Large-scale data decipher children's scale errors: A meta-analytic approach using the zero-inflated Poisson models

IF 3.1 1区 心理学 Q2 PSYCHOLOGY, DEVELOPMENTAL Developmental Science Pub Date : 2024-03-27 DOI:10.1111/desc.13499
Hiromichi Hagihara, Mikako Ishibashi, Yusuke Moriguchi, Yuta Shinya
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Abstract

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the previous research do not adequately capture the structure of the data. By conducting a secondary analysis of aggregated datasets across nine different studies (n = 528) and using more appropriate statistical methods, this study provides a more accurate description of the development of scale errors. We implemented the zero-inflated Poisson (ZIP) regression that could directly handle the count data with a stack of zero observations and regarded developmental indices as continuous variables. The results suggested that the developmental trend of scale errors was well documented by an inverted U-shaped curve rather than a simple linear function, although nonlinearity captured different aspects of the scale errors between the laboratory and classroom data. We also found that repeated experiences with scale error tasks reduced the number of scale errors, whereas girls made more scale errors than boys. Furthermore, a model comparison approach revealed that predicate vocabulary size (e.g., adjectives or verbs), predicted developmental changes in scale errors better than noun vocabulary size, particularly in terms of the presence or absence of scale errors. The application of the ZIP model enables researchers to discern how different factors affect scale error production, thereby providing new insights into demystifying the mechanisms underlying these phenomena. A video abstract of this article can be viewed at https://youtu.be/1v1U6CjDZ1Q

Research Highlights

  • We fit a large dataset by aggregating the existing scale error data to the zero–inflated Poisson (ZIP) model.
  • Scale errors peaked along the different developmental indices, but the underlying statistical structure differed between the in-lab and classroom datasets.
  • Repeated experiences with scale error tasks and the children's gender affected the number of scale errors produced per session.
  • Predicate vocabulary size (e.g., adjectives or verbs) better predicts developmental changes in scale errors than noun vocabulary size.

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大规模数据破解儿童量表误差:使用零膨胀泊松模型的元分析方法。
尺度错误是一种有趣的现象,即儿童试图对一个微小的物体进行特定的操作。目前有几种观点可以解释量表错误背后的发展机制;然而,对于不同因素如何相互作用并影响量表错误,目前还没有统一的说法,而且以往研究中使用的统计方法也不能充分捕捉数据的结构。本研究通过对九项不同研究的汇总数据集(n = 528)进行二次分析,并使用更合适的统计方法,对量表误差的发展提供了更准确的描述。我们采用了零膨胀泊松(ZIP)回归法,该方法可直接处理零观测值堆叠的计数数据,并将发展指数视为连续变量。结果表明,尽管非线性反映了实验室数据和课堂数据中量表错误的不同方面,但量表错误的发展趋向被倒 U 型曲线而非简单的线性函数很好地记录了下来。我们还发现,重复量表错误任务的经历减少了量表错误的数量,而女生比男生犯更多的量表错误。此外,模型比较法显示,谓词词汇量(如形容词或动词)比名词词汇量更能预测量表错误的发展变化,尤其是在量表错误的有无方面。ZIP模型的应用使研究人员能够辨别不同因素如何影响量表错误的产生,从而为揭示这些现象的内在机制提供新的见解。本文的视频摘要可在 https://youtu.be/1v1U6CjDZ1Q RESEARCH HIGHLIGHTS 上观看:我们将现有的尺度误差数据汇总到零膨胀泊松(ZIP)模型中,从而拟合了一个大型数据集。量表误差沿着不同的发展指数达到峰值,但实验室数据集和课堂数据集的基本统计结构有所不同。重复量表错误任务的经历和儿童的性别会影响每次训练产生的量表错误数量。谓词词汇量(如形容词或动词)比名词词汇量更能预测量表错误的发展变化。
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来源期刊
CiteScore
8.10
自引率
8.10%
发文量
132
期刊介绍: Developmental Science publishes cutting-edge theory and up-to-the-minute research on scientific developmental psychology from leading thinkers in the field. It is currently the only journal that specifically focuses on human developmental cognitive neuroscience. Coverage includes: - Clinical, computational and comparative approaches to development - Key advances in cognitive and social development - Developmental cognitive neuroscience - Functional neuroimaging of the developing brain
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