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Related Concept Videos

Ranks01:02

Ranks

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Longitudinal Research

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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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Ordinal Level of Measurement00:55

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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...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A Hierarchical Rater Model for Longitudinal Data.

Jodi M Casabianca1, Brian W Junker2, Ricardo Nieto1

  • 1a The University of Texas at Austin.

Multivariate Behavioral Research
|August 29, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces the longitudinal hierarchical rater model (L-HRM) to accurately estimate latent traits from psychological assessments, correcting for rater bias in longitudinal designs.

Keywords:
Autoregressivehierarchical rater modelitem response theorylatent trait estimationlongitudinalratingstime seriestrends

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychology

Background:

  • Psychological and educational research frequently aims to track changes or growth over time.
  • Human raters in assessments can introduce bias and variability, affecting score quality.
  • Existing models may not adequately address rater effects in longitudinal data.

Purpose of the Study:

  • To develop an extension of the hierarchical rater model (HRM) for longitudinal designs.
  • To estimate latent traits corrected for individual rater bias and variability.
  • To incorporate time-series processes for serial dependence and model overall growth.

Main Methods:

  • Extension of the hierarchical rater model (HRM) to the longitudinal HRM (L-HRM).
  • Inclusion of an autoregressive time series process for adjacent timepoints.
  • Parameterization to allow for overall growth estimation.
  • Evaluation using simulation studies for feasibility and performance.

Main Results:

  • The L-HRM demonstrates feasibility and effective performance in simulation studies.
  • Parameter recovery results show predictable bias and error patterns across conditions.
  • The model was successfully applied to character strength assessment data.

Conclusions:

  • The L-HRM provides a robust method for estimating latent traits from longitudinal assessments with rater effects.
  • The model accounts for rater bias, serial dependence, and growth over time.
  • Future research can further refine the L-HRM for improved accuracy and application.