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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

How Data are Classified: Numerical Data

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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

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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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.
Nursing assessment guides are generally based on holistic models rather than medical...
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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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Related Experiment Video

Updated: Feb 6, 2026

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions
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Homeless mortality data from East London.

Khalil Hassanally1,2, Miqdad Asaria3

  • 1Health E1, The Greenhouse, London, UK.

London Journal of Primary Care
|August 8, 2018
PubMed
Summary

Homeless individuals in East London experienced high mortality rates from substance misuse, liver disease, and cardiac issues. Those dying from substance misuse did so significantly younger than the average age of death for the homeless population.

Keywords:
Inequalityclinical audithousingmortality

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Area of Science:

  • Public Health
  • Epidemiology
  • Gerontology

Background:

  • Homeless mortality rates exhibit geographical variations, with previous data suggesting lower rates than the national average.
  • This study focuses on characterizing homeless mortality within the specific geographical area of East London.

Purpose of the Study:

  • To characterize the age at death and causes of death among homeless patients.
  • To analyze mortality data for individuals registered in two specialist homeless practices in Tower Hamlets and Hackney, London, between 2001 and 2016.

Main Methods:

  • A retrospective analysis of general practice electronic patient records was conducted.
  • Mortality data were extracted from electronic patient records of two East London general practices specializing in homeless care.

Main Results:

  • A total of 203 deaths were analyzed.
  • The average age at death was 47 years.
  • Leading causes of death included substance misuse (average age 38), liver disease (average age 49), and cardiac-related deaths (average age 51).

Conclusions:

  • Substance misuse was a significant factor contributing to premature death among the homeless population studied.
  • Individuals dying from substance misuse did so at a considerably younger age compared to the average age of death in this homeless cohort.