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Related Concept Videos

Response Surface Methodology01:16

Response Surface Methodology

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.
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Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus: Comparing...
Longitudinal Research02:20

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Observational Studies01:11

Observational Studies

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Cross-Sectional Research01:50

Cross-Sectional Research

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Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Updated: Jun 21, 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

Response shift was identified over multiple occasions with a structural equation modeling framework.

Ruth Barclay-Goddard1, Lisa M Lix, Robert Tate

  • 1Department of Physical Therapy, School of Medical Rehabilitation, University of Manitoba, R106-771 McDermot Avenue, Winnipeg, Manitoba, Canada. barclayg@cc.umanitoba.ca

Journal of Clinical Epidemiology
|July 21, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework using structural equation modeling to detect response shift (RS) over multiple time points. The research identified recalibration as a key factor in mental health changes after stroke.

Related Experiment Videos

Last Updated: Jun 21, 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

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Health Outcomes Research

Background:

  • Response shift (RS) can bias longitudinal health outcome measurements.
  • Accurate identification of RS is crucial for interpreting changes in mental health over time, particularly in clinical populations.
  • Existing methods for detecting RS are often limited in their ability to analyze data with more than two time points.

Purpose of the Study:

  • To develop and apply a novel framework utilizing structural equation modeling (SEM) for identifying response shift (RS) in longitudinal data with multiple time points.
  • To address critical methodological challenges in analyzing multi-time point data for RS, including model validation and multiple testing corrections.
  • To demonstrate the framework's application in understanding mental health changes post-stroke.

Main Methods:

  • Development of a model-based framework employing structural equation modeling (SEM) for response shift (RS) detection.
  • Application of the framework to longitudinal data from 678 individuals at 1, 3, 6, and 12 months post-stroke.
  • Incorporation of model validation and adjustments for multiple testing to enhance reliability.

Main Results:

  • The developed framework successfully identified both uniform and nonuniform recalibration.
  • Recalibration effects were observed at 6 and 12 months post-stroke, indicating significant response shifts.
  • The application provided insights into the timing and type of RS in mental health following stroke.

Conclusions:

  • The proposed SEM framework offers a robust method for identifying response shift (RS) in longitudinal studies with multiple time points.
  • Understanding the type and timing of RS is essential for optimizing treatment planning and clinical measurement strategies.
  • Model validation and correction for multiple testing bolster confidence in the identified mental health response shifts post-stroke.