Table 2_Identifying dyslexia-consistent reading profiles in mild intellectual disability:

Bartosz M. Radtke's study analyzes diagnostic data from 446 students with mild intellectual disability to identify dyslexia-consistent reading profiles. The data includes age-normed sten scores from the SB6/18 battery for decoding, reading fluency, reading comprehension, phonetic coding, and rapid automatized naming. The dataset was last updated on 2026-05-29.

Use Cases

• Developing severity-calibrated classification rules for reading disorders based on decoding and fluency scores.
• Conducting person-centered cluster analysis on reading and related cognitive markers.
• Investigating the incremental value of phonological and rapid automatized naming indicators for subgroup identification.
• Analyzing the prevalence of dyslexia-consistent profiles under different operational definitions of severity.

Strengths

• Dataset is based on a nationwide sample of 446 students with mild intellectual disability.
• Analysis uses age-normed sten scores from a standardized battery (SB6/18) across five reading-related domains.
• The study employs hierarchical clustering with bootstrap stability and compares six rule-based classification groupings.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small, at 674.0 KB, indicating limited scope.

Provenance

Source

Bartosz M. Radtke via figshare

Collection Method

Nationwide diagnostic data analyzed using hierarchical clustering and rule-based classification.

Freshness

Last updated 2026-05-29 06:11:40; freshness should be verified.

Geography

Nationwide (specific country not stated)