Deck 12: Examining Relationships in Quantitative Research

Question 1

A fundamental basis of regression analysis is the assumption of:

A) a curvilinear relationship between two weakly associated dependent variables.
B) a straight line relationship between the independent and dependent variables.
C) the lack of a relationship between independent variables.
D) a uniform normal distribution between dependent variables.
E) the existence of two independent variables for every dependent variable.

Answer 1

B

Question 2

Which of the following statements is True of the correlation analysis?

A) The null hypothesis for the Pearson correlation coefficient states that there is always a strong association between two variables.
B) The Pearson correlation coefficient measures the degree of linear association that ranges from 1.0 to 10.0.
C) The larger the correlation coefficient, the weaker the association between two variables.
D) The null hypothesis for the Pearson correlation coefficient states that the correlation coefficient is zero.
E) The Pearson correlation coefficient measures the degree of linear association between three variables.

Answer 2

D

Question 3

A researcher plots a scatter diagram of two variables. The dots on the plot are scattered roughly in a circle. This indicates that the relationship (covariation) between the two variables is:

A) linear and positive.
B) linear and negative.
C) circular and positive.
D) circular and negative.
E) very close to zero.

Answer 3

E

Question 4

Which of the following is True of the fundamentals of regression analysis?

A) A fundamental basis of regression analysis is the assumption of a circular relationship between the independent and dependent variables.
B) Regression uses an estimation procedure called ordinary least squares that guarantees the line it estimates will be the best fitting line.
C) The differences between actual and predicted values of the dependent variable are known as regression coefficients and are represented by b.
D) The regression coefficient is calculated by squaring errors of each dependent variable.
E) Any point that falls on the line of a regression analysis is the result of unexplained variance.

Question 5

A _____ relationship is one between two variables whereby the strength and/or direction of the relationship changes over the range of both variables.

A) linear
B) curvilinear
C) constant
D) proportional
E) collinear

Question 6

Which of the following is True of relationships between variables?

A) A linear relationship is much simpler to work with than a curvilinear relationship.
B) Relationships between variables lack direction.
C) A negative relationship exists between two variables if low levels of one variable are associated with low levels of another.
D) The larger the size of the correlation coefficient between two variables, the weaker the association between them.
E) In a linear relationship between two variables, the strength and direction of the relationship changes over the range of both variables.

Question 7

If a consistent and systematic relationship is not present between two variables, then:

A) a strong association is evident.
B) there is a moderate relationship.
C) an invisible relationship exists.
D) there is no relationship.
E) there is a weak association.

Question 8

In a certain town, when the number of automobiles owned went up, the number of service stations for automobiles also went up. This illustrates the concept of _____.

A) coalescence
B) cogitation
C) covariation
D) coexistence
E) convergence

Question 9

If the coefficient of correlation between two variables is -0.6, the coefficient of determination will be:

A) -0.6.
B) 0.4.
C) 0.36.
D) -0.36.
E) 0.6.

Question 10

The coefficient of determination:

A) describes the variation in the dependent variable caused by the control variable.
B) tells you the percentage of the total variation in the independent variable caused by the dependent variable.
C) ranges from -1.0 to +1.0.
D) ranges from .00 to 1.0.
E) is a stronger measure than the Pearson correlation coefficient.

Question 11

If a researcher is interested in measuring the effect of two independent variables on a dependent variable, he/she should use:

A) the Pearson correlation coefficient.
B) the Spearman correlation coefficient.
C) bivariate regression analysis.
D) multiple regression analysis.
E) simple regression.

Question 12

The statistical procedure that produces predictions with the lowest sum of squared differences between actual and predicted values in a regression equation is called:

A) SPSS.
B) unexplained variance.
C) ordinary least squares.
D) the slope.
E) regression coefficient.

Question 13

The Spearman rank order correlation coefficient differs from the Pearson correlation coefficient in that the Spearman rank order correlation:

A) primarily establishes a weak association between variables, whereas the Pearson correlation coefficient establishes a strong association between variables.
B) is used when variables have been measured using ordinal scales, whereas the Pearson correlation coefficient is used when variables have been measured using ratio scales.
C) assumes that variables have a normally distributed population, whereas the Pearson correlation coefficient assumes that variables have a uniform distribution.
D) is used for linear relationships, whereas the Pearson correlation coefficient is used for curvilinear relationships.
E) is a qualitative measure of the degree of variation, whereas the Pearson correlation coefficient is a quantitative measure of the degree of variation.

Question 14

Which of the following statements is True of model F statistics?

A) A larger F statistic indicates that the regression model has more explained variance than error variance.
B) An F statistic shows the change in the dependent variable for each unit change in the independent variable.
C) Analysis of linear relationships between a dependent variable and multiple independent variables requires that F statistics be smaller than beta coefficients.
D) Bivariate regression becomes multiple regression analysis when F statistics are used.
E) Standardization using beta coefficient augments the effects of using different scales of measurement.

Question 15

Which of the following is True of a beta coefficient?

A) The beta coefficient ranges from 1.00 to 3.00 and is a positive correlation coefficient.
B) The beta coefficient is an estimated correlation coefficient.
C) The beta coefficient is an F-ratio that has been recalculated to have a mean of 1 and a standard deviation of 0.
D) A positive beta means as the size of an independent variable decreases, then the size of the dependent variable increases.
E) A beta coefficient shows the change in the dependent variable for each unit change in the independent variable.

