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Related Concept Videos

Data Collection by Survey01:07

Data Collection by Survey

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...
Types of Surveys01:27

Types of Surveys

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...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Data Collection II01:29

Data Collection II

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 family,...
Data Collection by Observations01:08

Data Collection by Observations

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.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...

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Related Experiment Video

Updated: Jul 16, 2026

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ShinyDataMatcher: A user-friendly application for integrating survey data.

Lucia Guastadisegni1, Fedele Greco2, Carlo Trivisano2

  • 1Department of Statistics, University Carlos III of Madrid, Getafe, Madrid, Spain.

Plos One
|July 14, 2026
PubMed
Summary

ShinyDataMatcher is a new R Shiny application that simplifies integrating survey data using statistical matching. This tool empowers users to perform complex data matching tasks without coding, enhancing data analysis accessibility.

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Area of Science:

  • Statistics
  • Data Science
  • Computational Social Science

Background:

  • Integrating disparate survey datasets is crucial for comprehensive analysis.
  • Statistical matching is a powerful technique for data integration but often requires specialized programming skills.
  • Existing tools may lack user-friendliness or comprehensive functionality for statistical matching.

Purpose of the Study:

  • To introduce ShinyDataMatcher, an R Shiny application for user-friendly statistical matching of survey data.
  • To provide a no-code solution for the entire statistical matching workflow, from data preparation to synthetic dataset creation.
  • To lower technical barriers for applying statistical matching in research and official statistics.

Main Methods:

  • Development of an R Shiny application, ShinyDataMatcher.
  • Implementation of data import, exploration, processing, and variable harmonization features.
  • Integration of various macro- and micro-level statistical matching algorithms.
  • Inclusion of diagnostic tools for assessing matching quality and imputation uncertainty.

Main Results:

  • ShinyDataMatcher successfully guides users through the statistical matching process without requiring coding.
  • The application facilitates the integration of survey data, demonstrated by fusing Italian Household Budget Survey (HBS) and Survey on Household Income and Wealth (SHIW) data.
  • The tool enables the construction of synthetic matched datasets and accounts for imputation uncertainty using methods like repeated random hot-deck imputation.

Conclusions:

  • ShinyDataMatcher offers a transparent, accessible, and efficient environment for statistical matching.
  • The application democratizes the use of advanced data integration techniques for a wider range of users.
  • It has significant potential for applications in academic research and official statistics, improving data fusion capabilities.