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El. knyga: Marketing Analytics: Statistical Tools for Marketing and Consumer Behavior Using SPSS

, (Amsterdam University of Applied Sciences, Netherlands)
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"Marketing Analytics provides guidelines in the application of statistics using SPSS for students and professionals studying quantitative methods in Marketing and Consumer Behaviour. With simple language and a practical, screenshot-led approach, the bookpresents eleven multivariate techniques and the steps required to perform analysis. Each chapter contains a brief description of the technique, followed by the possible marketing research applications. One of these applications is then used in detail to illustrate its applicability in a research context, including the needed SPSS commands and illustrations. Each chapter also includes practical exercises, which require the readers to perform the technique and interpret the results, equipping students withthe necessary skills to apply statistics, by means of SPSS, in marketing and consumer research. Finally, there is a list of articles employing the technique, which can be used for further reading. This textbook provides introductory material for advancedundergraduate and postgraduate students studying marketing and consumer analytics, teaching methods along with practical software-applied training using SPSS. Support material includes two real datasets to illustrate the techniques' applications and PowerPoint slides providing a step-by-step guide to the analysis and commented outcomes"--

Marketing Analytics provides guidelines in the application of statistics using SPSS for students and professionals using quantitative methods in Marketing and Consumer Behaviour.

Preface vii
1 Creating and examining databases in SPSS 1(11)
1.1 Creating the SPSS spreadsheet and manipulating data
1(4)
1.2 Descriptive statistics with frequencies
5(2)
1.3 Cross tabulation
7(5)
2 Introduction to exploratory data analysis 12(15)
2.1 Exploratory data analysis
12(5)
2.2 Verification of assumptions
17(6)
2.3 Outliers
23(1)
2.4 Missing values
24(3)
3 Analysis of variance 27(21)
3.1 Application oft-test in SPSS
27(2)
3.2 Theoretical background - analysis of variance (ANOVA)
29(1)
3.3 Marketing application of ANOVA
30(1)
3.4 Application of ANOVA in SPSS
31(8)
3.5 Theoretical background - multivariate analysis of variance (MANOVA)
39(2)
3.6 Application of MANOVA in SPSS
41(7)
4 Regression analysis 48(15)
4.1 Theoretical background - simple regression analysis
48(2)
4.2 Theoretical background - multiple regression analysis
50(5)
4.3 Marketing application of regression analysis
55(1)
4.4 Application of multiple regression in SPSS
55(8)
5 Time series analysis 63(20)
5.1 Theoretical background - time series analysis
63(3)
5.2 Marketing application of time series analysis
66(1)
5.3 Application of time series analysis in SPSS
67(16)
6 Discriminant analysis 83(23)
6.1 Theoretical background - two groups discriminant analysis
83(2)
6.2 Theoretical background - multiple discriminant analysis
85(1)
6.3 Marketing application of discriminant analysis
86(1)
6.4 Application of discriminant analysis in SPSS
87(7)
6.5 Theoretical background - logistic regression
94(2)
6.6 Application of logistic regression in SPSS
96(10)
7 Cluster analysis 106(25)
7.1 Theoretical background - cluster analysis
107(5)
7.2 Marketing application of cluster analysis
112(1)
7.3 Application of cluster analysis in SPSS - hierarchical approach
112(11)
7.4 Application of cluster analysis in SPSS - nonhierarchical approach
123(8)
8 Exploratory Factor Analysis (EFA) 131(21)
8.1 Theoretical background - exploratory factor analysis
131(4)
8.2 Marketing application of exploratory factor analysis
135(1)
8.3 Application of exploratory factor analysis in SPSS
136(16)
9 Confirmatory Factor Analysis (CFA) 152(21)
9.1 Theoretical background - confirmatory factor analysis
152(2)
9.2 Marketing application of CFA
154(1)
9.3 Application of confirmatory factor analysis with AMOS
155(1)
9.4 The CFA model in AMOS
155(1)
9.5 The CFA analysis
156(6)
9.6 The CFA output
162(11)
10 Structural Equation Modeling (SEM) 173(18)
10.1 Theoretical background - structural equation modeling
173(1)
10.2 Marketing application of SEM
174(1)
10.3 Application of structural equation modeling with AMOS
175(1)
10.4 The SEM model in AMOS
176(3)
10.5 The SEM analysis
179(2)
10.6 The SEM output
181(10)
Appendix 1: Fitness center questionnaire 191(3)
Appendix 2: Supermarket questionnaire 194(3)
Bibliography 197(2)
Index 199
José Marcos Carvalho de Mesquita is Professor of Marketing at FUMEC University, Brazil, and visiting researcher at University of Connecticut, USA.

Erik Kostelijk is Associate Professor of Marketing at the Amsterdam School of International Business of the University of Applied Sciences in Amsterdam, the Netherlands.
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