Question 16

In calculating the Pearson correlation coefficient, we assume that:

A) when the correlation coefficient is weak, there is a consistent, systematic relationship between the two variables.
B) the relationship we are trying to measure is curvilinear.
C) the variables we want to analyze have a binomially distributed population.
D) the variables have been measured using interval- or ratio-scaled measures.
E) when the correlation coefficient is strong and significant, the two variables are associated in a curvilinear fashion.

Question 17

While studying the relationship between advertising and sales growth, a researcher determines that the relationship is sometimes weak and at other times moderate. This variation from one situation to another is the variation in the _____ of the relationship between advertising and sales growth.

A) strength of association
B) presence
C) type
D) direction
E) dispersion

Question 18

In bivariate regression analysis, the procedure used to determine the best-fitting line is called the:

A) least squares procedure.
B) squared error procedure.
C) sum of errors procedure.
D) least error procedure.
E) minimum error procedure.

Question 19

With regard to the least squares procedure, any data point that does not fall on the regression line is the result of:

A) specific variance.
B) nonresidual variance.
C) unexplained variance.
D) sum of the squared errors.
E) multicollinearity.

Question 20

_____ is a statistical technique that uses information about the relationship between an independent or predictor variable and a dependent variable to make predictions.

A) Non-parametric hypothesis coefficient
B) Covariation
C) Beta coefficient analysis
D) Bivariate regression analysis
E) Multiple regression analysis

Question 21

Covariation refers to the degree of association between two variables.

Question 22

The strength of association between two variables is determined by the size of the correlation coefficient.

Question 23

To measure whether a relationship between two variables exists, we rely on the concept of statistical significance.

Question 24

Regression analysis assumes a linear relationship is a bad description of the relationship between two variables.

Question 25

It is possible for a correlation to be statistically significant and still lack substantive significance.

Question 26

In a regression model, if independent variables exhibit multicollinearity, then:

A) the strength of association of variables changes according to the beta coefficient.
B) determination of the best fitting line requires maximization of the vertical distances of all the points from the line.
C) standardization augments the effects of using different scales of measurement.
D) computing factor scores is the least effective approach to regression analysis.
E) the estimation of separate regression coefficients for the correlated variables becomes difficult.

Question 27

A scatter plot wherein the dots form an ellipse indicates a positive relationship between variables.

Question 28

Scatter diagrams are a visual way to describe the relationship between two variables and the covariation they share.

Question 29

Large samples result in more confidence that a relationship exists, even if it is weak.

Question 30

Multicollinearity is a(n):

A) statistical procedure that estimates regression equation coefficients which produce the lowest sum of squared differences between the actual and predicted values of the dependent variable.
B) statistical technique which analyzes the linear relationship between a dependent variable and multiple independent variables by estimating coefficients for the equation for a straight line.
C) estimated regression coefficient that has been recalculated to have a mean of zero and a standard deviation of 1.
D) statistic that compares the amount of variation in the dependent measure "explained" or associated with the independent variables to the "unexplained" or error variance.
E) situation in which several independent variables are highly correlated with each other.

Question 31

Regression analysis assumes there is a straight line relationship between the independent and dependent variables.

Question 32

The pattern of covariation around the regression line which is not constant around the regression line and varies in some way when the values change from small to medium and large is known as _____.

A) multiple regression
B) homoskedasticity
C) heteroskedasticity
D) normal distribution
E) constant association

Question 33

The smaller the size of the coefficient of determination, the stronger the linear relationship between the two variables being examined.

Question 34

In a regression analysis, the horizontal distance between the estimated regression line and the actual data points is the unexplained variance called error.

Question 35

The least squares procedure determines the best-fitting line by maximizing the vertical distances of all the data points from the line.

Question 36

The use of the Pearson correlation coefficient assumes the variables have a normally distributed population.

Question 37

_____ refers to the pattern of covariation that is constant around the regression line, whether the values are small, medium, or large.

A) Linearity
B) Curvilinearity
C) Multicollinearity
D) Heteroskedasticity
E) Homoskedasticity

Question 38

When two variables have a curvilinear relationship, the formula that best describes the linkage is very simple.

Question 39

The use of a simple regression model assumes that the error terms associated with making predictions are dependently distributed.

Question 40

Which of the following is an advantage of the partial least squares method of structural equation modeling?

A) Solutions are possible with the simplest models, which are based on a few questions.
B) The method is parametric, so it can be applied to data that is not normally distributed.
C) The measurement requirements are very rigid and are designed to work exclusively with nominal data.
D) Only one type of analysis is possible.
E) Solutions can be obtained with both small and large samples.

Question 41

In multiple regression, the value of a beta coefficient can never be greater than 1.

Question 42

When the correlations between independent variables in regression are high enough to cause problems, one approach is to create summated scales consisting of the independent variables that are highly correlated.

Question 43

Discuss the relationship between the Pearson correlation coefficient and the coefficient of determination.

Question 44

Multiple regression analysis is an extension of bivariate regression.

Question 45

Discuss the concept of multicollinearity.

Question 46

A problem area for marketing researchers in multiple regression is when the independent variables are highly correlated among themselves.

Question 47

What are the several assumptions made while calculating the Pearson correlation coefficient?

Question 48

The calculation of a solution using the partial least squares method of structural equation modeling is similar to ordinary least squares regression, but is extended to obtain a solution for path models with more than two stages and variables measured with more than a single question.

Question 49

What are the advantages of using the partial least squares method of structural equation modeling?

Question 50

Discuss multiple regression analysis.