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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...
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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Application of a Longitudinal IRTree Model: Response Style Changes Over Time.

Allison J Ames1, Brian C Leventhal2

  • 1University of Arkansas, Fayetteville, AR, USA.

Assessment
|October 19, 2021
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Summary
This summary is machine-generated.

This study introduces the longitudinal item response tree (IRTree) model to analyze complex cognitive processes in longitudinal data. Accounting for response styles improves the accuracy of measuring changes in substantive traits over time.

Keywords:
Bayesian estimationitem response treelongitudinal modelresponse style

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Area of Science:

  • Psychometrics
  • Longitudinal Data Analysis
  • Cognitive Psychology

Background:

  • Traditional psychometric models often oversimplify cognitive response processes by assuming a single substantive trait drives item responses.
  • Analysis of ordinal responses has been limited to single time points and single traits.
  • Existing models struggle to capture complex response behaviors across multiple data collection waves.

Purpose of the Study:

  • To introduce and validate a novel longitudinal item response tree (IRTree) model for analyzing complex response processes in longitudinal studies.
  • To investigate whether changes in response styles are proportional to changes in the substantive trait of interest.
  • To assess the impact of accounting for response styles on the estimation of substantive trait growth.

Main Methods:

  • Application of the longitudinal item response tree (IRTree) framework to model complex response processes.
  • Utilizing a six-item sexual knowledge scale from the National Longitudinal Study of Adolescent to Adult Health across two waves.
  • Empirical validation of the longitudinal IRTree model's ability to capture trait and response style dynamics.

Main Results:

  • Sexual knowledge significantly increased from the first to the second wave of data collection.
  • Midpoint and extreme response styles decreased across the two waves.
  • Failure to account for response style significantly biased the estimation of substantive trait growth.

Conclusions:

  • The longitudinal IRTree model effectively captures both substantive trait changes and complex response styles (midpoint and extreme) over time.
  • Accurate estimation of trait growth in longitudinal studies necessitates accounting for response style variations.
  • This novel model offers a more comprehensive approach to understanding cognitive processes in repeated measures designs.