Comparing Analysis Frames for Visual Data Sets: Using Pupil Views Templates to Explore Perspectives of Learning

Autor/inn/en	Wall, Kate; Higgins, Steve; Remedios, Richard; Rafferty, Victoria; Tiplady, Lucy
Titel	Comparing Analysis Frames for Visual Data Sets: Using Pupil Views Templates to Explore Perspectives of Learning
Quelle	In: Journal of Mixed Methods Research, 7 (2013) 1, S.22-42 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1558-6898
DOI	10.1177/1558689812450211
Schlagwörter	Mixed Methods Research; Research Methodology; Visual Aids; Data Analysis; Educational Research; Elementary School Students; Secondary School Students; Learning; Gender Differences; Age Differences; Thinking Skills; Foreign Countries; United Kingdom (England) + Suchen Sie Ihr Suchwort? Research method; Forschungsmethode; Anschauungsmaterial; Auswertung; Bildungsforschung; Pädagogische Forschung; Sekundarschüler; Lernen; Geschlechterkonflikt; Age; Difference; Age difference; Altersunterschied; Denkfähigkeit; Ausland
Abstract	A key challenge of visual methodology is how to combine large-scale qualitative data sets with epistemologically acceptable and rigorous analysis techniques. The authors argue that a pragmatic approach drawing on ideas from mixed methods is helpful to open up the full potential of visual data. However, before one starts to "mix" the stages of analysis one needs to be aware of the strengths and weaknesses provided by the various qualitative and quantitative perspectives. This article therefore provides a methodological discussion based on empirical research experiences with one visual data set: Pupil Views Templates (Wall and Higgins). The authors investigate two different approaches to the analysis of these data: inductive and deductive processes. The two approaches are applied separately to the same data set and observations are made regarding the affordances and constraints of each process, and the findings and implications for developing visual analysis in this area are presented. The authors show how both processes provide useful insight, but without clear strategy as to how they can be combined to achieve the intent of the research, the true potential of visual data will remain unlocked. (Contains 2 figures and 4 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10