Question 1

A researcher has measured college aptitude, a continuous measure, on students within all high schools in the state and wants to examine the relationship between GPA and college aptitude. Which one of the following procedures is recommended given this scenario?

A) discriminant analysis
B) multilevel modeling
C) MANOVA
D) exploratory factor analysis

B

Accepted Answer

Multilevel modeling is recommended in this scenario because it allows for the examination of the relationship between two continuous variables while taking into account the nested structure of the data (students within schools). This approach also allows for the examination of individual differences among students within schools and can provide more accurate estimates of the relationship between GPA and college aptitude. The other options, such as discriminant analysis, MANOVA, and exploratory factor analysis, are not appropriate for examining the relationship between two continuous variables.

Question 2

A researcher wants to examine the mean difference between treatment and control participants on time on task, number of behavioral referrals, and sense of wellness. Which one of the following procedures is recommended given this scenario?

A) discriminant analysis
B) multilevel modeling
C) MANOVA
D) exploratory factor analysis

C

Accepted Answer

MANOVA is used when there are multiple dependent variables (in this case, time on task, number of behavioral referrals, and sense of wellness) and one independent variable (treatment vs. control group). MANOVA can determine if there is a significant difference between the two groups and also which dependent variable(s) contribute to the difference. Discriminant analysis is used to classify cases into groups based on a set of predictor variables. Multilevel modeling is used when there is a nested data structure (such as students within classrooms within schools) and can examine how variables operate at different levels. Exploratory factor analysis is used to explore the underlying structure of a set of variables and identify factors that explain the variance in the data. None of these procedures make sense for the scenario described.

Question 3

A researcher wishes to predict group classification based on pretreatment covariates. Which one of the following procedures is recommended given this scenario?&#10;A) discriminant analysis&#10;B) multilevel modeling&#10;C) MANOVA&#10;D) exploratory factor analysis

Accepted Answer

Discriminant analysis is specifically designed for predicting group membership based on a set of predictor variables. It is the most appropriate procedure in this scenario, compared to the other options which are not specifically designed for group prediction.

Question 4

A researcher wants to apply a variable-level approach to group variables based on participants. Which one of the following procedures is recommended given this scenario?&#10;A) discriminant analysis&#10;B) multilevel modeling&#10;C) MANOVA&#10;D) exploratory factor analysis

Accepted Answer

Exploratory factor analysis is used to identify underlying relationships between variables, grouping them based on patterns of correlations among participants.

Question 5

A researcher wants to predict whether or not someone will vote at the polls versus mailing in their ballot. Which one of the following procedures is recommended given this scenario?&#10;A) cluster analysis&#10;B) confirmatory factor analysis&#10;C) logistic regression&#10;D) propensity score analysis

Accepted Answer

Logistic regression is specifically designed for predicting binary outcomes (such as whether or not someone will vote) based on multiple predictor variables. Cluster analysis is more appropriate for finding groups or patterns in data, confirmatory factor analysis is used for assessing the validity of scales, and propensity score analysis is used for controlling confounding variables in observational studies.

Question 6

A researcher wants to apply a person-level approach to group participants based on observed variables. Which one of the following procedures is recommended given this scenario?&#10;A) cluster analysis&#10;B) confirmatory factor analysis&#10;C) logistic regression&#10;D) propensity score analysis

Accepted Answer

Cluster analysis is the recommended procedure for applying a person-level approach to group participants based on observed variables. It allows for the grouping of participants based on similarities in their responses to observed variables, rather than predetermined factors or variables.

Question 7

A researcher wants to classify variables into common groups based on theoretical evidence. Which one of the following procedures is recommended given this scenario?&#10;A) cluster analysis&#10;B) confirmatory factor analysis&#10;C) logistic regression&#10;D) propensity score analysis

Accepted Answer

Confirmatory factor analysis is a statistical technique used to confirm the structural relationships between measured variables and their underlying latent constructs. It is commonly used in research to test theories and classify variables into common groups based on theoretical evidence. Unlike cluster analysis, which is exploratory in nature, confirmatory factor analysis is based on theoretical frameworks and pre-specified hypotheses, making it a suitable choice for this scenario. Logistic regression and propensity score analysis are not relevant to this scenario as they are used for different purposes, such as predicting an outcome or adjusting for confounding variables.

Question 8

A researcher has conducted a study with participants self-selecting into groups. However, they are interested in making causal inference from this data. Which one of the following procedures is recommended given this scenario?

A) cluster analysis
B) confirmatory factor analysis
C) logistic regression
D) propensity score analysis

Accepted Answer

Propensity score analysis can help to control for self-selection bias by creating a matched comparison group based on pre-treatment characteristics. This can help to make causal inference from the observational data. Cluster analysis and confirmatory factor analysis are not appropriate for this scenario as they are exploratory techniques that do not address causal inference. Logistic regression could be used to analyze the data, but it does not control for self-selection bias in the same way that propensity score analysis does.

Question 9

A researcher was not able to randomly assign cases to groups, but is interested in matching cases even though they self-selected to groups. Which one of the following procedures is recommended given this scenario?

A) confirmatory factor analysis
B) discriminant analysis
C) multilevel modeling
D) propensity score analysis

Accepted Answer

Propensity score analysis is a statistical technique that attempts to estimate the likelihood or probability of a subject being assigned to a specific treatment or group, given a set of observed covariates. When randomization is not possible, propensity score analysis can be used to create matched groups of cases that are similar on relevant characteristics. Therefore, propensity score analysis is recommended in this scenario.

Question 10

A researcher wants to predict years of employment based on age and number of college courses completed. Which one of the following procedures is recommended given this scenario?&#10;A) discriminant analysis&#10;B) multilevel modeling&#10;C) multiple linear regression&#10;D) propensity score analysis

Accepted Answer

Multiple linear regression is the best choice because the researcher wants to predict a continuous outcome variable (years of employment) based on multiple predictor variables (age and number of college courses completed). Disciminant analysis is used to classify observations into groups based on predictor variables, but that is not the case in this scenario. Multilevel modeling is used when there is nested data, which is when observations are nested within groups or clusters, but that is not mentioned in this scenario. Propensity score analysis is used to adjust for selection bias in observational studies, which is not relevant in this scenario. Therefore, multiple linear regression is the most appropriate procedure for this research question.

Deck 1: Multivariate Statistics