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Focus on variability: New tools to study intra-individual variability in developmental data
Institution:1. Neuropsychology and Cognitive Health Program, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada;2. Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada;1. Professor of Applied Linguistics in Education, School of Education and Lifelong Learning, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, United Kingdom;2. Jilin University, China;1. School of Foreign Studies, South China Normal University, No. 55 Zhongshan Avenue, Guangzhou, Guangdong 510631, China;2. Key Research Center of Guangdong Province for Language, Cognition and Assessment, China;3. Department of Applied Linguistics, The Pennsylvania State University, 234 Sparks Building, University Park, PA 16802, USA;1. School of Psychological Sciences, Monash University, Building 17, Clayton Campus, Wellington Road, VIC 3800, Australia;2. Queensland Brain Institute, The University of Queensland, Brisbane 4072, Australia;3. Melbourne School of Psychological Sciences, University of Melbourne, Redmond Barry Building, Parkville, VIC 3010, Australia
Abstract:In accordance with dynamic systems theory, we assume that variability is an important developmental phenomenon. However, the standard methodological toolkit of the developmental psychologist offers few instruments for the study of variability. In this article we will present several new methods that are especially useful for visualizing and describing intra-individual variability in individual time-serial data of repeated observations. In order to illustrate these methods, we apply them to data of early language development. After reviewing the common techniques and measures, we present new methods that show variability in developmental time-series data: the moving min–max graph, and the progmax–regmin graph. In addition, we demonstrate a technique that is able to detect sudden increases of variability: the critical frequency method. Also, we propose a technique that is based on a central assumption of the measurement-error-hypothesis: namely the symmetric distribution of error. Finally, as traditional statistical techniques have little to offer in testing variability hypotheses, we examine the possibilities that are provided by random sampling techniques. Our aim with the present discussion of variability and the demonstration of some simple yet illustrative techniques is to help researchers focus on rich additional sources of information that will lead to more interesting hypotheses and more powerful testing procedures, adapted to the unique nature of developmental data.
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