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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

187
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...
187
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.0K
Longitudinal Research02:20

Longitudinal Research

12.0K
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...
12.0K
Coefficient of Correlation01:12

Coefficient of Correlation

6.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.2K
Correlations02:20

Correlations

33.3K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
33.3K
Two-Way ANOVA01:17

Two-Way ANOVA

2.7K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.7K

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Updated: Jul 23, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Longitudinal Canonical Correlation Analysis.

Seonjoo Lee1,2, Jongwoo Choi1,2, Zhiqian Fang1,2

  • 1Columbia University and New York State Psychiatric Institute, New York, U.S.A.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|July 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces longitudinal canonical correlation analysis (LCCA) to find links between complex health datasets. LCCA effectively uncovers correlation patterns in high-dimensional longitudinal data, aiding disease research.

Keywords:
Alzheimer’s diseaseCanonical correlation analysisLongitudinal data analysis

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Analyzing longitudinal data with varying time resolutions presents statistical challenges.
  • Identifying correlations between complex, high-dimensional datasets requires advanced methods.

Purpose of the Study:

  • To develop and validate a novel method for canonical correlation analysis tailored for longitudinal data.
  • To uncover latent correlation structures in multivariate longitudinal variables sampled irregularly.

Main Methods:

  • Modeled multivariate longitudinal trajectories using random effects models.
  • Developed longitudinal canonical correlation analysis (LCCA) to identify correlated linear combinations in latent space.
  • Validated LCCA through numerical simulations on high-dimensional longitudinal datasets.

Main Results:

  • LCCA effectively recovers underlying correlation patterns between two high-dimensional longitudinal datasets.
  • The method successfully handles data sampled at different time resolutions and irregular grids.
  • Identified longitudinal profiles of brain changes and amyloid accumulation in Alzheimer's Disease Neuroimaging Initiative data.

Conclusions:

  • LCCA is a powerful tool for exploring associations in complex longitudinal health data.
  • The method provides insights into the temporal relationships between biological markers.
  • This approach has significant implications for understanding disease progression and developing biomarkers.