{"id":43,"date":"2020-07-13T18:18:12","date_gmt":"2020-07-13T18:18:12","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/abnormalpsych\/chapter\/reading-correlational-research\/"},"modified":"2022-07-26T20:05:12","modified_gmt":"2022-07-26T20:05:12","slug":"reading-correlational-research","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/chapter\/reading-correlational-research\/","title":{"raw":"Correlational Research","rendered":"Correlational Research"},"content":{"raw":"<div class=\"textbox learning-objectives\">\r\n<h3>Learning Objectives<\/h3>\r\n<ul>\r\n \t<li style=\"font-weight: 400;\">Explain correlational research, including what a correlation coefficient tells us about the relationship between variables<\/li>\r\n<\/ul>\r\n<\/div>\r\n<h2>Correlational Research<\/h2>\r\nOne of the primary methods used to study abnormal behavior is the\u00a0correlational method.\u00a0<strong>Correlation<\/strong> means that there is a relationship between two or more variables (such between the variables of negative thinking and depressive symptoms), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A <strong>correlation coefficient<\/strong> is a number from negative one to positive one that indicates the strength and direction of the relationship between variables. The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as\u00a0<em>r<\/em>).\r\n\r\nThe number portion of the correlation coefficient indicates the strength of the relationship. The closer the number is to one (be it negative or positive), the more strongly related the variables are, and the more predictable changes in one variable will be as the other variable changes. The closer the number is to zero, the weaker the relationship, and the less predictable the relationships between the variables becomes. For instance, a correlation coefficient of 0.9 indicates a far stronger relationship than a correlation coefficient of 0.3. If the variables are not related to one another at all, the correlation coefficient is zero. The example above about\u00a0negative thinking and depressive symptoms is an example of two variables that we might expect to have a relationship to each other.\u00a0<span style=\"font-size: 1em;\">When higher values in one variable (negative thinking) are associated with higher values in the other variable (depressive symptoms), there is a\u00a0<\/span><i style=\"font-size: 1em;\">positive correlation<\/i><span style=\"font-size: 1em;\">\u00a0between the variables.<\/span>\r\n\r\nThe sign\u2014positive or negative\u2014of the correlation coefficient indicates the direction of the relationship.\u00a0<span style=\"font-size: 1em;\">Positive correlations carry positive signs; negative correlations carry negative signs.\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">A <\/span><strong style=\"font-size: 1rem; text-align: initial;\">positive correlation<\/strong><span style=\"font-size: 1rem; text-align: initial;\"> means that the variables move in the same direction. Put another way, it means that as one variable increases so does the other, and conversely, when one variable decreases so does the other. A <\/span><strong style=\"font-size: 1rem; text-align: initial;\">negative correlation<\/strong><span style=\"font-size: 1rem; text-align: initial;\"> means that the variables move in opposite directions. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other and vice versa.<\/span>\r\n\r\nOther examples of positive correlations\u00a0are the relationship between\u00a0depression and disturbance in normal sleep patterns.\u00a0One might expect then that scores on a measure of depression would be positively correlated with scores on a measure of sleep disturbances.\r\n\r\nOne might expect a negative correlation to exist between\u00a0<span style=\"font-size: 1em;\">between depression and self-esteem.\u00a0<\/span><span style=\"font-size: 1em;\">The more depressed people are, the lower their scores are on the\u00a0Rosenberg self-esteem scale\u00a0(RSES), a\u00a0self-esteem\u00a0measure widely used in social-science research.\u00a0<\/span>Keep in mind that a negative correlation is not the same as no correlation. For example, we would probably find no correlation between\u00a0<span style=\"font-size: 1em;\">depression\u00a0<\/span>and someone's<span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1em;\">height.\u00a0<\/span>\r\n\r\nIn correlational research,\u00a0<span style=\"font-size: 1em;\">scientists passively observe and measure phenomena.<\/span><span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0Here, we do not intervene and change behavior, as we do in experiments. In correlational research, we identify patterns of relationships, but we usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.