Question 1

Which of the following represents a simple analogy for factor analysis?

Accepted Answer

Factor analysis simplifies a large set of variables by identifying the underlying structure, much like sieving flour to use only what's necessary for baking.

Question 2

Which adjustment method is used when factor scores can still correlate with each other?

Accepted Answer

The Bartlett method allows factor scores to correlate with each other, unlike the Anderson-Rubin method, which produces uncorrelated factor scores.

Question 3

An R-matrix could be used to assess a participant's response to a survey on personality and character.

Accepted Answer

An R-matrix, which stands for a correlation matrix, can be used in psychological research to assess relationships between different survey items or scales, including those measuring aspects of personality and character.

Question 4

Error is also known as?

Accepted Answer

Error in statistical and research contexts is often referred to as random variance, which captures the idea that the error is due to random fluctuations or influences that cannot be accounted for by the model or experimental design.

Question 5

Exploratory factor analysis assumes what?

Accepted Answer

Exploratory factor analysis assumes that any measured variable may be associated with any factor, allowing for the identification of underlying relationships between variables without predefined assumptions on how variables are related to factors.

Question 6

Identification of error avoids what?

Accepted Answer

The answer of Identification of error avoids what?
...

Question 7

In the study described in Q2,you ask questions based on themes of interest in PhD topic,sociability with other PhD students,ability to talk about PhD studies enthusiastically,ability to talk about other students' studies,interest in an academic career beyond studies,interest in other PhD students' academic careers.You find a negative correlation between scores on the R-matrix around ability to talk about PhD studies enthusiastically,interest in an academic career beyond studies and interest in PhD topic,and a positive correlation between sociability with other PhD students,ability to talk about other students' studies and interest in other PhD students' academic careers.These two correlations are called?

Accepted Answer

The correlations described are referred to as "factors" in statistical analysis, where related variables are grouped together based on their correlations.

Question 8

Factors determined from correlation with other variables in the R-matrix are the same as the independent variables in factorial ANOVA.

Accepted Answer

Factors determined from correlation with other variables in the R-matrix are related to factor analysis, which is a technique for identifying underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. This is different from the independent variables in factorial ANOVA, which are pre-specified by the researcher and are used to investigate their effects on a dependent variable, often without assuming or exploring underlying correlations between these independent variables.

Question 9

Based on your answer to Q4,why is this the case?

Accepted Answer

The diagonal elements in an R-matrix (correlation matrix) represent the correlation of each variable with itself, which is always perfect (correlation coefficient of 1), hence they correlate perfectly.

Question 10

The use of factor analysis in the social sciences is common because social science studies often research correlations with which type of variable?

Accepted Answer

Factor analysis is commonly used in the social sciences for studying latent variables, which are not directly observable but are inferred from other variables that are observed.

Question 11

Why is there no intercept in the equation for describing factors?

Accepted Answer

The answer of Why is there no intercept in the...

Question 12

Squared multiple correlation (SMC)is used to estimate what?

Accepted Answer

Squared Multiple Correlation (SMC) is used to estimate the communality values for each variable, which represents the proportion of variance in a given variable that can be explained by all the other variables in the analysis.

Question 13

Within the common factor model,measured variables are expressed as what?

Accepted Answer

Measured variables within the common factor model are expressed as a function of common factors (which are shared among variables), unique factors (which are specific to each variable), and errors of measurement. This model is used in factor analysis to understand the underlying structure of observed variables.

Question 14

Orthogonal rotation and oblique rotation are two types of what?

Accepted Answer

Orthogonal rotation and oblique rotation are two types of factor rotation used in factor analysis to simplify the factors and make them more interpretable. Orthogonal rotation assumes factors are uncorrelated, while oblique rotation allows for correlations between factors.

Question 15

Principal component analysis is concerned with establishing which linear components exist within data.

Accepted Answer

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Question 16

Taking the scores from the R-matrix produced in Q8,and the two correlations found,what is the next stage in the analysis?

Accepted Answer

Representing the correlations graphically is a common next step in data analysis after calculating correlations, as it helps in visualizing the relationship between variables.

Question 17

When factor scores have a mean of 0 and a standard deviation of 1,which adjustment method has been used on factor scores?

Accepted Answer

The Anderson-Rubin method standardizes factor scores to have a mean of 0 and a standard deviation of 1.

Question 18

What is principal component analysis?

Accepted Answer

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

Question 19

In such a study,questions are grouped into themes of sociability,morals,comprehension and perception of themselves.When constructing an R-matrix,the diagonal line from top left to bottom right will show which figure?

Accepted Answer

In an R-matrix, which is a correlation matrix, the diagonal line from the top left to the bottom right represents the correlation of each variable with itself. Since any variable is perfectly correlated with itself, the value along the diagonal is always 1.000, indicating a perfect correlation.

Question 20

What does ML stand for?

Accepted Answer

ML stands for Maximum Likelihood, a statistical method for estimating the parameters of a statistical model.

Quiz 18: Exploratory Factor Analysis

Which of the following represents a simple analogy for factor analysis?

Which adjustment method is used when factor scores can still correlate with each other?

An R-matrix could be used to assess a participant's response to a survey on personality and character.

Error is also known as?

Exploratory factor analysis assumes what?

Identification of error avoids what?

Factors determined from correlation with other variables in the R-matrix are the same as the independent variables in factorial ANOVA.

Based on your answer to Q4,why is this the case?

The use of factor analysis in the social sciences is common because social science studies often research correlations with which type of variable?

Why is there no intercept in the equation for describing factors?

Squared multiple correlation (SMC) is used to estimate what?

Within the common factor model,measured variables are expressed as what?

Orthogonal rotation and oblique rotation are two types of what?

Principal component analysis is concerned with establishing which linear components exist within data.

Taking the scores from the R-matrix produced in Q8,and the two correlations found,what is the next stage in the analysis?

When factor scores have a mean of 0 and a standard deviation of 1,which adjustment method has been used on factor scores?

What is principal component analysis?

In such a study,questions are grouped into themes of sociability,morals,comprehension and perception of themselves.When constructing an R-matrix,the diagonal line from top left to bottom right will show which figure?

What does ML stand for?