{"id":7080,"date":"2021-12-10T01:57:43","date_gmt":"2021-12-10T01:57:43","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=7080"},"modified":"2021-12-10T02:25:44","modified_gmt":"2021-12-10T02:25:44","slug":"spss-amos-assessing-normality-of-data","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/spss-amos-assessing-normality-of-data\/","title":{"rendered":"SPSS AMOS Assessing Normality of Data"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"7080\" class=\"elementor elementor-7080\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-azhmcgu elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"azhmcgu\" 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\">Assessing Normality of Data using SPSS 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-gvc3nhu elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"gvc3nhu\" 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 Normality<\/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<p><b>A probability distribution that is symmetric about the mean<\/b>, showing that data near the mean are more frequent in occurrence than data far from the mean.<\/p><p>Normality is assessed using<\/p><ul><li>\u0096Skewness<\/li><li>\u0096Kurtosis<\/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-1a79531 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1a79531\" 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-6be212e\" data-id=\"6be212e\" 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-6220e3d elementor-widget elementor-widget-heading\" data-id=\"6220e3d\" 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\">Skewness and Kurtosis<\/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-7dfb52c\" data-id=\"7dfb52c\" 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-2d673ef elementor-widget elementor-widget-text-editor\" data-id=\"2d673ef\" 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 style=\"list-style-type: none;\"><ul><li><b>Skew<\/b>\u2014is the tilt in the distribution. Maybe on the left or on the right (Top graph in the figure).<\/li><li><b>Kurtosis<\/b>\u2014is the peakedness of a distribution. This heaviness or lightness in the tails usually means that your data looks flatter (or less flat) compared to the normal distribution (Bottom part of the figure).<\/li><\/ul><\/li><\/ul><p><br \/><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-442022c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"442022c\" 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-7688403\" data-id=\"7688403\" 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-d2223d3 elementor-widget elementor-widget-heading\" data-id=\"d2223d3\" 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\">Normality Assessment<\/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-720c435\" data-id=\"720c435\" 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-f503658 elementor-widget elementor-widget-text-editor\" data-id=\"f503658\" 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>The normality assessment is made by assessing the measure of skewness for every item. The <i>absolute value of skewness 1.0 <\/i>or lower indicates the data is normally distributed.<\/li><li>However, SEM using the Maximum Likelihood Estimator (MLE) like AMOS is fairly robust to <b>skewness greater than 1.0 in absolute value<\/b> if the sample size is large and the <b>Critical Region (CR) for the skewness does not exceed 8.0. <\/b><\/li><li>Meaning, the researcher could proceed into further analysis (SEM) since the estimator used is MLE. <b>Normally the sample size greater than 200 is considered large enough in MLE even though the data distribution is slightly non-normal.<\/b><\/li><li>Thus, for sample size greater than 200, the researcher could proceed further analysis with the absolute skewness up to +\/-2 although, some experts have suggested a value of +\/-3.<\/li><li>Another method for normality assessment is by looking at the <b>kurtosis statistic.<\/b> However, SEM using Maximum Likelihood Estimator (MLE) is also robust to kurtosis violations of multivariate normality as long the sample size is large.<\/li><li>For kurtosis, the range is \u221210 to +10 to still be considered normally distributed (Collier, 2020). Based on our results, we can see that both the skew and kurtosis are in an acceptable range to be considered \u201cnormal\u201d.<\/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-8f94d2f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8f94d2f\" 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-e995e92\" data-id=\"e995e92\" 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-ce70e0a elementor-widget elementor-widget-heading\" data-id=\"ce70e0a\" 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\">Normality Assessment - Mahalanobis Distance<\/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-b9ee55b\" data-id=\"b9ee55b\" 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-9ad7270 elementor-widget elementor-widget-text-editor\" data-id=\"9ad7270\" 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 the distribution is found to depart from normality, the researcher could assess the Mahalanobis distance to identify for the potential outliers in dataset.<\/li><li>AMOS computes the distance for every observation in dataset from the centroid. The centroid is the center of all data distribution.<\/li><li>It tabulates the distance of potential outliers from the centroid together with the probability for an observation suspected to be an outlier in the first column and the probability that an observation of similar extremity would occur given a multivariate normal population (the second column).<\/li><li>The outlier occurs when the distance of certain observation is too far compared to the majority other observations in a dataset.