<\/span>\r\n\r\nAs mentioned earlier, correlations have predictive value. So, what if you wanted to test whether spending on others is related to happiness, but you don\u2019t have $20 to give to each participant? You could use a correlational design\u2014which is exactly what Professor Dunn did, too. She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were.\r\n<h2 id=\"more-details-about-the-correlation\">More Details about the Correlation<\/h2>\r\nTo find out how well two variables correspond, we can plot the relationship between the two scores on what is known as a scatterplot (Figure 1). In the scatterplot, each dot represents a data point. (In this case it\u2019s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information\u2014in this case, information about how good the person rated the past month (<em>x<\/em>-axis) and how happy the person felt in the past month (<em>y<\/em>-axis). Which variable is plotted on which axis does not matter.\r\n<figure>\r\n\r\n[caption id=\"\" align=\"aligncenter\" width=\"458\"]<img title=\"Scatterplot of the association between happiness and ratings of the past month, a positive correlation (r = .81)\" src=\"https:\/\/nobaproject.com\/images\/shared\/images\/000\/001\/445\/original.jpg\" alt=\"Scatterplot of the association between happiness and ratings of the past month, a positive correlation (r = .81)\" width=\"458\" height=\"455\" \/> <strong>Figure 1<\/strong>. Scatterplot of the association between happiness and ratings of the past month, a positive correlation (<em>r<\/em> = .81). Each dot represents an individual.[\/caption]\r\n\r\n<figcaption><\/figcaption><\/figure>\r\nFor the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.\r\n\r\nIn a scatterplot, the dots form a pattern that extends from the bottom left to the upper right (just as they do in Figure 1). The\u00a0<em>r<\/em>\u00a0value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the\u00a0<em>r<\/em>\u00a0value is 0.81.\r\n\r\nFigure 2 shows a<span style=\"font-size: 1em;\">\u00a0negative correlation,<\/span><span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">the association between the average height of males in a country (<em>y<\/em>-axis) and the pathogen prevalence, or commonness of disease,\u00a0of that country (<em>x<\/em>-axis). In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The\u00a0<\/span><em style=\"font-size: 1rem; text-align: initial;\">r<\/em><span style=\"font-size: 1rem; text-align: initial;\">\u00a0value for a negative correlation is indicated by a negative number\u2014that is, it has a minus (\u2212) sign in front of it. Here, it is \u22120.83.<\/span>\r\n<figure>\r\n\r\n[caption id=\"\" align=\"aligncenter\" width=\"549\"]<img title=\"Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (r = \u2013.83).\" src=\"https:\/\/nobaproject.com\/images\/shared\/images\/000\/001\/446\/original.jpg\" alt=\"Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (r = \u2013.83).\" width=\"549\" height=\"457\" \/> <strong>Figure 2<\/strong>. Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (<em>r<\/em> = \u20130.83). Each dot represents a country (Chiao, 2009).[\/caption]\r\n\r\n<figcaption><\/figcaption><\/figure>\r\nThe strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn\u2019s correlational study, spending on others positively correlated with happiness: the more money people reported spending on others, the happier they reported to be. At this point, you may be thinking to yourself, \"I know a very generous person who gave away lots of money to other people but is miserable!\" Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line.[footnote]Scollon, C. N. (2020). Research designs. In R. Biswas-Diener &amp; E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http:\/\/noba.to\/acxb2thy[\/footnote]\r\n\r\n<section><section>\r\n<div class=\"textbox tryit\">\r\n<h3>Try It<\/h3>\r\nhttps:\/\/assess.lumenlearning.com\/practice\/0c330eef-8cbd-4927-9b16-9f960e455894\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/f4e734ac-fb6c-4f92-97f5-fd8618754603\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/95ec10f4-76af-408e-9da8-7a3ff9c10565\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/f19d9e06-45a9-4bbf-a580-64df2280dbf2\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/a1725da7-c7cb-458a-9e72-afc2f149eeb2\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/5e272f2b-3941-484a-b30b-b8a80741adfd\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/b0fd0cd8-846d-4fdb-8932-332b7c916155\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/8746f138-5280-4b10-8e2d-4fac81b1240c\r\n\r\n<\/div>\r\n<h2>Problems with correlation<\/h2>\r\nIf generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? From a correlation alone, we can\u2019t be certain. For example, in the first case it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness\u00a0<em>and<\/em>\u00a0generosity, creating the illusion of a direct link between the two. For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation\u2014an often repeated phrase among psychologists.