ReviewAnnals of dyslexia2020
Capitalizing on the promise of item-level analyses to inform new understandings of word reading development.
Review in Annals of dyslexia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response Models.Psychometrika · 2025Article
- Modeling Item-Level Spelling Variance in Adults: Providing Further Insights into Lexical Quality.Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
This special issue was expressly designed to illustrate how item-level analytic models can be employed to answer new questions in the field of reading. To accomplish this, we present a compilation of six empirical studies that explore how item-level analyses can be used to advance the field's understanding of word reading development. In this introduction to the special issue, I summarize the unique advantages of item-level analyses and discuss how the papers in this special issue are valuable examples of how item-focused analyses can be used in a variety of ways to address important questions across orthographies, word characteristics, and tasks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.