<\/li><li>The deletion of few extreme outliers in the model might improve the multivariate normality. Once the outlier is identified, the researcher could go back to dataset and get them deleted (based on the observation number).<\/li><li>The new measurement model is re-specified using the cleaned dataset. The process could be repeated. However, there is no necessity to examine Mahanolobis Distance if the non-normality issue does not arise.<\/li><li>This statistic represents the squared distance from the centroid of a data set. The bigger the distance, the farther the item is from the mean distribution.<\/li><li>AMOS also presents two additional statistics, p1 and p2.<\/li><li><b>A good rule of thumb is that If you have p1 and p2 values that are less than .001, these are cases denoted as outliers (Collier, 2020).<\/b><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a272d80 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a272d80\" 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-a0969f5\" data-id=\"a0969f5\" 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-f68f8d1 elementor-widget elementor-widget-heading\" data-id=\"f68f8d1\" 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\">Failure to Satisfy Normality Assumption<\/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-a9a026d\" data-id=\"a9a026d\" 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-c030cba elementor-widget elementor-widget-text-editor\" data-id=\"c030cba\" 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>As a summary, in the case when the normality assumption is not fulfilled, the researchers still have many options to take.<\/li><li>One of them is to remove the non-normal items from the measurement model (based on the measure of skewness) and continue with the analysis.<\/li><li>Another option is to remove the farthest observation from the center (outlier) of distribution.<\/li><li>However, the most popular method lately is to continue with the analysis with MLE (without deleting any item and also without removing any observation) and re-confirm the result of analysis through Bootstrapping.<\/li><li>Bootstrapping is the re-sampling process on the existing dataset using the method of sampling with replacement. The statistical procedure would compute the mean and standard deviation for every sample of size n to create the new sampling distribution.<\/li><li>The researcher could instruct Amos to collect 1000 random sample from the dataset and re-do the analysis.<\/li><li>Since the sample size is large (1000), the new sampling distribution would be closer to normal distribution. Amos would analyze the Bootstrapping data and produce the confidence intervals as well as the significance for every parameter involved in the analysis.<\/li><li>The researcher could compare the actual results with the bootstrapped results to confirm the analysis. If the results differ, the bootstrapped result will be 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-c9a5567 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c9a5567\" 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-58bc65a\" data-id=\"58bc65a\" 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-ca1bdbc elementor-widget elementor-widget-heading\" data-id=\"ca1bdbc\" 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-82934d5\" data-id=\"82934d5\" 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-d7e72e4 elementor-widget elementor-widget-text-editor\" data-id=\"d7e72e4\" 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-7efe22c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7efe22c\" 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-bf7228f\" data-id=\"bf7228f\" 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-62bfacc elementor-widget elementor-widget-heading\" data-id=\"62bfacc\" 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-66 elementor-top-column elementor-element elementor-element-26a3b66\" data-id=\"26a3b66\" 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-b5a1991 elementor-widget elementor-widget-video\" data-id=\"b5a1991\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/gtHvqhglAbY&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-3fadaa8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3fadaa8\" 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-37344cc\" data-id=\"37344cc\" 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-439f3ce elementor-widget elementor-widget-heading\" data-id=\"439f3ce\" 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-9f8c72e\" data-id=\"9f8c72e\" 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-a76b196 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"a76b196\" 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-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>Assessing Normality of Data using SPSS AMOS What is Normality A probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. Normality is assessed using \u0096Skewness \u0096Kurtosis Skewness and Kurtosis Skew\u2014is the tilt in the distribution. Maybe on the left [&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-7080","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>SPSS AMOS Assessing Normality of Data - 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\/spss-amos-assessing-normality-of-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SPSS AMOS Assessing Normality of Data - ResearchWithFawad\" \/>\n<meta property=\"og:description\" content=\"Assessing Normality of Data using SPSS AMOS What is Normality A probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. Normality is assessed using \u0096Skewness \u0096Kurtosis Skewness and Kurtosis Skew\u2014is the tilt in the distribution. 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Normality is assessed using \u0096Skewness \u0096Kurtosis Skewness and Kurtosis Skew\u2014is the tilt in the distribution. 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