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Learning Disabilities

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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Related Experiment Video

Updated: Nov 24, 2025

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Addressing data deficiencies in assistive technology by using statistical matching methodology: a case study from

Monica Jamali-Phiri¹, Juba Alyce Kafumba¹, Malcolm MacLachlan^2,3

¹Centre for Social Research, Chancellor College, University of Malawi, Zomba, Malawi.

Disability and Rehabilitation. Assistive Technology

|December 28, 2020

Summary

Statistical matching successfully created a composite dataset to assess assistive technology use in Malawi. This method addresses data gaps for children with disabilities in resource-poor settings.

Keywords:

Assistive technology children with disabilities distinct data-sources statistical matching sub-Saharan Africa

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Area of Science:

Rehabilitation science
Disability studies
Health informatics

Background:

Sub-Saharan Africa faces a data gap regarding assistive technology (AT) use among children with disabilities.
Understanding AT access is crucial for improving support and interventions for this population.

Purpose of the Study:

To address the data gap on assistive technology use among children with disabilities in sub-Saharan Africa.
To contribute to efforts examining access to assistive technologies in this region.

Main Methods:

Utilized data from Malawi's 2017 Living Conditions among Persons with Disabilities survey and the 2015-16 Malawi Demographic and Health survey.
Employed statistical matching via a random hot deck technique to integrate the two datasets.

Main Results:

The statistical matching technique produced a composite dataset with a low uncertainty value (2.2%).
Accuracy assessment confirmed the composite dataset's marginal distribution of AT use closely mirrors the donor dataset (Overlap index = 0.997).

Conclusions:

Statistical matching effectively generates reliable data in data-constrained environments, enabling the measurement of AT access for children with disabilities.
This technique enhances the efficient use of secondary data, allowing researchers in resource-poor settings to address new questions regarding AT use and influencing factors.