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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
What is Variation?01:14

What is Variation?

Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Pharmacodynamics in Geriatric Patients: Effects of Age01:27

Pharmacodynamics in Geriatric Patients: Effects of Age

Age-related pharmacokinetic changes are extensively documented, but understanding age-related pharmacodynamic alterations is relatively limited. This knowledge gap can be partly attributed to the complexity of developing appropriate measures of drug responses compared to bioanalytical methods for determining drug concentrations.Most information regarding age-related differences in human pharmacodynamics originates from cross-sectional studies. However, these studies assume that observed mean...

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Measurement of Lifespan in Drosophila melanogaster
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Published on: January 7, 2013

Models, measures, and methods: variability in aging research.

Edward Alan Miller1, William G Weissert

  • 1Department of Health Management and Policy, School of Public Health, the University of Michigan, Ann Arbor 48109-2029, USA. eddiemil@umich.edu

Home Health Care Services Quarterly
|July 23, 2003
PubMed
Summary
This summary is machine-generated.

This review reveals significant variability in how researchers measure predictors for adverse health outcomes like nursing home placement and mortality. Lack of standardized models and measurement strategies hinders consistent, reliable aging research.

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Area of Science:

  • Gerontology
  • Health Services Research
  • Biostatistics

Background:

  • Longitudinal studies are crucial for understanding aging processes and predicting adverse health outcomes.
  • Existing research on predictors of nursing home placement, hospitalization, functional impairment, and mortality shows inconsistencies.
  • A comprehensive review of measurement strategies is needed to improve the reliability and validity of aging research.

Purpose of the Study:

  • To review models and measurement strategies for predictors of adverse outcomes in aging research.
  • To examine the operationalization and coding of common variables and scales.
  • To identify trends in predictor selection and the use of conceptual models.

Main Methods:

  • Systematic review of 167 multivariate equations from 78 longitudinal studies (1985-1998).
  • Analysis of studies assessing risk factors for nursing home placement, hospitalization, functional impairment, and mortality.
  • Examination of variable operationalization, coding, and the use of conceptual models.

Main Results:

  • Considerable variability found in the operationalization and coding of variables like age, income, Activities of Daily Living (ADL), and the Short-Portable Mental Status Questionnaire (SPMSQ).
  • Few researchers utilized explicit conceptual models for variable selection.
  • Demographics, physical, and cognitive functioning were studied more frequently than service, market, or policy characteristics.

Conclusions:

  • The lack of standardization in measurement poses challenges to the validity and reliability of aging research findings.
  • Limited use of conceptual models leads to inconsistent predictor inclusion in analyses.
  • Improvements in measurement standardization and the application of conceptual models are recommended for future aging research.