5HRo2s0hUfN5rG8bw3+pduf7mYvb0+\/RooIlht4lscVvP2rXrcHOZhva\/fmS4q7zTznxyid9a1kWnSV\/2\/Pn6iv4726bya4U6TF22nvWua1m30Y3J3c35WacXgYqd4uyiQdRtMo7nyjQcg31zY7o7HVW+TtDmKdSs3Bu\/1846zWHd2BZUrNuZFRs34bp2LRs3r6SzdiUMOq8iH8gJ9sRUQ5vZ3kXTSdKn3jR5j4WGhxy7Y9bOieID4cdn9+BnwxHKehxzZR01f9HH9VY0PisH8VMtG3xevj3Wy5RVJo3QoJusn9gFrZ9suPqOhQvvDAUFEYwyMKGn01nlczIDdmP0rQVut+Q1OeG6G9r\/VWeAowtu611xXe\/G\/HFdqPijGZ7Bb0sFxdqPYnWn41B35UFD\/OV1GJRvzoHHRcfBhyd2R9vEnsJjTO8ZvdCoNYDgYgMof7oOfWcoKNxcuQkvCLhylF6NdKml6KTeJj\/lEXNsm6Cp25LlHj6vr+UpJRQcWWDL9x0nopzglUTSr50ezReelP8\/PYhu5hbYeylGhPLu06VePabtU8xpZ4e\/HgoyHmJrqYmhzYQSdWbomltvLbdyr8lNINj\/OgsGN+c\/DUbwsPCrkUXxe4s6mAzeWsr6pDSWDTZDq\/fiEguaPZ2s+dFyDG8eugRuG0\/VXztwOU7eCuY98cayRi2mn4gg8uR8NA074bJ6Jrq1W+IdkYz33G6oWY5DuX49\/hZNGujyu8cD5WuGn5hHhbot8VZU5ocHnPiuqj4TFrqycd1a1rm5Mb2nORV+ssa3eNucm0Sw\/zUWDm+FepOhPFCEirynJ2n+vQb2niFFj427jln92p\/bQsP3CAVvnH1wekF\/6jQeQ3gugAzP8VZUrtGQCc7OTLe3w97eHqcZs1jkfoHkt7YpSfzRypzWQ\/YWu6\/k2QevhwIg9xXeG5fg4DifHbs307e5Ie2dPJU7VsKD4\/Rp1Ajb0fbMmj4SQ526TN8boHyHB7vsMPrZmmulNFwpYb6snuHIXBc3tq+ZjoG+GetvFIWCPRM6Ym49R9mw5r84TceKtZnrVdR5nJ7RH22dsYS9c9ItFW83Rxrr69By4HyCX8mjQWlnH9zYNJJyjXoRULjj5cdzZMMKpk1fyPadWxhoqEfXkR6vhQK9Lo7EFCvDo\/2O6GtYcz0JkD5lSCstajbvzUxnR+zt7LCzd2Dm7D\/wOhv69kWEpYQCgIynPjgPaYd6LQtm7bpOmVe0kEUwyUCffg4lFzLd3eXA9xW0GTjZCYfp9tjZ2ePgPIP5a9x5mg5FoWDna2U8MNOaSh2nEafY166tG0W5+kO4+2aLknibjvVVMeo4hBlODtjZ2WE\/3ZFZsxfj7fNC+TBZQjDLJvahloYRw5YdIUnRY1xfO4byFQ0YZjdDWT5H55ks3niQaMV7nV08iDqWIwkp3AjS50xrWRfbmceUrx+wcRI1q\/Th1msHWhksHWbJT8ZtmT7DCXt7O+zspzNj1iw2u99CAqTf2Yl5LUt23S26JsH\/dvaBHVHF6qPvipGoaQ\/hgbJXSmB2V0vqNOlGI309es31fueq9nCfE8yY5szyzbtYM9UW\/arduf6OU73fJxSMNqyLjV3RCGCK3w7qfmvJZkUoiD63iir\/qU630fY4OkyX7yuOzsxetoX7L8voZvPjOVqs7gwy0sP69\/3FQsF6DMo3YU9QYWKT4TWuG3qmDoqOSIbXxK4YmL0esN7r7ANJMmf2rmSK0zy27drJwFZ10RvuxjuXV0ri8Fw+HjNtXazHuhKRqqjYpYaCnnxvNaaos8t5Sp+2erRY7K24I5uTy8dRz8KK6TNnMdq2CfpmQ\/GNURyaKEcKFIEsKYBOZqoYdhisrDN2hXXmRlGdKamAe3+6M93BkTVbdzN\/RAcqNhpFcOERkDSK4W1Maeh0qJTnprJkUEMajd3yxvVqpGyb0pafrZ15c4wi6uQC9Mq34OSzwm8yh82jO1K3aR96Nq9H6\/Hu5EvjmdSyPi17DqFFLUNGbS7abqWdffD4yBwqmLTCO1K+vW\/vnEi5KrUZPFlR\/+3l9X\/BUi+ep8mAAh6d28fMaY6s2rKDheM6UavFCB4qOrOcRwdoUMGEtVeLHTx9nmcf\/O+h4NT84qEA7u13RE2rDW+OYObl5iKRvq2LkXJidm90G4whvFjntWFSq9fOPngzFNzaNBlNg574KDrIBX3MafvajpXDmRXjMNTWo12\/CWw9cYucYkV4sMsOw586cTn2jXIVRGPfoQEtxm6XNxDRJzE3MmPj7cKeuPRQ0OGNUHDKuR\/aumWHgtyMFJIKO41XF7H8RZU+K6\/Li1FKKLjuNpLyjXpzR1ET\/HZOQ8OsH4VfydImpnQZWWxIf2ZXaneZUVRxJNE4d6pLrTbzkB\/4ZbHu99ZotHEqsQI+