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

Multiple Comparison Tests01:13

Multiple Comparison Tests

4.3K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.3K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

169
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
169
Introduction to z Scores01:05

Introduction to z Scores

981
A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
981
Introduction to z Scores01:06

Introduction to z Scores

10.8K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
10.8K
z Scores and Unusual Values01:07

z Scores and Unusual Values

10.9K
The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
10.9K
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

17.8K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
17.8K

You might also read

Related Articles

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

Sort by
Same author

Evidence-Based Footwear Recommendations for Older Adults: Enhancing Mobility, Comfort, and Fall Prevention.

Journal of the American Geriatrics Society·2025
Same author

How to Use the Index of Productive Syntax to Select Goals and Monitor Progress in Preschool Children.

Language, speech, and hearing services in schools·2022
Same author

Reliability of judgments of stuttering-related variables: The effect of language familiarity.

Journal of fluency disorders·2021
Same author

Improving Automatic IPSyn Coding.

Language, speech, and hearing services in schools·2020
Same author

Attempted use of PACE for riboswitch discovery generates three new translational theophylline riboswitch side products.

BMC research notes·2018
Same author

Circulating microRNAs as biomarkers in traumatic brain injury.

Neuropharmacology·2018

Related Experiment Video

Updated: Dec 26, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

903

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring.

Jenny A Roberts1, Evelyn P Altenberg1, Madison Hunter2

  • 1Department of Speech-Language-Hearing Sciences, Hofstra University, Hempstead, NY.

Language, Speech, and Hearing Services in Schools
|March 19, 2020
PubMed
Summary

Machine scoring of child language syntax using the Computerized Language ANalysis (CLAN) tools is less accurate than manual scoring. Researchers should report accuracy measures to understand CLAN

More Related Videos

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice
06:11

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice

Published on: October 19, 2019

20.9K
Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs
08:44

Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs

Published on: May 9, 2017

16.4K

Related Experiment Videos

Last Updated: Dec 26, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

903
Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice
06:11

Nest Building Behavior as an Early Indicator of Behavioral Deficits in Mice

Published on: October 19, 2019

20.9K
Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs
08:44

Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs

Published on: May 9, 2017

16.4K

Area of Science:

  • Linguistics
  • Computational Linguistics
  • Developmental Psychology

Background:

  • The Index of Productive Syntax (IPSyn) is a measure of grammatical development.
  • Automatic scoring tools like Computerized Language ANalysis (CLAN) aim to streamline IPSyn analysis.
  • Accuracy of automated linguistic analysis tools is crucial for research and clinical applications.

Purpose of the Study:

  • To evaluate the accuracy of machine-scored Index of Productive Syntax (IPSyn) using CLAN tools.
  • To compare machine scoring results against manual scoring benchmarks.
  • To introduce and utilize novel metrics for assessing automated syntactic analysis.

Main Methods:

  • Comparison of machine-scored and manually-scored IPSyn data from 20 transcripts (10 children, 30 and 42 months).
  • Analysis using traditional metrics (absolute point difference, point-to-point accuracy) and new metrics (Machine Item Accuracy - MIA, Cascade Failure Rate).
  • Examination of differences in total scores, subscale scores (Noun Phrase, Verb Phrase, Question/Negation, Sentence Structures), and individual structures.

Main Results:

  • Machine scoring showed a mean absolute point difference of 3.65 and 72.6% point-to-point agreement with manual scoring.
  • Machine Item Accuracy (MIA) was 74.9%, with significantly more erroneous items than missed items.
  • Noun Phrase and Verb Phrase subscales demonstrated higher accuracy than Question/Negation and Sentence Structures subscales.

Conclusions:

  • The CLAN program's automatic scoring of IPSyn demonstrated notable inaccuracies compared to manual scoring.
  • Recommendations for CLAN improvement include addressing second exemplar violations and implementing cascaded credit.
  • Researchers and clinicians should routinely report detailed accuracy metrics, including MIA, to understand the limitations of machine-scored syntax.