Question 1

Describe how a path analysis can be used to test mediational hypotheses.

Accepted Answer

The answer of Describe how a path analysis can be...

Question 2

What is structural equation modeling? Why would a researcher use this type of statistical analysis?

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Question 3

Describe a research study that could be analyzed using structural equation modeling.

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Question 4

What is prospective research? Why would a researcher use this type of statistical analysis?

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Question 5

Describe two limitations of path analysis and structural equation modeling.

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Question 6

Explain why it would be important to examine whether there are any curvilinear or nonadditive relationships between the variables you plan to analyze using path analysis or structural equation modeling.

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Question 7

Explain why omitting relevant variables from a path analysis or structural equation model can be problematic.

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Question 8

Explain why it is important to test both preferred and plausible alternative models when analyzing data using path analysis and structural equation modeling.

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Question 9

List two ways alternative models can be ruled out in path analysis and structural equation modeling other than directly testing those models.

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Question 10

Factor analysis is a

Accepted Answer

Factor analysis is a statistical technique used to identify clusters of variables that are highly correlated with each other but relatively uncorrelated with others. It can help identify underlying factors or dimensions that explain the common variance among a set of measured variables. It is often used in psychology and social sciences to explore relationships between latent variables and observed variables.

Question 11

In factor analysis, a factor is

Accepted Answer

A factor in factor analysis is a dimension that underlies a set of inter-correlated variables. Factors are considered latent (unobservable) variables that explain the patterns of correlations among observed (measured) variables. Factors can be thought of as underlying constructs or dimensions that are not directly measured but are reflected in the patterns of correlations among observed variables.

Question 12

Exploratory factor analysis can be used to

Accepted Answer

Exploratory factor analysis can be used for all of the mentioned purposes. It can summarize the relationships among variables, condense variables to simplify analysis, and examine the validity of a new measure by identifying underlying factors.

Question 13

Exploratory factor analysis can be used to

Accepted Answer

Exploratory factor analysis is used to identify underlying factors or dimensions in a set of variables and condense a large number of variables into a few variables for easier data analysis. It is not used to test hypotheses or examine the generalizability of a measure.

Question 14

If your goal is to condense scores on a large number of dependent measures into a smaller set of variables in order to simply data analysis, the best approach to take is

Accepted Answer

Principle components analysis is the best approach to condense scores on a large number of dependent measures into a smaller set of variables as it identifies the underlying components that explain the greatest amount of variance in the measures. This method is especially useful when there are high intercorrelations between the measures. Common factors analysis and confirmatory factor analysis are more appropriate when there is prior knowledge or theory about the underlying factors. Parallel analysis is a technique used to determine the number of factors to extract, but it is not used for condensing scores on dependent measures.

Question 15

Which of the following statements about exploratory factor analysis is TRUE?

Accepted Answer

Multicollinearity can be a problem for exploratory factor analysis just as it is for multiple regression analysis. In fact, multicollinearity among variables can lead to difficulties in identifying the underlying factors in the data.

Question 16

In exploratory factor analysis, factor extraction

Accepted Answer

Factor extraction is the process of identifying a smaller number of underlying factors that explain the covariance among a larger number of observed variables. This process involves creating an initial set of factors from the raw data.

Question 17

In exploratory factor analysis, factor rotation

Accepted Answer

Factor rotation helps in clarifying the content of factors that have been extracted from the raw data. It helps in identifying the underlying structure of the data and how different variables are related to each other. It is used to simplify the interpretation of the factors by bringing together the variables that are strongly correlated and separating those that are weakly correlated. However, factor rotation does not determine the number of factors to be interpreted or create an initial set of factors from the raw data. It also does not identify and eliminate outliers from the data set.

Question 18

Which of the following statements about orthogonal rotation in exploratory factor analysis is TRUE?

Accepted Answer

Orthogonal rotation in exploratory factor analysis creates a set of factors that are uncorrelated with one another, making it easier to interpret the distinctiveness of each factor. It is still commonly used today despite the popularity of other rotation methods such as oblique rotation. The choice A is incorrect because although it may not be the most popular method, it is still frequently used. The choice B is incorrect because orthogonal rotation specifically creates uncorrelated factors, making it impossible for them to be correlated with one another. The choice C is incorrect because the number of strong correlations in the data set does not determine the appropriateness of orthogonal rotation; rather, it is a matter of the researcher's preference and interpretation goals.

Question 19

Which of the following statements about oblique rotation in exploratory factor analysis is TRUE?

Accepted Answer

Oblique rotation creates factors that are correlated with one another, which is useful when the factors are conceptually related and may affect each other. This type of rotation is still commonly used in exploratory factor analysis.

Question 20

In factor analysis, an eigenvalue represents the

Accepted Answer

An eigenvalue in factor analysis indicates how much of the variance in the data is explained by each factor. Higher eigenvalues indicate that the corresponding factor accounts for more of the variance in the dataset. Therefore, option C is the correct choice.

Quiz 12: Factor Analysis, Path Analysis, and Structural Equation Modeling

Describe how a path analysis can be used to test mediational hypotheses.

What is structural equation modeling? Why would a researcher use this type of statistical analysis?

Describe a research study that could be analyzed using structural equation modeling.

What is prospective research? Why would a researcher use this type of statistical analysis?

Describe two limitations of path analysis and structural equation modeling.

Explain why it would be important to examine whether there are any curvilinear or nonadditive relationships between the variables you plan to analyze using path analysis or structural equation modeling.

Explain why omitting relevant variables from a path analysis or structural equation model can be problematic.

Explain why it is important to test both preferred and plausible alternative models when analyzing data using path analysis and structural equation modeling.

List two ways alternative models can be ruled out in path analysis and structural equation modeling other than directly testing those models.

Factor analysis is a

In factor analysis, a factor is

Exploratory factor analysis can be used to

Exploratory factor analysis can be used to

If your goal is to condense scores on a large number of dependent measures into a smaller set of variables in order to simply data analysis, the best approach to take is

Which of the following statements about exploratory factor analysis is TRUE?

In exploratory factor analysis, factor extraction

In exploratory factor analysis, factor rotation

Which of the following statements about orthogonal rotation in exploratory factor analysis is TRUE?

Which of the following statements about oblique rotation in exploratory factor analysis is TRUE?

In factor analysis, an eigenvalue represents the