LzPn\/UOBLJ+kpJSiYXuXQXz7SzsuxJa6vEbxnAgmGejRx967xJ+S77pj\/qs+80+\/cRwgzSZHCpDAlC5mGE0oGmUoeOWLjY4G1g6HlPrXPh0AAAy5SURBVA37tXWjKGf6G3fevJ5PwStmdDXHsM+qEkdnuVm5yJCRlpCkbIgijs1GtVxddvrL94GX1zZSp5Ixa2+8MTlUkE2u4s1LCwVTW7weCu5snECNil25XdhAygqQIePC8qH8ot+LgDc6FklmFgWANMGfzqYGDHEtmkMm9jzNvn\/32QeHHG0wbetYtE\/kRzKhjQF1hq1\/7Wgs6rIb2t+ooFKtA5eiy+6uCqJv0KlBPcZska\/Yjjk8E+NKXfF7x3q\/vxQKbm3HuFxRKMh7fgGr6jqM3nb3jVfPJTvv7VFGXnf6cl0xhLm0qSmdfz+g\/HtpoeDAuG7o1XNQHp0Ge85AW6Mdp18UvY\/PphGUbz6ozFDwwGM2dXQ64604L3Tp4GZoD9v89icgIystmcI1srJH+9GtUIMJexUdyHuFggj6tNWj5eKi+paXEMj4Do2obdKSMbNW4RtebH9+85REaTwzu1lg2GdlKXXm7W1F+uOjtNWqzx8nngEQsHkM5RuMIly5tEEeCiyne5XybHkosBzlVuI9Hx2ag1olMzbfLh7JklnZ35KqLex4XuwJKfc8qPetCir\/NmF7oHynDNhlTzkVFcoZDiIgsVjpSwsFh2dTwaQ1pxShINFvBzqqRiy99MYRZW4uUiAn9ASNtEyZrViXc3\/HWLSajyCscEFtxgOG1tOn18LzxT7qbSzMajPWqygUZKcmk5Lx18\/E+QChIJWFTY3RMnQs9XSl5DvbMalswLprRZHh+KweaJkOI7TwC8h6zqwuZlSu1YSRk6fj6GjP8D7tsXHeQXIZ\/UNW5GV61NOjQbcxbNzhzkHP3fRqpMqvfRcoL150ddFgKv\/YU9lQnl82mHI\/W7L1sj++57ZiUf17DHvM4M6DKPmRcuJt+jZthfNuv1Lf897WCWj+uxUXY97YnXOfMsxcC53OzvgFBrBn7gB+qFCFcStPEp2YBUjZPrIlhm2clOEpP\/IELVSq47y\/aJHI8WndqaY2nJAyQsEr\/0P0a9GV8dOdmDn5NzQrGzHr8GMAJI+PUF1bnWkni4LGFdffUDHpiqJv4tLqwZSr2ojtF\/3xvbCVBj9\/h6mVM9dC4wA4tqgXFWvUZegkJxwcpjOgoxlVqjdnx62iKYnUxydpq6eGfktbpjo44mA3HptW1szZ6ldGKIhiQGtNjJXTNTl4zZ1EZ9thODnPZFgHU9QtxxBU1mpFWTijNWpgM\/FYKX\/MYJ9zT77\/SZe+oybj4OjA2CG96NrXgXuZAMk497DgR\/2WTHJyxMFhMp0ttFCz\/I3rL4pa48urBqNSuzf+pVwUMs53FxYaVajfsT\/2jo5MnzqGTs27s\/poKAC+W2fS1aY\/052cmdCvFeo6XTkfoZhNliazaUInvq9kxKCxU3FwdGD0wO7YDplHmKKD957XCy3j33ikDAVPGWehQyf7ouCa6LcL858r02bABBztJrFw4yUkQEFCECNa1KaKiRXj7RxwnD6F\/l068vusI4qjyXwuu05EV8OYEfPW4O7pxd4VE6iqos3848\/evs2BY7O7UfFXQ4ZNdsDBwZHfOpnxs7oVB4PeiIXpgXSv8QONJu9951VF856foYF6VTrb7ybwjg\/z+llQ4RtzVngHkpDx9grg7dCTqqpDeFQYCp6cpqaOGmMPPZLfURDOEE0d2k04rHxOss8mtFWMWe9TOCeUz4U1Y\/jpB3VshozDwdGRiSMH0qnLaK5Gvb1Bldedhmy9UFh3vqduWyeuhsgH3OMvrkZDpR7bAwvjk4x9v7enht5k5fQBaeFMszandmNbVrntxMvLE\/tepqhY9uNBGaHg9sax\/PK9ISu8b+J3yZ0WNX\/i5zYTuP74GblvqTIR57fTo3UPJjs54TSmJ6pVLVh9WbHoMcmX9t9pMnilr\/LxXrM7o9J0GBHKUBCOTZMaNJhXVN8C9zhj1nQEN+JK2U5ZIXRqWJ1mhdMNwMubu7HQrKqsMw6KOrPq8KO3ftZU\/10Yf1eJQS7HuXv7MpPb6KCi2Y7d14LkZ9TIXjCgcW2MJ+0v5dkp\/NGrLsaDXUtOreTEsnxEW9S0GzN2miMODg4M7d4UtZot2H79jZELWSJzuxih1mEOhessZfG+tK1VjQ6zTr7ezr3yob6RKn22Fk2JBB9wREW3EcefK0+TYY+9DT9U0af\/6Ck4Ktqn9j3n8VQCOQ890Pm+GgNdjhJ0+zIT2+lRTrs1u8\/cJ00iA2Tc2uOMtpoBwxxXsMfDC4+1DlSq9itjCkcKMkMYbVWfZkNWkFBG31mWDxAKMtk1ehA9B2wsdXFceog3gzv151BQ0aqo