Integrating thematic analysis with cluster analysis of unstructured interview datasetsCitation formats

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Integrating thematic analysis with cluster analysis of unstructured interview datasets : an evaluative case study of an inquiry into values and approaches to learning mathematics. / Prevett, Pauline; Black, Laura; Hernandez-Martinez, Paul; Pampaka, Maria; Williams, Julian.

In: International Journal of Research and Method in Education, 30.06.2020.

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@article{69704935a20c4749b4101c14a8efe7b7,
title = "Integrating thematic analysis with cluster analysis of unstructured interview datasets: an evaluative case study of an inquiry into values and approaches to learning mathematics",
abstract = "A novel approach to integrating Cluster Analysis (CA) within qualitative inquiry is presented, grounded in a large, unstructured dataset from open and rather unstructured interviews. This dataset was previously subjected to typical (theory sensitive) thematic analyses. Transformed into quantitative binary matrix structures, the CA offers robustness and transparency as it systematically exhausts the whole dataset in a replicable procedure. However, then the transformation becomes bi-directional, as resulting clusters provoke new qualitative interpretations and even further quantitative analyses. This approach led to theoretically interpretable results that significantly extended previous understandings of relations between 'values' and 'learning approach' relating to mathematics learner identity. This integrated methodology is evaluated for its significance to the substantive field, but is discussed more widely for social science research drawing on such interview datasets in general.",
keywords = "cluster analysis, mixed methods, thematic analysis, mathematics identities",
author = "Pauline Prevett and Laura Black and Paul Hernandez-Martinez and Maria Pampaka and Julian Williams",
year = "2020",
month = jun,
day = "30",
doi = "10.1080/1743727X.2020.1785416",
language = "English",
journal = "International Journal of Research and Method in Education",
issn = "1743-727X",
publisher = "Routledge",

}

RIS

TY - JOUR

T1 - Integrating thematic analysis with cluster analysis of unstructured interview datasets

T2 - an evaluative case study of an inquiry into values and approaches to learning mathematics

AU - Prevett, Pauline

AU - Black, Laura

AU - Hernandez-Martinez, Paul

AU - Pampaka, Maria

AU - Williams, Julian

PY - 2020/6/30

Y1 - 2020/6/30

N2 - A novel approach to integrating Cluster Analysis (CA) within qualitative inquiry is presented, grounded in a large, unstructured dataset from open and rather unstructured interviews. This dataset was previously subjected to typical (theory sensitive) thematic analyses. Transformed into quantitative binary matrix structures, the CA offers robustness and transparency as it systematically exhausts the whole dataset in a replicable procedure. However, then the transformation becomes bi-directional, as resulting clusters provoke new qualitative interpretations and even further quantitative analyses. This approach led to theoretically interpretable results that significantly extended previous understandings of relations between 'values' and 'learning approach' relating to mathematics learner identity. This integrated methodology is evaluated for its significance to the substantive field, but is discussed more widely for social science research drawing on such interview datasets in general.

AB - A novel approach to integrating Cluster Analysis (CA) within qualitative inquiry is presented, grounded in a large, unstructured dataset from open and rather unstructured interviews. This dataset was previously subjected to typical (theory sensitive) thematic analyses. Transformed into quantitative binary matrix structures, the CA offers robustness and transparency as it systematically exhausts the whole dataset in a replicable procedure. However, then the transformation becomes bi-directional, as resulting clusters provoke new qualitative interpretations and even further quantitative analyses. This approach led to theoretically interpretable results that significantly extended previous understandings of relations between 'values' and 'learning approach' relating to mathematics learner identity. This integrated methodology is evaluated for its significance to the substantive field, but is discussed more widely for social science research drawing on such interview datasets in general.

KW - cluster analysis, mixed methods, thematic analysis, mathematics identities

U2 - 10.1080/1743727X.2020.1785416

DO - 10.1080/1743727X.2020.1785416

M3 - Article

JO - International Journal of Research and Method in Education

JF - International Journal of Research and Method in Education

SN - 1743-727X

M1 - 0

ER -