Question 1

Outliers affect both the magnitude and the sign of a calculated correlation.

Accepted Answer

Outliers can significantly influence the correlation coefficient by altering both its strength (magnitude) and direction (sign), potentially leading to misleading interpretations of the data relationship.

Question 2

On a scatter plot, an elliptical pattern of data points indicates the presence of heteroscedasticity.

Accepted Answer

An elliptical pattern in a scatter plot typically indicates a linear relationship with constant variance, not heteroscedasticity. Heteroscedasticity is indicated by a pattern where the spread of data points increases or decreases across the plot.

Question 3

To avoid the problem of error of measurement, you should develop a well-defined measurement model.

Accepted Answer

A well-defined measurement model helps ensure that the variables are accurately and consistently measured, reducing the likelihood of measurement errors.

Question 4

Multivariate statistics are especially suited to research with small samples.

Accepted Answer

Multivariate statistics typically require larger sample sizes to accurately estimate the relationships between multiple variables and to ensure the reliability and validity of the results.

Question 5

In most cases, the results of a factor analysis are easier to interpret without rotation of factors.

Accepted Answer

Rotation in factor analysis, such as varimax or oblimin, is often used to make the output more interpretable by simplifying the factor structure and making the loadings more distinct.

Question 6

Oblique rotation assumes that measures are uncorrelated and consequently that the factors extracted are uncorrelated.

Accepted Answer

Oblique rotation allows for the factors to be correlated, unlike orthogonal rotation which assumes factors are uncorrelated.

Question 7

Principal components analysis is most appropriate when you have theoretically or empirically based predictions.

Accepted Answer

Principal components analysis (PCA) is a data reduction technique used to identify patterns in data without requiring theoretical or empirical predictions. It is exploratory and does not rely on prior assumptions.

Question 8

For most applications in psychological research, you should use the raw score regression weights to interpret a multiple regression analysis.

Accepted Answer

Raw score regression weights can be difficult to interpret due to differences in scale; standardized coefficients are often preferred for clearer interpretation in psychological research.

Question 9

A beta weight for a given predictor variable may be high because it directly produces most of the variance in a dependent variable.

Accepted Answer

A beta weight measures the direct effect of a predictor variable on a dependent variable, controlling for the effects of other predictor variables. A high beta weight indicates that the predictor variable has a strong direct effect on the dependent variable.

Question 10

An alternative to using beta weights to determine the unique contribution of each predictor is to use squared semipartial correlations instead.

Accepted Answer

Squared semipartial correlations can be used to determine the unique contribution of each predictor by measuring the proportion of variance in the dependent variable uniquely explained by each predictor, similar to beta weights.

Question 11

R-square provides an index of the amount of variability in the dependent variable accounted for by the predictor variables.

Accepted Answer

R-square, also known as the coefficient of determination, measures the proportion of variance in the dependent variable that is predictable from the independent variables in a regression model.

Question 12

The Roy-Bargman test should be used in the absence of a theoretically or empirically based order of entry.

Accepted Answer

The answer of The Roy-Bargman test should be used in...

Question 13

Multivariate analysis of variance (MANOVA) circumvents problems with the homogeneity assumptions and error-term selection and is used for within-subjects designs.

Accepted Answer

The answer of Multivariate analysis of variance (MANOVA) circumvents problems...

Question 14

Curved arrows in a path model indicate causal relationships.

Accepted Answer

Curved arrows in a path model typically indicate correlations or covariances, not causal relationships. Causal relationships are usually represented by straight arrows.

Question 15

Variables that begin a causal chain are called endogenous variables.

Accepted Answer

Endogenous variables are those whose values are determined within the model, often influenced by other variables in the system, whereas variables that begin a causal chain are typically exogenous variables.

Question 16

Path analysis is designed to sift through data for interesting relationships among variables.

Accepted Answer

Path analysis is a statistical technique used to evaluate causal models by examining the relationships between variables, not for sifting through data for interesting relationships.

Question 17

Structural equation modeling (SEM) is a variant of path analysis.

Accepted Answer

Structural equation modeling (SEM) extends path analysis by allowing for the inclusion of latent variables and more complex relationships between observed and unobserved variables.

Question 18

The values of measured variables fail to determine the value of a latent variable.

Accepted Answer

The answer of The values of measured variables fail to...

Question 19

Structural equation modeling (SEM) is normally used as an exploratory procedure.

Accepted Answer

Structural equation modeling (SEM) is primarily used as a confirmatory technique to test hypotheses and theoretical models, rather than for exploratory purposes.

Question 20

In structural equation modeling (SEM), researchers can evaluate the relationships among measured variables and hypothetical constructs within the models that they test.

Accepted Answer

Structural equation modeling (SEM) is a statistical technique that allows researchers to assess and estimate the relationships among observed variables and latent constructs, which are hypothetical and not directly measured.

Quiz 15: Using Multivariate Design and Analysis