Analytical Design in Applied Marketing Research


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

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  • 27/07/10
  • Analytical Design in Applied Marketing Research

    1. 1. Analytical Design - Quant Week 6 (2) Dr. Kelly Page Cardiff Business School E: T: @drkellypage T: @caseinsights FB:
    2. 2. <ul><li>Get an overview of the data analysis procedure; </li></ul><ul><li>Develop an understanding of the importance and nature of quality control checks; </li></ul><ul><li>Understand the data entry process and data entry alternatives; </li></ul><ul><li>Learn how data are tabulated; </li></ul><ul><li>Learn how to set up and interpret cross tabulations; </li></ul><ul><li>Comprehend the basic techniques of statistical analysis. </li></ul>Lecture Objectives
    3. 3. Data Analysis – Overview The Key Steps: 1 2 3 4 5 Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry
    4. 4. Data Analysis - Overview <ul><li>Step One: </li></ul><ul><ul><li>Validation: Confirming the interviews/surveys occurred </li></ul></ul><ul><ul><li>Editing: Determining the questionnaires were completed correctly </li></ul></ul><ul><li>Step Two: </li></ul><ul><ul><li>Coding: Grouping and assigning numeric codes to the question responses. </li></ul></ul><ul><li>Step Three: </li></ul><ul><ul><li>Data Entry: Process of converting data to an electronic form </li></ul></ul><ul><ul><li>Can use scanning devices to enter data </li></ul></ul><ul><ul><li>Scanning the questionnaire into a database ( such as with bubble sheets) </li></ul></ul><ul><li>Step Four: </li></ul><ul><ul><li>Clean the Data: Check for data entry errors or data entry inconsistencies </li></ul></ul><ul><ul><li>Machine cleaning – a computerized check of the data </li></ul></ul><ul><li>Step Five: </li></ul><ul><ul><li>Data tabulations and statistical analysis . </li></ul></ul>
    5. 5. Variables & Values <ul><li>Variables are measurable factors, attributes, properties or characteristics of an item, individual or system (what we measure) </li></ul><ul><li>Values are the results of measuring or observing a variable – the data scores (how we measure it – response format) </li></ul><ul><li>Examples </li></ul><ul><li>Account Balance : a variable describing how much money you have in the bank </li></ul><ul><li>£203.45 – The Value of your Account Balance at 11.32 a.m. on Friday 13 th February </li></ul><ul><li>Broadcast Media – a variable which denotes the type media channel that someone owns </li></ul><ul><li>Sony TV – the specific value according to which set they own </li></ul><ul><li>Customer Satisfaction (CS) – a variable which denotes how satisfied a customers experience with X was </li></ul><ul><li>Positive = the values of positive or negative, high or low could be the value of customer satisfaction and is dependent on how we measured it </li></ul>
    6. 6. Considerations <ul><li>Research objectives </li></ul><ul><li>Type of data (e.g., Nominal, Ordinal, Interval, Ratio) </li></ul><ul><li>Sample size (e.g., min=100) </li></ul><ul><li>Sampling method (e.g., non-probability) </li></ul>
    7. 7. 1) I want to describe the data! <ul><li>Frequency Analysis – Univariate (one variable) </li></ul><ul><ul><li>Count </li></ul></ul><ul><ul><li>Percentages </li></ul></ul><ul><ul><li>Missing </li></ul></ul><ul><li>Cross-tabulations – Bivariate (two variables) </li></ul><ul><ul><li>2x count </li></ul></ul><ul><ul><li>2x percentage </li></ul></ul><ul><li>Descriptive Statistics – Univariate (one variable) </li></ul><ul><ul><li>Mean, median, mode, kurtosis, standard deviation, skewness, and variance. </li></ul></ul><ul><li>Graphical Presentation </li></ul><ul><ul><li>How to visually display the descriptive profile of the data </li></ul></ul>
    8. 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.
    9. 9. b). Cross Tabulations Examination of the responses to one question relative to the responses to one or more questions in a survey set. <ul><li>Bi-variate cross-tabulation: </li></ul><ul><ul><li>Cross tabulation two items - “Business Category” and “Gender” </li></ul></ul><ul><li>Multi-variate cross-tabulation: </li></ul><ul><ul><li>Additional filtering criteria - “Veteran Status” - Now filtering three items. </li></ul></ul>
