Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Random and Systematic Errors01:20

Random and Systematic Errors

10.9K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
10.9K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.7K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
73.7K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

71
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
71
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.6K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.6K
Distance Corrections01:15

Distance Corrections

27
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
27

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Use of intraoperative vancomycin powder and its effects on the incidence of surgical site infection in orthopaedic trauma: a systematic review with meta-analysis.

OTA international : the open access journal of orthopaedic trauma·2026
Same author

Joint analysis of dispersed count-time data using a bivariate latent factor model.

The British journal of mathematical and statistical psychology·2025
Same author

Beta-Binomial Model for Count Data: An Application in Estimating Model-Based Oral Reading Fluency.

Educational and psychological measurement·2025
Same author

Equating Oral Reading Fluency Scores: A Model-Based Approach.

Educational and psychological measurement·2024
Same author

Penalized likelihood methods for modeling count data.

Journal of applied statistics·2023
Same author

Bias for Treatment Effect by Measurement Error in Pretest in ANCOVA Analysis.

Educational and psychological measurement·2022
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026
Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026
Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026
Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026
Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

15.7K

Incorporating calibration errors in oral reading fluency scoring.

Xin Qiao1, Akihito Kamata2, Cornelis Potgieter3,4

  • 1University of South Florida, Tampa, Florida, USA.

The British Journal of Mathematical and Statistical Psychology
|May 10, 2024
PubMed
Summary
This summary is machine-generated.

Calibration errors in oral reading fluency (ORF) assessments can bias scores. Accounting for these errors provides more accurate standard errors (SEs) for ORF scores, especially with smaller calibration samples.

Keywords:
count dataitem response theorymeasurement errororal reading fluencyresponse time

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

756
Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish
14:43

Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish

Published on: July 18, 2020

8.0K

Related Experiment Videos

Last Updated: Jun 26, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

15.7K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

756
Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish
14:43

Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish

Published on: July 18, 2020

8.0K

Area of Science:

  • Educational Measurement
  • Psychometrics
  • Reading Fluency Research

Background:

  • Oral reading fluency (ORF) assessments are crucial for identifying struggling readers and evaluating educational interventions.
  • Current scoring methods often treat calibrated passage parameters as fixed, potentially overlooking calibration errors.
  • Unaccounted calibration errors can lead to biased ORF scores and underestimated standard errors (SEs).

Purpose of the Study:

  • To develop and evaluate a method for incorporating calibration errors into latent variable scores for ORF assessments.
  • To derive accurate SEs for ORF scores that account for calibration uncertainty.
  • To assess the impact of calibration errors on score estimates and SEs under varying conditions.

Main Methods:

  • Utilized an approach that integrates calibration errors into latent variable score calculations.
  • Employed the delta method to derive SEs for ORF scores, explicitly including calibration uncertainty.
  • Conducted a simulation study to examine the recovery of point estimates and SEs for latent variable and ORF scores.

Main Results:

  • Ignoring calibration errors resulted in underestimated SEs for both latent variable scores and ORF scores.
  • The underestimation of SEs was more pronounced when the calibration sample size was small.
  • The proposed method demonstrated improved accuracy in SE estimation by accounting for calibration uncertainty.

Conclusions:

  • It is essential to acknowledge and incorporate calibration errors in ORF scoring to obtain reliable estimates.
  • Underestimating SEs can have significant implications for intervention evaluation and reader screening.
  • Future ORF assessment development should prioritize methods that address calibration uncertainty for enhanced score precision.