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Related Concept Videos

Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Introduction to Surveying, Plane Surveying and Geodetic Surveys01:27

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Surveying is the art and science of mapping the earth's surface. It involves measuring distances, angles in horizontal or vertical directions, and levels to understand the shape and size of land features. Surveying techniques are essential for various tasks, such as identifying the levels of a land area with reference to a specific point, and mapping undulations and water bodies.There are two main types of surveying: plane surveys and geodetic surveys. Plane surveys assume the earth is flat,...
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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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Survey Safety01:28

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Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
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Errors and Mistakes in Surveying01:19

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Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of...
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Weighted Mean00:57

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Frailty Assessment in an Aging Mouse Model
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Weighted estimation for multivariate shared frailty models for complex surveys.

Jing Wang1

  • 1The University of Texas at Arlington, Arlington, TX, 76019, USA. jing.wang@uta.edu.

Lifetime Data Analysis
|April 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a weighted multivariate frailty model to address biases in clustered survival data from complex surveys. The new method ensures consistent parameter estimation for correlated events, improving accuracy in longitudinal studies.

Keywords:
Multivariate frailty modelNewton–Raphson algorithmPseudolikelihoodSampling weight

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Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Multivariate frailty models analyze clustered survival data, examining relationships between event hazards and covariates.
  • These models can yield biased estimates when applied to complex survey data due to sampling design dependencies.

Purpose of the Study:

  • To develop a consistent parameter estimation method for multivariate frailty models using complex survey data.
  • To address and correct for potential biases introduced by informative or non-informative sampling designs.

Main Methods:

  • The study proposes weighting multivariate frailty models by the inverse probability of selection, adhering to the pseudolikelihood principle.
  • Estimation involves maximizing penalized partial and marginal pseudolikelihood functions.
  • Performance was evaluated using Monte Carlo simulations and data from the Early Childhood Longitudinal Study.

Main Results:

  • The proposed weighted estimator demonstrates consistency.
  • The estimator is shown to be approximately unbiased in simulations and real-world data analysis.
  • This approach effectively mitigates biases associated with complex survey sampling in frailty models.

Conclusions:

  • The weighted multivariate frailty model provides a robust and accurate method for analyzing clustered survival data from complex surveys.
  • This technique enhances the reliability of parameter estimation in longitudinal studies involving correlated events.