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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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 in the...
Nominal Level of Measurement00:56

Nominal Level of Measurement

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 scale is...
Measures of Intelligence01:29

Measures of Intelligence

Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this; it...
Interval Level of Measurement00:55

Interval Level of Measurement

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 the...
Ratio Level of Measurement00:54

Ratio Level of Measurement

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. For...
Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:

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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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Published on: September 27, 2020

Multilevel interventions: measurement and measures.

Martin P Charns1, Mary K Foster, Elaine C Alligood

  • 1Center for Organization, Leadership and Management Research, US Department of Veterans Affairs, Boston, MA, USA.

Journal of the National Cancer Institute. Monographs
|May 25, 2012
PubMed
Summary
This summary is machine-generated.

Multilevel cancer intervention research faces measurement challenges. Studies predominantly use individual-level measures, neglecting group and organizational levels, hindering effective healthcare system interventions.

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Area of Science:

  • Health Services Research
  • Intervention Science
  • Cancer Care Research

Background:

  • Multilevel intervention research offers a realistic approach to healthcare challenges.
  • However, applying multilevel strategies to cancer care interventions presents unique measurement hurdles and possibilities.
  • Effective implementation requires robust research design and precise measurement.

Purpose of the Study:

  • To assess the current state of measurement in multilevel intervention research within cancer care.
  • To identify measurement gaps and opportunities in this field.
  • To inform best practices for future multilevel intervention studies.

Main Methods:

  • A systematic review of 1,072 cancer care articles published between 2005 and 2010.
  • Identification of 234 multilevel intervention articles, with 40 specifically in cancer care.
  • Supplementary literature review across health services, social psychology, and organizational behavior for relevant measures.

Main Results:

  • The majority of measures in multilevel cancer intervention studies focused on the individual level.
  • Measures at group, organization, and community levels were infrequently utilized.
  • Scant discussion was found regarding the independence, validity, and reliability of the measures employed.

Conclusions:

  • Measurement in multilevel intervention research, particularly in cancer care, is complex.
  • Key considerations include measure independence, validity, reliability, sample size, and statistical power.
  • Successful multilevel research necessitates clear identification of constructs and measures across all levels, alongside attention to cross-level interactions and a theory-driven, interdisciplinary approach with mixed methods.