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Longitudinal Studies01:26

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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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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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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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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Related Experiment Video

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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On disaggregating between-person and within-person effects with longitudinal data using multilevel models.

Lijuan Peggy Wang1, Scott E Maxwell1

  • 1Department of Psychology, University of Notre Dame.

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|March 31, 2015
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Summary

This study clarifies how to separate between-person and within-person effects using multilevel models. It compares centering and detrending methods, offering guidance for accurate longitudinal data analysis.

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Area of Science:

  • Psychology
  • Statistics
  • Longitudinal Data Analysis

Background:

  • Disaggregating between-person and within-person effects is crucial for understanding psychological phenomena.
  • Multilevel models are commonly used for analyzing longitudinal data.
  • Existing methods for separating these effects require careful consideration of data preprocessing techniques.

Purpose of the Study:

  • To extend the discussion on disaggregating between- and within-person effects using multilevel models.
  • To analyze the impact of different centering and detrending approaches on effect disaggregation.
  • To provide practical recommendations for researchers analyzing longitudinal data.

Main Methods:

  • Conceptual and analytical examination of three centering approaches: no centering, grand-mean centering, and person-mean centering.
  • Evaluation of various detrending approaches: no detrending, detrending predictor (X) only, detrending outcome (Y) only, and detrending both X and Y.
  • Application of methods to two real psychology datasets and simulation studies.

Main Results:

  • Different centering and detrending methods yield varying results in disaggregating between- and within-person effects.
  • The choice of method significantly impacts the interpretation of effects in longitudinal studies.
  • Simulation studies confirmed the differential performance of approaches across various conditions.

Conclusions:

  • The study provides clear recommendations on when and how to apply centering and detrending techniques.
  • Proper application of these methods is essential for accurate estimation of between- and within-person effects.
  • Researchers are advised to carefully consider these preprocessing steps for robust longitudinal data analysis.