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Related Concept Videos

Data Collection by Survey01:07

Data Collection by Survey

7.0K
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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Schemas01:42

Schemas

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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Self-Schemas02:16

Self-Schemas

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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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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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Data Reporting and Recording01:24

Data Reporting and Recording

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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...
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Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Updated: Sep 16, 2025

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Standardizing Survey Data Collection to Enhance Reproducibility: Development and Comparative Evaluation of the

Yibei Chen1, Dorota Jarecka1, Sanu Ann Abraham2

  • 1McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.

Journal of Medical Internet Research
|July 11, 2025
PubMed
Summary
This summary is machine-generated.

ReproSchema standardizes survey data collection for improved research reproducibility. This schema-centric framework ensures data consistency and interoperability across diverse scientific disciplines.

Keywords:
FAIRdata collectionfindability, accessibility, interoperability, and reusabilityreproducibilityschemasurvey

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Area of Science:

  • Biomedical and Health Informatics
  • Data Science
  • Research Methodology

Background:

  • Inconsistent survey data collection hinders reproducibility in biomedical, clinical, behavioral, and social sciences.
  • ReproSchema offers a schema-centric framework for standardized, reproducible survey data collection.
  • It provides a reusable assessment library and computational tools for validation and conversion.

Purpose of the Study:

  • To evaluate ReproSchema's effectiveness in improving research reproducibility and reliability.
  • To compare ReproSchema against 12 existing platforms for data collection consistency.
  • To demonstrate ReproSchema's application in mental health, longitudinal studies, and neuroimaging research.

Main Methods:

  • Described ReproSchema's core components: schema-based design, reusable assessments (>90), and validation/conversion tools.
  • Compared 12 platforms against 14 FAIR principles and 8 survey functionalities.
  • Applied ReproSchema to 3 use cases: NIMH-Minimal, ABCD/HEAL, and CB-P-DAS Checklist.

Main Results:

  • ReproSchema met all 14 FAIR criteria and supported 6 of 8 key survey functionalities.
  • Demonstrated flexibility in standardizing mental health assessments, tracking longitudinal data, and creating interactive checklists.
  • Ensured compatibility with existing tools like REDCap and FHIR.

Conclusions:

  • ReproSchema enhances research reproducibility through a structured, schema-driven approach to survey data collection.
  • It integrates version control, metadata management, and interoperability for consistent, compatible data.
  • Future developments will expand its utility for transparent, scalable, and reproducible research.