{"id":3620,"date":"2021-08-30T04:41:23","date_gmt":"2021-08-30T04:41:23","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=3620"},"modified":"2023-06-14T03:10:56","modified_gmt":"2023-06-14T03:10:56","slug":"pearson-correlation-analysis-using-spss","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/pearson-correlation-analysis-using-spss\/","title":{"rendered":"Pearson Correlation Analysis using SPSS"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"3620\" class=\"elementor elementor-3620\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8yz7ue8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8yz7ue8\" 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-80293b9\" data-id=\"80293b9\" 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-de8583b elementor-widget elementor-widget-heading\" data-id=\"de8583b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\"><b>Pearson Correlation Analysis using SPSS<\/b><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7c98a889 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7c98a889\" data-element_type=\"section\" data-e-type=\"section\">\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-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-390a477e\" data-id=\"390a477e\" 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<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-87526a9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"87526a9\" 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-inner-column elementor-element elementor-element-1faadd53\" data-id=\"1faadd53\" 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-554b9863 elementor-widget elementor-widget-image\" data-id=\"554b9863\" 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\/09\/Correlation-Analysis.jpg\" class=\"attachment-full size-full wp-image-5037\" alt=\"Correlation Analysis using SPSS\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/09\/Correlation-Analysis.jpg 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/09\/Correlation-Analysis-300x169.jpg 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/09\/Correlation-Analysis-1024x576.jpg 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/09\/Correlation-Analysis-768x432.jpg 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-50 elementor-inner-column elementor-element elementor-element-70a0fdef\" data-id=\"70a0fdef\" 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-8128bb0 elementor-widget elementor-widget-text-editor\" data-id=\"8128bb0\" 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<h4>Pearson Correlation Analysis using SPSS<\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-48de1528 elementor-widget elementor-widget-text-editor\" data-id=\"48de1528\" 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 tutorial guides the researchers on the concept, interpretation, and reporting Pearson Correlation Analysis using SPSS.<\/p><p>Tutorials include<\/p><ol><li>Concept of Correlation Analysis.<\/li><li>Pearson Correlation and Its Interpretation.<\/li><li>Correlation Matrix<\/li><li>Reporting Correlation Results<\/li><\/ol><div>Here is a complete playlist on Correlation Analysis. Click <a href=\"https:\/\/youtube.com\/playlist?list=PLb7vm6tsQ3KteGLAjHtuokVzC0Y_8-hhl\">Here<\/a><\/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\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-rqd48i7 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"rqd48i7\" 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-85b149e\" data-id=\"85b149e\" 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-f1cecd6 elementor-widget elementor-widget-heading\" data-id=\"f1cecd6\" 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\">What is Correlation?\n<\/h3>\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-39c4fce\" data-id=\"39c4fce\" 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-dba6bdb elementor-widget elementor-widget-text-editor\" data-id=\"dba6bdb\" 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>Correlation is a measure of relationship between two variables. It has wide application in business and statistics. Now what would you do when you have two variables that are needed to be studied together. How would you use SPSS to identify relationship, association, link or bonding of two variables at one time. The analysis of relationship between two variables is termed as correlation analysis.\u00a0\u00a0 Correlation analysis is used to describe the strength and direction of the linear relationship between two variables.<\/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-d65dcb1 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d65dcb1\" 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-b605739\" data-id=\"b605739\" 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-2ca87c0 elementor-widget elementor-widget-heading\" data-id=\"2ca87c0\" 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\">Example Scenarios\n<\/h3>\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-436d9ac\" data-id=\"436d9ac\" 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-1215af1 elementor-widget elementor-widget-text-editor\" data-id=\"1215af1\" 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>Following are few scenarios in which we would go for Correlation Analysis<\/p><ul><li>A university would like to know if social responsibility is associated with university reputation in the community<\/li><li>A marketing manager would like to know if a hike in prices has anything to do with decrease in product sales<\/li><li>A HR manager would like to know if an increase in employee pay would result in lower absenteeism<\/li><li>A social science researcher would like to know if increase in age results in decrease of conflicts at work\/home<\/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-47f27bd elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"47f27bd\" 