Equation Modeling in Statistics MCQs
Equation Modeling in Statistics MCQs
Welcome to MCQss.com, your resource for MCQs on equation modeling in statistics. This page provides a collection of interactive MCQs designed to assess your understanding of the concepts and techniques used in structural equation modeling (SEM) and path analysis.
Equation modeling, specifically structural equation modeling (SEM), is a powerful statistical technique used to examine complex relationships among variables. It allows researchers to investigate both the direct and indirect effects of variables on an outcome of interest. Our MCQs cover various aspects of equation modeling, including model specification, estimation methods (e.g., maximum likelihood estimation), goodness-of-fit assessment, and model interpretation.
Path analysis is a related technique that focuses on estimating and interpreting direct and indirect effects within a set of variables. It allows researchers to test specific hypotheses about causal relationships among variables. Our MCQs explore topics related to path analysis, including the identification of paths, estimation of path coefficients, and assessment of model fit.
Engaging with these MCQs will not only test your knowledge but also enhance your understanding of equation modeling techniques. Whether you are a student, researcher, or practitioner, these MCQs will help you deepen your expertise in utilizing equation modeling for data analysis and model testing.
Take the opportunity to challenge yourself and explore the MCQs on equation modeling in statistics. Test your understanding, learn from the explanations provided, and further develop your skills in applying equation modeling techniques to analyze complex data structures.
Start exploring the MCQs now and expand your knowledge of equation modeling in statistics.
1: This is a matrix that summarizes all the variances and all the possible covariances for a list of variables is known as:
A. Variance
B. Covariance Matrix
C. Both
D. None of these
2: A measurement model in a structural equation model consists of a latent variable and its indicator variables are called ___________ .
A. Structural Model
B. Measurement Model
C. Ordinary Least Squares (OLS)
D. Measured Variables
3: The X variables in a structural equation model that are measured in the study. In an Amos path model, these are represented as a rectangle called _________ .
A. Structural Model
B. Measurement Model
C. Ordinary Least Squares (OLS)
D. Measured Variables
4: Structural Model is the part of a structural equation model that represents causal (and correlational) paths among latent variables and measured variables.
A. True
B. False
5: A statistic is the best ordinary least squares estimate if it minimizes the sum of squared prediction errors is known as:
A. Noncausal Path
B. Ordinary Least Squares (OLS)
C. Confirmatory Factor Analysis (CFA)
D. None of these
6: A model that represents indicator variables with paths (loadings) that relate them to latent variables (factors) is called _______ .
A. Noncausal Path
B. Ordinary Least Squares (OLS)
C. Confirmatory Factor Analysis (CFA)
D. None of these
7: In a causal model, a bidirectional arrow is used to represent a situation where two variables are non causally associated with each other; they are
A. Correlated
B. Confounded
C. Redundant
D. All of these
8: In a causal model, a unidirectional arrow that points from X toward Y denotes the theoretical existence of a causal connection in which X causes or influences Y (X → Y).
A. True
B. False
9: For each parameter, the modification index indicates how much overall model fit can be improved by changing that parameter is known as:
A. Saturated Model
B. Independence Model
C. Modification Index
D. None of these
10: A model that includes direct paths from each variable to every other variable. It represents the (usually uninteresting) hypothesis that everything is related to everything is called ____________ .
A. Saturated Model
B. Independence Model
C. Modification Index
D. None of these
11: In structural equation modeling, a model that has no paths among any variables; it represents that assumption that none of the variables are related, causally or non causally is Known as:
A. Null model
B. Nil model
C. Independence Model
D. All of these
12: An empirical method for estimation of standard errors and confidence intervals, used in situations where there is no simple formula to calculate these is called ___________ .
A. Fully Identified Model
B. Model Identification
C. Bootstrapping
D. Just-Identified Model
13: Model identification is difficult to assess completely (Kenny & Milan, 2012). One part of this involves comparison of the number of parameters to be estimated versus the number of distinct sample moments is known as:
A. Fully Identified Model
B. Model Identification
C. Bootstrapping
D. Just-Identified Model
14: Assessment of model identification often involves complex considerations and can be quite difficult (Kenny & Milan, 2012) is called _______________ .
A. Fully Identified Model
B. Model Identification
C. Bootstrapping
D. Just-Identified Model
15: Model Fit is the degree to which the variance/covariance matrix that is reproduced from the paths and coefficients of a structural model matches the variances and covariances estimated using the original data set.
A. True
B. False
16: A model is just identified if it includes direct paths between all variables and has 0 degrees of freedom is known as:
A. Fully Identified Model
B. Model Identification
C. Bootstrapping
D. Just-Identified Model
17: A model is overidentified if there are more pieces of information in the data (variances and covariances) than free parameter estimates and the model df are positive is known as:
A. Equivalent Models
B. Recursive Model
C. Overidentified Model
D. Underidentified Model
18: Underidentified Model is a model that has more free parameters to estimate than pieces of information in the data; it does not have a unique solution.
A. True
B. False
19: Models are equivalent when they have identical goodness of fit but include paths that correspond to different hypotheses about which variables are causally related, or direction of causality is known as:
A. Equivalent Models
B. Recursive Model
C. Overidentified Model
D. Underidentified Model
20: If a structural model has causal loops, it is called _____________ .
A. Nonrecursive
B. Recursive Model
C. Both
D. None of these
21: In binary logistic regression, Model A (e.g., Yi = a + b1 × X1) is nested in Model B (e.g., Yi = a + b1 × X1 + b2 × X2) if all the variables in Model A are also included in Model B, but Model B has one or more additional variables that are not included in Model A.
A. True
B. False
22: An analysis that has one or more implied correlations greater than zero in absolute value and/or negative error variance estimates is called ___________ .
A. Constraints on SEM Model Parameters
B. Chi-Square Goodness-of-Fit Index
C. Inadmissible Solution
D. Bentler Comparative Fit Index (CFI)
23: One of several indexes of fit that describes how well the overall model paths and coefficients reproduce the variance/covariance information in the data is known as:
A. Constraints on SEM Model Parameters
B. Chi-Square Goodness-of-Fit Index
C. Inadmissible Solution
D. Bentler Comparative Fit Index (CFI)
24: Root Mean Square Error of Approximation (RMSEA) model fit index calculates the size of the standardized residual correlations. It ranges from 0 (perfect fit) to 1 (poor fit).
A. True
B. False
25: A goodness-of-fit index based on information that compares model fit for the specified model with model fit for the null model the (which represents an assumption that none of the variables are related) is known as:
A. Constraints on SEM Model Parameters
B. Chi-Square Goodness-of-Fit Index
C. Inadmissible Solution
D. Bentler Comparative Fit Index (CFI)
26: Kline (2006) recommends that this should also be reported; SRMR > .10 may indicate poor fit. Amos does not provide SRMR is called __________ .
A. Constraints on SEM Model Parameters
B. Standardized Root Mean Square Residual (SRMR)
C. Both
D. None of these
27: There are many ways model parameters can be constrained. One type of constraint is created when there is no direct path between two variables is called ____________ .
A. Constraints on SEM Model Parameters
B. Standardized Root Mean Square Residual (SRMR)
C. Both
D. None of these
28: Moderation involves a situation in which the slope to predict Y from X1 differs across scores on the X2 variable.
A. True
B. False
29: The opposite process rule says to solve for ________.
A. A known variable by replicating the process used to form the original equation
B. An unknown variable by reversing the process used to form the original equation
C. An unknown variable by replicating the process used to form the original equation
D. A known variable by reversing the process used to form the original equation
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Equation Modeling in Statistics MCQs