[footnote]Scollon, C. N. (2020). Research designs. In R. Biswas-Diener &amp; E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http:\/\/noba.to\/acxb2thy[\/footnote]\r\n<h2>Correlation Does Not Indicate Causation<\/h2>\r\nCorrelational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about <strong>cause and effect<\/strong>. While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a <strong>confounding variable<\/strong>, is actually causing the systematic movement in our variables of interest. In the\u00a0<span style=\"font-size: 1em;\">depression and negative thinking<\/span><span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">example mentioned earlier, <\/span><span style=\"font-size: 1em;\">stress<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0is a confounding variable that could account for the relationship between the two variables.\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0<\/span>\r\n\r\nEven when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our example about the relationship between\u00a0depression and disturbance in normal sleep patterns.\u00a0<span style=\"font-size: 1em;\">It seems reasonable to assume that s<\/span><span style=\"font-size: 1em;\">leep disturbance might cause a higher score on a measure of depression, just as a high degree of depression might cause more disturbed sleep patterns<\/span><span style=\"font-size: 1em; text-align: initial;\">, but if we were limited to\u00a0<\/span><strong style=\"font-size: 1em; text-align: initial;\">correlational research<\/strong><span style=\"font-size: 1em; text-align: initial;\">, we would be overstepping our bounds by making this assumption.\u00a0<\/span><span style=\"font-size: 1em;\">Both depression and sleep disturbance could be due to an underlying physiological disorder o<\/span><span style=\"font-size: 1em;\">r any to other third variable that you have not measured<\/span><span style=\"font-size: 1em; text-align: initial;\">.<\/span>\r\n\r\n<span style=\"font-size: 1em;\">Unfortunately, people mistakenly make claims of causation as a function of correlations all the time.<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0<\/span><span style=\"font-size: 1em;\">While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality.\u00a0<\/span><span style=\"font-size: 1em;\">The correlational method does not involve manipulation of the variables of interest. In the previous example, the experimenter does not manipulate people\u2019s depressive symptoms or sleep patterns.\u00a0<\/span><span style=\"font-size: 1em; text-align: initial;\">Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how\u00a0<\/span><span style=\"font-size: 1em;\">investigators use experimental methods in which the experimenter manipulates one or more variables of interest and observes their effects on other variables or outcomes under controlled conditions.<\/span>\r\n<div class=\"textbox examples\">\r\n<h3>watch IT<\/h3>\r\nIn this video, we discuss one of the best methods psychologists have for predicting behaviors: correlation. But does that mean that a behavior is absolutely going to happen? Let's find out!\r\n\r\n<iframe src=\"\/\/plugin.3playmedia.com\/show?mf=5572154&amp;p3sdk_version=1.10.1&amp;p=20361&amp;pt=375&amp;video_id=HSNp5MlTE-c&amp;video_target=tpm-plugin-vzk1y75p-HSNp5MlTE-c\" width=\"800px\" height=\"450px\" frameborder=\"0\" marginwidth=\"0px\" marginheight=\"0px\"><\/iframe>\r\n\r\nYou can view the <a href=\"https:\/\/course-building.s3-us-west-2.amazonaws.com\/Abnormal+Psychology\/transcripts\/5CorrelationvsCausationPsy101_transcript.txt\" target=\"_blank\" rel=\"noopener\">transcript for \"#5 Correlation vs. Causation - Psy 101\" here (opens in new window)<\/a>.\r\n\r\n<\/div>\r\n<\/section><section>\r\n<div class=\"textbox tryit\">\r\n<h3>Try It<\/h3>\r\nhttps:\/\/assess.lumenlearning.com\/practice\/722b3100-c666-4aab-94cb-fc8aa6294021\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/ad669841-b050-4cdb-8046-13efe44ea2e0\r\n\r\n<\/div>\r\n<div class=\"textbox key-takeaways\">\r\n<h3>Think It Over<\/h3>\r\nConsider why correlational research is often used in the study of abnormal behavior. If correlational designs do not demonstrate causation, why do researchers make causal claims regarding their results? Are there instances when correlational results could demonstrate causation?