Jove
Visualize
Contact Us

Related Concept Videos

Measuring Reaction Rates03:09

Measuring Reaction Rates

33.6K
Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical...
33.6K
The Integrated Rate Law: The Dependence of Concentration on Time02:39

The Integrated Rate Law: The Dependence of Concentration on Time

48.8K
While the differential rate law relates the rate and concentrations of reactants, a second form of rate law called the integrated rate law relates concentrations of reactants and time. Integrated rate laws can be used to determine the amount of reactant or product present after a period of time or to estimate the time required for a reaction to proceed to a certain extent. For example, an integrated rate law helps determine the length of time a radioactive material must be stored for its...
48.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

335
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
335
Pharmacodynamic Models: Logarithmic Concentration–Effect Model01:15

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

67
The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration,...
67
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

69
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
69
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

57
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
57

You might also read

Related Articles

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

Sort by
Same author

Letter identity and position coding in the parafovea.

Journal of experimental psychology. Learning, memory, and cognition·2024
Same author

Looking for immediate and downstream evidence of lexical prediction in eye movements during reading.

Quarterly journal of experimental psychology (2006)·2024
Same author

Lexical processing across the visual field.

Journal of experimental psychology. Human perception and performance·2023
Same author

How do task demands and aging affect lexical prediction during online reading of natural texts?

Journal of experimental psychology. Learning, memory, and cognition·2022
Same author

Are there independent effects of constraint and predictability on eye movements during reading?

Journal of experimental psychology. Learning, memory, and cognition·2022
Same author

A multitask comparison of word- and character-frequency effects in Chinese reading.

Journal of experimental psychology. Learning, memory, and cognition·2022
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 Experiment Video

Updated: Apr 5, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

To transform or not to transform: using generalized linear mixed models to analyse reaction time data.

Steson Lo1, Sally Andrews1

  • 1School of Psychology, University of Sydney Sydney, NSW, Australia.

Frontiers in Psychology
|August 25, 2015
PubMed
Summary

Generalized linear mixed-effect models (GLMM) offer a superior alternative to linear mixed-effect models (LMM) for analyzing skewed reaction time data. GLMMs avoid data transformation, preventing misleading conclusions in psychological research.

Keywords:
RT transformationsadditive factorsgeneralized linear mixed-effect modelsinteraction effectsmental chronometry

More Related Videos

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
08:01

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Published on: August 12, 2016

9.6K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Related Experiment Videos

Last Updated: Apr 5, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K
A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli
08:01

A Method for Manipulating Blood Glucose and Measuring Resulting Changes in Cognitive Accessibility of Target Stimuli

Published on: August 12, 2016

9.6K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Area of Science:

  • Cognitive Psychology
  • Statistical Modeling

Background:

  • Linear mixed-effect models (LMMs) are common in psychology for multi-level data.
  • Current LMM guidelines recommend non-linear transformations for skewed reaction time (RT) data, potentially causing theoretical issues.
  • Transformations can alter findings, as seen in Balota et al. (2013) where RT analysis effects changed.

Purpose of the Study:

  • To introduce Generalized Linear Mixed-Effect Models (GLMM) as a solution for analyzing skewed RT data.
  • To demonstrate how GLMMs satisfy normality assumptions without data transformation.
  • To reanalyze Balota et al.'s (2013) data using GLMM to illustrate its benefits.

Main Methods:

  • Utilized Generalized Linear Mixed-Effect Models (GLMM).
  • Applied GLMM to reanalyze reaction time datasets from Balota et al. (2013).
  • Focused on models that satisfy normality assumptions without data transformation.

Main Results:

  • GLMM analyses preserve the original metric of reaction time data.
  • Avoided the misleading conclusions caused by non-linear transformations of RT data.
  • Successfully reanalyzed Balota et al.'s data, aligning with theoretical expectations.

Conclusions:

  • GLMMs provide a more accurate approach for analyzing skewed psychological data, particularly reaction times.
  • This method allows for robust assessment of individual differences without compromising data integrity.
  • GLMMs offer a flexible framework for psychological research involving complex data structures.