Exam 4

A  Ordinal data

B  Ranked scores

C  The chi-square value

D  All of the above

A  0.92

B  0.63

C  0.14

D  0.86

A  Neither

B  Its sign (+ or – )

C  its size

D  Both

A  X is NOT a good predictor of Y

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

C  X is a good predictor of Y

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

A  for nominal data

B  for tables that are 2 x 2

C  when random sampling has been used

D  all of the above

A  interval level data

B  nominal level data

C  both nominal and interval

D  ordinal level data

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

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

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

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

A  its size

B  its sign (+ or -)

C  neither its sign (+ or -) or size

D  both its size and sign (+ or -)

A  Pearson’s r

B  Cramer’s V

C  contingency coefficient

D  Spearman’s rank order correlation coefficient

A  Phi coefficient

B  Spearman’s r

C  Contingency Coefficient

D  Pearson’s r

A  Spearman’s r

B  Goodman’s and Kruskal’s Gamma

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  there is a large sample

D  the table has the same number of rows and columns

A  the independent variable must be categorical in nature

B  the independent variable is influenced by the dependent variable

C  one variable is believed to be influenced by the other

D  the variables being investigated must be correlated

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

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

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

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

A  the worse the obtained data fit the regression line

B  the ore the independent variable predicts the independent variable

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

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

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

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

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

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

A  curvilienear relationship

B  a negative relationship

C  not enough information to answer question

D  a positive relationship

A  varies from -1.0 to +1.0

B  presents the direction of the relationship

C  presents the strength of the relationship

D  all of the above

A  -0.31

B  -0.28

C  (+0.58)

D  (+0.12)

A  correlations provide statements of causation

B  correlations can be negative

C  correlations can never exceed 1.0

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

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

B  positive

C  inverse

D  negative