\r\n\r\n<\/div>\r\n<div class=\"textbox learning-objectives\">\r\n<h3>Glossary<\/h3>\r\n<div class=\"ui-toggle-wrapper\">\r\n\r\n<strong>cause-and-effect relationship:\u00a0<\/strong>changes in one variable cause the changes in the other variable; can be determined only through an experimental research design\r\n\r\n<strong>confirmation bias:\u00a0<\/strong>tendency to ignore evidence that disproves ideas or beliefs\r\n\r\n<strong>confounding variable:\u00a0<\/strong>unanticipated outside factor that affects both variables of interest,\\; often gives the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables\r\n\r\n<strong>correlation: <\/strong>the relationship between two or more variables; when two variables are correlated, one variable changes as the other does\r\n\r\n<strong>correlation coefficient:\u00a0<\/strong>number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by <em>r<\/em>\r\n\r\n<strong>negative correlation:\u00a0<\/strong>two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation\r\n\r\n<strong>positive correlation:\u00a0<\/strong>two variables change in the same direction, both becoming either larger or smaller\r\n\r\n<\/div>\r\n<\/div>\r\n<\/section><\/section>","rendered":"<div class=\"textbox learning-objectives\">\n<h3>Learning Objectives<\/h3>\n<ul>\n<li style=\"font-weight: 400;\">Explain correlational research, including what a correlation coefficient tells us about the relationship between variables<\/li>\n<\/ul>\n<\/div>\n<h2>Correlational Research<\/h2>\n<p>One of the primary methods used to study abnormal behavior is the\u00a0correlational method.\u00a0<strong>Correlation<\/strong> means that there is a relationship between two or more variables (such between the variables of negative thinking and depressive symptoms), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A <strong>correlation coefficient<\/strong> is a number from negative one to positive one that indicates the strength and direction of the relationship between variables. The association between two variables can be summarized statistically using the correlation coefficient (abbreviated as\u00a0<em>r<\/em>).<\/p>\n<p>The number portion of the correlation coefficient indicates the strength of the relationship. The closer the number is to one (be it negative or positive), the more strongly related the variables are, and the more predictable changes in one variable will be as the other variable changes. The closer the number is to zero, the weaker the relationship, and the less predictable the relationships between the variables becomes. For instance, a correlation coefficient of 0.9 indicates a far stronger relationship than a correlation coefficient of 0.3. If the variables are not related to one another at all, the correlation coefficient is zero. The example above about\u00a0negative thinking and depressive symptoms is an example of two variables that we might expect to have a relationship to each other.\u00a0<span style=\"font-size: 1em;\">When higher values in one variable (negative thinking) are associated with higher values in the other variable (depressive symptoms), there is a\u00a0<\/span><i style=\"font-size: 1em;\">positive correlation<\/i><span style=\"font-size: 1em;\">\u00a0between the variables.<\/span><\/p>\n<p>The sign\u2014positive or negative\u2014of the correlation coefficient indicates the direction of the relationship.\u00a0<span style=\"font-size: 1em;\">Positive correlations carry positive signs; negative correlations carry negative signs.\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">A <\/span><strong style=\"font-size: 1rem; text-align: initial;\">positive correlation<\/strong><span style=\"font-size: 1rem; text-align: initial;\"> means that the variables move in the same direction. Put another way, it means that as one variable increases so does the other, and conversely, when one variable decreases so does the other. A <\/span><strong style=\"font-size: 1rem; text-align: initial;\">negative correlation<\/strong><span style=\"font-size: 1rem; text-align: initial;\"> means that the variables move in opposite directions. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other and vice versa.<\/span><\/p>\n<p>Other examples of positive correlations\u00a0are the relationship between\u00a0depression and disturbance in normal sleep patterns.\u00a0One might expect then that scores on a measure of depression would be positively correlated with scores on a measure of sleep disturbances.<\/p>\n<p>One might expect a negative correlation to exist between\u00a0<span style=\"font-size: 1em;\">between depression and self-esteem.\u00a0<\/span><span style=\"font-size: 1em;\">The more depressed people are, the lower their scores are on the\u00a0Rosenberg self-esteem scale\u00a0(RSES), a\u00a0self-esteem\u00a0measure widely used in social-science research.