With the data in a form that is now useful, the researcher can begin the process of analyzing the data to determine what has been learned. The method used to analyze data depends on the approach used to collect the information (secondary research; primary quantitative research; primary qualitative research). For primary research the selection of method of analysis also depends on the type of research instrument used to collect the information.
Essentially there are two types of methods of analysis – descriptive and inferential.
Descriptive Data Analysis
Not to be confused with descriptive research, descriptive analysis, as the name implies, is used to describe the results obtained. In most cases, the results are merely used to provide a summary of what has been gathered (e.g., how many liked or dislike a product) without making a statement of whether the results hold up to statistical evaluation. For quantitative data collection the most common methods used for this basic level of analysis are visual representations, such as charts and tables, and measures of central tendency including averages (i.e., mean value). For qualitative data collection, where analysis may consist of the researcher’s own interpretation of what was learned, the information may be coded or summarized into grouping categories.
Inferential Data Analysis
While descriptive data analysis can present a picture of the results, to really be useful the results of research should allow the researcher to accomplish other goals such as:
- Using information obtained from a small group (i.e., sample of customers) to make judgments about a larger group (i.e., all customers).
- Comparing groups to see if there is a difference in how they respond to an issue.
- Forecasting what may happen based on collected information.
To move beyond simply describing results requires the use of inferential data analysis where advanced statistical techniques are used to make judgments (i.e., inferences) about some issue (e.g., is one type of customer different from another type of customer). Using inferential data analysis requires a well-structured research plan that follows the scientific method. Also, most (but not all) inferential data analysis techniques require the use of quantitative data collection.
As an example of the use of inferential data analysis, a marketer may wish to know if North American, European, and Asian customers differ in how they rate certain issues. The marketer uses a survey that includes a number of questions asking customers from all three regions to rate issues on a scale of 1 to 5. If a survey is constructed properly the marketer can compare each group using statistical software that tests whether differences exists. This analysis offers much more insight than simply showing how many customers from each region responded to each question.