Practice Quiz Chapter 14

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. How are interaction variables created?

a) By multiplying the value of an independent variable with itself correct incorrect

b) By multiplying the values on two or more variables together for every case correct incorrect

c) By multiplying the values of two or more dependent variables correct incorrect

d) By adding the values of two or more independent variables correct incorrect

e) By subtracting the values of the independent variables from one another correct incorrect

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. How do researchers use interaction variables to capture the joint influence of two categorical variables?

a) The researcher first creates an interaction-dummy variable and then uses this variable to capture all the possible combinations of attributes correct incorrect

b) The researcher first creates an interaction variable and then uses that interaction variable to capture all the possible combinations of attributes correct incorrect

c) The researcher first creates a dummy variable for one of the categorical variables and then uses that dummy variable to capture all the possible combinations of attributes correct incorrect

d) The researcher first creates dummy variables for each categorical variable and then uses those dummy variables to capture all the possible combinations of attributes correct incorrect

e) The researcher first creates dummy variables for each categorical variable and then uses those dummy variables to create a series of interaction variables that capture all the possible combinations of attributes correct incorrect

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. How do researchers investigate the joint influence of two ratio-level variables?

a) They multiply the values on the two ratio-level variables to create the interaction variable correct incorrect

b) They add the values on the two ratio-level variables to create the interaction variable correct incorrect

c) They subtract the values on the two ratio-level variables to create the interaction variable correct incorrect

d) They divide the values on the two ratio-level variables to create the interaction variable correct incorrect

e) They square the values on the two ratio-level variables to create the interaction variable correct incorrect

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. One of the following statements can't be said when an interaction variable is used as an independent variable–which one is it?

a) The regression slope coefficients and the associated significance tests for the independent variables no longer refer to the overall relationship between each independent variable and the dependent variable correct incorrect

b) The slope coefficients and the associated significance tests refer to the partial relationship between the independent variable and the dependent variable, for a group or condition that is defined by another variable correct incorrect

c) The interpretation of the statistical significance test associated with the slope coefficient of the interaction variable is closer to the typical interpretation of a significance test for a regression slope coefficient correct incorrect

d) When an interaction variable is statistically significant, researchers are relatively confident that, in the population, the relationship between dependent variable and one of the variables in the interaction variable does not differ correct incorrect

e) When an interaction variable is statistically significant, researchers are relatively confident that, in the population, the relationship between dependent variable and one of the variables in the interaction variable differs depending on the value on the other variable(s) used to create the interaction variable correct incorrect

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. What is a conditional relationship?

a) When the slope coefficients and the associated significance tests refer to the partial relationship between the independent variables–for a group or condition that is defined by the dependent variables correct incorrect

b) When the slope coefficients and the associated significance tests refer to the partial relationship between the dependent variables–for a group or condition that is defined by the independent variables correct incorrect

c) When the slope coefficients and the associated significance tests refer to the full relationship between the dependent variables–for a group or condition that is defined by another variable correct incorrect

d) When the slope coefficients and the associated significance tests refer to the full relationship between the independent variables– for a group or condition that is defined by another variable correct incorrect

e) When the slope coefficients and the associated significance tests refer to the partial relationship between the independent and the dependent variables–for a group or condition that is defined by another variable correct incorrect

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. What does a researcher think whenever they see linear regression using a quadratic or squared independent variable?

a) They think the relationship between that variable and the dependent variable is linear rather than curvilinear correct incorrect

b) They think the relationship between that variable and the dependent variable is curvilinear rather than linear correct incorrect

c) They think the relationship between that variable and the dependent variable is a quadratic one correct incorrect

d) They think the relationship between that variable and the dependent variable is multilinear rather than linear correct incorrect

e) They think the relationship between that variable and the dependent variable is linear correct incorrect

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. Calculate the value of the quadratic variable if the original linear variable has a value of 10?

a) 10 correct incorrect

b) 1 correct incorrect

c) 100 correct incorrect

d) 0.100 correct incorrect

e) 1000 correct incorrect

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. Calculate the value of the quadratic variable if the original linear variable has a value of 100?

a) 10 correct incorrect

b) 100,000 correct incorrect

c) 10,000 correct incorrect

d) 5,000 correct incorrect

e) 1000 correct incorrect

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. Why is it only sensible to predict curvilinear relationships between a ratio-level independent variable and a ratio-level dependent variable?

