Marketing Analytics Statistical Tools for Marketing and Consumer Behaviour using SPSS 1st Edition By Carvalho de Mesquita, José Marcos
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Marketing Analytics Statistical Tools for Marketing and Consumer Behaviour using SPSS 1st Edition By Carvalho de Mesquita, José Marcos

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211
File size
4.32 MB
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Digital PDF
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eBook[PDF]
About this ebook
The first edition of Marketing Analytics: Statistical Tools for Marketing and Consumer Behavior Using SPSS by José Marcos Carvalho de Mesquita and Erik Kostelijk outlines 11 core multivariate statistical techniques tailored for market research and consumer behavior analysis. The book uses a hands-on, screenshot-guided methodology across its primary chapters: [1, 2, 4, 5]
1. Database Creation and Data Manipulation
  • SPSS Spreadsheets: Instructions on manually entering data, importing external files (like Excel), and managing the Data View and Variable View interfaces.
  • Descriptive Statistics: Running frequencies and distributions to understand initial data characteristics.
  • Cross-Tabulation: Examining relationships between categorical variables to discover consumer patterns. [1, 3, 4]
2. Exploratory Data Analysis (EDA)
  • Verification of Assumptions: Evaluating datasets for essential conditions like normality, homoscedasticity, and linearity.
  • Data Cleaning: Tools and workflows to identify and treat outliers and handle missing values in social science datasets. [1, 2]
3. Core Multivariate & Analytical Techniques
  • Analysis of Variance (ANOVA): Utilizing t-tests, standard ANOVA, and Multivariate Analysis of Variance (MANOVA) to compare distinct consumer groups.
  • Regression Analysis: Evaluating predictive relationships between marketing inputs and consumer behavior outcomes.
  • Time Series Analysis: Tracking and forecasting consumer data trends over specific periods.
  • Discriminant Analysis: Categorizing consumers into distinct, mutually exclusive groups based on known characteristics.
  • Cluster Analysis: Segmenting markets by clustering consumers with similar buying patterns or traits.
  • Exploratory Factor Analysis (EFA): Reducing data complexity to identify underlying psychographic or behavioral factors.
  • Confirmatory Factor Analysis (CFA): Testing and verifying whether a pre-defined framework fits the collected market data.
  • Structural Equation Modeling (SEM): Mapping complex, multi-layered causal relationships among consumer attitudes and purchase intents. [1, 3, 4, 5]
Included Appendices & Real Datasets
  • Fitness Center Questionnaire: The complete survey framework and dataset used for practical exercises.
  • Supermarket Questionnaire: A consumer behavior instrument and dataset utilized for step-by-step tutorial modeling. [1, 2]

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Topics

Analysis of Variance (ANOVA) Cluster Analysis: Segmenting markets by clustering consumers Confirmatory Factor Analysis (CFA): Testing and verifying Cross-Tabulation: Examining relationships between categorical variables Data Cleaning: Tools and workflows to identify and treat outliers Descriptive Statistics: Running frequencies and distributions Discriminant Analysis: Categorizing consumers into distinct Exploratory Data Analysis (EDA) Exploratory Factor Analysis (EFA): Reducing data complexity Regression Analysis: Evaluating predictive relationships SPSS Spreadsheets: Instructions on manually entering data Structural Equation Modeling (SEM): Mapping complex multi-layered causal relationships Time Series Analysis: Tracking and forecasting consumer data trends Utilizing t-tests standard ANOVA and Multivariate Analysis of Variance (MANOVA) Verification of Assumptions: Evaluating datasets
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