Business
Answer 1)
A report is the written or virtual documentation of the research process that clearly defines the target problem, approach development, research design, fieldwork and data preparation and analysis. Important to note, even though summarizing of the research process in the report format is the final step in the marketing research process, however, this is the only step that ensures the documentation of the entire research process and provides a written validation of the efforts put in by the market researcher and thus, attracts significant importance from the point of view of the entire organization. Below we comprehensively discuss the importance of the report making as the part of marketing research project:
i) Source of documentation and validation:
Once the research completes his research on the target problem and is satisfied with the overall outcome, he needs to convert all his effort in a tangible format. The report making process thus provides documentary evidence of the researcher’s effort which he can then submit to the management for review.
ii) Source of decision making
Every marketing research process is initiated with the objective to find a solution to an existing problem or explore the success of business opportunity. Henceforth, managers of many departments are dependent on the outcome of the research process and until these managers are provided with a documented layout of the entire research process, they may not be able to take any further decision and the utility of the entire research process will be diminished.
iii) Source of future reference
Research report also serves as a source for future reference as the management may wish to develop the research process again by taking assistance or necessary utilities from the past research report.
Henceforth, considering the above aspects, it is evident that every market researcher should detail his entire research process as well as the conclusions and recommendation in a logical sequence through the research report.
Answer 2)
The very purpose of the data analysis process is to produce information that will further guide the future decision making activity of the organization. Considering the importance of the data analysis process, it is important that the researcher follows a set pattern while selecting the data analysis strategy.
The very step of the selection of the data strategy begins with a consideration of the first three steps in the process: Definition of the target problem (Step 1), Development of the approach (Step II) and Research Design (Step III). Until this point, the researcher should use the research layout as the benchmark, though he might introduce changes in light of any modification to the research layout over the period of the research process.
The next step relates to understanding the characteristic of the data and what statistical method favors the nature of the data. For instance, ANOVA may be appropriate for analyzing experimental data from causal designs.
Once the researcher has understood the characteristic of the data involved, the next step is to consider the statistical techniques, their purpose and assumptions involved. The researcher should relate the data characteristic and see if the purpose and assumptions of statistical technique suits the data involved.
Finally, the background and philosophy of the researcher endow the final effect on the choice of the data analysis strategy. If the researcher is experienced and have handled multiple research projects of different domains, he may wish to apply more advanced statistical methods. On the other hand, a first time researcher may settle for a single data analysis technique.
Answer 3)
Missing responses are one of the crucial aspects while dealing with the data. These responses represent variables that are unknown because of ambiguous answers by the respondents or if their responses were not recorded properly. Important to note, if around 10% of the total response fall in the category of non-response error, in that case, it will be difficult to proceed with the data collected and analyze it. Below we have discussed the different approaches for handling the data:
i)Substituting with a neutral value:
This is the simplest method for dealing with the non-response error. As part of this method, the mean response of the variable is substituted with for the missing responses. Accordingly, the mean and other statistics remain unchanged.
ii) Substitute with an imputed response:
Under this method, the pattern of responses of the respondents to the other questions is used to impute the response for possible missing data. The biggest advantage of this method is that the imputed responses can be calculated statistically by determining the relationship of the missing variable to other answered variables. However, this method is time consuming and is prone to serious bias on behalf of the researcher.
iii) Casewise deletion:
As part of casewise deletion, respondents with any missing data are completely eliminated from the analysis. However, in case of significant non-response, eliminating all the respondents will turn the remaining data meaningless.
iv) Pairwise deletion:
Under this method, instead of discarding all the cases with missing responses, the researcher only uses the data of the respondents with complete responses for the data variables. However, the methods works appropriately when the sample size is large, there are only a few responses and there is no significant relationship with the variables.
Considering the above four approaches for handling the data, I believe that Pairwise deletion works best as unlike other methods it has the least probability of inclusion of researcher bias and moreover, it does not distort the data in any way.
References
Malhotra, Naresh. Marketing Research: An Applied Orientation. Pearson, 2010.