\u00a0<\/span>Keep in mind that a negative correlation is not the same as no correlation. For example, we would probably find no correlation between\u00a0<span style=\"font-size: 1em;\">depression\u00a0<\/span>and someone&#8217;s<span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1em;\">height.\u00a0<\/span><\/p>\n<p>In correlational research,\u00a0<span style=\"font-size: 1em;\">scientists passively observe and measure phenomena.<\/span><span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0Here, we do not intervene and change behavior, as we do in experiments. In correlational research, we identify patterns of relationships, but we usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.<\/span><\/p>\n<p>As mentioned earlier, correlations have predictive value. So, what if you wanted to test whether spending on others is related to happiness, but you don\u2019t have $20 to give to each participant? You could use a correlational design\u2014which is exactly what Professor Dunn did, too. She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were.<\/p>\n<h2 id=\"more-details-about-the-correlation\">More Details about the Correlation<\/h2>\n<p>To find out how well two variables correspond, we can plot the relationship between the two scores on what is known as a scatterplot (Figure 1). In the scatterplot, each dot represents a data point. (In this case it\u2019s individuals, but it could be some other unit.) Importantly, each dot provides us with two pieces of information\u2014in this case, information about how good the person rated the past month (<em>x<\/em>-axis) and how happy the person felt in the past month (<em>y<\/em>-axis). Which variable is plotted on which axis does not matter.<\/p>\n<figure>\n<div style=\"width: 468px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" title=\"Scatterplot of the association between happiness and ratings of the past month, a positive correlation (r = .81)\" src=\"https:\/\/nobaproject.com\/images\/shared\/images\/000\/001\/445\/original.jpg\" alt=\"Scatterplot of the association between happiness and ratings of the past month, a positive correlation (r = .81)\" width=\"458\" height=\"455\" \/><\/p>\n<p class=\"wp-caption-text\"><strong>Figure 1<\/strong>. Scatterplot of the association between happiness and ratings of the past month, a positive correlation (<em>r<\/em> = .81). Each dot represents an individual.<\/p>\n<\/div><figcaption><\/figcaption><\/figure>\n<p>For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy.<\/p>\n<p>In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right (just as they do in Figure 1). The\u00a0<em>r<\/em>\u00a0value for a positive correlation is indicated by a positive number (although, the positive sign is usually omitted). Here, the\u00a0<em>r<\/em>\u00a0value is 0.81.<\/p>\n<p>Figure 2 shows a<span style=\"font-size: 1em;\">\u00a0negative correlation,<\/span><span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">the association between the average height of males in a country (<em>y<\/em>-axis) and the pathogen prevalence, or commonness of disease,\u00a0of that country (<em>x<\/em>-axis). In this scatterplot, each dot represents a country. Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The\u00a0<\/span><em style=\"font-size: 1rem; text-align: initial;\">r<\/em><span style=\"font-size: 1rem; text-align: initial;\">\u00a0value for a negative correlation is indicated by a negative number\u2014that is, it has a minus (\u2212) sign in front of it. Here, it is \u22120.83.<\/span><\/p>\n<figure>\n<div style=\"width: 559px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" title=\"Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (r = \u2013.83).\" src=\"https:\/\/nobaproject.com\/images\/shared\/images\/000\/001\/446\/original.jpg\" alt=\"Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (r = \u2013.83).\" width=\"549\" height=\"457\" \/><\/p>\n<p class=\"wp-caption-text\"><strong>Figure 2<\/strong>. Scatterplot showing the association between average male height and pathogen prevalence, a negative correlation (<em>r<\/em> = \u20130.83). Each dot represents a country (Chiao, 2009).<\/p>\n<\/div><figcaption><\/figcaption><\/figure>\n<p>The strength of a correlation has to do with how well the two variables align. Recall that in Professor Dunn\u2019s correlational study, spending on others positively correlated with happiness: the more money people reported spending on others, the happier they reported to be. At this point, you may be thinking to yourself, &#8220;I know a very generous person who gave away lots of money to other people but is miserable!&#8221; Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line.