a) Because categorical independent variables are incorporated into regressions using dummy variables correct incorrect

b) Because categorical independent variables are incorporated into regressions using quadratic variables correct incorrect

c) Because categorical dependent variables are incorporated into regressions using quadratic variables correct incorrect

d) Because categorical dependent variables are incorporated into regressions using dummy variables correct incorrect

e) Because categorical dependent variables are not incorporated into regressions correct incorrect

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. What is necessary to predict the curvilinear relationship between a ratio-level independent variable and a ratio-level dependent variable?

a) Two versions of the independent variable: the original version and the quadratic version correct incorrect

b) Two versions of the dependent variable: the original version and the quadratic version correct incorrect

c) Two versions of the independent and dependent variable: the original version and the quadratic version correct incorrect

d) Two versions of the independent variable: the original version and the curvilinear version correct incorrect

e) Two versions of the dependent variable: the original version and the curvilinear version correct incorrect

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. What does a linear regression that uses both the linear and quadratic version of the independent variable produce?

a) Two constant coefficients and a slope coefficient correct incorrect

b) A constant coefficient and two slope coefficients correct incorrect

c) A constant coefficient and a slope coefficient correct incorrect

d) Two constant coefficients and two slope coefficients correct incorrect

e) No constant coefficients and no slope coefficients correct incorrect

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. Using a linear regression prediction equation for both the linear and quadratic versions of an independent variable, calculate the predicted value on the dependent variable when the value of the original or linear variable is 10.

a) 100 correct incorrect

b) 1000 correct incorrect

c) 10,000 correct incorrect

d) 1,050 correct incorrect

e) 5,010 correct incorrect

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. Using a linear regression prediction equation for both the linear and quadratic versions of an independent variable, calculate the predicted value on the dependent variable when the value of the original or linear variable is 40.

a) 12,600 correct incorrect

b) 16,200 correct incorrect

c) 1,620 correct incorrect

d) 1,660 correct incorrect

e) 1,260 correct incorrect

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. Using a linear regression prediction equation for both the linear and quadratic versions of an independent variable, calculate the predicted value on the dependent variable when the value of the original or linear variable is 100.

a) 1,050 correct incorrect

b) 10,500 correct incorrect

c) 5,100 correct incorrect

d) 500,100 correct incorrect

e) 100,500 correct incorrect

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. Interpreting the slope coefficients of quadratic variables is difficult without using graphs. With this in mind–which of the following statements is not true?

a) It's possible to get a general idea about the shape of a relationship by looking at the sign of the slope coefficient of the corresponding linear variable correct incorrect

b) When the slope coefficient of a quadratic variable is negative, the predicted relationship has an upward curve, with the tails of the line moving up and away from the straight line correct incorrect

c) When the slope coefficient of a quadratic variable is negative, the predicted relationship has an upward curve, with the tails of the line moving down and towards the straight line correct incorrect

d) When the slope coefficient of a quadratic variable is negative, the predicted relationship has a downward curve, with the tails dropping further below the straight line correct incorrect

e) The size of the slope coefficient of a quadratic variable indicates how quickly or slowly the regression line curves away from the straight line correct incorrect

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. Why do quadratic variables allow researchers to use regression to model relationships that more accurately reflect real-world processes?

a) Because they are restricted to predicting straight-line relationships in the context of linear regression correct incorrect

b) Because they are no longer restricted to predicting straight-line relationships, even in the context of linear regression correct incorrect

c) Because they are no longer restricted to predicting curvilinear relationships, even in the context of linear regression correct incorrect

d) Because they are restricted to predicting curvilinear relationships in the context of linear regression correct incorrect

e) Because they are restricted to predicting curvilinear relationships in the context of multiple linear regression correct incorrect

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. What is a linear transformation?

a) A non-linear transformation where the relative sequence of cases but not the relative distance between the cases remains the same in the original variable and the transformed variable correct incorrect

b) A non-linear transformation where the relative sequence of cases and the relative distance between the cases remains the same in the original variable and the transformed variable correct incorrect

c) A transformation where the relative sequence of cases and the relative distance between the cases remains the same in the original variable and the transformed variable correct incorrect

d) A transformation where the relative sequence of cases changes but the relative distance between the cases remains the same in the original variable and the transformed variable correct incorrect

e) A transformation where neither the relative sequence of cases nor the relative distance between the cases remains the same in the original variable and the transformed variable correct incorrect

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. Logarithmic transformations are the most common non-linear transformations utilized by researchers for variables that are right-skewed. Which one of the following statements about this type of transformation is not valid?