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-eeb50a0\" data-id=\"eeb50a0\" 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-9457d1d elementor-widget elementor-widget-heading\" data-id=\"9457d1d\" 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\">Pearson Correlation<\/h3>\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-a559350\" data-id=\"a559350\" 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-369c975 elementor-widget elementor-widget-text-editor\" data-id=\"369c975\" 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>Pearson r is designed for interval\/ratio level (continuous) variables. It can also be used if you have one continuous variable (Employee Commitment) and one dichotomous variable (e.g. Type of Organization: Public\/Private). The correlation is expressed in the form of a coefficient. Symbolized as <i>r<\/i>, a correlation coefficient is normally reported as a decimal number somewhere between +1.00 and -1.00.<\/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-a9fd0a6 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a9fd0a6\" 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-ae0ea73\" data-id=\"ae0ea73\" 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-2f913f2 elementor-widget elementor-widget-heading\" data-id=\"2f913f2\" 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<h4 class=\"elementor-heading-title elementor-size-default\">Direction of Relationship<\/h4>\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-01f81a5\" data-id=\"01f81a5\" 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-6acc831 elementor-widget elementor-widget-text-editor\" data-id=\"6acc831\" 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>Positive, Negative or Zero Correlation<\/b><\/p><ul><li>The positive and negative signs indicate positive and negative correlation respectively. Positive sign shows that with increase in one variable, the other increases as well, while negative correlation indicates that with an increase in one variable, the other decreases. However, the value itself provides an indication of the strength of relationship.<\/li><li>If the correlation is 1 or \u20131, this shows perfect correlation. A perfect correlation shows that the value of one variable can be completely determined by the other variable.<\/li><li>If the correlation coefficient is 0, this shows there is no relationship between the two variables. Hence, one variable provides no support in predicting the second variable.<\/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-613bf2f elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"613bf2f\" 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-c715999\" data-id=\"c715999\" 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-eebaabd elementor-widget elementor-widget-heading\" data-id=\"eebaabd\" 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<h4 class=\"elementor-heading-title elementor-size-default\">Interpretation of the Correlation Coefficient\n<\/h4>\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-7190215\" data-id=\"7190215\" 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-794a68a elementor-widget elementor-widget-text-editor\" data-id=\"794a68a\" 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>It is important that we know what is meant by the coefficient, what it tells about the data. To have an answer, generally, the coefficient of correlation is interpreted in verbal description. Correlation Coefficient can easily be interpreted using the following table.<\/p><table width=\"1103\"><tbody><tr><td width=\"282\"><b>Size of Correlation<\/b><\/td><td width=\"821\"><b>Interpretation<\/b><\/td><\/tr><tr><td width=\"282\"><b>1<\/b><\/td><td width=\"821\">Perfect Positive\/Negative Correlation<\/td><\/tr><tr><td width=\"282\"><b>\u00b1 .90 to\u00a0 \u00b1 .99<\/b><\/td><td width=\"821\">Very High Positive\/Negative Correlation<\/td><\/tr><tr><td width=\"282\"><b>\u00b1 .70 to \u00b1 .90<\/b><\/td><td width=\"821\">High Positive\/Negative Correlation<\/td><\/tr><tr><td width=\"282\"><b>\u00b1 .50 to \u00b1 .70<\/b><\/td><td width=\"821\">Moderate Positive\/Negative Correlation<\/td><\/tr><tr><td width=\"282\"><b>\u00b1 .30 to \u00b1 .50<\/b><\/td><td width=\"821\">Low Positive\/Negative Correlation<\/td><\/tr><tr><td width=\"282\"><b>\u00b1 .10 to \u00b1 .30<\/b><\/td><td width=\"821\">Very low Positive\/Negative Correlation<\/td><\/tr><tr><td width=\"282\"><b>\u00b1 .0 to \u00b1 .10<\/b><\/td><td width=\"821\">Markedly Low and Negligible Positive\/Negative Correlation<\/td><\/tr><\/tbody><\/table><p>It is important to note that correlation does not provide any information about cause and effect. The correlation does not speak of the influence one variable has over the other. It is important that you remember that correlation is not equal to causation.<\/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-3fb8908 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3fb8908\" 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-ad1d048\" data-id=\"ad1d048\" 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-111c2ea elementor-widget elementor-widget-heading\" data-id=\"111c2ea\" 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<h4 class=\"elementor-heading-title elementor-size-default\">Correlation is not Cause and Effect Analysis<\/h4>\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-24f24f8\" data-id=\"24f24f8\" 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-aaa62f2 elementor-widget elementor-widget-text-editor\" data-id=\"aaa62f2\" 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>It is important to note that correlation does not provide any information about cause and effect. The correlation does not speak of the influence one variable has over the other. It is important that you remember that correlation is not equal to causation.