61uc6Dl4ES+K7VP5qRHsW+1M+wbmmDXuzNT5rviEvHzn+eyp4T4snTacdi0a06hhA8zqmdJj0T7lop67uxfQqd0MHisa2fzkMNZMHkTnftPYe+QkbosmYztwIsdvRSIDsmJvMqSpNhXVamNp1YUxziteO00w\/MQqbJuNxz\/hzZLJiLl5gN+62DBq1gaOH\/PAcURvRjhv5mmyvHn0XjqeARM3Kq\/nIIm7wbgmNridLzqyve7qRJeui4gso18sSI\/h2Jo5dG5cF22Ttizcc4XCCzZKnl\/Fpld3XH2Kyhx4cD4NB0\/nkSIp5ac8YdWkgXT+bQq7jxxn08Kp9O49gYOB8k7\/6MLeVNVrSJ9uVujWMqTH2HlcvBdXohwJoddYYjeUxsbGtO42hCUbDxCeVMY5zwWvmDfBlkEbipLuy7uXmD+pP3V0dGjRx45Lj95xkStZFMt6dMNh1bXS\/56fwvk9K+nfqQn6DVry+7Q\/OHItTNEpJuFk25DK9azo2a4h2roNGDV7PfdiX1\/yHeixAMs+s3j0lgY66u5Z5oztS4M6dWnfZxSrdpwgKl3+HadH+rN2xmjq69fCpMMwPG++cSpVbjwnty6hp1VDDBu2ZYzjYk7deqYcpfDZPpOeg+bzrLA1k8ay4vfe2K2\/XOxF8rh9bC2dGphg0rwXW84+Vv4l++UD3BZOpk39ujS2ssV52RYCIosPlmbje2Q9Q3p1poFlIywtzDCtZ4u7b9lj9nEPLrFkxhha1tFFu7YFQ+yXcaWU7yr00CyqqDbE\/d77nN6Xx639LnS17sGs9e4c83RjhE0fnDZ5k1TG0brPhhlYd13IM0VukEb7Ytu7Ky6XnynuiGKhbW+mrCpaPZ5+\/xi9LAdx6H6xbSHLxvfoJn63bY2+WRMGjpuB+5kgssoI5PK6M6BY3ZlG717j8bojr8MpAZ70aDgE79DCnUfGucVT6NZ3zWsHTZKEELYvmkqXts1p1Kgh5vVNaTZ6AU\/Lqj7Zkax3HoZNz7HsPHScnS6OdOk9hgM+YUje0lDmJ0awf5kDbcwM0Wtgg+sR\/6LphrQHTGljy4Ji8+CXttjTePQiYgoTXV4UDqO78fv2wvqWj7\/7bGqVq4C2sTldbIewdOsRItMUT8iJxG6EDWN2FRuN4o0603ukvM6klREbC7I4s30O1u37sHy7F0d3uNC750CWuF9GnhdfMW9UHwatPl\/KkzPYMfM3Bi04WPrUSm4iZ7YtxqZpXbR06jNwykIuPSp9\/w8+c4yD3sWvFpjNxa1b+fPxG2E4+T5DB3Rl1omiBX\/PLm2kUZ+RXI8rtkPlJXFm93L6tmlIfYsW\/G6\/iGN+4fIALc3myrY5tO\/QjyVbDnBo50oG9unDoh2XKbogbx6Bx7cyuk9XGjdshKWFOaaWrXAtnIrJi2b5xAEMX+BJxl+8ItXX+4NIspK1qCDxHhPaWNKqzxR27PNk2\/rlDOnahEr6rdl79\/0vFVPKS3+mSi\/ogRk26NvM+DAXx\/msyM8+MJ5YcpGi8AFIYpjcvDbGvVZS9i8plPSPqTJKn7LEn+69H55wwcS4MePnueHhsZsVM8fQVFeD5gNW8vKvj1gLn5GvNxSUIumaK2rVzDlS\/KA47x5NdNTosvotR6ZfoMJTEmO+uJ+LKDwlceenLsgXKerCajR+1GPF5dIuTib88+WxYWRL9Pusee3eR3smU7l6ay7GfnENxldJhIJiCpLuMa61BY172LH7kDfnTh5k0aSeaNWzxvP+P\/fHVv5X7vZWqLWeTNRfnJP6fMUzto0BmsM2f7hrvQsAyHJjcW6nR43Ws4kXG\/eL9fD4cuoYWTB26Wa8z57n4E5X+jQzpe3wdcSLkYIvgggFb8iKvc+25XMYNaA\/\/foPY+rs5Vy493Ud+Vx0s6fXtHXEf3G\/F5HCGrsh9Ft2UoSCDyz7+UUGtbRm1enP8dc5hQ8nn4eXDzBn+nh62\/Zj6IiJLN10iBeZX1xj8dUSoUAQBEEQBECEAkEQBEEQFEQoEARBEAQBEKFAEARBEASF\/wPBqAXE5TOhtwAAAABJRU5ErkJggg==\" alt=\"\" \/><\/p><p><b>Covariances\u2014<\/b>the amount of change in one variable that is consistently related to the change in another variable (the degree to which the two variables change together on a reliable and consistent basis).