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Modelling Change: New Opportunities in the Analysis of Microgenetic Data.

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Modelling Change: New Opportunities in the Analysis of Microgenetic Data. / Cheshire, Andrea; Muldoon, Kevin P.; Francis, Brian; Lewis, Charlie N.; Ball, Linden J.

In: Infant and Child Development, Vol. 16, No. 1, 02.2007, p. 119-134.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Cheshire, A, Muldoon, KP, Francis, B, Lewis, CN & Ball, LJ 2007, 'Modelling Change: New Opportunities in the Analysis of Microgenetic Data.', Infant and Child Development, vol. 16, no. 1, pp. 119-134. https://doi.org/10.1002/icd.498

APA

Vancouver

Author

Cheshire, Andrea ; Muldoon, Kevin P. ; Francis, Brian ; Lewis, Charlie N. ; Ball, Linden J. / Modelling Change: New Opportunities in the Analysis of Microgenetic Data. In: Infant and Child Development. 2007 ; Vol. 16, No. 1. pp. 119-134.

Bibtex

@article{e4d5322685674a0992897c2694f2fdbc,
title = "Modelling Change: New Opportunities in the Analysis of Microgenetic Data.",
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{\textquoteright}s cognitive development. The first example illustrates the use of quasi-binomial modelling to examine 6- and 7-year olds{\textquoteright} 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{\textquoteright} counting skills. This technique was employed to examine different influences on children{\textquoteright}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.",
keywords = "microgenetic, modelling, cognitive development, change",
author = "Andrea Cheshire and Muldoon, {Kevin P.} and Brian Francis and Lewis, {Charlie N.} and Ball, {Linden J.}",
year = "2007",
month = feb,
doi = "10.1002/icd.498",
language = "English",
volume = "16",
pages = "119--134",
journal = "Infant and Child Development",
issn = "1522-7227",
publisher = "JOHN WILEY & SONS LTD",
number = "1",

}

RIS

TY - JOUR

T1 - Modelling Change: New Opportunities in the Analysis of Microgenetic Data.

AU - Cheshire, Andrea

AU - Muldoon, Kevin P.

AU - Francis, Brian

AU - Lewis, Charlie N.

AU - Ball, Linden J.

PY - 2007/2

Y1 - 2007/2

N2 - 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.

AB - 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.

KW - microgenetic

KW - modelling

KW - cognitive development

KW - change

U2 - 10.1002/icd.498

DO - 10.1002/icd.498

M3 - Journal article

VL - 16

SP - 119

EP - 134

JO - Infant and Child Development

JF - Infant and Child Development

SN - 1522-7227

IS - 1

ER -