<a class=\"footnote\" title=\"Scollon, C. N. (2020). Research designs. In R. Biswas-Diener &amp; E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http:\/\/noba.to\/acxb2thy\" id=\"return-footnote-43-1\" href=\"#footnote-43-1\" aria-label=\"Footnote 1\"><sup class=\"footnote\">[1]<\/sup><\/a><\/p>\n<section>\n<section>\n<div class=\"textbox tryit\">\n<h3>Try It<\/h3>\n<p>\t<iframe id=\"assessment_practice_0c330eef-8cbd-4927-9b16-9f960e455894\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/0c330eef-8cbd-4927-9b16-9f960e455894?iframe_resize_id=assessment_practice_id_0c330eef-8cbd-4927-9b16-9f960e455894\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_f4e734ac-fb6c-4f92-97f5-fd8618754603\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/f4e734ac-fb6c-4f92-97f5-fd8618754603?iframe_resize_id=assessment_practice_id_f4e734ac-fb6c-4f92-97f5-fd8618754603\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_95ec10f4-76af-408e-9da8-7a3ff9c10565\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/95ec10f4-76af-408e-9da8-7a3ff9c10565?iframe_resize_id=assessment_practice_id_95ec10f4-76af-408e-9da8-7a3ff9c10565\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_f19d9e06-45a9-4bbf-a580-64df2280dbf2\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/f19d9e06-45a9-4bbf-a580-64df2280dbf2?iframe_resize_id=assessment_practice_id_f19d9e06-45a9-4bbf-a580-64df2280dbf2\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_a1725da7-c7cb-458a-9e72-afc2f149eeb2\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/a1725da7-c7cb-458a-9e72-afc2f149eeb2?iframe_resize_id=assessment_practice_id_a1725da7-c7cb-458a-9e72-afc2f149eeb2\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_5e272f2b-3941-484a-b30b-b8a80741adfd\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/5e272f2b-3941-484a-b30b-b8a80741adfd?iframe_resize_id=assessment_practice_id_5e272f2b-3941-484a-b30b-b8a80741adfd\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_b0fd0cd8-846d-4fdb-8932-332b7c916155\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/b0fd0cd8-846d-4fdb-8932-332b7c916155?iframe_resize_id=assessment_practice_id_b0fd0cd8-846d-4fdb-8932-332b7c916155\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_8746f138-5280-4b10-8e2d-4fac81b1240c\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/8746f138-5280-4b10-8e2d-4fac81b1240c?iframe_resize_id=assessment_practice_id_8746f138-5280-4b10-8e2d-4fac81b1240c\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<\/div>\n<h2>Problems with correlation<\/h2>\n<p>If generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? From a correlation alone, we can\u2019t be certain. For example, in the first case it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness\u00a0<em>and<\/em>\u00a0generosity, creating the illusion of a direct link between the two. For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation\u2014an often repeated phrase among psychologists.<a class=\"footnote\" title=\"Scollon, C. N. (2020). Research designs. In R. Biswas-Diener &amp; E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http:\/\/noba.to\/acxb2thy\" id=\"return-footnote-43-2\" href=\"#footnote-43-2\" aria-label=\"Footnote 2\"><sup class=\"footnote\">[2]<\/sup><\/a><\/p>\n<h2>Correlation Does Not Indicate Causation<\/h2>\n<p>Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about <strong>cause and effect<\/strong>. While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a <strong>confounding variable<\/strong>, is actually causing the systematic movement in our variables of interest. In the\u00a0<span style=\"font-size: 1em;\">depression and negative thinking<\/span><span style=\"font-size: 1em;\">\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">example mentioned earlier, <\/span><span style=\"font-size: 1em;\">stress<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0is a confounding variable that could account for the relationship between the two variables.\u00a0<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0<\/span><\/p>\n<p>Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our example about the relationship between\u00a0depression and disturbance in normal sleep patterns.\u00a0<span style=\"font-size: 1em;\">It seems reasonable to assume that s<\/span><span style=\"font-size: 1em;\">leep disturbance might cause a higher score on a measure of depression, just as a high degree of depression might cause more disturbed sleep patterns<\/span><span style=\"font-size: 1em; text-align: initial;\">, but if we were limited to\u00a0<\/span><strong style=\"font-size: 1em; text-align: initial;\">correlational research<\/strong><span style=\"font-size: 1em; text-align: initial;\">, we would be overstepping our bounds by making this assumption.\u00a0<\/span><span style=\"font-size: 1em;\">Both depression and sleep disturbance could be due to an underlying physiological disorder o<\/span><span style=\"font-size: 1em;\">r any to other third variable that you have not measured<\/span><span style=\"font-size: 1em; text-align: initial;\">.