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

Response Surface Methodology01:16

Response Surface Methodology

387
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:
387
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

491
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
491
Regression Analysis01:11

Regression Analysis

6.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

143
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...
143
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.3K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.3K
One-Way ANOVA01:18

One-Way ANOVA

10.2K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
10.2K

You might also read

Related Articles

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

Sort by
Same author

Multiple imputation of multilevel data with single-level models: A fully conditional specification approach using adjusted group means.

Behavior research methods·2026
Same author

Trajectories of change within cognitive behavioral therapy for psychosis.

Schizophrenia bulletin·2025
Same author

Improving the probability of reaching correct conclusions about congruence hypotheses: Integrating statistical equivalence testing into response surface analysis.

Psychological methods·2025
Same author

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares.

Psychological methods·2024
Same author

Investigating the effects of congruence between within-person associations: A comparison of two extensions of response surface analysis.

Psychological methods·2024
Same author

Extraversion, social interactions, and well-being during the COVID-19 pandemic: Did extraverts really suffer more than introverts?

Journal of personality and social psychology·2023

Related Experiment Video

Updated: Nov 12, 2025

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development
06:00

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development

Published on: March 17, 2023

655

Response Surface Analysis with Missing Data.

Sarah Humberg1, Simon Grund2

  • 1Department of Psychology, University of Münster.

Multivariate Behavioral Research
|March 19, 2021
PubMed
Summary
This summary is machine-generated.

When analyzing psychological data with missing values, the substantive-model-compatible (SMC) approach for Response Surface Analysis (RSA) is recommended over the "just another variable" (JAV) method for more accurate results.

Keywords:
Response surface analysismaximum likelihoodmissing datamultiple imputationpolynomial regression

More Related Videos

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.4K
Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.1K

Related Experiment Videos

Last Updated: Nov 12, 2025

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development
06:00

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development

Published on: March 17, 2023

655
Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.4K
Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.1K

Area of Science:

  • Psychological research methods
  • Quantitative psychology
  • Statistical modeling

Background:

  • Response Surface Analysis (RSA) is increasingly used in psychology to test congruence hypotheses, such as person-job fit and dyadic similarity.
  • RSA relies on nonlinear polynomial regression, but its application with incomplete data is not well understood.
  • Existing methods for handling missing data in RSA, like the 'just another variable' (JAV) approach, may have limitations.

Purpose of the Study:

  • To compare the performance of different missing data handling strategies within Response Surface Analysis (RSA).
  • To evaluate the 'just another variable' (JAV) and 'substantive-model-compatible' (SMC) approaches for multiple imputation (MI) and maximum-likelihood (ML) estimation in RSA.
  • To assess the impact of these methods on parameter estimation, response surface shape, and congruence hypothesis testing.

Main Methods:

  • A simulation study was conducted to compare various missing data handling techniques in RSA.
  • The study focused on two primary approaches: the 'just another variable' (JAV) method and the 'substantive-model-compatible' (SMC) method.
  • Performance was evaluated based on the accuracy of parameter estimates, the fidelity of the response surface, and the validity of congruence hypothesis tests.

Main Results:

  • The 'just another variable' (JAV) approach demonstrated potential distortions in parameter estimates and conclusions regarding the response surface shape.
  • The 'substantive-model-compatible' (SMC) approach generally performed well across the evaluated outcomes.
  • A real-data example illustrated the practical application and differences between the JAV and SMC methods.

Conclusions:

  • The 'substantive-model-compatible' (SMC) approach is recommended for handling missing data in Response Surface Analysis (RSA) due to its superior performance.
  • Researchers should exercise caution when using the 'just another variable' (JAV) approach in RSA with incomplete data, as it may yield inaccurate findings.
  • The study provides practical guidance and recommendations for applying these missing data methods in psychological research utilizing RSA.