Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 2 |
Descriptor
Oral Reading | 2 |
Reading Fluency | 2 |
Reading Tests | 2 |
Bayesian Statistics | 1 |
Computer Assisted Testing | 1 |
Equated Scores | 1 |
Error of Measurement | 1 |
Goodness of Fit | 1 |
Markov Processes | 1 |
Monte Carlo Methods | 1 |
Scoring | 1 |
More ▼ |
Source
Grantee Submission | 2 |
Author
Akihito Kamata | 2 |
Cornelis Potgieter | 2 |
Xin Qiao | 2 |
Joseph Nese | 1 |
Yusuf Kara | 1 |
Publication Type
Speeches/Meeting Papers | 2 |
Reports - Evaluative | 1 |
Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Xin Qiao; Akihito Kamata; Cornelis Potgieter – Grantee Submission, 2023
Oral reading fluency (ORF) assessments are commonly used to screen at-risk readers and to evaluate the effectiveness of interventions as curriculum-based measurements. As with other assessments, equating ORF scores becomes necessary when we want to compare ORF scores from different test forms. Recently, Kara et al. (2023) proposed a model-based…
Descriptors: Error of Measurement, Oral Reading, Reading Fluency, Equated Scores
Xin Qiao; Akihito Kamata; Yusuf Kara; Cornelis Potgieter; Joseph Nese – Grantee Submission, 2023
In this article, the beta-binomial model for count data is proposed and demonstrated in terms of its application in the context of oral reading fluency (ORF) assessment, where the number of words read correctly (WRC) is of interest. Existing studies adopted the binomial model for count data in similar assessment scenarios. The beta-binomial model,…
Descriptors: Oral Reading, Reading Fluency, Bayesian Statistics, Markov Processes