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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

12.7K
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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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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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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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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Statistical Analysis: Overview01:11

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
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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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Measurement in Intensive Longitudinal Data.

Daniel McNeish1, David P Mackinnon1, Lisa A Marsch2

  • 1Arizona State University, Department of Psychology.

Structural Equation Modeling : a Multidisciplinary Journal
|November 5, 2021
PubMed
Summary
This summary is machine-generated.

This study integrates measurement models into dynamic structural equation modeling (DSEM) for more rigorous analysis of intensive longitudinal data. This approach enhances construct measurement and allows for time- and person-invariant scale assessments.

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Area of Science:

  • Psychological science
  • Statistical modeling
  • Behavioral research

Background:

  • Intensive longitudinal data are increasingly common, often analyzed with dynamic structural equation modeling (DSEM).
  • Existing DSEM applications frequently use simple sums or averages of scale items, neglecting proper measurement models.
  • This limits the rigor and scope of construct measurement in intensive longitudinal studies.

Purpose of the Study:

  • To demonstrate incorporating measurement models into DSEM for robust construct measurement.
  • To assess scale invariance across time and individuals within DSEM frameworks.
  • To provide a practical example using ecological momentary assessment data.

Main Methods:

  • Integration of measurement models within the DSEM framework.
  • Application of dynamic structural equation modeling (DSEM).
  • Utilizing Mplus software for model fitting and interpretation.

Main Results:

  • The proposed method allows for more rigorous measurement of constructs in intensive longitudinal data.
  • It enables the assessment of scale invariance across time and individuals, which is not possible with simple aggregation.
  • The example demonstrates the practical implementation and interpretation of these models.

Conclusions:

  • Incorporating measurement models into DSEM significantly improves the rigor of intensive longitudinal data analysis.
  • This approach is crucial for accurately measuring constructs and understanding their stability.
  • The findings have implications for research on mood, affect, and other time-varying constructs.