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Related Concept Videos

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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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.
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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
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How Data are Classified: Categorical Data01:11

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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.
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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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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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Missing data as data.

Anahid Basiri1, Chris Brunsdon2

  • 1School of Geographical and Earth Sciences, The University of Glasgow, Glasgow, G12 8QQ Glasgow, UK.

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|September 20, 2022
PubMed
Summary
This summary is machine-generated.

Digital lives offer rich data but face challenges like missing information and bias. This study reframes missing data as valuable, revealing reasons for its absence and providing a realistic sample size assessment for under-represented datasets.

Keywords:
biasbig data paradoxcrowdsourced datamissing dataunder-representation

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Area of Science:

  • Social sciences
  • Digital sociology
  • Computational social science

Background:

  • Modern digital lives generate high-frequency, high-granularity societal data.
  • This data, while large, is often sparse, biased, and user-generated.
  • Under-representation and missing data are critical challenges in digital research.

Discussion:

  • Missing data is often overlooked or treated as a limitation.
  • Analyzing the patterns and reasons for missingness can yield significant insights.
  • This approach offers a more nuanced understanding of data limitations.

Key Insights:

  • Proposes a novel perspective: viewing missing data as informative.
  • Identifies the underlying causes of data gaps and under-representation.
  • Enables a more realistic estimation of effective sample size in digital studies.

Outlook:

  • Improves the interpretation of findings from large, digital datasets.
  • Enhances the validity and reliability of computational social science research.
  • Provides a framework for addressing data bias and sparsity in future studies.