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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.6K
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...
4.6K
Experimental Designs01:16

Experimental Designs

18.3K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
18.3K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

305
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
305
Randomized Experiments01:13

Randomized Experiments

9.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.1K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.2K
Response Surface Methodology01:16

Response Surface Methodology

710
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
710

You might also read

Related Articles

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

Sort by
Same author

Estimating marginal effects with zero-inflated models: A tutorial with the R package mzim.

Behavior research methods·2026
Same author

Using Generalized Linear Mixed Models in the Analysis of Count and Rate Data in Single-case Eperimental Designs: A Step-by-step Tutorial.

Evaluation & the health professions·2024
Same author

Multilevel modeling in single-case studies with zero-inflated and overdispersed count data.

Behavior research methods·2024
Same author

Multilevel modeling in single-case studies with count and proportion data: A demonstration and evaluation.

Psychological methods·2023
Same author

Visualization of α-synuclein trafficking via nanogold labeling and electron microscopy.

STAR protocols·2023
Same author

C-O Coupling/[4+2] Cycloaddition Tandem Reactions via Oxidative Dearomatization of BINOLs: Access to Bridged Polycyclic Compounds.

The Journal of organic chemistry·2023
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Feb 25, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K

Generalized least squares transformation for single-case experimental design: Introducing the R package lmeSCED.

Chendong Li1, Eunkyeng Baek2, Wen Luo1

  • 1Department of Educational Psychology, Texas A&M University, 718E Harrington Tower, College Station, TX, 77843-4225, USA.

Behavior Research Methods
|February 23, 2026
PubMed
Summary
This summary is machine-generated.

Multilevel models (MLMs) for single-case experimental designs (SCEDs) can be improved. A new R package, lmeSCED, addresses autocorrelation and small sample sizes, providing accurate fixed-effect inference and random-effect variance components.

Keywords:
AutocorrelationSingle-case experimental designSmall sample adjustment

More Related Videos

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

6.1K
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

Related Experiment Videos

Last Updated: Feb 25, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

6.1K
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

Area of Science:

  • Behavioral and social sciences
  • Statistical modeling
  • Psychology

Background:

  • Single-case experimental designs (SCEDs) generate repeated observations, suitable for multilevel models (MLMs).
  • SCED data often exhibit autocorrelation and small sample sizes, leading to biased standard errors and inflated Type I error rates in fixed effects.
  • Existing statistical software has limitations in simultaneously addressing these SCED data challenges.

Purpose of the Study:

  • To evaluate a two-step statistical approach combining generalized least squares (GLS) transformation for autocorrelation and Satterthwaite's adjustment for small samples.
  • To implement these methods, alongside novel tests for random effects, in a user-friendly R package named lmeSCED.
  • To assess the performance of this approach using Monte Carlo simulations and demonstrate its practical application.

Main Methods:

  • A two-step method involving GLS transformation to handle AR(1) residuals and Satterthwaite's adjustment for fixed-effects inference.
  • Development of the lmeSCED R package, incorporating a boundary-corrected restricted likelihood-ratio test and parametric bootstrapping for random effects.
  • Monte Carlo simulation studies to evaluate parameter recovery and Type I error rates.

Main Results:

  • Applying MLMs to GLS-transformed SCED data resulted in unbiased parameter recovery.
  • The proposed methods maintained Type I error rates at nominal levels.
  • The lmeSCED package effectively handles autocorrelation and small sample sizes in SCED data.

Conclusions:

  • The evaluated two-step method provides a statistically sound approach for analyzing SCED data with multilevel models.
  • The lmeSCED R package offers a practical and effective tool for researchers, enhancing the reliability of SCED data analysis.
  • This work addresses critical limitations in existing statistical approaches for SCED data, paving the way for more accurate research findings.