<\/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-9cdebbf elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9cdebbf\" 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-a36649a\" data-id=\"a36649a\" 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-04a7307 elementor-widget elementor-widget-heading\" data-id=\"04a7307\" 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<h4 class=\"elementor-heading-title elementor-size-default\">Assumptions of Pearson Correlation<\/h4>\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-9aa884a\" data-id=\"9aa884a\" 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-cda852c elementor-widget elementor-widget-text-editor\" data-id=\"cda852c\" 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 two variables shall be on Interval or Ratio scale or <\/li><li>One variable can be continuous whereas the other can be binary (dichotomous).<\/li><li>The continuous variable in the person correlation analysis shall be normally distributed. <\/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-d19768c elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d19768c\" 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-6c9ddee\" data-id=\"6c9ddee\" 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-0d032de elementor-widget elementor-widget-heading\" data-id=\"0d032de\" 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<h4 class=\"elementor-heading-title elementor-size-default\">Pearson Correlation Example<\/h4>\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-0ed694b\" data-id=\"0ed694b\" 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-f1d4928 elementor-widget elementor-widget-text-editor\" data-id=\"f1d4928\" 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><strong>The Problem:<\/strong><\/p><p>Investigate the relationship between Servant Leadership (SL) and Self Efficacy (SE).<\/p><p><strong>Hypothesis<\/strong><\/p><p><strong>H<sub>A<\/sub>:<\/strong> There is a significant relationship between Servant Leadership and Self Efficacy.<\/p><p><strong>\u00a0<\/strong><strong>Information Required:<\/strong><\/p><ul><li>Two continuous variables (In this case, Servant Leadership and Self Efficacy.<\/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-787ce76 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"787ce76\" 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-535bfb6\" data-id=\"535bfb6\" 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-85ee4a0 elementor-widget elementor-widget-heading\" data-id=\"85ee4a0\" 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\">Pearson Correlation Analysis using SPSS - Steps<\/h3>\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-8480284\" data-id=\"8480284\" 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-0073c53 elementor-widget elementor-widget-text-editor\" data-id=\"0073c53\" 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<ol><li>Go to <strong>Analyze \u2192 Correlate \u2192 Bivariate<\/strong><\/li><li>Select the variables for which the correlation is to be studied from the list box on the left\u00a0 and press the arrow to move them to the list box on the right side. Correlation requires at least two variables in the box on the right.<\/li><li>Select the variable<strong>s,<\/strong> Press the Arrow button to the add the variable to the <strong>Variables<\/strong> list box<\/li><li>Make sure you have selected Pearson Coefficient checkbox from the <strong>Correlation Coefficient<\/strong> group box.<\/li><\/ol>\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-4db09da elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4db09da\" 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-713275a\" data-id=\"713275a\" 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-ee9385a elementor-widget elementor-widget-heading\" data-id=\"ee9385a\" 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<h4 class=\"elementor-heading-title elementor-size-default\">Choosing the Options<\/h4>\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-4c82c6a\" data-id=\"4c82c6a\" 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-8542e61 elementor-widget elementor-widget-text-editor\" data-id=\"8542e61\" 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<ol><li>Next, while choosing between one-tailed and two-tailed test of significance, One-tailed test is select if the direction of relationship (Positive or Negative is known) otherwise if the direction of relationship is unknown select two-tailed test.<\/li><li>Finally<strong> Flag significant correlations <\/strong>to show an asterisk next to each correlation that is significant at the 0.05 significance level and two asterisks next to each correlation that is significant at the 0.01 significance level.<\/li><li><strong>Press OK<\/strong><\/li><\/ol>\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-27f14ba elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"27f14ba\" 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-f4e9e41\" data-id=\"f4e9e41\" 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-25062e8 elementor-widget elementor-widget-heading\" data-id=\"25062e8\" 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<h4 class=\"elementor-heading-title elementor-size-default\">Correlation Matrix\n<\/h4>\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-1c96e0f\" data-id=\"1c96e0f\" 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-2d9fd4d elementor-widget elementor-widget-text-editor\" data-id=\"2d9fd4d\" 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>\n<li>Correlation is often used to explore the relationship among more than two variables. To do so, add more than two variables in the variables list box while selecting the variables form the list of variables.