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Related Concept Videos

What is Variation?01:14

What is Variation?

Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Regression Toward the Mean01:52

Regression Toward the Mean

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...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...

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Related Experiment Video

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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

Variations in data collection can influence outcome measures of BMI measuring programmes.

Nick Townsend1, Harry Rutter, Charlie Foster

  • 1University of Oxford, British Heart Foundation Health Promotion Research Group, Oxford. nicholas.townsend@dphpc.ox.ac.uk

International Journal of Pediatric Obesity : IJPO : an Official Journal of the International Association for the Study of Obesity
|August 13, 2011
PubMed
Summary

Data collection variations significantly impact obesity prevalence measures in children. Digit preference in weight recording greatly affects results, especially for younger children, highlighting the need for standardized measurement protocols.

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Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • The World Health Organization (WHO) advocates for standardized obesity surveillance systems.
  • Variations in data collection methods across regions can compromise the accuracy of obesity prevalence outcome measures.

Purpose of the Study:

  • To analyze the impact of data collection variations on Body Mass Index (BMI) z-score in children.
  • To quantify the regional variation in BMI z-score attributable to data collection inconsistencies within England's National Child Measurement Programme (NCMP).

Main Methods:

  • Multilevel analysis of 2007/08 National Child Measurement Programme (NCMP) data.
  • Examined 478,381 Reception (4-5 years) and 496,297 Year 6 (10-11 years) pupils from 17,279 primary schools across 152 regions in England.
  • Adjusted for individual and school-level variables to isolate the effect of data collection variations.

Main Results:

  • Data collection variables explained 29.7% of regional BMI z-score variation in Reception pupils and 5.3% in Year 6 pupils.
  • Digit preference in weight rounding was the most significant factor, accounting for 26.4% of regional variation in Reception pupils and 4.0% in Year 6 pupils.
  • Individual measurement variations can be magnified at regional and national levels.

Conclusions:

  • Data collection inconsistencies, particularly digit preference, substantially influence obesity prevalence estimates in children.
  • Measurement programmes must prioritize identifying and minimizing data collection variations to enhance the accuracy of public health surveillance.
  • Key factors to address include participation rates, measurement timing, and precise recording of measurements.