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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.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
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Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Fitting ordinal regression analysis to anthropometric data.

A Pradhan1

  • 1Department of Community Medicine, KIST Medical College, Lalitpur, Nepal. amiseason@yahoo.com

Journal of Nepal Health Research Council
|August 30, 2012
PubMed
Summary
This summary is machine-generated.

Childhood malnutrition remains a concern in Nepal, with factors like maternal education, household wealth, and birth size significantly impacting nutritional status. Addressing these can improve child health outcomes.

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Area of Science:

  • Public Health
  • Pediatrics
  • Nutritional Epidemiology

Background:

  • National surveys in Nepal reveal persistent issues with child nutrition, with high rates of stunting, underweight, and wasting.
  • The Nepal Demographic and Health Survey 2006 highlighted that 49% of children under five are stunted, 39% are underweight, and 13% are wasted.
  • Understanding the determinants of child nutritional status is crucial for effective public health interventions.

Purpose of the Study:

  • To analyze the key factors associated with the nutritional status of children under five years of age in Nepal.
  • To identify socioeconomic and demographic predictors of stunting, underweight, and wasting in young children.

Main Methods:

  • Cross-sectional study utilizing secondary data from the 2006 Nepal Demographic and Health Survey.
  • Ordinal regression analysis was employed to model the ordered nutritional status variables (stunting, underweight, wasting).
  • Statistical software including STATA 9 and SPSS versions 13 and 17 were used for data analysis.

Main Results:

  • Ordinal regression effectively modeled underweight and wasting, identifying significant predictors.
  • The model for stunting required adjustments due to unmet assumptions, with wealth index and maternal education found to be significant factors.
  • High wealth index, larger birth size, and maternal education were associated with reduced underweight prevalence.

Conclusions:

  • Maternal education, household wealth index, and child's size at birth are significant determinants of nutritional status in children under five.
  • Interventions targeting socioeconomic factors and maternal education can help mitigate childhood malnutrition in Nepal.
  • Addressing wealth disparities and promoting maternal education are key strategies for improving child nutrition.