Mixed methods research approaches and applications

Technical Writing

Mixed Methods

The topic of this study is Market Segmentation Typologies in the Sports Industry.

The purpose of this study was to develop a consumer typology founded on analysis of consumer participation motivation data by way of a mixed-method approach.

This study describes a methodical approach to developing a consumer segmentation typology using both demographic variables as well as self-identified motivations for sport and fitness participation by way of a multivariate statistical method.

Specific Research Questions / Philosophical Underpinnings

The research questions in this study were how to generate a deeper understanding of consumers in the sports industry and how to analyze and use this information?

Sample

The mixed-method approach used in this study utilized qualitative data in order to help validate succeeding quantitative cluster analysis, and drew upon cluster profiles in order to establish the structure for market segmentation.

Procedure

Data for this study were gathered as part of a larger data collection endeavor. A four-page questionnaire was sent to 2,000 Runner's World subscribers. The questionnaire included a cover letter, a small incentive and a postage-paid return envelope. A follow-up postcard was mailed just about two weeks after the initial questionnaire mailing.

Variables/Concepts

Variables used in this study included demographic variables and self identified motivations for sport and fitness participation.

Instrument(s) analysis

Assessing the reliability of the cluster solutions was done by looking at ranges of cluster solutions. A snake-plot of the four segment solution by the fundamental ten motivations designates a rich solution that shows differences across groups. The external validity of the cluster solution was determined by integrating the closed form data with the cluster solutions founded on qualitative data. The credibility of the cluster profiles was done with representative quotes from the qualitative data. This helped to support the labeling endeavor and strengthen the insights provided by the quantitative data.

Data analysis