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Marketing Research

Marketing Research. Aaker, Kumar, Day Ninth Edition Instructor’s Presentation Slides. Chapter Sixteen. Fundamentals of Data Analysis. Data Analysis. A set of methods and techniques used to obtain information and insights from data Helps avoid erroneous judgements and conclusions

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Marketing Research

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  1. Marketing Research Aaker, Kumar, Day Ninth Edition Instructor’s Presentation Slides

  2. Chapter Sixteen Fundamentals of Data Analysis http://www.drvkumar.com/mr9/

  3. Data Analysis • A set of methods and techniques used to obtain information and insights from data • Helps avoid erroneous judgements and conclusions • Can constructively influence the research objectives and the research design • Major Data Preparation techniques: • Data editing • Coding • Statistically adjusting the data http://www.drvkumar.com/mr9/

  4. Data Editing • Identifies omissions, ambiguities, and errors in responses • Conducted in the field by interviewer and field supervisor and by the analyst prior to data analysis • Problems identified with data editing: • Interviewer Error • Omissions • Ambiguity • Inconsistencies • Lack of Cooperation • Ineligible Respondent http://www.drvkumar.com/mr9/

  5. Coding • Coding closed-ended questions involves specifying how the responses are to be entered • Open-ended questions are difficult to code • Lengthy list of possible responses is generated http://www.drvkumar.com/mr9/

  6. Statistically Adjusting the Data Weighting • Each response is assigned a number according to a pre-specified rule • Makes sample data more representative of target population on specific characteristics • Modifies number of cases in the sample that possess certain characteristics • Adjusts the sample so that greater importance is attached to respondents with certain characteristics http://www.drvkumar.com/mr9/

  7. Statistically Adjusting the Data (contd.) Variable Re-specification • Existing data is modified to create new variables • Large number of variables collapsed into fewer variables • Creates variables that are consistent with study objectives • Dummy variables are used (binary, dichotomous, instrumental, quantitative variables) • Use (d-1) dummy variables to specify (d) levels of qualitative variable http://www.drvkumar.com/mr9/

  8. Statistically Adjusting the Data (contd.) Scale Transformation • Scale values are manipulated to ensure comparability with other scales • Standardization allows the researcher to compare variables that have been measured using different types of scales • Variables are forced to have a mean of zero and a standard deviation of one • Can be done only on interval or ratio scaled data • Standardized score, http://www.drvkumar.com/mr9/

  9. Simple Tabulation • Consists of counting the number of cases that fall into various categories • Uses: • Determine empirical distribution (frequency • distribution) of the variable in question • Calculate summary statistics, particularly the • mean or percentages • Aid in "data cleaning" aspects http://www.drvkumar.com/mr9/

  10. Frequency Distribution • Reports the number of responses that each question received • Organizes data into classes or groups of values • Shows number of observations that fall into each class • Can be illustrated simply as a number or as a percentage or histogram • Response categories may be combined for many questions • Should result in categories with worthwhile number of respondents http://www.drvkumar.com/mr9/

  11. Frequency Distribution http://www.drvkumar.com/mr9/

  12. Descriptive Statistics • Statistics normally associated with a frequency distribution to help summarize information in the frequency table • Includes: • Measures of central tendency mean, median and mode • Measures of dispersion (range, standard deviation, and coefficient of variation) • Measures of shape (skewness and kurtosis) http://www.drvkumar.com/mr9/

  13. Cross Tabulations • Statistical analysis technique to study the relationships among and between variables • Sample is divided to learn how the dependent variable varies from subgroup to subgroup • Frequency distribution for each subgroup is compared to the frequency distribution for the total sample • The two variables that are analyzed must be nominally scaled http://www.drvkumar.com/mr9/

  14. Factors Influencing the Choice of Statistical Technique Type of Data • Classification of data involves nominal, ordinal, interval and ratio scales of measurement • Nominal scaling is restricted in that mode is the only meaningful measure of central tendency • Both median and mode can be used for ordinal scale • Non-parametric tests can only be run on ordinal data • Mean, median and mode can all be used to measure central tendency for interval and ratio scaled data http://www.drvkumar.com/mr9/

  15. Factors Influencing the Choice of Statistical Technique (Contd.) Research Design • Depends on: • Whether dependent or independent samples are used • Number of observations per object • Number of groups being analyzed • Number of variables • Control exercised over variable of interest http://www.drvkumar.com/mr9/

  16. Factors Influencing the Choice of Statistical Technique (Contd.) Assumptions Underlying the Test Statistic • Two-sample t-test : • The samples are independent. • The characteristics of interest in each population have normal distribution. • The two populations have equal variances. http://www.drvkumar.com/mr9/

  17. Overview of Statistical Techniques Univariate Techniques • Appropriate when there is a single measurement of each of the 'n' sample objects or there are several measurements of each of the `n' observations but each variable is analyzed in isolation • Nonmetric data - measured on nominal or ordinal scale • Metric data - measured on interval or ratio scale • Determine whether single or multiple samples are involved • For multiple samples, choice of statistical test depends on whether the samples are independent or dependent http://www.drvkumar.com/mr9/

  18. Classification of Univariate Statistical Techniques http://www.drvkumar.com/mr9/

  19. Overview of Statistical Techniques (Contd.) Multivariate Techniques • A collection of procedures for analyzing association between two or more sets of measurements that have been made on each object in one or more samples of objects • Uses: • To group variables or people or objects • To improve the ability to predict variables (such as usage) • To understand relationships between variables (such as • advertising and sales) http://www.drvkumar.com/mr9/

  20. Classification of Multivariate Statistical Techniques http://www.drvkumar.com/mr9/

  21. Classification of Multivariate Techniques (Contd.) Dependence Techniques • One or more variables can be identified as dependent variables and the remaining as independent variables • Choice of dependence technique depends on the number of dependent variables involved in analysis Interdependence Techniques • Whole set of interdependent relationships is examined • Further classified as having focus on variable or objects http://www.drvkumar.com/mr9/

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