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Psychology

<OpenstaxParagraph>
	<OpenstaxTerm>Correlation</OpenstaxTerm> means that there is a relationship between two or more variables (such as ice cream consumption and crime), 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 <OpenstaxTerm>correlation coefficient</OpenstaxTerm> is a number from -1 to +1 that indicates the strength and direction of the relationship between variables. The correlation coefficient is usually represented by the letter <OpenstaxEm>r</OpenstaxEm>.
</OpenstaxParagraph>
<OpenstaxParagraph>
	The number portion of the correlation coefficient indicates the strength of the relationship. The closer the number is to 1 (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 relationship 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 0. The example above about ice cream and crime is an example of two variables that we might expect to have no relationship to each other.
</OpenstaxParagraph>
<OpenstaxParagraph>
	The sign—positive or negative—of the correlation coefficient indicates the direction of the relationship (<a href="#CNX_Psych_02_04_scatter">Figure 2.12</a>). A <OpenstaxTerm>positive correlation</OpenstaxTerm> 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 <OpenstaxTerm>negative correlation</OpenstaxTerm> 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.
</OpenstaxParagraph>
<OpenstaxParagraph>
	The example of ice cream and crime rates is a positive correlation because both variables increase when temperatures are warmer. Other examples of positive correlations are the relationship between an individual’s height and weight or the relationship between a person’s age and number of wrinkles. One might expect a negative correlation to exist between someone’s tiredness during the day and the number of hours they slept the previous night: the amount of sleep decreases as the feelings of tiredness increase. In a real-world example of negative correlation, student researchers at the University of Minnesota found a weak negative correlation (<OpenstaxEm>r</OpenstaxEm> = -0.29) between the average number of days per week that students got fewer than 5 hours of sleep and their GPA (Lowry, Dean, &amp; Manders, 2010). Keep in mind that a negative correlation is not the same as no correlation. For example, we would probably find no correlation between hours of sleep and shoe size.
</OpenstaxParagraph>
<OpenstaxParagraph>
	As mentioned earlier, correlations have predictive value. Imagine that you are on the admissions committee of a major university. You are faced with a huge number of applications, but you are able to accommodate only a small percentage of the applicant pool. How might you decide who should be admitted? You might try to correlate your current students’ college GPA with their scores on standardized tests like the SAT or ACT. By observing which correlations were strongest for your current students, you could use this information to predict relative success of those students who have applied for admission into the university.
</OpenstaxParagraph>
<OpenstaxFigure id="CNX_Psych_02_04_scatter" src="https://openstax.org/apps/archive/20240226.174525/resources/97caa41a4392d7220b4c21871bb6c94c0a0a3412" alt="Three scatterplots are shown. Scatterplot (a) is labeled “positive correlation” and shows scattered dots forming a rough line from the bottom left to the top right; the x-axis is labeled “weight” and the y-axis is labeled “height.” Scatterplot (b) is labeled “negative correlation” and shows scattered dots forming a rough line from the top left to the bottom right; the x-axis is labeled “tiredness” and the y-axis is labeled “hours of sleep.” Scatterplot (c) is labeled “no correlation” and shows scattered dots having no pattern; the x-axis is labeled “shoe size” and the y-axis is labeled “hours of sleep.”" mediaType="image/jpeg" width="975" height="300" />
<OpenstaxFigureText width="975">
	Figure 2.12 Scatterplots are a graphical view of the strength and direction of correlations. The stronger the correlation, the closer the data points are to a straight line. In these examples, we see that there is (a) a positive correlation between weight and height, (b) a negative correlation between tiredness and hours of sleep, and (c) no correlation between shoe size and hours of sleep.
</OpenstaxFigureText>
<OpenstaxNote>
	<OpenstaxNoteTitle> Link to Learning </OpenstaxNoteTitle> 
	<OpenstaxParagraph>
		Manipulate this <a href="http://openstax.org/l/scatplot">interactive scatterplot</a> to practice your understanding of positive and negative correlation.
	</OpenstaxParagraph>
</OpenstaxNote>
<OpenstaxH3>Correlation Does Not Indicate Causation </OpenstaxH3>
<OpenstaxParagraph>
	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 <OpenstaxTerm>cause and effect</OpenstaxTerm>. While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a <OpenstaxTerm>confounding variable</OpenstaxTerm>, is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.
</OpenstaxParagraph>
<OpenstaxParagraph>
	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 discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to <OpenstaxTerm>correlational research</OpenstaxTerm>, we would be overstepping our bounds by making this assumption.
</OpenstaxParagraph>
<OpenstaxParagraph>
	Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, research found that people who eat certain breakfast cereal may have a reduced risk of heart disease (Anderson, Hanna, Peng, &amp; Kryscio, 2000). Cereal companies are likely to share this information in a way that maximizes and perhaps overstates the positive aspects of eating cereal. But does cereal really cause better health, or are there other possible explanations for the health of those who eat cereal? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. 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 scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.
</OpenstaxParagraph>
<OpenstaxFigure id="CNX_Psych_02_04_cereal" src="https://openstax.org/apps/archive/20240226.174525/resources/8b9f7edcdb1d52de91dc6a890d91d06cdf18ddb0" alt="A photograph shows a bowl of cereal." mediaType="image/jpeg" width="325" height="244" />
<OpenstaxFigureText width="325">
	Figure 2.13 Does eating cereal really cause someone to be healthier? (credit: Tim Skillern)
</OpenstaxFigureText>
<OpenstaxH3>Illusory Correlations </OpenstaxH3>
<OpenstaxParagraph>
	The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. <OpenstaxTerm>Illusory correlations</OpenstaxTerm>, or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (<a href="#CNX_Psych_02_04_moon">Figure 2.14</a>).
</OpenstaxParagraph>
<OpenstaxFigure id="CNX_Psych_02_04_moon" src="https://openstax.org/apps/archive/20240226.174525/resources/27d9385e8ab8ba755aad482bd8ecad4859be14bb" alt="A photograph shows the moon." mediaType="image/jpeg" width="325" height="216" />
<OpenstaxFigureText width="325">
	Figure 2.14 Many people believe that a full moon makes people behave oddly. (credit: Cory Zanker)
</OpenstaxFigureText>
<OpenstaxParagraph>
	There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton &amp; Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.
</OpenstaxParagraph>
<OpenstaxParagraph>
	Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as <OpenstaxTerm>confirmation bias</OpenstaxTerm>. Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).
</OpenstaxParagraph>

