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Markets Research
Engineering & Social Sciences Program
Madrid, Spain
Dates: 1/16/20 - 5/30/20
Markets Research
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Marketing
Instruction in: English
Course Code: 13185
Transcript Source: Partner Institution
Course Details: Level 300, 400
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Marketing, Statistics I, Statistics II
DESCRIPTION
Topic 1. Introduction to Marketing Research
1.1. The process of Marketing Research
1.2. Classification of Marketing Research Studies
1.3. Problem definition
1.4. Preparing a marketing research report
Topic 2. Exploratory, descriptive and causal research techniques
2.1. Exploratory designs: In-depth interviews, Focus groups and observation
2.2. Descriptive designs: Surveys and panels
2.3. Explicative designs: Experiments and conjoint analysis techniques
Topic 3. Fieldwork and data preparation
3.1. The nature of fieldwork
3.2. Data coding
3.3. Computerized databases
Topic 4. Univariate hypotheses testing
4.1. Frequency
4.2. Tendency
4.3. Dispersion
Topic 5. Bivariate hypotheses testing
5.1. Testing for significant differences
5.2. Testing for association
Topic 6. Multivariate preferences analysis with SPSS
6.1. Multiple regression analysis
6.2. Analysis of variance and covariance (ANOVA, MANOVA, etc.)
Topic 7. Multivariate positioning analysis with SPSS
7.1. Principal components analysis
7.2. Perceptual maps
Topic 8. Multivariate segmentation analysis
8.1. Cluster analysis
8.2. Discriminant analysis
1.1. The process of Marketing Research
1.2. Classification of Marketing Research Studies
1.3. Problem definition
1.4. Preparing a marketing research report
Topic 2. Exploratory, descriptive and causal research techniques
2.1. Exploratory designs: In-depth interviews, Focus groups and observation
2.2. Descriptive designs: Surveys and panels
2.3. Explicative designs: Experiments and conjoint analysis techniques
Topic 3. Fieldwork and data preparation
3.1. The nature of fieldwork
3.2. Data coding
3.3. Computerized databases
Topic 4. Univariate hypotheses testing
4.1. Frequency
4.2. Tendency
4.3. Dispersion
Topic 5. Bivariate hypotheses testing
5.1. Testing for significant differences
5.2. Testing for association
Topic 6. Multivariate preferences analysis with SPSS
6.1. Multiple regression analysis
6.2. Analysis of variance and covariance (ANOVA, MANOVA, etc.)
Topic 7. Multivariate positioning analysis with SPSS
7.1. Principal components analysis
7.2. Perceptual maps
Topic 8. Multivariate segmentation analysis
8.1. Cluster analysis
8.2. Discriminant analysis