<\/p><p><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/p><p><b>Independent vs. Dependent Latent Variables<\/b><\/p><p>Independent variables (also called exogenous variables) are the constructs that influence another variable. Dependent variables (called endogenous variables) are constructs influenced by independent variables.<\/p><p><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e01a403 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e01a403\" 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-a71011a\" data-id=\"a71011a\" 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-cbe790f elementor-widget elementor-widget-heading\" data-id=\"cbe790f\" 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\">Sample AMOS Model<\/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-6ad045d\" data-id=\"6ad045d\" 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-128fad1 elementor-widget elementor-widget-text-editor\" data-id=\"128fad1\" 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><b><img decoding=\"async\" class=\"aligncenter\" 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\" alt=\"\" \/><\/b><\/p><ul><li>Note: X1 and Y are latent constructs. In Amos syntax, latent constructs are represented by the ellipses. The latent construct X1 is measured using items X11 to X15, while latent construct Y is measured using items Y1 to Y5. The measured items are represented by rectangles in the model. Sometimes (in literature) the measured items are called latent indicators.<\/li><li>Key: X1 = <b>Exogenous <\/b>construct, while X11 to X15 is a set of 5 items to measure latent construct X1<\/li><li>In the Amos diagram, e1 to e5 are errors in measurement for items X11 to X15<\/li><li>Y = <b>Endogenous <\/b>construct, while Y1 to Y5 is a set of 5 items to measure latent construct Y<\/li><li>In the Amos diagram, e6 to e10 are errors in measurement for items Y1 to Y5<\/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-e46ff33 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e46ff33\" 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-2767719\" data-id=\"2767719\" 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-b64a0af elementor-widget elementor-widget-heading\" data-id=\"b64a0af\" 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-563ddc0\" data-id=\"563ddc0\" 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-2136358 elementor-widget elementor-widget-text-editor\" data-id=\"2136358\" 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-c7504ab elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c7504ab\" 