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

Confidence Coefficient01:24

Confidence Coefficient

10.1K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.0K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.0K
Confidence Intervals01:21

Confidence Intervals

9.7K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
9.7K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

3.9K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
3.9K
Confirmation Biases01:31

Confirmation Biases

7.6K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
7.6K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.9K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
8.9K

You might also read

Related Articles

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

Sort by
Same author

The performance of Bayesian fit indices in approximate measurement invariance testing with many groups in cross-cultural research.

Behavior research methods·2026
Same author

Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish.

Multivariate behavioral research·2026
Same author

Who plays a more crucial role in adolescent well-being: Interactions with parents or peers? An investigation of adolescents aged 10 to 18 years.

Applied psychology. Health and well-being·2026
Same author

Unraveling Symptom Heterogeneity and Core Features of Adolescent Social Anxiety: Insights From Latent Profile and Network Analyses.

Scandinavian journal of psychology·2025
Same author

A Beta Mixture Model for Careless Respondent Detection in Visual Analogue Scale Data.

Psychometrika·2025
Same author

Bayesian factor mixture modeling with response time for detecting careless respondents.

Behavior research methods·2025

Related Experiment Video

Updated: Dec 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K

A partially confirmatory approach to scale development with the Bayesian Lasso.

Jinsong Chen1, Zhihan Guo1, Lijin Zhang1

  • 1Department of Psychology.

Psychological Methods
|July 14, 2020
PubMed
Summary

This study introduces a new partially confirmatory factor analysis approach using Bayesian Lasso methods. It simultaneously estimates loading and residual structures, offering a flexible tool for scale development.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.1K

Related Experiment Videos

Last Updated: Dec 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.1K

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Factor analysis encompasses exploratory and confirmatory approaches, with varying levels of researcher input.
  • Bayesian regularization methods offer enhanced flexibility in scale development.

Purpose of the Study:

  • To propose a partially confirmatory factor analysis approach integrating Bayesian Lasso methods.
  • To simultaneously address loading and residual structures in scale development.

Main Methods:

  • Utilizing Bayesian Lasso for regression and covariance matrices.
  • Implementing a one-step procedure with specified item loadings and a two-step procedure with factor loadings.
  • Employing Bayesian hierarchical formulation with Markov Chain Monte Carlo estimation and Lasso or regular priors.

Main Results:

  • The proposed approach effectively estimates both loading and residual structures simultaneously.
  • Evaluated through simulated and real data, demonstrating validity and robustness.

Conclusions:

  • The partially confirmatory approach provides a flexible and powerful method for scale development.
  • It enhances the integration of prior knowledge within factor analysis frameworks.