    10. 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. 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
    12. 12. 2. I want to test the differences between Groups of people or things! <ul><li>T-test </li></ul><ul><ul><li>Differences between two groups (e.g., males and females) </li></ul></ul><ul><ul><li>Measure of difference: T-statistic </li></ul></ul><ul><li>ANOVA </li></ul><ul><ul><li>Differences between two or more groups (e.g., age groups) </li></ul></ul><ul><ul><li>Measure of difference: F-statistic </li></ul></ul><ul><li>Measures of Difference </li></ul><ul><li>T-statistic (t-test) </li></ul><ul><li>F-statistic (Anova) </li></ul><ul><li>Significance of Difference (p>0.01) </li></ul><ul><li>Needs to look at means or run additional statistic to identify ‘where’ the difference are! </li></ul>
    13. 13. E.g., T-test
    14. 14. E.g., ANOVA
    15. 15. 3. I want to test if a relationship exists between 2 or more variables <ul><li>Correlation </li></ul><ul><ul><li>2 x variables </li></ul></ul><ul><ul><li>If interval or ratio data – pearson </li></ul></ul><ul><ul><li>If ordinal data = spearman </li></ul></ul><ul><li>Simple Regression Analysis </li></ul><ul><ul><li>1 x independent variable </li></ul></ul><ul><ul><li>1 x dependent variable </li></ul></ul><ul><li>Multiple Regression Analysis </li></ul><ul><ul><li>Multiple x independent variable </li></ul></ul><ul><ul><li>1 x dependent variable </li></ul></ul><ul><li>Measures of Association </li></ul><ul><li>Correlation coefficient ( r) </li></ul><ul><li>Regression coefficient (r 2 ) </li></ul><ul><li>Strength of association (0-1) </li></ul><ul><li>Direction of Association (+/-) </li></ul><ul><li>Significance of Association (p>0.01) </li></ul>
    16. 16. E.g., Correlation
    17. 17. E.g., Simple Regression
    18. 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. 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
    20. 20. 4. I want to group people OR objects <ul><li>Cluster Analysis </li></ul><ul><ul><li>Group people or objects based differences between and similarities within (segmentation) </li></ul></ul><ul><li>Factor Analysis </li></ul><ul><ul><li>Group data to most important related to criterion (11 items = 2 dimensions of satisfaction) </li></ul></ul><ul><li>Perceptual Mapping </li></ul><ul><ul><li>Visual representation of perceptions by groups (brand associations) </li></ul></ul><ul><li>Conjoint Analysis </li></ul><ul><ul><li>Value of people’s rankings of important product attributes (consumer choice > price, quality, location) </li></ul></ul>
    21. 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. 22. E.g., Cluster Analysis
    23. 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.
    24. 25. E.g., Cinema Attribute Importance (1)
    25. 26. E.g., Cinema Attribute Importance (2)
    26. 27. Perceptual Mapping Procedure of producing visual representations of consumer perceptions of products, brands, companies, or other objects / issues. . . . . . . . . . . . . . . . Men Women . . . . . . . . . . . . . Setting Markers
    27. 28. Conjoint Analysis Procedure use to quantify the value consumers associate with different levels of product/service attributes or features.
    28. 29. Analytical Design – Key Points! <ul><li>What type of data do you have? </li></ul><ul><ul><li>Ratio, Interval, Ordinal, Nominal = Statistical power </li></ul></ul><ul><li>What do you want to find out? </li></ul><ul><ul><li>Describe how data is distributed </li></ul></ul><ul><ul><li>Group differences between two or more groups </li></ul></ul><ul><ul><li>Relationships between two or more variables </li></ul></ul><ul><ul><li>Who falls into which grouping </li></ul></ul><ul><ul><li>Customer preference criterion </li></ul></ul><ul><li>Other Considerations: </li></ul><ul><li>How much missing data? </li></ul><ul><li>How big is sample size? </li></ul><ul><li>How was data collected – random or non-random? </li></ul>
    29. 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. Kelly Page (cc)