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Marketing Research: An Applied Approach 5th Edition Malhotra, Naresh K;Nunan, Daniel(Contributor);Birks, David F(Contributor) - Solutions
1.8 Describe the ways in which the reliability and validity of MDS solutions can be assessed.
1.7 What guidelines are used for deciding on the number of dimensions in which to obtain an MDS solution?
1.6 What factors influence the choice of an MDS procedure?
1.5 Describe the direct and derived approaches to obtaining MDS input data.
1.4 Describe the steps involved in conducting MDS.
1.3 What is meant by a spatial map?
1.2 Identify two marketing research problems where MDS could be applied.Explain how you would apply MDS in these situations.
1.1 For what purposes are MDS procedures used?
1.9 appreciate how software is used in multidimensional scaling and conjoint analysis.
1.8 define the concept of hybrid conjoint analysis and explain how it simplifies the data collection task;
1.7 describe the procedure for conducting conjoint analysis, including formulating the problem, constructing the stimuli, deciding the form of input data, selecting a conjoint analysis procedure, interpreting the results and assessing reliability and validity;
1.6 discuss the basic concepts of conjoint analysis, contrast it with MDS and discuss its various applications;
1.5 understand the relationship between MDS discriminant analysis and factor analysis;
1.4 explain correspondence analysis and discuss its advantages and disadvantages;
1.3 explain the MDS scaling of preference data and distinguish between internal and external analysis of preferences;
1.2 describe the steps involved in MDS of perception data, including formulating the problem, obtaining input data, selecting an MDS procedure, deciding on the number of dimensions, labelling the dimensions and interpreting the configuration and assessing reliability and validity;
1.1 discuss the basic concept and scope of multidimensional scaling (MDS) in marketing research and describe its various applications;
1.4 In a small group, discuss the following issues: ‘The consequences of inappropriate validation of cluster analysis solutions can be disastrous’ and‘User-friendly statistical packages can create cluster solutions in situations where naturally occurring clusters do not exist’.
1.3 You are a marketing research analyst for a major airline. You have been set the task of determining consumers’ attitudes towards budget airlines. Construct a 15-item scale for this purpose. In a group of five students, obtain data on this scale and standard demographic characteristics from 25
1.2 Analyse the Benetton data (taken from Exercise 4, Chapter 22). Consider only the following variables: awareness, attitude, preference, intention and loyalty towards Benetton.a Cluster the participants based on the identified variables using hierarchical clustering. Use Ward’s method and
1.1 Analyse the data in Table 25.1 using the following hierarchical methods:a Single linkage (nearest neighbour).b Complete linkage (furthest neighbour).c Method of centroid.
1.15 How is cluster analysis used to group variables?
1.14 Describe some procedures available for assessing the quality of clustering solutions.
1.13 What are some of the additional variables used for profiling the clusters?
1.12 What role may qualitative methods play in the interpretation of clusters?
1.11 What is involved in the interpretation of clusters?
1.10 What guidelines are available for deciding the number of clusters?
1.9 What are the two major disadvantages of non-hierarchical clustering procedures?
1.8 Why is the average linkage method usually preferred to single linkage and complete linkage?
1.7 Upon what basis may a researcher decide which variables should be selected to formulate a clustering problem?
1.6 Present a classification of clustering procedures.
1.5 What is the most commonly used measure of similarity in cluster analysis?
1.4 Briefly define the following terms: dendrogram, icicle plot, agglomeration schedule and cluster membership.
1.3 What are some of the uses of cluster analysis in marketing?
1.2 What is a ‘cluster’?
1.1 Discuss the similarity and difference between cluster analysis and discriminant analysis.
1.6 appreciate how software is used to carry out factor analysis.
1.5 discuss the applications of non-hierarchical clustering and clustering of variables;
1.4 describe the purpose and methods for evaluating the quality of clustering results and assessing reliability and validity;
1.3 explain the procedure for conducting cluster analysis, including formulating the problem, selecting a distance measure, selecting a clustering procedure, deciding on the number of clusters, interpreting clusters and profiling clusters;
1.2 discuss the statistics associated with cluster analysis;
1.1 describe the basic concept and scope of cluster analysis and its importance in marketing research;
1.5 In a small group, identify the uses of factor analysis in each of the following major decision areas in marketing:a Market segmentation b Product decisions c Promotions decisions d Pricing decisions e Distribution decisions f Service delivery decisions.
1.4 You are a marketing research analyst for a manufacturer of fashion clothing targeted at teenage boys. You have been asked to develop a set of 10 statements for measuring psychographic characteristics and lifestyles that you feel would relate to their fashion personas. The participants would be
1.3 Analyse the Benetton data (taken from Exercise 4, Chapter 20). Consider only the following variables: awareness, attitude, preference, intention and loyalty towards Benetton.a Analyse these data using principal components analysis, using the varimax rotation procedure.b Interpret the factors
1.2 In a study of the relationship between household behaviour and shopping behaviour, data on the following lifestyle statements were obtained on a seven-point scale (1 = disagree, 7 = agree):V1 I would rather spend a quiet evening at home than go out to a party.V2 I always check prices, even on
1.1 Complete the following portion of an output from principal components analysis:a Draw a scree plot based on these data.b How many factors should be extracted? Explain your reasoning. Variable Communality Factor Eigenvalue % of variance V 1.0 V 1.0 V3 1.0 V4 1.0 V5 1.0 V6 1.0 V7 1.0 V8 1.0
