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Related Concept Videos

Data Collection by Survey01:07

Data Collection by Survey

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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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Archival Research01:40

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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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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.
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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Longitudinal Research02:20

Longitudinal Research

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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...
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Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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Data Collection III01:05

Data Collection III

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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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Improving Metadata Infrastructure for Complex Surveys: Insights from the Fragile Families Challenge.

Alexander T Kindel1, Vineet Bansal1, Kristin D Catena1

  • 1Princeton University, Princeton, NJ, USA.

Socius : Sociological Research for a Dynamic World
|May 22, 2023
PubMed
Summary

Researchers redesigned a metadata system for the Fragile Families Study, treating metadata as data. This improves data preparation efficiency and accuracy for complex datasets and analysis, enabling new machine learning applications.

Keywords:
computational social sciencedata sharingmetadataquantitative methodologysurvey research

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Area of Science:

  • Social Sciences
  • Data Science
  • Computational Social Science

Background:

  • Existing metadata systems hinder efficient data preparation for complex datasets.
  • Growing data analysis practices necessitate improved metadata management.

Purpose of the Study:

  • To redesign a metadata system for the Fragile Families and Child Wellbeing Study.
  • To enhance data preparation efficiency and reduce errors in data analysis.

Main Methods:

  • Treating metadata as data by releasing comprehensive variable information.
  • Developing a format amenable to both automated and manual data processing.
  • Incorporating participant experiences from the Fragile Families Challenge.

Main Results:

  • The redesigned metadata system simplifies data preparation tasks.
  • The approach reduces the potential for errors in data analysis.
  • Facilitates machine learning applications for longitudinal survey data.

Conclusions:

  • Treating metadata as data significantly improves data preparation.
  • The open-sourced tools support broader research in social sciences and data analysis.
  • Encourages further research into data preparation methodologies.