2.3.1 Correlational Research

1 Introduction to Psychology

1.1 What Is Psychology?

1.1.1 Why Study Psychology?

1.2 History of Psychology

1.2.1 Wundt and Structuralism

1.2.2 Functionalism

1.2.3 Freud and Psychoanalytic Theory

1.2.4 Wertheimer, Koffka, Köhler, and Gestalt Psychology

1.2.5 Pavlov, Watson, Skinner, and Behaviorism

1.2.6 Maslow, Rogers, and Humanism

1.2.7 The Cognitive Revolution

1.2.8 Multicultural And Cross-Cultural Psychology

1.2.9 Women in Psychology

1.3 Contemporary Psychology

1.3.1 Biopsychology and Evolutionary Psychology

1.3.2 Sensation and Perception

1.3.3 Cognitive Psychology

1.3.4 Developmental Psychology

1.3.5 Personality Psychology

1.3.6 Social Psychology

1.3.7 Industrial-Organizational Psychology

1.3.8 Health Psychology

1.3.9 Sport and Exercise Psychology

1.3.10 Clinical Psychology

1.3.11 Forensic Psychology

1.4 Careers in Psychology

1.4.1 Other Careers in Academic Settings

1.4.2 Career Options Outside of Academic Settings

2 Psychological Research

2.1 Why Is Research Important?

2.1.1 Use of Research Information

2.1.2 NOTABLE RESEARCHERS

2.1.3 The Process of Scientific Research

2.2 Approaches to Research

2.2.1 Clinical or Case Studies

2.2.2 Naturalistic Observation

2.2.3 Surveys

2.2.4 Archival Research

2.2.5 Longitudinal and Cross-Sectional Research

2.3 Analyzing Findings

2.3.2 Causality: Conducting Experiments and Using the Data

2.3.3 Reliability and Validity

2.4 Ethics

2.4.1 Research Involving Human Participants

2.4.2 Research Involving Animal Subjects

3 Biopsychology

3.1 Human Genetics

3.1.1 Genetic Variation

3.1.2 Gene-Environment Interactions

3.2 Cells of the Nervous System

3.2.1 Neuron Structure

3.2.2 Neuronal Communication

3.2.3 Neurotransmitters and Drugs

3.3 Parts of the Nervous System

3.3.1 Peripheral Nervous System

3.4 The Brain and Spinal Cord

3.4.1 The Spinal Cord

3.4.2 The Two Hemispheres

3.4.3 Forebrain Structures

3.4.4 Midbrain and Hindbrain Structures

3.4.5 Brain Imaging

3.5 The Endocrine System

3.5.1 Major Glands

4 States of Consciousness

4.1 What Is Consciousness?

4.1.1 Biological Rhythms

4.1.2 Problems With Circadian Rhythms

4.2 Sleep and Why We Sleep

4.2.1 What is Sleep?

4.2.2 Why Do We Sleep?

4.3 Stages of Sleep

4.3.1 NREM Stages of Sleep

4.3.2 REM Sleep

4.4 Sleep Problems and Disorders

4.4.1 Insomnia

4.4.2 Parasomnias

4.4.3 Sleep Apnea

4.4.4 Narcolepsy

4.5 Substance Use and Abuse

4.5.1 Substance Use Disorders

4.5.2 Drug Categories

4.6 Other States of Consciousness

4.6.1 Hypnosis

4.6.2 Meditation

5 Sensation and Perception

5.1 Sensation versus Perception

5.1.1 Sensation

5.1.2 Perception

5.2 Waves and Wavelengths

5.2.1 Amplitude and Wavelength

5.2.2 Light Waves

5.2.3 Sound Waves

5.3 Vision

5.3.1 Anatomy of the Visual System

5.3.2 Color and Depth Perception

5.4 Hearing

5.4.1 Anatomy of the Auditory System

5.4.2 Pitch Perception

5.4.3 Sound Localization

Question	Answer
tendency to ignore evidence that disproves ideas or beliefs	confirmation bias
unanticipated outside factor that affects both variables of interest, often giving 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	confounding variable
relationship between two or more variables; when two variables are correlated, one variable changes as the other does	correlation
number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r	correlation coefficient
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	negative correlation
two variables change in the same direction, both becoming either larger or smaller	positive correlation