Correlational Studies Relationship between two variables. Extent to which two factors vary together Allows Prediction NO causation Third-variable problem Tell us the relationship between two variables. What happens to one variable if you change the other? How well does each variable predict the other? Tells us NOTHING about causation…only prediction. A could cause B; B could cause A (bidirectionality problem); C could cause both A and B (third variable problem) Pearson’s r correlation values range from –1.00 to 1.00.

Key Terms Variable – Anything that can vary (quantitative) Scatterplot – Graphed cluster of dots. Each dot represents the value of two variables. Correlation Coefficient – Statistical index of the relationship between two variables. Pearson’s r: ranges from -1.0 to 1.0. Closer to -1 or 1, the stronger the relationship

Correlation Coefficients The closer the value of r is to –1 or to +1, the stronger the correlation. Negative or inverse correlation: as one variable goes up, the other goes down. Positive or direct correlation: As one variable goes up, the other goes up. Look at the absolute value to determine strength. Take the absolute value to determine the strength. The closer the value is to –1 or to +1, the stronger the correlation. -.72 has the same strength as +.72 A correlation at or near zero means that no relationship exists between the variables. Negative or inverse correlation: variables are moving in opposite direction. High values of one variable are associated with low values of the other. Positive or direct correlation: Variables are moving together—either up or down.

Graphs of Correlations Positive: GPA and ACT scores Depression and Suicide Education and Salary Negative (Inverse): Self-Esteem and Depression Exercise and Obesity Graphs of Correlations

https://www.socscistatistics.com/tests/pearson/Default2.aspx

Identify the type of correlation. What are the possible interpretations? The more a mother smokes, the more her children are likely to display behavioral problems. The more psychology courses students take during college, the higher scores they get on a measure of interpersonal sensitivity. Decreased sleep is associated with increased weight gain.

Spurious Correlations Correlation exists but because of a 3rd variable Examples The more ice cream eaten, the greater number of shark attacks. The greater number of teeth lost in children, the greater the vocabulary size. The more appliances people have in their homes in Taiwan, the fewer children they have. http://www.tylervigen.com/spurious-correlations (more examples) These occur when variables are related through some association with another variable; they are not directly related to each other.

Keep In Mind… Illusory Correlation: When we believe there is a relationship between two things, we seek out instances that confirm our belief. Regression Toward the Mean: Average results are more typical than non- average results Chili/AL football analogy

Experimentation One variable is manipulated to see the effect on the other variable Only research method that allows you to infer CAUSATION

Types of Variables Independent: (IV) manipulated by experimenter; affects DV Dependent: (DV) variable that is measured & recorded Subject Variables: variable such as gender, age race, etc. that cannot be manipulated, but could still affect DV Extraneous (Confounding) variables: factor NOT controlled by experimenter that could influence results (alternative explanations) Subject variables: a type of IV such as gender, age, race, etc. that cannot be manipulated by the experimenter but is still taken into account as having a possible effect on the DV

True Experiments: Four Requirements Manipulate at least one IV Measure at least 1 DV At least 2 groups: treatment & control Random assignment—hallmark of experiment Control confounds Random assignment—hallmark of experiment. Each participant is assigned completely at random to be in any of the various experimental or control groups in the study. Different from random selection, in which each person in a population has an equal chance of being selected for the study.

The Power of the Placebo Naturally observe possible relationship between violent video games and aggression Do correlational study Conduct true experiment The Power of the Placebo

Threats to Experimentation Confounding Experimenter bias Demand Characteristics Need double-blind study to combat experimenter bias and deception to counteract demand characteristics. Experimenter bias: Experimenter consciously or subconsciously ruins own experiment by treating groups differently Demand Characteristics: Subjects try to guess your hypothesis or they change their behavior in some way because they know they are being studied. Demand characteristics are a deliberate attempt by the subject to change behavior. The Hawthorne Effect is similar, but it’s an unconscious behavior on the subject’s part…behavior is changed because people know they are being studied, but they don’t actually mean to change their behavior.

The Power of the Placebo Improvement due only to expectation that it will work 35% of improvement Drug studies give sham treatments to control for placebo effects Placebo effect has been documented for many years and accounts for about 35% of any given improvement. Tuttle et al. found that clinical trials in 2013 for treatment of chronic pain produced only 9% greater pan relief than placebo. In 1996, it was 27% greater. The media bombardment promising relief of X drug for pain has probably strengthened the placebo effect. The Power of the Placebo

The Nocebo The experience of negative side effects that are only based on expectation Nocebo effect is around 20%. Knowing a drug’s side effect profile can put ideas is a person’s mind that she should expect those side effects. For example, if you knew that a drug could cause sleeplessness, you might have insomnia because you expect not to be able to sleep while taking this medication.

GOALS Internal Validity: the degree to which your experiment tests what it is supposed to test External validity: the degree to which your results can be generalized to the real world. Reliability: your experiment can be replicated