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Related Concept Videos

Data Collection by Survey01:07

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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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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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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Data Collection by Observations01:08

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Grouped data with survey revision.

Chung-Han Liang1, Da-Wei Wang2, Mei-Lien Pan3

  • 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

BMC Medical Research Methodology
|January 16, 2023
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Summary
This summary is machine-generated.

Questionnaire revisions in longitudinal studies can cause systematic shifts. The Version Alignment Method (VAM) estimates and corrects these revision effects, improving data consistency for more reliable research findings.

Keywords:
Data cleaningData preprocessingGrouped dataMatrix decompositionStochastic processSurvey revision

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Area of Science:

  • Social Sciences
  • Statistics
  • Survey Methodology

Background:

  • Longitudinal studies frequently employ surveys, but questionnaire revisions are common.
  • These revisions can introduce systematic shifts and inconsistencies in measured data.
  • Existing methods offer limited solutions for mitigating revision-induced biases.

Purpose of the Study:

  • To develop a method to mitigate the effects of questionnaire revisions in longitudinal surveys.
  • To propose the Version Alignment Method (VAM) as a data preprocessing tool.
  • To reduce systematic errors and improve the consistency of estimated measures.

Main Methods:

  • Modeled questionnaire revisions as a stochastic process with transition matrices.
  • Developed the Version Alignment Method (VAM) to separate and utilize revision-related transitions.
  • Verified VAM using simulation data and a real-world MJ dataset with multiple revisions.

Main Results:

  • VAM significantly reduced annual average differences caused by revisions (0.593 to 0.115).
  • Enabled comparison of item frequencies before and after revisions by aligning response intervals.
  • Achieved a low average estimation error (0.0044 in L-infinity norm).

Conclusions:

  • Questionnaire revisions introduce response bias and information loss, impacting longitudinal study estimations.
  • VAM effectively estimates and reduces aggregate systematic errors from revisions.
  • VAM enhances the reliability and consistency of measures in longitudinal survey data.