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-6972f75\" data-id=\"6972f75\" 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-d387b5d elementor-widget elementor-widget-heading\" data-id=\"d387b5d\" 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-f5ce619\" data-id=\"f5ce619\" 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-46890f8 elementor-widget elementor-widget-video\" data-id=\"46890f8\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/LrSN140-7jw&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-237a555 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"237a555\" 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-64ecf32\" data-id=\"64ecf32\" 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-8fb251e elementor-widget elementor-widget-heading\" data-id=\"8fb251e\" 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 AMOS 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-50 elementor-top-column elementor-element elementor-element-69ab73f\" data-id=\"69ab73f\" 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-27987fb elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"27987fb\" 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-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>Introduction to AMOS IBM SPSS AMOS Series &#8211; Introduction to AMOS. The session discusses What is AMOS? Understanding Diagram Symbols AMOS Terminologies Sample Model in AMOS and What it Means? Introduction to AMOS The session discusses the very basics of AMOS including the different concepts, terminologies, and a sample AMOS model What is AMOS? AMOS [&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-6538","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>IBM SPSS AMOS Series - 4 - Introduction to AMOS - ResearchWithFawad<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-4-introduction-to-amos\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"IBM SPSS AMOS Series - 4 - Introduction to AMOS - ResearchWithFawad\" \/>\n<meta property=\"og:description\" content=\"Introduction to AMOS IBM SPSS AMOS Series &#8211; Introduction to AMOS. The session discusses What is AMOS? Understanding Diagram Symbols AMOS Terminologies Sample Model in AMOS and What it Means? Introduction to AMOS The session discusses the very basics of AMOS including the different concepts, terminologies, and a sample AMOS model What is AMOS? 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