Technological and methodological advances enable new substantive research questions to be posed, and new study designs to be implemented, in educational research. In this paper I review emerging methods relevant for capturing learning and teaching processes over time —the sequences of learning events— which take place in multiple contexts.

To do so, the concepts of nomothetic and ideographic research are traced through the use of Cattell’s (1952) cube, posing persons, variables and time as the three key dimensions for determining study-designs. For educational research, a fourth dimension —context— is important to consider given the nested structures (e.g. student-teacher dyads, peerrelations, student-groups, classrooms, teachers, and schools) learning and teaching occurs in. Several developments of quantitative methods enable researchers to a) establish quality of measurement (e.g. factor analysis, item response models), b) across sequences of time-points (e.g. autoregressive models), c) in complex multilevel structures (e.g. multilevel models, random effects models), also using estimators which are robust for small-n studies (e.g. Bayesian models). Educational researchers are encouraged to design studies fitting multilevel models for hierarchically and cross-classified data, and to think in terms of intraindividual learning processes.

Cite this article as: Malmberg, L. (2018). Métodos cuantitativos para el registro de procesos y contextos en la investigación educativa | Quantitative Methods for Capturing Processes and Contexts in Educational Research. Revista Española de Pedagogía, 76 (271), 449-462. doi: 10.22550/REP76-3-2018-03

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Author Biography

Lars-Erik Malmberg es Profesor Agregado de Métodos Cuantitativos en Educación en el Departamento de Educación de la University of Oxford. Tiene más de 70 publicaciones (artículos revisados por pares, capítulos de libros e informes) y es el redactor jefe de la revista Journal of Learning and Instruction. Su labor investigadora se centra actualmente en las perspectivas intraindividuales en procesos de aprendizaje y en el modelado de datos intraindividuales utilizando modelos de ecuaciones estructurales multinivel.


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Palabras clave | Keywords

educationalresearch, intensivelongitudinaldata, multilevelmodel, quantitativemethods, statisticalmodels