Secondary Data and Market Research Techniques
Secondary Data: Advantages and Disadvantages
Secondary Data are data that have already been collected for purposes other than the problem at hand.
Advantages of Secondary Data
- Easily accessible and relatively inexpensive.
- Can help in identifying and better defining the problem.
- Helps in developing an approach to the problem.
- Answers certain research questions and tests some hypotheses.
- Allows for more insightful interpretation of primary data.
Disadvantages of Secondary Data
- Usefulness may be limited in relevance and accuracy.
- Objectives, nature, and methods used to collect the data may not be appropriate to the present situation.
- May lack accuracy and may not be completely current or dependable.
Criteria for Evaluating Secondary Data
Specifications: Methodology Used to Collect the Data
Should be critically examined to identify possible sources of bias. These checks provide information on the reliability and validity of the data and help determine whether they can be generalized to the problem at hand.
Error: Accuracy of the Data
The researcher must determine whether the data are accurate enough for the purpose of the current study. Errors can occur in the approach, research design, sampling, data collection, analysis, and reporting stages of the project. It’s difficult to evaluate the data if the researcher did not participate in the research.
Currency: When the Data Was Collected
Data may not be current, and the time between data collection and publishing may be long. Data may not be updated frequently enough for the purpose of the problem at hand. Marketing research requires current data; therefore, the value may diminish if data becomes dated.
Objective: The Purpose for Which the Data Were Collected
Data is collected with objectives in mind. The objective will determine the purpose for which that information is relevant and useful. It may not be appropriate in another situation.
Nature: The Content of Data
The nature or content of the data should be examined with special attention to the definition of key variables, the units of measurement, categories used, and the relationship examined.
Dependability: How Dependable Are the Data?
Overall indication of the dependability of the data can be assessed by examining the expertise, credibility, reputation, and trustworthiness of the source. This information can be obtained by checking with others who have used the information provided.
Qualitative vs. Quantitative Research
Qualitative Research
Objective: To gain a qualitative understanding of underlying reasons and motivations.
- Small sample of non-representative cases.
- Unstructured data collection.
- Non-statistical data analysis.
- Outcome: Develop an initial understanding.
Quantitative Research
Objective: To quantify the data and generalize the results from the sample to the population of interest.
- Large sample of representative cases.
- Structured data collection.
- Statistical data analysis.
- Outcome: Recommend a final course of action.
Depth Interview Techniques
Laddering
Questioning progresses from product characteristics to user characteristics. This helps the researcher tap into the consumer’s network of meanings, psychological, and emotional reasons that affect their purchasing decisions. Researchers want to know more than simple quality and low-price reasons. Example: Cosmetics.
Hidden Issue Questioning
Focuses on personal “sore spots,” not easily revealed, so research should use specific tactics.
Symbolic Analysis
Attempts to analyze the symbolic meaning of objects by comparing them with their opposites. Example: Comparing email with traditional letters for family communication.
Observation Techniques
Structured Observation
The researcher specifies in detail what is to be observed and how measurements are to be recorded. Example: An auditor performing inventory analysis in a store. It’s appropriate when the problem has been clearly identified.
Unstructured Observation
The observer monitors all aspects that seem relevant to the problem at hand. Example: Observing children playing with new toys. It’s appropriate when the problem has yet to be formulated.
Disguised Observation
Respondents are not aware that they are being observed, for example, using one-way mirrors.
Undisguised Observation
The respondents are aware that they are under observation.
Natural Observation
Involves observing behavior as it takes place in the environment. Example: Observing the behavior of respondents eating fast food.
Contrived Observation
Respondents’ behavior is observed in an artificial environment, such as a test kitchen.