<\/span><\/p>\n<p><span style=\"font-size: 1em;\">Unfortunately, people mistakenly make claims of causation as a function of correlations all the time.<\/span><span style=\"font-size: 1rem; text-align: initial;\">\u00a0<\/span><span style=\"font-size: 1em;\">While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality.\u00a0<\/span><span style=\"font-size: 1em;\">The correlational method does not involve manipulation of the variables of interest. In the previous example, the experimenter does not manipulate people\u2019s depressive symptoms or sleep patterns.\u00a0<\/span><span style=\"font-size: 1em; text-align: initial;\">Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how\u00a0<\/span><span style=\"font-size: 1em;\">investigators use experimental methods in which the experimenter manipulates one or more variables of interest and observes their effects on other variables or outcomes under controlled conditions.<\/span><\/p>\n<div class=\"textbox examples\">\n<h3>watch IT<\/h3>\n<p>In this video, we discuss one of the best methods psychologists have for predicting behaviors: correlation. But does that mean that a behavior is absolutely going to happen? Let&#8217;s find out!<\/p>\n<p><iframe loading=\"lazy\" src=\"\/\/plugin.3playmedia.com\/show?mf=5572154&amp;p3sdk_version=1.10.1&amp;p=20361&amp;pt=375&amp;video_id=HSNp5MlTE-c&amp;video_target=tpm-plugin-vzk1y75p-HSNp5MlTE-c\" width=\"800px\" height=\"450px\" frameborder=\"0\" marginwidth=\"0px\" marginheight=\"0px\"><\/iframe><\/p>\n<p>You can view the <a href=\"https:\/\/course-building.s3-us-west-2.amazonaws.com\/Abnormal+Psychology\/transcripts\/5CorrelationvsCausationPsy101_transcript.txt\" target=\"_blank\" rel=\"noopener\">transcript for &#8220;#5 Correlation vs. Causation &#8211; Psy 101&#8221; here (opens in new window)<\/a>.<\/p>\n<\/div>\n<\/section>\n<section>\n<div class=\"textbox tryit\">\n<h3>Try It<\/h3>\n<p>\t<iframe id=\"assessment_practice_722b3100-c666-4aab-94cb-fc8aa6294021\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/722b3100-c666-4aab-94cb-fc8aa6294021?iframe_resize_id=assessment_practice_id_722b3100-c666-4aab-94cb-fc8aa6294021\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_ad669841-b050-4cdb-8046-13efe44ea2e0\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/ad669841-b050-4cdb-8046-13efe44ea2e0?iframe_resize_id=assessment_practice_id_ad669841-b050-4cdb-8046-13efe44ea2e0\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<\/div>\n<div class=\"textbox key-takeaways\">\n<h3>Think It Over<\/h3>\n<p>Consider why correlational research is often used in the study of abnormal behavior. If correlational designs do not demonstrate causation, why do researchers make causal claims regarding their results? Are there instances when correlational results could demonstrate causation?<\/p>\n<\/div>\n<div class=\"textbox learning-objectives\">\n<h3>Glossary<\/h3>\n<div class=\"ui-toggle-wrapper\">\n<p><strong>cause-and-effect relationship:\u00a0<\/strong>changes in one variable cause the changes in the other variable; can be determined only through an experimental research design<\/p>\n<p><strong>confirmation bias:\u00a0<\/strong>tendency to ignore evidence that disproves ideas or beliefs<\/p>\n<p><strong>confounding variable:\u00a0<\/strong>unanticipated outside factor that affects both variables of interest,\\; often gives the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables<\/p>\n<p><strong>correlation: <\/strong>the relationship between two or more variables; when two variables are correlated, one variable changes as the other does<\/p>\n<p><strong>correlation coefficient:\u00a0<\/strong>number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by <em>r<\/em><\/p>\n<p><strong>negative correlation:\u00a0<\/strong>two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation<\/p>\n<p><strong>positive correlation:\u00a0<\/strong>two variables change in the same direction, both becoming either larger or smaller<\/p>\n<\/div>\n<\/div>\n<\/section>\n<\/section>\n\n\t\t\t <section class=\"citations-section\" role=\"contentinfo\">\n\t\t\t <h3>Candela Citations<\/h3>\n\t\t\t\t\t <div>\n\t\t\t\t\t\t <div id=\"citation-list-43\">\n\t\t\t\t\t\t\t <div class=\"licensing\"><div class=\"license-attribution-dropdown-subheading\">CC licensed content, Original<\/div><ul class=\"citation-list\"><li>Modification, adaptation, and original content. <strong>Authored by<\/strong>: Sonja Ann Miller for Lumen Learning. <strong>Provided by<\/strong>: Lumen Learning. <strong>License<\/strong>: <em><a target=\"_blank\" rel=\"license\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY: Attribution<\/a><\/em><\/li><\/ul><div class=\"license-attribution-dropdown-subheading\">CC