How to Analyze Qualitative Market Research Data

Informal meeting in an advertising agency
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Once qualitative data has been collected, the market researcher is typically faced with a large amount of data that has to be analyzed and interpreted for the ultimate users of the market research insights. Three approaches to qualitative data analysis are discussed here.

Quantitative Data Analysis

In this method of analyzing qualitative research data, the information gathered is tabulated according to the results for different variables in the dataset. This provides a comprehensive picture of the data and assists in the process of identifying patterns

A common way of displaying the data to facilitate the analysis is by using a frequency distribution, which is an organized tabulation of the number of the responses or scores according to each variable category.  Tabulation provides a structured way to determine data accuracy, identify data outliers, gauge the spread of the scores or responses, and observe categorical frequency.

Qualitative Content Analysis

When the content analysis is treated as a quantitative method of analysis, it provides a way to systematically and objectively analyzes media content. This version of content analysis used standardized measurements to code, characterize and compare texts. 

When a qualitative approach is taken to content analysis, the focus is on analyzing both the explicit or manifest content of a text as well as interpreting the latent meaning of texts that which can be interpolated from the text, but that is not explicitly stated in it. The emphasis of content analysis is data coding, which may explain a major limitation of this approach—its inability to provide a rich understanding of the meanings of texts

Constant Comparative Method

This qualitative data analysis method is a structured iterative process in which researchers compare each new bit of data with data that has already been examined in the study:

  • Open Coding: Each data bit is coded and then assigned to a relevant topic category or discarded if no relevance is observed. This coding occurs in accordance with how data bits compare with the accumulating body of analyzed data
  • Axial Coding: As the data bits are analyzed, new overarching topic categories will emerge. Once the data has all been coded and assigned to topic categories, the researcher examines the categories for emerging themes. Theoretical saturation occurs when no new data appears to be emerging from the examined data. 
  • Selective Coding: In this last coding stage, the topic categories and the categorical interrelationships are used to create a storyline that tells or explains the phenomenon that is the focus of the research.

Application of Analysis Approach

A key to the successful analysis of qualitative data is understanding when a method of analysis should be used and when it is better to choose another data analysis approach:

  • Quantitative Data Analysis:  Quantitative data analysis using interval data that are continuous that has a logical order with standardized differences between values, but that does not have a natural zero. Items on a Likert scale are a good example of interval data.
  • Qualitative Content Analysis: In healthcare research, texts appropriate for content analysis include grant proposals, published manuscripts, minutes from meetings, transcripts of conversations, medical encounters, interviews, and focus groups. Appropriate texts for analysis in healthcare fields also include messages communicated to the masses via newspapers, magazines, radio, television and the internet.
  • Constant Comparative Method: The constant comparative method of data analysis can be used with structured responses, such as closed-end survey questions, or unstructured responses, such as those obtained when survey participants answer the open-ended items on a questionnaire. That said, a constant comparative data analysis process has perhaps the most utility when used with extensive accounts that consist of unstructured data, such as interview transcripts.

Presentation of Findings

The way that the data analysis findings or outcomes are presented can make the difference between research that is used and research that is put on the shelf. A rule of thumb is to present data in a way that will be understandable and usable to the least sophisticated people who will receive the data analysis findings:

  • Quantitative Data Analysis: Data are frequently displayed in a manner that condenses the data from the constructed frequency and percent distributions.
  • Qualitative Content Analysis: Data can be presented in tables and matrices. This is useful particularly when quotations are used to articulate the findings by interweaving. What this means is that refinement of the analysis may well occur even as the manuscript is still being written in final form.
  • Constant Comparative Method: The presentation of findings in a constant comparative data analysis process is focused on revealing the themes that have emerged from the data. While visual displays of data may be used, the findings are typically tied to specific excerpts from the data set, which explicitly illustrate the themes. These excerpts are including in the narrative discussion of the results section of the research manuscript and/or article.

    Fit of Analysis Method With Data Collected

    Tailoring the data analysis method to the data that has been collected and to the research questions and ultimate purpose results in stronger insights that can be used with:

    • Quantitative Data Analysis is a good fit for closed-ended question items in surveys.
    • Qualitative Content Analysis is a good fit for Interview response data.
    • Constant Comparative Analysis is a good fit for open-ended question items in surveys and with interview responses.


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