&nbsp;<\/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-075d6db elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"075d6db\" 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-8e8af1e\" data-id=\"8e8af1e\" 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-a8bbbdf elementor-widget elementor-widget-heading\" data-id=\"a8bbbdf\" 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\">Report Pearson Correlation Analysis using SPSS\n<\/h3>\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-101078c\" data-id=\"101078c\" 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-644297e elementor-widget elementor-widget-text-editor\" data-id=\"644297e\" 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><strong>The Problem:<\/strong><\/p><p>Investigate the relationship between servant leadership and self-efficacy.<\/p><p><strong>H<sub>1<\/sub>:<\/strong> There is significant relationship between servant leadership and self-efficacy.<\/p><p>Pearson product correlation of servant leadership and self-efficacy was found to be moderately positive and statistically significant (<em>r <\/em>= .534, p &lt; .001). Hence, H1 was supported. This shows that an increase in servant leadership behavior would lead to a higher self-efficacy in the followers.<\/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-u1mbqq4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"u1mbqq4\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-narrow\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-da37464\" data-id=\"da37464\" 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-f834bf9 elementor-widget elementor-widget-video\" data-id=\"f834bf9\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=2BeYY8JgokU&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-ec7fd8e\" data-id=\"ec7fd8e\" 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-f046b1e elementor-widget elementor-widget-video\" data-id=\"f046b1e\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=KmJ5eEHJDEE&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-8afec47\" data-id=\"8afec47\" 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-89bb5fb elementor-widget elementor-widget-video\" data-id=\"89bb5fb\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/zflP716yPsA&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-2bac805 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2bac805\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-narrow\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-890fe70\" data-id=\"890fe70\" 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-8611add elementor-widget elementor-widget-video\" data-id=\"8611add\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/lnHx2AU3ezQ&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-7e1504d\" data-id=\"7e1504d\" 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-0f26ead elementor-widget elementor-widget-video\" data-id=\"0f26ead\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/IKQ44bT4DY4&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-2779bd2\" data-id=\"2779bd2\" 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-7dbd7d4 elementor-widget elementor-widget-video\" data-id=\"7dbd7d4\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/AGF4PnoCoQc&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-c0db9c4 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c0db9c4\" 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-ded740e\" data-id=\"ded740e\" 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-359da55 elementor-widget elementor-widget-heading\" data-id=\"359da55\" 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\">Groupwise Correlation Analysis\n<\/h3>\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-63dacbb\" data-id=\"63dacbb\" 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-974722f elementor-widget elementor-widget-text-editor\" data-id=\"974722f\" 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>There may be situations when you need to compare the correlation coefficient between two groups. For instance, we may need to compare how the correlation between two variables differs between male and female respondents.<\/p><p><b>Statistical Significance for the difference between Groups<\/b><\/p><ul><li>While you now know how to find a correlation coefficient in each of the groups, but still we do not know if the difference in the relationship between groups is significant. Unfortunately, SPSS will not do this step for you, so it is done manually.<\/li><li>First, we will be converting the r values into z scores and then we use an equation to calculate the observed value of Z. The value obtained will be assessed using a set decision rule to determine the likelihood that the difference in the correlation noted between the two groups could have been due to chance.<\/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-dbe89a3 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dbe89a3\" 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-b2713e7\" data-id=\"b2713e7\" 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-b52b22a elementor-widget elementor-widget-heading\" data-id=\"b52b22a\" 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<h4 class=\"elementor-heading-title elementor-size-default\"><b>Groupwise Correlation - Convert each of the r values into z values<\/b><\/h4>\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-3e5f60f\" data-id=\"3e5f60f\" 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-33202a6 elementor-widget elementor-widget-text-editor\" data-id=\"33202a6\" 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>\u00a0<\/p><ul><li><p>The first step is to convert the correlation coefficients (r) into the Z scores. From the SPSS output, find the r-value (ignore any negative sign out the front) and N for Group 1 (males) and Group 2 (females).