Preparing the Research Report

Once the data have been tabulated, interpreted and analyzed, the marketing researcher is required to prepare his report embodying the findings of the research study and recommendations. As a poor report on an otherwise good research will considerably undermine its utility, it is necessary that the researcher gives sufficient thought and care to its preparation.

Although report writing needs some skill, which can be developed with practice, the researcher should follow the main principles of writing a report. Some of these principles are objectivity, coherence, clarity in the presentation of ideas and use of charts and diagrams. The essence of a good research report is that it effectively communicates its research findings. As management is generally not interested in details of the research design and statistical findings, the research report should not be loaded with such details, otherwise there is a strong likelihood of its remaining unattended on the manager’s desk. In view of this, the researcher has to exercise extra care to make the report a useful and a worthwhile document for the management.

Sometimes, a detailed marketing research study throws up one or more areas where further investigation is needed. Since research on those areas or aspects could have been fitted into the original project, a separate follow-up study has to be attempted.

The marketing research process, as described above, involves various steps, though strict adherence to each of these steps may not be necessary. A researcher may deviate from the above sequence and steps depending on his specific needs. It should be remembered that as research proceeds from the selection of the theme through the collection and analysis of data to the preparation of a report, the focus of attention will move from one activity to the other. This implies that the researcher does not always concentrate exclusively on one particular phase of research until its completion.

Further, while it is beneficial to draw a detailed plan and sequence of various activities in marketing research, it is hardly so if it requires such financial backing as the firm cannot afford. There is no point in attempting something which cannot be completed on account of financial constraints or limitations of time.

Another point worth emphasizing is that howsoever elaborate a research design may be, its successful implementation depends in no small measure on its management. In fact, management of research, whether in marketing or in any other field, is of great importance

Errors in the Research Process

Hitherto, we have discussed the marketing process. The researcher should ensure that the research should ensure that the research does not have a high degree of error. If no care is exercised in minimizing errors that are likely to crop up at every stage then they are bound to assume phenomenal proportions.

The errors are of two types:

• Sampling Error
• Non-Sampling Errors

Sampling Errors:  Marketing research studies are based on samples of people or products or stores The results emerging form such studies are then generalized, i.e. applied to the entire population. For example, if a study is done amongst Maruti car owners in a city to know their average monthly expenditure on the maintenance of their car, it can be done either by covering all Maruti car owners residing in that city or by choosing a sample, say 10%, of the total Maruti car owners. In the latter case, the study may give a different average than the actual average if the entire population is covered. The difference between the sample value and the corresponding population value is known as the sampling error.

Non-Sampling Errors: Non-sampling errors are those errors which occur in different stages of research except in the selection of sampling. These errors are many and varied. A non-sampling error can arise right at the beginning when the problem is defined wrongly. It can also occur in any of the subsequent stags such as in designing a questionnaire, non-response of the questionnaire, in the analysis and interpretation of data, etc

Remember:

• Sampling error is measurable while it is not easy to measure a non-sampling error.
• Sampling error decrease as the sample size increases, while it is not necessary in case of non-sampling error.

Types of Non-sampling Errors

Defective problem definition: Problem on which research is to be undertaken should be precisely defined. For example, a study in unemployment must be clear as to the concept of unemployment, the reference period, and the geographic area to be covered, and so on. If any of these concepts has wrong connotation, the results of the study would turn out to be wrong.

Defective population definition: If the population is not well-defined and does not fit to the objects of research study then an error occurs. Suppose a study is undertaken to know the views of industrial workers on incentives offered by a company. The study defines its population as male employees and interviews are held amongst them. The exclusion of female employees would be a source of error.

Frame error: The sampling frame is the list of all units comprising the population from which a sample to be taken. If the sampling frame is incomplete or inaccurate, its use will give rise to this type of error. For example, consider the voters’ list as sampling frame. If a survey is to be undertaken to collect information from different sections of the society, then the voters’ list will be inappropriate. This is because young people below 15 years of age will be left out from the survey.

Surrogate information error: This type of error occurs when the information sought by the researcher is different from the information needed to solve the problem. For example, when price of a brand is taken to represent its quality In such a case, it is presumed that higher the price of the brand, the better is its quality. This may or may not be true.

Non-response error: Non-response error occurs when respondent refuses to cooperate with the interviewer by not answering his questions. In case of mail survey particularly, the extent of non-response is usually high.

Measurement error: This is caused when the information gathered is different from the information sought. For example, respondent are asked to indicate whether they own a colour television set. Some of them respond in the affirmative just to boost their image before an interviewer, even though they may not own a colour television set.

Experimental error: An experiment aims at measuring the impact of one or more independent variable on a dependent variable. For example, take the case of the impact of training on the performance of salesmen. During the period when the training is given, there may be a decline in competition and as result sales performance may improve. The result of such an experimental study will be misleading.

Poor questionnaire design: As you know that a questionnaire is an instrument to collect data from respondent in a survey. If the questionnaire is defective, the data collected on that basis will be misleading. For example, if one or more questions are wrongly worded conveying a different meaning than what was sought to be conveyed, wrong data will be collected through responses to such questions.

Interviewer bias: This error occurs on account of interviewer’s influence in conducting an interview or wrong recording by him.

Data processing error: After the data have been collected, they are to be processed. This involves coding the responses, recording the codes, etc. so that data collection can be transformed into suitable tables. Mistake can occur during the processing stage of data.

Data analysis error: As in the case of data processing, errors can occur on account of wrong analysis of data. Apart form simple mistakes in summation, division, etc., more complex errors can occur. For example, the application of a wrong statistical technique can cause such errors.

Interpretation error: Sometimes wrong interpretation of data can cause this type of error. In order to support a particular line of action, the researcher may deliberately misinterpret data.