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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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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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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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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A Latent-Change Scaling Model for Longitudinal Multiple Choice Data.

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    Area of Science:

    • Psychometrics
    • Longitudinal Data Analysis
    • Statistical Modeling

    Background:

    • Traditional methods often struggle to capture dynamic preference shifts in repeated-choice data.
    • Understanding population heterogeneity and individual change trajectories is crucial for accurate analysis.

    Purpose of the Study:

    • To present a novel latent-change scaling model for analyzing longitudinal multiple-choice data.
    • To integrate latent class analysis and low-dimensional scaling within a longitudinal framework.
    • To model both population heterogeneity and individual preference changes over time.

    Main Methods:

    • Development of a latent-change scaling model combining latent class analysis and scaling techniques.
    • Application of a longitudinal framework to track changes in response category preferences.
    • Utilizing a national panel dataset for empirical illustration.

    Main Results:

    • The model successfully characterizes cross-sectional population heterogeneity.
    • It effectively captures underlying change processes and individual differences in category utilities over time.
    • Demonstrated the model's utility in analyzing national panel data.

    Conclusions:

    • The proposed latent-change scaling model provides a robust framework for analyzing repeated-measures multiple-choice data.
    • It offers valuable insights into population heterogeneity and dynamic individual preference shifts.
    • The model can be extended to incorporate multiple indicators for more comprehensive analysis.