Correlational Research PSYC-31574-003
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This 2 page Class Notes was uploaded by Amy Turk on Monday April 4, 2016. The Class Notes belongs to PSYC-31574-003 at Kent State University taught by Dr. Tanjeem Azad in Spring 2016. Since its upload, it has received 11 views. For similar materials see Research Methods In Psychology in Psychlogy at Kent State University.
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Date Created: 04/04/16
CORRELATIONAL RESEARCH ● Focuses on whether two variables are related, and if so, how strongly they are related ● Assessing the relationship between variables that are measured ○ Ex. smoking history and health problems ● Variables are not experimentally manipulated Why It’s Important ● Certain independent variables cannot be manipulated by a researcher ○ Gender ○ Ethnic background ○ Intelligence ● Ethical constraints of research questions and manipulating variables ■ Ex. “Do laws that require people to wear seat belts actually reduce traffic fatalities?” ● Discovering relationships between variables has influenced policies ○ Ex. “Correlation of smoking habits with lung cancer led to warning on cigarette packs” ● Very useful for making predictions Correlation Coefficient ● Indicates the degree to which two variables are related ● r = always between -1, 0, 1 ● Direction: determined by the sign ofr ○ Positive r= positive correlation ■ High scores on one variable tend to have high scores on the other variable ■ Low scores on one tend to have low scores on the other ○ Negative r= negative correlation ■ Inverse relationship ● High scores on one, low scores on the other ● Vice versa Strength ● Magnitude determined by the absolute value of r(ignore the sign) ● The closer r is to 1, the stronger the correlation ● When r = o, variables are not related ● A strong correlation has minimal scattering of data points ● Weak: more scattering of data points Problem with r ● Not on a ratio scale, but on an interval scale ● There is no natural zero ● Cannot make comparisons ● By squaring r, we convert it to a ration scale Coefficient of Determination ● (r) ● Statistic that tells you how much of the variance in variable A can be explained by variable B 2 ● (r)= systematic variance ○ The amount of variance in one variable that is explained by the variance in the other variable 2 ● If 0, thenr)= 0 ○ No relationship 2 ● If = 1 , th(r)= 100%