Question 1

Which of the following are key challenges in combining predictors in multiple regression as discussed by Shultz, Whitney, and Zickar (2021)? ________.

Accepted Answer

A)  Choosing predictors that do not overlap with each other 
B)  Choosing predictors that will maximize prediction 
C)  Making sure to avoid capitalizing on chance 
D)  All of the Above 
A)  Choosing predictors that do not overlap with each other 
B)  Choosing predictors that will maximize prediction 
C)  Making sure to avoid capitalizing on chance 
D)  All of the Above D

Question 2

What are problems that multicollinearity cause with multiple regression in terms of prediction.

Accepted Answer

Multicollinearity relates to the amount of empirical overlap predictors have with each other. When there is large overlap, the amount of the overall R² is going to be lower than would be expected by looking at individual correlations. In addition, including redundant predictors can make interpretation of regression coefficients difficult. Finally, by including redundant predictors, you are unnecessarily wasting participant time without a gain in prediction.

Question 3

The adjusted R² controls for which of the following factors to correct the obtained R² ______________.

Accepted Answer

A)  reliability of predictor variables 
B)  reliability of predictors and outcome variable 
C)  sample size and number of predictors 
D)  multicollinearity among predictors 
A)  reliability of predictor variables 
B)  reliability of predictors and outcome variable 
C)  sample size and number of predictors 
D)  multicollinearity among predictors C

Question 4

A regression equation that was computed on one sample should be applied to a new sample to determine whether the regression coefficients from the first sample capitalize on chance. What factor is associated with less capitalization on chance?

Accepted Answer

A)  Large number of predictor variables 
B)  Large sample size 
C)  Samples that are older in age 
D)  Skewed predictor variables. 
A)  Large number of predictor variables 
B)  Large sample size 
C)  Samples that are older in age 
D)  Skewed predictor variables.

Question 5

When building a multiple regression equation to increase prediction, you should choose variables that ______.

Accepted Answer

A)  have high correlations with outcome variables 
B)  have weak correlations with other predictor variables 
C)  can be reliably measured 
D)  all of the above 
A)  have high correlations with outcome variables 
B)  have weak correlations with other predictor variables 
C)  can be reliably measured 
D)  all of the above

Question 6

If you are interested in the original metrics of your variables, you should use ______ regression coefficients.

Accepted Answer

A)  unstandardized 
B)  standardized 
C)  centered 
D)  uncentered 
A)  unstandardized 
B)  standardized 
C)  centered 
D)  uncentered

Question 7

Why might you use a standardized regression equation versus an unstandardized regression equation?

Accepted Answer

Standardized regression equations are re

Question 8

What are key statistics that should be used to evaluate the effectiveness of a regression equation for prediction?

Accepted Answer

The R² and its accompanying statistical

Question 9

Suppose you have four predictor variables and you want to use them to statistical predict an outcome variable. Which of the following techniques would be best to generate a predication equation?

Accepted Answer

A)  Correlation 
B)  Multiple Regression 
C)  Exploratory Factor Analysis 
D)  Discriminant Function Analysis 
A)  Correlation 
B)  Multiple Regression 
C)  Exploratory Factor Analysis 
D)  Discriminant Function Analysis

Question 10

If your boss wants to figure out how much variance your regression equation should account for in a new sample, which statistic should you report to her?

Accepted Answer

A)  R² 
B)  Cross-validated R² 
C)  Adjusted R² 
D)  Standard Error of Prediction 
A)  R² 
B)  Cross-validated R² 
C)  Adjusted R² 
D)  Standard Error of Prediction

Question 11

If we want to see the percentage of variance in our outcome variable accounted for by our predictor variables, we need to examine which of the following statistics?

Accepted Answer

A)  rₓᵧ 
B)  B 
C)  R² 
D)  R 
A)  rₓᵧ 
B)  B 
C)  R² 
D)  R

Question 12

Suppose you are designing a selection battery that could be used to predict success for a particular job, food service preparation employee. You have several suggested predictor variables and your boss suggested that you use multiple regression to develop an equation used for weighting and hiring. You need to collect data on existing employees and so, knowing what you've read in Shultz, Whitney, and Zickar (2021) what are some steps and principles that will help guide data collection.

Accepted Answer

First, you would need to establish what

Question 13

When three predictors are highly correlated with each other, which of the following is likely?

Accepted Answer

A)  interpretation of individual regression coefficients is difficult 
B)  the overall R² of the prediction equation may be inflated 
C)  more predictor variables are needed to achieve significance 
D)  All of the Above 
A)  interpretation of individual regression coefficients is difficult 
B)  the overall R² of the prediction equation may be inflated 
C)  more predictor variables are needed to achieve significance 
D)  All of the Above

Question 14

Which of the following statistics provides the most direct estimate of the average amount of error in regression equation predictions?

