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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Friedman Two-way Analysis of Variance by Ranks01:21

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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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Ordinal Level of Measurement00:55

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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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...
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses.

Vassilis G S Vasdekis1, Silvia Cagnone2, Irini Moustaki3

  • 1Department of Statistics, Athens University of Economics and Business, 76 Patission Street, 10434, Athens, Greece. vasdekis@aueb.gr.

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Summary

This study introduces a composite likelihood method for analyzing ordinal longitudinal data, offering a feasible alternative to maximum likelihood estimation with minimal bias.

Keywords:
composite likelihoodgoodness-of-fit measureslatent variableslongitudinalordinal data

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Ordinal longitudinal data presents challenges due to complex interdependencies.
  • Traditional multivariate methods can be computationally intensive.

Purpose of the Study:

  • To propose a composite likelihood estimation approach for ordinal longitudinal responses.
  • To evaluate the performance and feasibility of this new method.

Main Methods:

  • Utilizes bivariate marginal probabilities instead of multivariate ones within a latent variable model.
  • Incorporates time-dependent latent variables linked via an autoregressive model.
  • Employs item-specific random effects to capture item interdependencies.

Main Results:

  • Composite likelihood estimators demonstrate small bias and mean square error in simulations.
  • The proposed method is shown to be a viable alternative to full maximum likelihood.
  • Model selection criteria and lower-order residuals are effectively used for model assessment.

Conclusions:

  • The composite likelihood approach is a practical and efficient method for analyzing ordinal longitudinal data.
  • This method provides a robust framework for understanding complex dependencies in such data.