Market Research: Data Analysis Methods

Market Research Data Analysis Methods

What is Data Analysis ? • A set of methods and techniques used to obtain information and insights from data. • Helps to avoid erroneous judgments and conclusions. • Can constructively influence the research objectives and the research design. • Interpret the data using the advanced statistical analyses like correlation, regression, cluster analysis, factor analysis etc.

Importance of Data Analysis ? • Convert the mountain of data into meaningful entities. • Reduce data into meaningful results. • Drive decisions with better analysis. • Turn data into opportunities.

Preparing the data for Analysis 1. Data Editing / checking. Editing process is conducted by the data analyst prior to the analysis process. Editing process helps to identify the data omissions, interviewer errors, errors in the response, uncertainty in answering, ineligible respondents and logical flow/routing of the questionnaire.

Preparing the data for Analysis 2. Coding Coding refers to the process of assigning numerals or other symbols to answers so that responses can be put into a limited number of categories or classes. Coding is necessary for efficient analysis and through it several replies are reduced to a small number of classes which contains the critical information's required for the analysis.

Preparing the data for Analysis 2. Coding (Cont…) Open ended questions are difficult to code. A lengthy list of possible responses are generated for this purpose and then the codes are selected from this list for the appropriate answers. Each response is assigned with a number (column number) for the convenience of analysis.

Preparing the data for Analysis 3. Statistically Adjusting the data for analysis. Weighting Is a data projection technique used to extrapolate the sample data representative of the target population/Universe. A weighting factor is derived for this purpose [population (of the stratum)/sample (of the Stratum)] and then it is applied to the sample data in order to extrapolate it.

Tabulation 4. Tabulation is the process of summarizing raw data and displaying the same in a compact form(i.e., in the form of statistical tables) for further analysis. a. Simple Tabulation. Gives information about one or more groups of independent questions. b. Complex Tabulation. Gives information about two or more inter-related characteristics of data.

Tabulation (Contd..) General Principles. 1. Every table should have a clear and concise title placed just above the body of the table. 2. Every table should be given a distinct table number to facilitate easy reference. 3. The column heading (breaks/captions) and the row Headings(stubs) should be brief and clear. 4. Explanatory footnotes. 5. Units of measurement/base should be indicated under heading. 6. Source of data can be indicated just below the table. 7. The columns can be numbered for easy reference .

Tabulation (Contd..) General Principles(Contd…). 8. Base title should match with the base value. 9. Percentages can be kept close to the data. 10.All column figures, decimals and (+) or (-) signs must be properly aligned. 11.Abbreviations should be avoided to the extent possible and ditto marks should not be used in the table. 12. Miscellaneous and exceptional(DK/NS) items should be kept at the bottom of the table.

Cross Tabulation Method • 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

Nominal Scale One of the Measurement Scales , which simply assigns number symbols to events in order to label them. These numbers are just convenient labels for the particular class of events and there is no quantitative value for them. Chi-Square test is the most common test of statistical significance that can be applied with nominally scaled numbers. Ex : Assignment of numbers to basketball players in order to identify them.

Measurement Scales • Nominal Scale  Nominal scaling is restricted to the mode as the only measure of central • Ordinal Scaletendency  Rank orders represents ordinal scale and it’s frequently used in qualitative research. Both median and mode can be used for ordinal scale. Non-parametric tests can only be run on ordinal data. • Interval scale • Ratio scale  Mean, median and mode can all be used to measure central tendency for interval and ratio scaled data

Data Analysis Qualitative data lends itself to non-parametric tests and Quantitative data lends itself to parametric tests in statistics Data Qualitative Nominal variables

Quantitative Ordinal variables

Non-Parametric Tests (Chi-Square test)

Interval variables

Ratio variables

Parametric Tests t-tests and Analysis of Variance (ANOVA)

Types of Analysis 1. Univariate analysis. - Analysis involving a single variable 2. Bivariate analysis. - Analysis involving two variables

3. Multi-variate analysis. - Analysis involving more than two variables

Univariate Analysis If the analysis involves only a single variable, then it is called as a Univariate analysis. Useful when each variables are analyzed separately.

Following are the examples of univariate analysis. 1. Mean 2. Variance 3. Standard deviation 4. Median 5. Mode Etc…

Bi-variate Analysis If the analysis involves two variables, then it is called as a Bi-variate analysis. Following are the examples of bi-variate analysis. 1. Linear/Simple regression 2. Correlation coefficient 3. Chi-square 4. Covariance Etc…

Multi-variate Analysis If the analysis involves more than two variables, then it is called as a multi-variate analysis. Following are the examples of Multi-variate analysis.

1. Multiple regression 2. Correspondence analysis 3. Factor analysis 4. Cluster analysis 5. Discriminant analysis Etc…

Why Use Multivariate Analysis ? 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)