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Data Analysis

Data Analysis. AMA Collegiate Marketing Research Certificate Program. Module Objectives. Introduce key data analysis procedures Provide a basic understanding of how to interpret data. Data Analysis Metrics. What Will We Be Covering?. Frequencies Mode Median Mean Standard Deviation

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Data Analysis

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  1. Data Analysis AMA Collegiate Marketing Research Certificate Program

  2. Module Objectives • Introduce key data analysis procedures • Provide a basic understanding of how to interpret data

  3. Data Analysis Metrics

  4. What Will We Be Covering? • Frequencies • Mode • Median • Mean • Standard Deviation • Cross-Tabulations • T-Tests • One-way ANOVA Data Type Plays a Key Role in Data Analysis and Analytics

  5. Frequencies

  6. Frequencies = Data Counts and Percent Relevant for all types of data

  7. Mode

  8. Mode = Most Frequent Response Mode = 0-25%

  9. Median

  10. Median = Middle Response Numerical value that identifies split of high vs. low half of sample responses Notes: (1) 18-34 has 32.1% of responses (2) 45+ has 47% of responses (3) Thus, 50th percentile falls in the 35-44 age range (32.1% - 53%) Median = 35-44

  11. Mean

  12. Convenience Store Food Mean = Average Must be interval or ratio data Also frequencies, mode (3), median (4) for children food choices

  13. Standard Deviation

  14. Standard Deviation = Variation/Dispersion • Represents how much variation or dispersion there is in the distribution of responses. Less variation = lower standard deviation. • Standard deviation is “standardized” with a mean “0” and standard deviation of “1”

  15. Standard Deviation and Margin of Error • Need interval or ratio data • Margin of error goes up as standard deviation goes up • Harder to detect group difference (i.e., male vs. female satisfaction) as standard deviation goes up +/- 3% +/- 5% +/- 7%

  16. Cross-Tabulations

  17. Cross-Tabulations • Comparing the distribution of responses across two categorical variables • Goal is to see if there is a statistical differences in the distribution of responses across groups • Male vs. females and preferred movie type • Age groups and smart phone ownership • Home owners vs. renters and Cable vs. Dish

  18. Cross-Tabulations Considerations • Typically two categorical variables • Gender (M/F) and Movies (Comedy, Drama, Romance) • Although you can use interval scales in cross-tabulations, t-tests and ANOVA are more commonly used because of the ability to calculate means • Identify independent and dependent variable • V = which groups you are comparing (gender, age) • DV = what are you comparing (movies, smart phone)

  19. Example: Buying Channel by Year Need to determine statistical significance

  20. SPSS Output Where Buy: Hobbyist vs. Professional Conclusion: Because p of .000 < .05, can conclude that hobbyists are more likely to buy in store than professionals (74.6% vs. 68.5%)

  21. T-Tests

  22. T-Tests • Comparison of mean scores across two groups • Grouping variable = Independent variables • Grouping variable must be categorical • Male vs. female, smart phone vs. no smart phone • Dependent variable = must be something for which you can calculate a mean (interval/ratio) • Average satisfaction score, average importance rating, average number of times dining out per week

  23. Independent Sample T-test • Two different (independent) groups • Male vs. female • Smart phone buyers vs. non-smart phone buyers Conclusions: Professionals put greater importance on quality, service and speed; Hobbyists on price

  24. SPSS Output Purchase Likelihood Hobbyist Vs. Professional Conclusion: Professionals are more likely to buy power tools. No significant difference for wood Sig (p value) must be < than .05

  25. Paired T-test • Paired T-test = same group, two different scores • Customer satisfaction 2011 vs. 2012 (same customers) • Comparison of attributes vs. competitor (see below)

  26. SPSS Output Comparing Perceptions of Price vs. Other Attributes Conclusion: Perceptions of price are lower than hand tool variety, power tool variety, and product quality

  27. One-way ANOVA

  28. One-Way ANOVA • Comparison of means across multiple groups • Purchase likelihood by • Age: by 18-24, 25-45 and 45+ years old • Bus type: Government, university, manufacturing Conclusion: Each has a more likely purchase: G = Phones U = Computers M = Services

  29. SPSS Output Comparing Overall Value by Channel Conclusion: At least one significant difference exists Conclusion: Retail buyers have the highest perceptions of overall value; catalog second (but need to look at contrasts)

  30. You will get to see how to run these analyses in SPSS, including click by click tutorials and results

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