Accepted Answer

A)  Standard Error of the Estimate 
B)  Standard Error of Measurement 
C)  Standard Error of Dependency 
D)  Standard Error of Develoment 
A)  Standard Error of the Estimate 
B)  Standard Error of Measurement 
C)  Standard Error of Dependency 
D)  Standard Error of Develoment

Question 15

What are strategies for avoiding capitalizing on chance in multiple regression?

Accepted Answer

The respondent should mention several st

Which of the following are key challenges in combining predictors in multiple regression as discussed by Shultz, Whitney, and Zickar (2021)? ________.

What are problems that multicollinearity cause with multiple regression in terms of prediction.

The adjusted R² controls for which of the following factors to correct the obtained R² ______________.

A regression equation that was computed on one sample should be applied to a new sample to determine whether the regression coefficients from the first sample capitalize on chance. What factor is associated with less capitalization on chance?

When building a multiple regression equation to increase prediction, you should choose variables that ______.

If you are interested in the original metrics of your variables, you should use ______ regression coefficients.

Why might you use a standardized regression equation versus an unstandardized regression equation?

What are key statistics that should be used to evaluate the effectiveness of a regression equation for prediction?

Suppose you have four predictor variables and you want to use them to statistical predict an outcome variable. Which of the following techniques would be best to generate a predication equation?

If your boss wants to figure out how much variance your regression equation should account for in a new sample, which statistic should you report to her?

If we want to see the percentage of variance in our outcome variable accounted for by our predictor variables, we need to examine which of the following statistics?

When three predictors are highly correlated with each other, which of the following is likely?

Which of the following statistics provides the most direct estimate of the average amount of error in regression equation predictions?

What are strategies for avoiding capitalizing on chance in multiple regression?

Introduction and Overview

Statistics Review for PSYC Measurement

Sychological Scaling

Test Preparation and Specification

Reliability Overview: Classical Test Theory

Estimating Reliability

Content Validation

Criterion-Related Validation

Construct Validation

VG Psychometric Meta-Analysis Copy

Tests Bias Unfairness and Equivalence

Developing Tests of Maximal Performance

Classical Test Theory Item Analysis

Scoring Tests

Developing Measures of Typical Performance

Response Biases

Exploratory Factor Analysis

Confirmatory Factor Analysis

Item Response Theory

Applications of Item Response Theory: Computer Adaptive Testing and Differential Item Functioning

Generalizability Theory

Filters

Exam 17: Multiple Regression

Which of the following are key challenges in combining predictors in multiple regression as discussed by Shultz, Whitney, and Zickar (2021)? ________.

What are problems that multicollinearity cause with multiple regression in terms of prediction.

The adjusted R² controls for which of the following factors to correct the obtained R² ______________.

A regression equation that was computed on one sample should be applied to a new sample to determine whether the regression coefficients from the first sample capitalize on chance. What factor is associated with less capitalization on chance?

When building a multiple regression equation to increase prediction, you should choose variables that ______.

If you are interested in the original metrics of your variables, you should use ______ regression coefficients.

Why might you use a standardized regression equation versus an unstandardized regression equation?

What are key statistics that should be used to evaluate the effectiveness of a regression equation for prediction?

Suppose you have four predictor variables and you want to use them to statistical predict an outcome variable. Which of the following techniques would be best to generate a predication equation?

If your boss wants to figure out how much variance your regression equation should account for in a new sample, which statistic should you report to her?

If we want to see the percentage of variance in our outcome variable accounted for by our predictor variables, we need to examine which of the following statistics?

When three predictors are highly correlated with each other, which of the following is likely?

Which of the following statistics provides the most direct estimate of the average amount of error in regression equation predictions?

What are strategies for avoiding capitalizing on chance in multiple regression?

Introduction and Overview

Statistics Review for PSYC Measurement

Sychological Scaling

Test Preparation and Specification

Reliability Overview: Classical Test Theory

Estimating Reliability

Content Validation

Criterion-Related Validation

Construct Validation

VG Psychometric Meta-Analysis Copy

Tests Bias Unfairness and Equivalence

Developing Tests of Maximal Performance

Classical Test Theory Item Analysis

Scoring Tests

Developing Measures of Typical Performance

Response Biases

Exploratory Factor Analysis

Confirmatory Factor Analysis

Item Response Theory

Applications of Item Response Theory: Computer Adaptive Testing and Differential Item Functioning

Generalizability Theory

Filters

If we want to see the percentage of variance in our outcome variable accounted for by our predictor variables, we need to examine which of the following statistics? 