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Related Concept Videos

Measurement: Standard Units03:38

Measurement: Standard Units

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Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
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Ratio Level of Measurement00:54

Ratio Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
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Units and Standards of Measurement01:10

Units and Standards of Measurement

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A physical quantity is defined either by specifying its measurement method or by stating how it is calculated from other measurements. For example, consider a metallic cube. We might define its mass and dimensions by specifying methods for measuring them, such as using a weighing machine and a meter scale. Then, we could define the volume by stating that it is the cube of its side, and we could calculate the density as the mass divided by the volume.
Measurements of physical quantities are...
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Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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Nominal Level of Measurement00:56

Nominal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
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Establishing gold standards for System-Level Measures: a modified Delphi consensus process.

Fiona Doolan-Noble1, Stuart Barson2, M Lyndon3

  • 1Department of General Practice and Rural Health, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.

International Journal for Quality in Health Care : Journal of the International Society for Quality in Health Care
|June 12, 2018
PubMed
Summary
This summary is machine-generated.

A modified Delphi consensus process successfully established aspirational gold standards for 15 System-Level Measures (SLMs) at Counties Manukau Health. This method provides a reliable framework for setting quality benchmarks in healthcare systems.

Keywords:
benchmarkinghealth policyhealthcare systemmeasurement of qualityquality indicators

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Area of Science:

  • Healthcare Quality Improvement
  • Health Systems Management
  • Public Health Policy

Background:

  • System-Level Measures (SLMs) are crucial for monitoring healthcare performance.
  • Establishing aspirational benchmarks, or 'gold standards,' is essential for driving quality improvement.
  • Counties Manukau Health (CM Health) in New Zealand sought to define gold standards for its SLMs.

Purpose of the Study:

  • To establish aspirational 'gold standards' for a suite of System-Level Measures (SLMs).
  • To apply these standards within Counties Manukau Health (CM Health), a New Zealand District Health Board.

Main Methods:

  • A multi-stage, multi-method modified Delphi consensus process was employed.
  • The process involved virtual communication (Round 1) and a facilitated face-to-face meeting (Round 2) in Auckland, NZ.
  • Participants included health professionals, managers, academics, and quality improvement experts.

Main Results:

  • Fifteen System-Level Measures (SLMs) across Population Health, Patient Experience, and Cost/Productivity domains were reviewed.
  • Agreement was reached on a gold standard for each of the 15 SLMs.
  • Twelve participants engaged in Round 1, and 19 in Round 2.

Conclusions:

  • The Delphi consensus process is an effective method for establishing gold standards for SLMs.
  • This approach can be successfully implemented by health boards, such as CM Health, to enhance performance measurement.