<\/p><p>To calculate the Z score, we can use the Fisher Z transformation. Open Excel and type the formula (= Fisher(Correlation Coefficient to be Converted)<\/p><ul><li>Males ( r ) = .608 n = 166<\/li><li>Females ( r ) = .104 n = 55<\/li><\/ul><ul><li>Males z = .705<\/li><li>Female z = .104<\/li><\/ul><\/li><\/ul><div><img decoding=\"async\" class=\"aligncenter\" 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alt=\"Calculat Z Score\" width=\"1121\" height=\"691\" \/><\/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-be76692 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"be76692\" 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-100 elementor-top-column elementor-element elementor-element-efa45f2\" data-id=\"efa45f2\" 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-c36759b elementor-widget elementor-widget-video\" data-id=\"c36759b\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/DMoQ9IT_aPQ&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-7603d90 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7603d90\" 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-100 elementor-top-column elementor-element elementor-element-1272723\" data-id=\"1272723\" 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-25c60eb elementor-widget elementor-widget-heading\" data-id=\"25c60eb\" 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 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\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6500b2e7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6500b2e7\" 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-100 elementor-top-column elementor-element elementor-element-487318a2\" data-id=\"487318a2\" 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-3618c19d elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"3618c19d\" 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-6028 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/binary-logistic-regression-analysis-in-spss\/\" class=\"menu-link\">Binary Logistic Regression Analysis in SPSS<\/a><\/li>\n<li class=\"page_item page-item-3688 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/categorical-predictor-dummy-variables-in-regression-using-spss\/\" class=\"menu-link\">Categorical Predictor\/Dummy Variables in Regression using SPSS<\/a><\/li>\n<li class=\"page_item page-item-3607 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/crosstabulation-and-chi-square-test-using-spss\/\" class=\"menu-link\">Crosstabulation and Chi-Square Test using SPSS<\/a><\/li>\n<li class=\"page_item page-item-6257 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/data-screening-and-handling-missing-data-using-spss\/\" class=\"menu-link\">Data Screening and Handling Missing Data using SPSS<\/a><\/li>\n<li class=\"page_item page-item-7418 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/how-to-check-linear-relationship-in-spss\/\" class=\"menu-link\">How to Check Linear Relationship in SPSS<\/a><\/li>\n<li class=\"page_item page-item-3767 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/how-to-perform-exploratory-factor-analysis-using-spss\/\" class=\"menu-link\">How to Perform Exploratory Factor Analysis using SPSS<\/a><\/li>\n<li class=\"page_item page-item-3899 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/how-to-perform-one-way-anova\/\" class=\"menu-link\">How to Perform One Way ANOVA<\/a><\/li>\n<li class=\"page_item page-item-3573 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/how-to-run-interpret-and-report-descriptive-statistics-using-spss\/\" class=\"menu-link\">How to Run, Interpret, and Report Descriptive Statistics using SPSS<\/a><\/li>\n<li class=\"page_item page-item-3556 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/identifying-and-correcting-data-entry-errors-in-spss\/\" class=\"menu-link\">Identifying and Correcting Data Entry Errors in SPSS<\/a><\/li>\n<li class=\"page_item page-item-6339 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/independent-samples-t-test-using-spss\/\" class=\"menu-link\">Independent Samples T-Test using SPSS<\/a><\/li>\n<li class=\"page_item page-item-3938 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/mann-whitney-u-test-using-spss\/\" class=\"menu-link\">Mann Whitney U Test using SPSS<\/a><\/li>\n<li class=\"page_item page-item-3634 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/partial-correlation-analysis-using-spss\/\" class=\"menu-link\">Partial Correlation Analysis using SPSS<\/a><\/li>\n<li class=\"page_item page-item-3653 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/concept-interpretation-reporting-regression-analysis-using-spss\/\" class=\"menu-link\">Regression Analysis using SPSS: Concept, Interpretation, Reporting<\/a><\/li>\n<li class=\"page_item page-item-3585 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/data-analysis-using-spss\/transform-continuous-variables-into-categorical-variables-in-spss\/\" class=\"menu-link\">Transform Continuous Variables into Categorical Variables using SPSS<\/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>Welcome to our page dedicated to explaining Pearson Correlation! Here you will find a clear and concise tutorial on this fundamental statistical concept. Our video lectures will guide you through the basics of Pearson Correlation and its applications in real-world scenarios. You will learn how to calculate Pearson Correlation, interpret its values, and use it to determine the strength and direction of relationships between variables. In addition, we&#8217;ll cover how to use Excel to perform Pearson Correlation calculations, saving you time and effort. Our aim is to equip you with the skills and knowledge needed to confidently apply Pearson Correlation in your own research or analysis. Whether you are new to statistics or just need a refresher, our tutorial and videos on Pearson Correlation will help you understand this topic with ease.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":3669,"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-3620","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - 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