An Analytical Tool for Visual Data Analysis: Application to the Mmogo-Method^®

Abstract

An analytical tool that enables experienced researchers and non-experts to analyse participant-created visual data has so far remained underexplored. Existing frameworks to analyse participant-generated visual data tend to use selected theoretical frameworks, guided by participants’ interpretations, to generalise results, test hypotheses, or to identify representational meanings. Analyses of visual data as separate units employ mainly content analysis or (social) semiotic analysis. This article presents a tool for conducting a systematic and transparent analysis of image-based research data by adopting an empiricist and pragmatist approach. We designed a step-by-step procedure to guide researchers individually or in a group to conduct an analysis of such data. Our prototype—which we named Created-Image Data Analysis (CIDA)—was developed by applying design-based research. The CIDA tool consists of five phases, each with an analytical focus and operational questions. Phase 1 covers the basic information; Phase 2 examines the elements and organisation of the visual representation; Phase 3 analyses its logic or cohesion; Phase 4 interprets meaning; and Phase 5 concludes with an evaluation. We applied CIDA to an example of visual data obtained by applying the Mmogo-method in which participants use materials such as clay, dried grass stalks, and beads of different sizes and colours to respond to a researcher-introduced prompt. The CIDA tool is applicable to all static participant-created visual data obtained during research, but it has not yet been tested for analysing moving image-based data. The tool qualifies as heuristic; it offers a systematic procedure to guide an analysis with data-grounded interpretations. The analysis is replicable and open to scrutiny. When the findings from the textual data are considered in combination with the participant-created visual data, the trustworthiness of the interpretations of these datasets is enhanced. This analytical tool enables a rigorous procedure applicable to visual data across subject disciplines and for different research purposes.