{"data":{"id":"10.17608/k6.auckland.c.3469797","type":"dois","attributes":{"doi":"10.17608/k6.auckland.c.3469797","prefix":"10.17608","suffix":"k6.auckland.c.3469797","identifiers":[{"identifier":"https://doi.org/10.17608/k6.auckland.c.3469797","identifierType":"DOI"}],"alternateIdentifiers":[{"alternateIdentifierType":"DOI","alternateIdentifier":"https://doi.org/10.17608/k6.auckland.c.3469797"}],"creators":[{"name":"Brown, Gavin","nameType":"Personal","givenName":"Gavin","familyName":"Brown","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"https://orcid.org/0000-0002-8352-2351","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"New Methods for New Methods"}],"publisher":"Figshare","container":{},"publicationYear":2016,"subjects":[{"subject":"140302 Econometric and Statistical Methods","subjectScheme":"FOR"},{"subject":"FOS: Economics and business","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Education"},{"subject":"130103 Higher Education","subjectScheme":"FOR"},{"subject":"FOS: Educational sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2016-09-21","dateType":"Created"},{"date":"2018-01-21","dateType":"Updated"},{"date":"2016","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC BY 4.0","rightsUri":"https://creativecommons.org/licenses/by/4.0"}],"descriptions":[{"description":"This collection provides work on the challenge of preparing doctoral students in education to develop competence in the advanced statistical methods they need to properly analyse the complex data that education generates.For example: Knowing whether a new curriculum program has\nimpact over the status quo requires matching students in the innovation schools\nwith similar students in a counterfactual situation (propensity matching),\nwhile taking into account that students are taught in groups which influence\nthe nature of an innovation (nested hierarchical analysis) and that some students\nwill be absent at data collection points (missing data analysis), and\ndetermining the effect of change (longitudinal value added analysis) may have\nto use non-equivalent measures of effectiveness (equating required) and take\ninto account multiple confounding factors.Thus, this collection provides source documents and working documents around the idea that new ways of teaching and designing curriculum are probably needed to assist future post-doctoral researchers to cope with the complexity of educational 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