licensed content, Shared previously<\/div><ul class=\"citation-list\"><li>Analyzing Findings. <strong>Authored by<\/strong>: OpenStax College. <strong>Located at<\/strong>: <a target=\"_blank\" href=\"http:\/\/cnx.org\/contents\/Sr8Ev5Og@5.49:mfArybye@7\/Analyzing-Findings\">http:\/\/cnx.org\/contents\/Sr8Ev5Og@5.49:mfArybye@7\/Analyzing-Findings<\/a>. <strong>License<\/strong>: <em><a target=\"_blank\" rel=\"license\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY: Attribution<\/a><\/em>. <strong>License Terms<\/strong>: Download for free at http:\/\/cnx.org\/contents\/4abf04bf-93a0-45c3-9cbc-2cefd46e68cc@5.48<\/li><li>Research Designs. <strong>Authored by<\/strong>: Christie Napa Scollon . <strong>Provided by<\/strong>: Singapore Management University. <strong>Located at<\/strong>: <a target=\"_blank\" href=\"https:\/\/nobaproject.com\/modules\/research-designs\">https:\/\/nobaproject.com\/modules\/research-designs<\/a>. <strong>Project<\/strong>: The Noba Project. <strong>License<\/strong>: <em><a target=\"_blank\" rel=\"license\" href=\"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/\">CC BY-NC-SA: Attribution-NonCommercial-ShareAlike<\/a><\/em><\/li><\/ul><div class=\"license-attribution-dropdown-subheading\">All rights reserved content<\/div><ul class=\"citation-list\"><li>Correlation vs. Causality: Freakonomics Movie. <strong>Located at<\/strong>: <a target=\"_blank\" href=\"https:\/\/www.youtube.com\/watch?v=lbODqslc4Tg\">https:\/\/www.youtube.com\/watch?v=lbODqslc4Tg<\/a>. <strong>License<\/strong>: <em>Other<\/em>. <strong>License Terms<\/strong>: Standard YouTube License<\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section><hr class=\"before-footnotes clear\" \/><div class=\"footnotes\"><ol><li id=\"footnote-43-1\">Scollon, C. N. (2020). Research designs. In R. Biswas-Diener &amp; E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http:\/\/noba.to\/acxb2thy <a href=\"#return-footnote-43-1\" class=\"return-footnote\" aria-label=\"Return to footnote 1\">&crarr;<\/a><\/li><li id=\"footnote-43-2\">Scollon, C. N. (2020). Research designs. In R. Biswas-Diener &amp; E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http:\/\/noba.to\/acxb2thy <a href=\"#return-footnote-43-2\" class=\"return-footnote\" aria-label=\"Return to footnote 2\">&crarr;<\/a><\/li><\/ol><\/div>","protected":false},"author":29,"menu_order":6,"template":"","meta":{"_candela_citation":"[{\"type\":\"cc\",\"description\":\"Analyzing Findings\",\"author\":\"OpenStax College\",\"organization\":\"\",\"url\":\"http:\/\/cnx.org\/contents\/Sr8Ev5Og@5.49:mfArybye@7\/Analyzing-Findings\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"Download for free at http:\/\/cnx.org\/contents\/4abf04bf-93a0-45c3-9cbc-2cefd46e68cc@5.48\"},{\"type\":\"copyrighted_video\",\"description\":\"Correlation vs. Causality: Freakonomics Movie\",\"author\":\"\",\"organization\":\"\",\"url\":\"https:\/\/www.youtube.com\/watch?v=lbODqslc4Tg\",\"project\":\"\",\"license\":\"other\",\"license_terms\":\"Standard YouTube License\"},{\"type\":\"cc\",\"description\":\"Research Designs\",\"author\":\"Christie Napa Scollon \",\"organization\":\"Singapore Management University\",\"url\":\"https:\/\/nobaproject.com\/modules\/research-designs\",\"project\":\"The Noba Project\",\"license\":\"cc-by-nc-sa\",\"license_terms\":\"\"},{\"type\":\"original\",\"description\":\"Modification, adaptation, and original content\",\"author\":\"Sonja Ann Miller for Lumen Learning\",\"organization\":\"Lumen Learning\",\"url\":\"\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"ea53cfc9-d6dc-4ab6-ba53-e9856a678d2c, 26f2d9ec-fcaf-421f-ad0e-e970137c7015","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-43","chapter","type-chapter","status-publish","hentry"],"part":35,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/pressbooks\/v2\/chapters\/43","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/wp\/v2\/users\/29"}],"version-history":[{"count":25,"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/pressbooks\/v2\/chapters\/43\/revisions"}],"predecessor-version":[{"id":7446,"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/pressbooks\/v2\/chapters\/43\/revisions\/7446"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/pressbooks\/v2\/parts\/35"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/pressbooks\/v2\/chapters\/43\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/wp\/v2\/media?parent=43"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/pressbooks\/v2\/chapter-type?post=43"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/wp\/v2\/contributor?post=43"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-abnormalpsych\/wp-json\/wp\/v2\/license?post=43"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}