A generalized nonparametric classification method of small samples using Qc matrix
A generalized nonparametric classification method of small samples using Qc matrix
Chen,Xingyu 1Yan,Yakun 2Kang,Chunhua1
作者信息
- 1. Zhejiang Philosophy and Social Science Laboratory for the Mental Health and Crisis Intervention of Children and Adolescents
- 2. Jinhua Education College
- 折叠
摘要
Objective: This study integrates the idea of QC matrix with generalized nonparametric classification method (GNPC), and extends GNPC to generalized sequential nonparametric classification method (seq-GNPC) that can be applied to graded scoring items, with a main focus on the problem of saturated models that are poorly discriminated under small samples and the parametric model cannot be estimated in small samples.Methods: Including simulation studies and empirical research. The conditions for the simulation study are as follows: The number of attributes K is set to 3 or 5, and the number of items J is set to 20 or 40. The ratio of fixed graded scoring items is 50%. Two types of QC matrices were also considered: restricted QC matrix and unrestricted QC matrix.Results: The simulation results show that seq-GNPC is better than the parametric method in small samples when the data pattern confirms the saturated model. When the data pattern confirms the reduce model, the parametric model shows a significant inability to converge in the small sample situation, yet seq-GNPC guarantees 100% estimation rate. The empirical results also show that seq-GNPC is more stable than seq-GDINA in the small-sample case, with higher attribute pattern remeasurement rates and smaller standard deviations.Limitations: This study counted the number of replicates that the parametric model was able to estimate for analysis under each condition, but it did not discuss in depth the specific circumstances under which the parametric model would no longer fail to diagnose the classification under each sample size, which may need to be explored further.Conclusions: The seq-GNPC method proposed in this paper has good applicability in small samples and can effectively solve the problem of diagnostic evaluation of graded or mixed scoring programs in small samples.Abstract
Objective: This study integrates the idea of QC matrix with generalized nonparametric classification method (GNPC), and extends GNPC to generalized sequential nonparametric classification method (seq-GNPC) that can be applied to graded scoring items, with a main focus on the problem of saturated models that are poorly discriminated under small samples and the parametric model cannot be estimated in small samples.Methods: Including simulation studies and empirical research. The conditions for the simulation study are as follows: The number of attributes K is set to 3 or 5, and the number of items J is set to 20 or 40. The ratio of fixed graded scoring items is 50%. Two types of QC matrices were also considered: restricted QC matrix and unrestricted QC matrix.Results: The simulation results show that seq-GNPC is better than the parametric method in small samples when the data pattern confirms the saturated model. When the data pattern confirms the reduce model, the parametric model shows a significant inability to converge in the small sample situation, yet seq-GNPC guarantees 100% estimation rate. The empirical results also show that seq-GNPC is more stable than seq-GDINA in the small-sample case, with higher attribute pattern remeasurement rates and smaller standard deviations.Limitations: This study counted the number of replicates that the parametric model was able to estimate for analysis under each condition, but it did not discuss in depth the specific circumstances under which the parametric model would no longer fail to diagnose the classification under each sample size, which may need to be explored further.Conclusions: The seq-GNPC method proposed in this paper has good applicability in small samples and can effectively solve the problem of diagnostic evaluation of graded or mixed scoring programs in small samples. 关键词
generalized nonparametric classification method/nonparametric classification method/graded/Qc matrix/seq-GDINAKey words
generalized nonparametric classification method/nonparametric classification method/graded/Qc matrix/seq-GDINA引用本文复制引用
Chen,Xingyu,Yan,Yakun,Kang,Chunhua.A generalized nonparametric classification method of small samples using Qc matrix[EB/OL].(2025-12-04)[2025-12-09].https://chinaxiv.org/abs/202512.00029.学科分类
计算技术、计算机技术
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