a) The first step in a logarithmic transformation is to express each value on the original variable as a common base number raised to an exponent correct incorrect

b) The second step in a logarithmic transformation is to express each value on the original variable as a common base number raised to an exponent correct incorrect

c) Any positive number can be used as the common base number, but social scientists regularly use either base 2 or base 10 correct incorrect

d) A logarithmic transformation using base 10 is denoted as log10 correct incorrect

e) The transformed variable is created by assigning each case the value of the exponent that is produced when the original value on the variable is represented as the common base number raised to an exponent correct incorrect

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. With logarithmic transformation using base 2, each one-unit increase in the transformed variable is equivalent to what?

a) To dividing the original value by two correct incorrect

b) To taking the square root of the original value correct incorrect

c) To quadrupling the original value correct incorrect

d) To halving the original value correct incorrect

e) To doubling the original value correct incorrect

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. What happens to the values of a variable's cases when it is log transformed?

a) Cases with low values on the original variable are moved farther apart in the new variable, and cases with high values on the original variable are moved closer together in the new variable correct incorrect

b) Cases with low values on the original variable are moved closer together in the new variable, and cases with high values on the original variable are moved further apart correct incorrect

c) Cases with low values on the original variable are removed from the new variable, and cases with high values on the original variable are moved further apart correct incorrect

d) Cases with low values on the original variable are moved farther apart from the new variable, and cases with high values on the original variable are removed correct incorrect

e) Researchers must decide whether to proceed with a linear or non-linear transformation after it is log transformed correct incorrect

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. An interaction effect occurs when the relationship between two variables changes after a third variable is considered.

a) True correct incorrect

b) False correct incorrect

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. Once an interaction variable has been created, it can be used as an independent variable in a regression.

a) True correct incorrect

b) False correct incorrect

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. Whenever an interaction variable is used in a regression, the variables used to create the interaction variable can't be an independent variable.

a) True correct incorrect

b) False correct incorrect

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. The interpretation of the slope coefficients is the same when a linear regression uses an interaction variable as an independent variable.

a) True correct incorrect

b) False correct incorrect

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. When a linear regression uses an interaction variable as an independent variable, the slope coefficients still show the change in the dependent variable that is associated with a one-unit increase in the independent variable.

a) True correct incorrect

b) False correct incorrect

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. Researchers never investigate the joint influence of two-ratio-level variables.

a) True correct incorrect

b) False correct incorrect

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. The slope coefficient of an interaction variable created using two ratio-level variables is easier to interpret and display visually.

a) True correct incorrect

b) False correct incorrect

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. Interaction variables allow quantitative social scientists to incorporate an understanding of intersectionality or intersectional identities for understanding people's experiences.

a) True correct incorrect

b) False correct incorrect

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. Researchers typically present regression results related to interaction variables using graphs because the slope coefficients alone can be difficult to meaningfully describe.

a) True correct incorrect

b) False correct incorrect

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. The slope coefficients change when regressions use interaction variables as dependent variables.

a) True correct incorrect

b) False correct incorrect

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. The test of statistical significance associated with the slope coefficient of an interaction variable shows the probability of randomly selecting a sample with the observed relationship, or one of greater magnitude, if there is a joint relationship between the variables used to create the interaction variable and the dependent variable in the population.

a) True correct incorrect

b) False correct incorrect

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. A curvilinear relationship is one in which the line of best fit between two variables is curved, not straight.

a) True correct incorrect

b) False correct incorrect

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. Linear regressions are not useful for predicting a curvilinear relationship.

a) True correct incorrect

b) False correct incorrect

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. The slope coefficient of the linear or original version of the independent variable indicates the angle of the predicted straight-line relationship between the independent variable and the dependent variable.

a) True correct incorrect

b) False correct incorrect

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. The slope coefficient of the quadratic version of the variable indicates the direction and shape of the predicted curvilinear relationship between the independent variables.

a) True correct incorrect

b) False correct incorrect

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. The values on the quadratic variable grow exponentially from the squaring of the variable's values.

a) True correct incorrect

b) False correct incorrect

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. Based on the exponential growth in the values on the quadratic variable, the predicted values on the dependent variable no longer correspond to a straight-line relationship.

a) True correct incorrect

b) False correct incorrect

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. The best way to assess the shape and the magnitude of a curvilinear relationship is to use the regression coefficients to calculate the predicted value on the dependent variable for several plausible values on the independent variable and to graph the relationship.

a) True correct incorrect

b) False correct incorrect

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. Curvilinear relationships capture situations where the relationship between two variables is positive at some values of the independent variable and negative at other values on the independent variable.

a) True correct incorrect

b) False correct incorrect

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. Ratio-level, nominal-level and ordinal-level variables can be transformed.

a) True correct incorrect

b) False correct incorrect