Modelling Change: New Opportunities in the Analysis of Microgenetic Data

Autor/inn/en	Cheshire, Andrea; Muldoon, Kevin P.; Francis, Brian; Lewis, Charlie N.; Ball, Linden J.
Titel	Modelling Change: New Opportunities in the Analysis of Microgenetic Data
Quelle	In: Infant and Child Development, 16 (2007) 1, S.119-134 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1522-7219
DOI	10.1002/icd.498
Schlagwörter	Cues; Logical Thinking; Computation; Cognitive Development; Models; Preschool Children; Young Children; Change; Case Studies; Statistical Analysis; Pretests Posttests; Testing; Feedback (Response); Control Groups; Experimental Groups + Suchen Sie Ihr Suchwort? Stichwort; Kognitive Entwicklung; Analogiemodell; Pre-school age; Preschool age; Child; Children; Pre-school education; Preschool education; Vorschulalter; Kind; Kinder; Vorschulkind; Vorschulkinder; Vorschulerziehung; Vorschule; Frühe Kindheit; Wandel; Case study; Fallstudie; Case Study; Statistische Analyse; Testdurchführung; Testen
Abstract	Despite the increasing use of the microgenetic methodology to examine change, the techniques employed to analyse microgenetic data remain fairly unsophisticated. This paper reviews the existing ways of analysing such data and describes their limitations. We use two recent studies to illustrate how modelling can avoid these problems and reveal important aspects of children's cognitive development. The first example illustrates the use of quasi-binomial modelling to examine 6- and 7-year olds' analogical reasoning development. This method offered insights into the way in which children develop, in terms of the rate and path of change, and how different instructional cues can affect their performance. The second study employs a random effects logistic model to analyse the development of preschoolers' counting skills. This technique was employed to examine different influences on children's use of counting to compare quantities. We argue that the key benefit of such modelling approaches is that they are able to tap into the process of change whilst not compromising statistical assumptions. (Contains 6 figures and 1 table.) (As Provided).
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Update	2017/4/10