Chrome Extension
WeChat Mini Program
Use on ChatGLM

Investigating Measurement Invariance of the IDS-2 Intelligence Scales Between Migrant and Non-Migrant Groups

Lily Gantscheva, Martin Steppan,Alexander Grob

Intelligence(2024)SCI 3区SCI 2区

Univ Basel | Dept Dev & Personal Psychol

Cited 0|Views1
Abstract
Intelligence plays a crucial role in various aspects of human life, impacting health, academic achievement, and socio-economic success. However, cultural and linguistic disparities in intelligence testing pose challenges, particularly for individuals from migrant backgrounds. This study replicates and extends the landmark study by Wicherts and Dolan (2010) exploring measurement invariance of the German language intelligence test, the Intelligence and Development Scales - 2 (IDS-2), between children and adolescents from migrant (N = 132) and non-migrant (N = 1898) groups. The results revealed partial strict measurement invariance in the IDS-2 intelligence scale subtests across the examined groups. The breach of full strict measurement invariance is primarily due to intercept differences on the verbally loaded subtests-Naming Categories, Naming Opposites, and Story Recall-highlighting the confounding impact of language complexity on test outcomes. These discrepancies resulted in a cumulative intercept difference disadvantaging migrant participants of approximately 4 IQ points on the Full-Scale IQ Score. The findings indicate that while the IDS-2 scales generally assess intelligence consistently across diverse groups, the influence of language complexity on the verbal subtests may result in a disadvantage for children and adolescents with migration backgrounds. To address these biases, we propose the development of non-verbal and culturally fair intelligence tests.
More
Translated text
Key words
Measurement invariance,IDS-2,Intelligence,Migration background,Intelligence tests,Fairness
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Heather Van Epps, Olaya Astudillo, Yaiza Del Pozo Martin, Joan Marsh
2022

被引用29 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest