Exam 4

A  Ranked scores

B  Ordinal data

C  The chi-square value

D  All of the above

A  0.92

B  0.14

C  0.63

D  0.86

A  Both

B  Its sign (+ or – )

C  its size

D  Neither

A  High scores on X are associated with high scores on Y

B  X is a good predictor of Y

C  low scores on X are associated with low scores on Y

D  X is NOT a good predictor of Y

A  when random sampling has been used

B  for nominal data

C  for tables that are 2 x 2

D  all of the above

A  interval level data

B  both nominal and interval

C  nominal level data

D  ordinal level data

A  the amount of change in Y for each unit change in X

B  the amount of change in X for each unit change in Y

C  the points where the regression line crosses the X axis when Y = 0

D  the point where the regression line crosses the Y axis when X = 0

A  its size

B  its sign (+ or -)

C  both its size and sign (+ or -)

D  neither its sign (+ or -) or size

A  Cramer’s V

B  Spearman’s rank order correlation coefficient

C  Pearson’s r

D  contingency coefficient

A  Pearson’s r

B  Phi coefficient

C  Contingency Coefficient

D  Spearman’s r

A  Goodman’s and Kruskal’s Gamma

B  Spearman’s r

C  Pearson’s r

D  Cramer’s V

A  the table is 2 x 2

B  the given table does not have the same number of row and columns

C  the table has the same number of rows and columns

D  there is a large sample

A  the variables being investigated must be correlated

B  one variable is believed to be influenced by the other

C  the independent variable is influenced by the dependent variable

D  the independent variable must be categorical in nature

A  the proportion of variance in Y that is NOT explained by X

B  the proporion of variance in Y that is attributed to error

C  the proportion of the variance in Y that is explained by X

D  the proportion of variance in X that is attributed to error

A  the less variance has been accounted for by the independent variable

B  the better the obtained data fit the regression line (AKA line of best fit)

C  the worse the obtained data fit the regression line

D  the ore the independent variable predicts the independent variable

A  the point where the regresssion line corsses the X-axis when Y=0

B  the amount of change in Y for each unit change in X

C  the point where the regression line crosses the Y-axis when X=0

D  the amount of change in X for each change in Y

A  not enough information to answer question

B  a positive relationship

C  a negative relationship

D  curvilienear relationship

A  presents the direction of the relationship

B  varies from -1.0 to +1.0

C  presents the strength of the relationship

D  all of the above

A  -0.28

B  (+0.12)

C  (+0.58)

D  -0.31

A  correlations can never exceed 1.0

B  correlations provide statements of causation

C  correlations can be negative

D  correlations closer to 0.0 are considered to be weak, while correlations closer to 1.0 are considered to be strong.

A  positive

B  negative

C  there is not enough information to estimate the nature of the correlation

D  inverse