Questions and Answers of Categorical Data Analysis

=+7. What is fit?
=+6. What is a spurious correlation? How might it be revealed using SEM?
=+5. What is a theory? How is a theory represented in a SEM framework?
=+4. How is structural equation modeling similar to the other multivariate techniques?
=+3. Describe conceptually how the estimated covariance matrix in a SEM analysis (Σk) can be computed. Why do we compare it to S?
=+2. What are the distinguishing characteristics of SEM?
=+1. What is the difference between a latent construct and a measured variable?
=+ List the six stages of structural equation modeling and understand the role of theory in the process.
=+ Know how to represent a SEM model visually with a path diagram.
=+ Understand that the objective of SEM is to explain covariance and how it translates into the fit of a model.
=+ Explain the types of relationships involved in SEM.
=+ Know the basic conditions for causality and how SEM can help establish a cause-and-effect relationship.
=+ Understand structural equation modeling and how it can be thought of as a combination of familiar multivariate techniques.
=+ Distinguish between variables and constructs.
=+Understand the distinguishing characteristics of SEM.
Describe how correspondence, or association, is derived from a contingency table.
+or column) in CA. Can categories always be directly compared based on proximity in the perceptual map?
+3. Describe the methods for interpretation of categories (row
+2. Describe how correspondence, or association, is derived from a contingency table.
+1. Compare and contrast CA and MDS techniques.
+ Explain correspondence analysis as a method of perceptual mapping.
+ Select between a decompositional or compositional approach.
+ Understand the basics of perceptual mapping with nonmetric data.
+7. Compare and contrast CA and MDS techniques.
+Compare this procedure with the procedure for factor analysis.
+6. How does the researcher identify the dimensions in MDS?
+5. How can the researcher determine when the optimal MDS solution has been obtained?
+4. How do metric and nonmetric MDS procedures differ?
+3. How are ideal points used in MDS procedures?
+2. What is the difference between preference data and similarities data, and what impact does it have on the results of MDS procedures?
+. How does MDS differ from other interdependence techniques (cluster analysis and factor analysis)?
+ Understand how to create a perceptual map.
+ Determine the comparability and number of objects.
+ Select between a decompositional or compositional approach.
+ Understand the differences between similarity data and preference data.
+ Define multidimensional scaling and describe how it is performed.
+7. How can researchers use graphical portrayals of the cluster procedure?
+6. What is the difference between the interpretation stage and the profiling and validation stages?
+5. How does a researcher decide the number of clusters to have in a solution?
+ Under which conditions would each approach be used?
+4. How does the researcher know whether to use hierarchical or nonhierarchical cluster techniques?
+3. What should the researcher consider when selecting a similarity measure to use in cluster analysis?
+2. What is the purpose of cluster analysis, and when should it be used instead of factor analysis?
+1. What are the basic stages in the application of cluster analysis?
+ Follow the guidelines for cluster validation.
+ Know how to interpret results from cluster analysis.
+ Understand the differences between hierarchical and nonhierarchical clustering techniques.
+ Understand why different distance measures are sometimes used.
+ Understand how interobject similarity is measured.
+ Identify the types of research questions addressed by cluster analysis.
+ Define cluster analysis, its roles, and its limitations.
+What are the most important issues to consider, along with each methodology’s strengths and weaknesses
+6. How would you advise a market researcher to choose among the three types of conjoint methodologies?
+Which are least well served by conjoint analysis?
+ What types of choice problems are best suited to analysis with conjoint analysis?
+5. What are the practical limits of conjoint analysis in terms of variables or types of values for each variable?
+to a marketing decision. In doing so, define the compositional rule you will use, the experimental design for creating profiles, and the analysis method. Use at least five respondents to support
+variables and two levels of each variable that is appropriate
+4. Design a conjoint analysis experiment with at least four
+3. Using either the simple numerical procedure discussed earlier or a computer program, analyze the data from the experiment in question 1.
+Which presentation method was easier for the respondents?
+How would you improve on the descriptions of the factors or levels?
+2. How difficult was it for respondents to handle the wordy and slightly abstract concepts they were asked to evaluate?
+information with both the trade-off and full-profile methods.
+the compositional rule you think they will use. Collect
+Each chapter includes specific references for the topics covered
+References General references are included at the end of the textbook
+Each chapter includes graphics to illustrate the numeric issues
+Illustrative topics are presented
+Illustrations Each chapter includes humorous pictures
+Introduces each subject in a general overview
+Depth Goes into great depth on each subject
+to their preferred textbook style for a class, and specify Factor Level
+1. Ask three of your classmates to evaluate choice combinations based on the following variables and levels relative
+ Recognize the limitations of traditional conjoint analysis and select the appropriate alternative methodology (e.g., choice-based or adaptive conjoint) when necessary.
+ Compare a main effects model and a model with interaction terms and show how to evaluate the validity of one model versus the other.
+ Apply a choice simulator to conjoint results for the prediction of consumer judgments of new attribute combinations.
+ Assess the relative importance of the predictor variables and each of their levels in affecting consumer judgments.
+ Explain the impact of choosing rank choice versus ratings as the measure of preference.
+ Understand how to create factorial designs.
+ Formulate the experimental plan for a conjoint analysis.
+ Know the guidelines for selecting the variables to be examined by conjoint analysis.
+ Explain the managerial uses of conjoint analysis.
+probability in a logistic regression procedure.
+5. Explain the concept of odds and why it is used in predicting
+4. What are the unique characteristics of interpretation in logistic regression?
+dependent and independent variables?
+3. How does logistic regression handle the relationship of the
+What are the advantages and disadvantages of this decision?
+2. When would you employ logistic regression rather than discriminant analysis?
+analysis, regression analysis, logistic regression analysis, and analysis of variance?
+1. How would you differentiate among multiple discriminant
+ Understand the strengths and weaknesses of logistic regression compared to discriminant analysis and multiple regression.
+comparisons to both multiple regression and discriminant analysis.
+ Interpret the results of a logistic regression analysis and assessing predictive accuracy, with
+ Describe the method used to transform binary measures into the likelihood and probability measures used in logistic regression.
+ Identify the types of variables used for dependent and independent variables in the application of logistic regression.
+ State the circumstances under which logistic regression should be used instead of multiple regression.
+8. How does discriminant analysis handle the relationship of the dependent and independent variables?
+7. Why should a researcher stretch the loadings and centroid data in plotting a discriminant analysis solution?

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