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Midterm

# Midterm2 key terms.docx

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School
Department
Psychology
Course
Psychology 2800E
Professor
Riley Hinson
Semester
Fall

Description
Correlational research: research to determine whether there is a relationship between two measures Scatterplot: a graph in which one of the measures is plotted on the abscissa (x-axis) and the other is plotted on the ordinate (y-axis), and each participant is located at the point corresponding to his/her x and y values The variable being predicted is the y-variable, also known as the criterion variable. The variable being used to make the prediction is termed the x variable, and is also known as the predictor variable. (Independent variable is also x, and the dependent variable is y) Slope: a slant that is an indicator of a relationship between the two variables on a scatterplot Positive relationship: one variable increases as the other increases Negative relationship: one variable decreases as the other decreases Correlation coefficient: used to numerically quantifying the degree of the relationship between the two variables Positive correlation / direct relationship: if the two variables both increase together or both decrease together, with a correlation coefficient between 0.0 and +1.0 Negative correlation or an inverse relationship: if the two variables change in opposite directions, with a correlation coefficient between 0.0 and -1.0 Linear relationship: what correlation coefficients quantify the strength of Scaleless: the value of the correlation coefficient is not affected by the units of measurement If both variables are measured on interval/ratio scales, then the Pearson product-moment correlation coefficient is used, which is most commonly used in psychological research Restriction of range problem: I the range of the x or y scores is limited to a small set of values, this may lead to an underestimation of the degree of correlation between x and y, resulting in ceiling or floor effects, or using participants that do not display the full range of possible outcome values Cases: most often the individual participants of the study, the bivariate data is produced by this in the calculation of a correlation Coefficient of determination: simply r squared, and it is used to identify the proportion of variability in the y scores that is accounted for by the linear relationship between x and y Regression analysis: predictions about the values of one variable based on information about values of the other variable if a correlation exists betwee
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