Marketing Research : An Applied Orientation 7th Global Edition Naresh K. Malhotra - Solutions

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1.13 Describe the cross-validation procedure. Describe double cross-validation.
1.12 What is multicollinearity? What problems can arise because of multicollinearity?
1.11 Explain the stepwise regression approach. What is its purpose?
1.10 What is gained by an examination of residuals?
1.9 State the null hypothesis in testing the significance of the overall multiple regression equation. How is this null hypothesis tested?
1.8 Explain the meaning of a partial regression coefficient. Why is it called that?
1.7 What is multiple regression? How is it different from bivariate regression?
1.6 What is meant by prediction accuracy? What is the standard error of the estimate?
1.5 How is the strength of association measured in bivariate regression? In multiple regression?
1.4 Explain the meaning of standardised regression coefficients.
1.3 What is the least squares procedure?
1.2 What are the main uses of regression analysis?
1.1 What is the product moment correlation coefficient? Does a product moment correlation of zero between two variables imply that the variables are not related to each other?
1.6 understand the use of software in analyses of correlation and regression.
1.5 discuss non-metric correlation and measures such as Spearman’s rho and Kendall’s tau;
1.4 describe specialised techniques used in multiple regression analysis, particularly stepwise regression, regression with dummy variables and analysis of variance and covariance with regression;
1.3 explain the nature and methods of multiple regression analysis and the meaning of partial regression coefficients;
1.2 explain the nature and methods of bivariate regression analysis and describe the general model, estimation of parameters, standardised regression coefficient, significance testing, prediction accuracy, residual analysis and model cross-validation;
1.1 discuss the concepts of product moment correlation, partial correlation and part correlation, and show how they provide a foundation for regression analysis;
Identify the ethical issues related to the interpretation and reporting of the research process and findings to the client and the use of these results by the client.
Describe how social media facilitate and enhance report preparation and presentation.
Understand the report preparation and presentation process in international marketing research.
Explain the reason for follow-up with the client and describe the assistance that should be given to the client and the evaluation of the research project.
Describe the approach to the marketing research report from the client’s perspective and the guidelines for reading the research report.
Discuss the nature and scope of the oral presentation and describe the “Tell ‘Em”and “KISS ‘Em” principles.
Discuss the basic requirements of report preparation, including report format, report writing, graphs, and tables.
Explain the role of software in conducting structural equation modeling and path analysis using SPSS, SAS, and other popular software.
Explain path analysis and discuss its relationship to SEM.
Discuss the relationship of SEM to other multivariate techniques.
Describe how to specify a structural model and assess its validity.
Explain the concept of model fit and the differences among absolute, incremental, and parsimony fit indices.
Know how to specify a measurement model and assess its validity.
Describe the process of conducting SEM and explain the various steps involved.
Discuss the basic statistics associated with SEM.
Explain the basic concepts in SEM such as theory, model, path diagram, exogenous versus endogenous constructs, dependence and correlational relationships, model fit, and model identification.
Define the nature and unique characteristics of structural equation modeling (SEM).
Explain the role of software in conducting multidimensional scaling and conjoint analysis using SPSS and SAS.
Define the concept of hybrid conjoint analysis and explain how it simplifies the data collection task.
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.
Discuss the basic concepts of conjoint analysis, contrast it with MDS, and discuss its various applications.
Understand the relationship among MDS, discriminant analysis, and factor analysis.
Explain correspondence analysis and discuss its advantages and disadvantages.
Explain the multidimensional scaling of preference data and distinguish between internal and external analysis of preferences.
Describe the steps involved in multidimensional scaling of perception data, including formulating the problem, obtaining input data, selecting an MDS procedure, deciding on the number of dimensions, labeling the dimensions and interpreting the configuration, and assessing reliability and validity.
Discuss the basic concept and scope of multidimensional scaling (MDS) in marketing research and describe its various applications.
Explain the role of software in conducting cluster analysis using SPSS and SAS.
Discuss the applications of nonhierarchical clustering and clustering of variables.
Describe the purpose and methods for evaluating the quality of clustering results and assessing reliability and validity.
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, and interpreting and profiling clusters.
Discuss the statistics associated with cluster analysis.
Describe the basic concept and scope of cluster analysis and its importance in marketing research.
Explain the role of software in conducting factor analysis and related procedures using SPSS and SAS.
Describe the procedure for determining the fit of a factor analysis model using the observed and the reproduced correlations.
