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Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Data Reporting and Recording01:24

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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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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Data Collection II01:29

Data Collection II

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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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Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression
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Data Visualization in Sociology.

Kieran Healy1, James Moody1

  • 1Duke University.

Annual Review of Sociology
|October 25, 2014
PubMed
Summary
This summary is machine-generated.

Sociology can improve its use of data visualization tools for exploring datasets and presenting statistical findings. Enhanced graphical standards and data sharing are key for advancing sociological insights and public engagement.

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Area of Science:

  • Social Sciences
  • Sociology
  • Data Visualization

Background:

  • Data visualization is crucial for social scientific research.
  • Sociology has historically underutilized visual tools despite early promise.
  • A gap exists in current sociological practice regarding effective data visualization.

Purpose of the Study:

  • To review the historical trajectory and current status of visualization in sociology.
  • To examine recent advancements in visualizing raw data and statistical models.
  • To differentiate between exploratory and presentational visualization methods.

Main Methods:

  • Historical review of visualization in sociology.
  • Analysis of contemporary visualization techniques for raw data.
  • Examination of methods for presenting statistical modeling results.
  • Distinction between data exploration and result presentation tools.

Main Results:

  • Sociology's use of visual tools has not kept pace with its potential.
  • Recent developments offer new ways to explore data and present findings.
  • A clear distinction exists between tools for data exploration and those for presentation.
  • Advances align with a broader trend toward open data and code sharing.

Conclusions:

  • There is a need to elevate the standard of graphical displays in sociological publications.
  • Increased sharing of code and data can foster better visualization practices.
  • Adoption of advanced visualization techniques can enhance sociological research and communication.