Mediation in Statistics MCQs
Mediation in Statistics MCQs
Welcome to MCQss.com, your go-to resource for MCQs on mediation in statistics. This page provides a collection of interactive MCQs to help you assess your knowledge and comprehension of mediation analysis, its concepts, methods, and applications in statistical research.
Mediation analysis is a statistical technique used to examine the underlying mechanisms and pathways through which an independent variable affects a dependent variable. It helps researchers understand how and why certain variables mediate or explain the relationship between the predictor and the outcome variable.
Our MCQs cover a broad range of topics related to mediation analysis, including the conceptual understanding of mediation, types of mediation models, assessment of mediation effects, statistical methods for testing mediation, and interpreting the results of mediation analysis.
By engaging with these MCQs, you can enhance your understanding of mediation in statistics, gain familiarity with different mediation models (such as Baron and Kenny's steps, bootstrapping, and Sobel test), and learn how to apply mediation analysis in various research contexts.
MCQss.com offers an interactive learning platform where you can practice, evaluate, and improve your knowledge of mediation in statistics. The MCQs provide immediate feedback, allowing you to assess your understanding, identify areas for improvement, and reinforce your learning.
Whether you are a student studying statistics, a researcher exploring causal relationships, or a practitioner involved in data analysis, these MCQs will help you develop a solid foundation in mediation analysis and strengthen your statistical skills.
Take advantage of the MCQs available on this page to test your knowledge, prepare for exams, or enhance your proficiency in mediation analysis. By mastering the concepts and methods of mediation in statistics, you can effectively investigate and understand the underlying mechanisms driving statistical relationships.
1: A causal model is often represented by a path diagram; unidirectional arrows represent hypotheses that variables are causally related, and bidirectional arrows represent hypotheses that variables are not causally associated.
A. True
B. False
2: A diagram in which hypothesized causal associations between variables are represented by unidirectional arrows is known as:
A. Path Model
B. Tracing Rule
C. Causal Model
D. None of these
3: In a mediated causal model, “a” coefficient corresponds to the path from X1 (the initial cause) to M (the hypothesized mediating variable).
A. True
B. False
4: In a mediated causal model, “b” coefficient corresponds to the path from X1 (the initial cause) to M (the hypothesized mediating variable).
A. True
B. False
5: In a mediation analysis, “ c' ” represents the direct path from X1 to Y, when the indirect path from X1 to Y (via the mediating variable M) is controlled for in the analysis.
A. True
B. False
6: The standard error of the raw score regression slope to predict the outcome variable Y from the mediating variable (X2 or M) is known as:
A. Sb
B. A
C. B
D. C’
7: A regression analysis for which the outcome variable is categorical; the goal of the analysis is prediction of group membership is called _________ .
A. Mediated Effect
B. Logistic Regression
C. Both
D. None of these
8: The t test for the raw score regression coefficient to predict the final outcome variable Y from the mediating variable (X2 or M) in a mediation model.
A. True
B. False
9: The effect of X1 on Y is said to be mediated if the effect of X1 on Y is carried by or transmitted by a mediating variable X2 or M is known as:
A. Direct Effect
B. Mediated Effect
C. Total Effect
D. None of these
10: Which is denoted c' in a mediated path model; it represents any additional predictive association between X1 and Y that is called __________.
A. Direct Effect
B. Mediated Effect
C. Total Effect
D. None of these
11: The overall strength of association between the initial predictor variable X1 and the final outcome variable Y in a mediated model (ignoring or not controlling for the mediating variable M or X2) is known as:
A. Direct Effect
B. Mediated Effect
C. Total Effect
D. None of these
12: Indirect Effect corresponds to the two-step path from X1 to Y, via the mediating variable X2 or M.
A. True
B. False
13: ____________ is a widely used significance test proposed by Sobel (1982) to test H0: ab = 0. This tests whether a mediating variable (X2) transmits the influence of the independent variable X1 to the Y outcome variable.
A. Sobel Test
B. Correctly Specified Model
C. Inconsistent Mediation
D. None of these
14: A model is correctly specified if it includes all the variables that should be taken into account (e.g., all variables that are confounded with or causally related to X1 or Y, or all variables that interact with X1 or Y) are called _________ .
A. Sobel Test
B. Correctly Specified Model
C. Inconsistent Mediation
D. None of these
15: Inconsistent mediation occurs when one of the signs for a path in a mediate model is negative; the direct (c') and indirect (a × b) effects of X1 on Y are opposite in sign.
A. True
B. False
16: SEab is one notation for the standard error of the ab product that represents that strength of the mediated path. Different methods of estimating this standard error have been proposed by __________ .
A. Sobel (1982)
B. Aroian (1947)
C. Goodman (1960)
D. All of these
17: An X2 variable is called a suppressor variable relative to X1 and Y if the partial correlation between X1 and Y controlling for X2 (r1Y.2) is larger in absolute magnitude than r1Y or if r1Y.2 is significantly different from 0 and opposite in sign to r1Y.
A. True
B. False
18: ________ is a test for H0: ab = 0 that uses a different method to estimate SEab from the one proposed by Sobel (1982).
A. Aroian Test
B. Goodman Test
C. Both
D. All of these
19: Goodman Test is a test for H0: ab = 0 that uses a different method to estimate SEab from the one proposed by Sobel (1982).
A. True
B. False
20: Tracing Rule is defined as Procedures for tracing all possible paths from each causal variable to each outcome variable in a path model. The (reproduced) total correlation between two variables in a path model using standardized variables is obtained by tracing all routes or paths between those variables, with some limitations.
A. True
B. False
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Mediation in Statistics MCQs
