Exam 4

A  Ordinal data

B  The chi-square value

C  Ranked scores

D  All of the above

A  0.14

B  0.92

C  0.86

D  0.63

A  Both

B  Neither

C  its size

D  Its sign (+ or – )

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

B  X is NOT a good predictor of Y

C  X is a good predictor of Y

D  High scores on X are associated with high scores on 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  ordinal level data

B  interval level data

C  nominal level data

D  both nominal and interval

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

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

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

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

A  its size

B  neither its sign (+ or -) or size

C  its sign (+ or -)

D  both its size and sign (+ or -)

A  contingency coefficient

B  Spearman’s rank order correlation coefficient

C  Pearson’s r

D  Cramer’s V

A  Phi coefficient

B  Contingency Coefficient

C  Spearman’s r

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 has the same number of rows and columns

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

C  there is a large sample

D  the table is 2 x 2

A  one variable is believed to be influenced by the other

B  the variables being investigated must be correlated

C  the independent variable must be categorical in nature

D  the independent variable is influenced by the dependent variable

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

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

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

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

A  the ore the independent variable predicts the independent variable

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

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

D  the worse the obtained data fit the regression line

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

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

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

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

A  a positive relationship

B  not enough information to answer question

C  a negative relationship

D  curvilienear 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  (+0.12)

B  -0.28

C  -0.31

D  (+0.58)

A  correlations can be negative

B  correlations can never exceed 1.0

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

D  correlations provide statements of causation

A  negative

B  inverse

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

D  positive