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Chapters 9 & 15 (Correlation- a statistical value that measures and…
Chapters 9 & 15
Correlation- a statistical value that measures and describes the direction and degree of relationship between two variables. The sign (+/−) indicates the direction of the relationship. The numerical value (0.0 to 1.0) indicates the strength or consistency of the relationship. The type (Pearson or Spearman) indicates the form of the relationship. Also known as correlation coefficient.
Positive Correlation- this occurs when the two variables tend to change in the same direction, so as the value of the X variable increases from one individual to another, the Y variable also tends to increase; when the X variable decreases, the Y variable also decreases
Negative Correlation- the two variables tend to go in opposite directions. As the X variable increases, the Y variable decreases, so it is an inverse relationship
Perfect Correlation- a relationship where the actual data points perfectly fit the specific form being measured. For a Pearson correlation, the data points fit perfectly on a straight line.
Pearson Correlation- measures the degree and the direction of the linear relationship between two variables
Sum of Products (SP)- A measure of the degree of covariability between two variables; the degree to which they vary together
Coefficient of Determination- measures the proportion of variability in one variable that can be determined from the relationship with the other variable
Partial Correlation- measures the relationship between two variables while controlling the influence of a third variable by holding it constant
Spearman Correlation- a correlation calculated for ordinal data, it is also used to measure the consistency of direction for a relationship
Point-Biserial Correlation- a correlation between two variables where one of the variables is dichotomous
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A correlation between two variables does not prove a causation relationship, it just shows that the variables are related to one another
t statistic- is used to test hypotheses about an unknown population mean and standard deviation is unknown.
The formula for the t statistic has the same structure as the z-score formula, except that the t statistic uses the estimated standard error in the denominator
Estimated Standard Error- it is used as an estimate of the real standard error when the value of the standard deviation is unknown
It is computed from the sample variance or sample standard deviation and provides an estimate of the standard distance between a sample mean M and the population mean
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Percentage of variance accounted for by the treatment- A measure of effect size that determines what portion of the variability in the scores can be accounted for by the treatment effect
Confidence Interval- an interval, or range of values centered around a sample statistic. It's purpose is that a sample statistic, such as a sample mean, should be relatively near to the corresponding population parameter
It uses the t equation, solved for the unknown mean