1.15 How is the fit of the factor analysis model examined?
1.14 What are surrogate variables? How are they determined?
1.13 When is it useful to calculate factor scores?
1.12 What guidelines are available for interpreting the factors?
1.11 Why is it useful to rotate the factors? Which is the most common method of rotation?
1.10 What is a scree plot? For what purpose is it used?
1.9 Explain how eigenvalues are used to determine the number of factors.
1.8 What is the major difference between principal components analysis and common factor analysis?
1.7 For what purpose is the Kaiser–Meyer–Olkin measure of sampling adequacy used?
1.6 Briefly define the following: eigenvalue, factor loadings, factor matrix and factor scores.
1.5 What is meant by the term ‘communality of a variable’?
1.4 What hypothesis is examined by Bartlett’s test of sphericity? For what purpose is this test used?
1.3 Describe the factor analysis model.
1.2 What are the major uses of factor analysis?
1.1 How is factor analysis different from multiple regression and discriminant analysis?
1.6 appreciate how software is used in factor analysis.
1.5 describe the procedure for determining the fit of a factor analysis model using the observed and the reproduced correlations;
1.4 explain the selection of surrogate variables and their application, with emphasis on their use in subsequent analysis;
1.3 understand the distinction between principal component factor analysis and common factor analysis methods;
1.2 discuss the procedure for conducting factor analysis, including problem formulation, construction of the correlation matrix, selection of an appropriate method, determination of the number of factors, rotation and interpretation of factors;
1.1 describe the concept of factor analysis and explain how it is different from analysis of variance, multiple regression and discriminant analysis;
1.4 In a small group, discuss the following issue: ‘Is it meaningful to determine the relative importance of predictors in discriminating between the groups? Why or why not?’
1.3 Analyse the Benetton data (taken from Exercise 4, Chapter 20). Do the three usage groups differ in terms of awareness, attitude, preference, intention and loyalty towards Benetton when these variables are considered simultaneously?
1.2 Given the following information, calculate the discriminant score for each participant. The value of the constant is 2.04. Unstandardised discriminant function coefficients Age 0.38 Income 0.44 Risk taking -0.39 Optimistic 1.26 Participant ID Age Income Risk taking Optimistic 0246 36 43.7 1337
1.1 In investigating the differences between heavy, light or non-users of frozen foods, it was found that the two largest standardised discriminant function coefficients were 0.97 for convenience orientation and 0.61 for income. Is it correct to conclude that convenience orientation is more
1.15 How does the stepwise discriminant procedure differ from the direct method?
1.14 When the groups are of equal size, how is the accuracy of chance classification determined?
1.13 Describe a common procedure for determining the validity of discriminant analysis.
1.12 How is the statistical significance of discriminant analysis determined?
1.11 Explain the concept of structure correlations.
1.10 What is a classification matrix?
1.9 Explain what is meant by an eigenvalue.
1.8 Define discriminant scores.
1.7 What is Wilks’ l? For what purpose is it used?
1.6 How should the total sample be split for estimation and validation purposes?
1.5 What are the steps involved in conducting discriminant analysis?
1.4 Describe the relationship of discriminant analysis to regression and ANOVA.
1.3 What is the main distinction between two-group and multiple discriminant analysis?
1.2 Describe four examples of the application of discriminant analysis.
1.1 What are the objectives of discriminant analysis?
1.6 understand the use of software to support discriminant and logit analysis.
1.5 describe the binary logit model and its advantages over discriminant and regression analysis;
1.4 explain stepwise discriminant analysis and describe the Mahalanobis procedure;
1.3 discuss multiple discriminant analysis and the distinction between two-group and multiple discriminant analysis;
1.2 outline the procedures for conducting discriminant analysis, including the formulation of the problem, estimation of the discriminant function coefficients, determination of significance, interpretation and validation;
1.1 describe the concept of discriminant analysis, its objectives and its applications in marketing research;
1.5 In a small group, discuss the following issues: ‘Regression is such a basic technique that it should always be used in analysing data’ and ‘What is the relationship between bivariate correlation, bivariate regression, multiple regression and analysis of variance?’
1.4 In a survey pre-test, data were obtained from 20 participants on preference for boots (V1) on a seven-point scale (1 = not preferred, 7 = greatly preferred). The participants also provided their evaluations of the boots on comfort (V2), style(V3) and durability (V4), also on seven-point scales
1.3 You come across a magazine article reporting the following relationship between annual expenditure on prepared dinners (PD) and annual income (INC):PD = 23.4 + 0.003INC The coefficient of the INC variable is reported as significant.a Does this relationship seem plausible? Is it possible to have
1.2 To understand the role of quality and price in influencing the patronage of shoe shops, 14 major shoe shops in a large city were rated in terms of preference to shop, quality of shoes sold and price fairness. All the ratings were obtained on an 11-point scale, with higher numbers indicating
1.1 A supermarket chain wants to determine the effect of promotion on relative competitiveness. Data were obtained from 15 cities on the promotional expenses relative to a major competitor (competitor expenses = 100) and on sales relative to this competitor (competitor sales = 100).You are assigned
1.15 Demonstrate the equivalence of regression with dummy variables to one-way ANOVA.
1.14 What are some of the measures used to assess the relative importance of predictors in multiple regression?
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