Expound the selection of surrogate variables and their application, with emphasis on their use in subsequent analysis.
Understand the distinction between principal component factor analysis and common factor analysis methods.
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.
Describe the concept of factor analysis and explain how it is different from analysis of variance, multiple regression, and discriminant analysis.
Explain the role of software in conducting discriminant and logit analysis using SPSS and SAS.
Describe the binary logit model and its advantages over discriminant and regression analysis.
Demonstrate stepwise discriminant analysis and the Mahalanobis procedure.
Discuss multiple discriminant analysis and the distinction between two-group and multiple discriminant analysis.
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.
Describe the concept of discriminant analysis, its objectives, and its applications in marketing research.
Explain the role of software in conducting correlation and regression using SPSS and SAS.
Discuss nonmetric correlation and measures such as Spearman’s rho and Kendall’s tau.
Describe specialized techniques used in multiple regression analysis, particularly stepwise regression, regression with dummy variables, and analysis of variance and covariance with regression.
Explain the nature and methods of multiple regression analysis and the meaning of partial regression coefficients.
Explain the nature and methods of bivariate regression analysis and describe the general model, estimation of parameters, standardized regression coefficient, significance testing, prediction accuracy, residual analysis, and model cross-validation.
Discuss the concepts of product moment correlation, partial correlation, and part correlation and show how they provide a foundation for regression analysis.
Explain the role of software in conducting analysis of variance and covariance using SPSS and SAS.
Discuss specialized ANOVA techniques applicable to marketing such as repeated measures ANOVA, nonmetric analysis of variance, and multivariate analysis of variance (MANOVA).
Explain key factors pertaining to the interpretation of results with emphasis on interactions, relative importance of factors, and multiple comparisons.
Describe analysis of covariance and show how it accounts for the influence of uncontrolled independent variables.
Describe n-way analysis of variance and the testing of the significance of the overall effect, the interaction effect, and the main effect of each factor.
Describe one-way analysis of variance, including decomposition of the total variation, measurement of effects, significance testing, and interpretation of results.
Discuss the scope of the analysis of variance (ANOVA) technique and its relationship to the t test and regression.
Explain the role of software in conducting frequency, cross-tabulation, and hypothesis testing using SPSS and SAS.
Understand data analysis associated with nonparametric hypothesis testing for one sample, two independent samples, and paired samples.
Describe data analysis associated with parametric hypothesis testing for one sample, two independent samples, and paired samples.
Explain data analysis associated with cross-tabulations and the associated statistics:chi-square, phi coefficient, contingency coefficient, Cramer’s V, and lambda coefficient.
Discuss data analysis associated with frequencies, including measures of location, measures of variability, and measures of shape.
Describe the significance of preliminary data analysis and the insights that can be obtained from such an analysis.
Explain the role of software in data preparation and analysis using SPSS and SAS.
Identify the ethical issues related to data processing, particularly the discarding of unsatisfactory responses, violation of the assumptions underlying the data analysis techniques, and evaluation and interpretation of results.
Elucidate data preparation in mobile marketing research.
Describe text coding, categorization, and other aspects of data preparation in social media research.
Understand the intracultural, pancultural, and cross-cultural approaches to data analysis in international marketing research.
Classify statistical techniques and give a detailed classification of univariate techniques as well as a classification of multivariate techniques.
Describe the procedure for selecting a data analysis strategy and the factors influencing the process.
State the reasons for and methods of statistically adjusting data: weighting, variable respecification, and scale transformation.
Discuss the data-cleaning process and the methods used to treat missing responses: substitution of a neutral value, imputed response, casewise deletion, and pairwise deletion.
Describe the guidelines for coding questionnaires, including the coding of structured and unstructured questions.
Explain questionnaire checking and editing, treatment of unsatisfactory responses by returning to the field, assigning missing values, and discarding unsatisfactory responses.
Discuss the nature and scope of data preparation and the data-preparation process.
Discuss the ethical aspect of fieldwork.
Elucidate fieldwork in mobile marketing research.
Illustrate fieldwork in relation to social media.
Explain the issues related to fieldwork when conducting international marketing research.
Describe the evaluation of fieldworkers in areas of cost and time, response rates, quality of interviewing, and the quality of data.
Discuss the supervision of fieldworkers in terms of quality control and editing, sampling control, control of cheating, and central office control.

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Marketing Research : An Applied Orientation 7th Global Edition Naresh K. Malhotra - Solutions

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