R&e Ch. 11

Front 1

beta weight?

Back 1

are coefficients indicating the magnitude of prediction for a variable after removing the effects of all other predictors assesses the proportion of variability in one variable that can be determined or explained by a second variable.

Front 2

Correlation?

Back 2

is defined as a statistical test to determine the tendency or pattern for two (or more) variables or two sets of data to vary consistently.

Front 3

correlation matrix?

Back 3

is a visual display of the correlation coefficients for all variables in a study.

Front 4

correlational research designs?

Back 4

are quant. Design in which investigators use a correlation statistical technique to describe and measure the degree of association (or relationship) between two or more variables or sets of scores.

Front 5

co-vary?

Back 5

means that a score can be predicted on one variable with knowledge about the individual’s score on another variable.

Front 6

criterion variable?

Back 6

is the outcome variable being predicted in correlational research.

Front 7

curvilinear distribution?

Back 7

a U-shaped distribution in scores on a graph.

Front 8

degree of association?

Back 8

means the association between two variables or sets of scores with a correlation coefficient of -1.00 to +1.00, with .00 indicating no linear associate at all.

Front 9

explanatory research design?

Back 9

is a type of correlational research in which the researcher is interested in the extent to which two variables (or more) co-vary, where variance or change in one variable is reflected in variance or change in the other.

Front 10

meta-analysis?

Back 10

is a type of research report in which the author integrates the findings of many (primary source) research studies.

Front 11

multiple regression or multiple correlation?

Back 11

is a statistical procedure for examining the combined relationship of multiple independent variables on a single dependent variable.

Front 12

negative correlation?

Back 12

are when the points move in the opposite direction- when x increases, y decreases and when x decreases y increases.

Front 13

negative linear relationship?

Back 13

results when low scores (or high scores) on one variable relate to high scores (or low scores) on the other variable.

Front 14

partial correlations?

Back 14

are used to determine the amount of variance that an intervening variable explains in both the independent and dependent variables.

Front 15

phi coefficient?

Back 15

is the correlation statistic used to determine the degree and direction of association when both variables being related are dichotomous measures.

Front 16

point-biserial correlation?

Back 16

is the correlation statistic used when one variable is measured as an interval or ratio scale and the other has two different values (a dichotomous scale).

Front 17

positive correlation?

Back 17

are when the points move in the same direction- when X increases, so does y, or alternatively, if x decreases, so does y.

Front 18

positive linear relationship?

Back 18

means that low (or high) scores on one variable relate to low (or high) scores on a second variable.

Front 19

prediction research design?

Back 19

is a type of correlational research in which the researcher identifies variables that will positively predict an outcome or criterion.

Front 20

predictor variable?

Back 20

is the variable the researcher uses to make a forecast about an outcome in correlational research.

Front 21

product moment correlation coefficient?

Back 21

is called the bivariate correlation, the zero order correlation, or simply the Pearson R, and it is indicated by an “r” for its notion. The statistic is calculated for two variables (rxy)by multiplying the z scores on X and Y for each case and then dividing by the number of cases minus 1.

Front 22

regression line?

Back 22

in correlation research is a line of “best fit” for all of the points of scores on a graph. This line comes the closest to all of the points on the plot and it is calculated by drawing a line that minimizes the squared distance of the points from the line.

Front 23

regression table?

Back 23

show the overall amount of variance explained in a dependent variable by all independent variables, called R2(R squared).

Front 24

spearman rho?

Back 24

is the correlation statistic used for nonlinear data when the data are measured on ordinal scales (rank-ordered scales).

Front 25

uncorrelated relationship?

Back 25

of scores means that the scores in the distribution are independent of each other.

Front 26

What are characteristics of correlational designs?

Back 26

-Display of scores
-Associations between sources
-Multiple variable analysis