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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...
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...
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...
Quartile01:15

Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...

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Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
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Published on: May 10, 2019

Inequality measurement for ordered response health data.

Ramses H Abul Naga1, Tarik Yalcin

  • 1Department of Economics and International Development, University of Bath, United Kingdom. rhan20@bath.ac.uk

Journal of Health Economics
|October 8, 2008
PubMed
Summary

Measuring health inequalities from self-reported health status (SRHS) data is challenging due to the ordered nature of responses. This study introduces new inequality indices that are invariant to the chosen numerical scale, improving health inequality measurement.

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

Area of Science:

  • Health Economics
  • Biostatistics
  • Social Determinants of Health

Background:

  • Self-reported health status (SRHS) is an ordered categorical variable.
  • Measuring health inequalities using SRHS data requires a numerical scale, which can be problematic.
  • Conventional inequality indices may yield inconsistent results due to scale sensitivity.

Purpose of the Study:

  • To introduce a novel parametric family of inequality indices for ordered response variables.
  • To develop indices that are invariant to the choice of numerical scale.
  • To address the limitations of existing methods in measuring health inequalities from SRHS data.

Main Methods:

  • Development of a parametric family of inequality indices based on Allison and Foster's ordering.
  • Derivation of key members within the proposed parametric family.
  • Application of the new indices to empirical data from the Swiss Health Survey.

Main Results:

  • The proposed inequality indices satisfy a crucial invariance property with respect to the numerical scale.
  • The methodology provides a more robust approach to measuring health inequalities from SRHS data.
  • Empirical application demonstrates the practical utility of the new indices.

Conclusions:

  • The newly developed inequality indices offer a more reliable method for assessing health disparities using self-reported health status.
  • This approach mitigates the scale-dependency issues inherent in previous measurement techniques.
  • The findings have implications for public health policy and the accurate measurement of health inequalities.