This lecture introduces some of the basic considerations in quantitative analytical design in Applied Marketing Research

1. Analytical Design - Quant Week 6 (2) Dr. Kelly Page Cardiff Business School E: pagekl@cardiff.ac.uk T: @drkellypage T: @caseinsights FB: kelly@caseinsights.com

3. Data Analysis – Overview The Key Steps: 1 2 3 4 5 Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry

8. a). Frequency Analysis One Way Frequency Tables/Graphs A table showing the number (n) or percentage (%) of respondents choosing each answer to a survey question.

10. c). Descriptive Statistics Effective means of summarizing large sets of data. Key measures include: mean, median, mode, kurtosis, standard deviation, skewness, and variance. Measures of Central Tendency! Mean Median Mode Measures of Dispersion! Variance Range Standard Deviation Skewness

11. d). Graphical Representation Line, Pie, and Bar Charts Line Charts: Good for demonstrating linear relationships. Pie Charts: Good for special relationships among data points. Bar Charts: Good for side by side relationships / comparisons

13. E.g., T-test

14. E.g., ANOVA

16. E.g., Correlation

17. E.g., Simple Regression

18. No Apparent Relationship Between X and Y X Y Perfect Positive Relationship Between X and Y X Y Perfect Negative Relationship Between X and Y Parabolic Relationship Between X and Y X Y

19. General Positive Relationship Between X and Y X Y No Apparent Relationship Between X and Y X Y Y X Negative Curvilinear Relationship Between X and Y General Negative Relationship Between X and Y X Y

21. Cluster Analysis The general term for statistical procedures that classify objects or people into some number of mutually exclusive and exhaustive groups on the basis of two or more classification variables. Cluster 1: Men Cluster 2: Women Cluster 3: People with Green Cars

22. E.g., Cluster Analysis

24. Factor Analysis Procedure for grouping & simplifying data by reducing a large set of values/items to a smaller set of factors/dimension of a variable by identifying dimensions in the data . Factor Loading: Correlation between factor scores and the original variables.

25. E.g., Cinema Attribute Importance (1)

26. E.g., Cinema Attribute Importance (2)

27. Perceptual Mapping Procedure of producing visual representations of consumer perceptions of products, brands, companies, or other objects / issues. . . . . . . . . . . . . . . . Men Women . . . . . . . . . . . . . Setting Markers

28. Conjoint Analysis Procedure use to quantify the value consumers associate with different levels of product/service attributes or features.

30. The content of this work is of shared interest between the author, Kelly Page and other parties who have contributed and/or provided support for the generation of the content detailed within. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 2.0 UK: England & Wales. http://creativecommons.org/ Kelly Page (cc)