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  • Validity of Measured vs. Self-Reported Weight and Height and . . .
    Despite the minor discrepancies in self-reported data seen at the individual level, this study showed a significant connection between measured and reported weight and height Strong correlations (>0 9) were found between measured weight, height, and BMI and self-reported values, respectively
  • Dealing with different types of data discrepancy - Usermaven
    Data discrepancy arises when two or more comparable data sets don’t match For instance, discrepancies may occur when different analytics platforms or dashboards display varying values for the same metric This misalignment can stem from setting differences, such as date ranges or attribution windows
  • Biases in self-reported height and weight measurements and . . .
    From the reported height and weight biases, it is clear that BMI would be significantly over-reported in India and significantly under-reported in Russia In China and South Africa overall, the signs are the same for height and weight differences
  • Deciding Whether or Not to Weight a Sample - The Data Story Guide
    We should weight a survey when: There are discrepancies between survey results and facts For example: Fact: 12% of US adults live in California; Result: 25% of respondents live in California; The discrepancy is believed to be caused by the survey having interviewed too few or too many people in one or more groups in the population For example
  • Self-Reported Versus Actual Weight and Height Data Contribute . . .
    The present study found significant differences between two approaches to defining weight misperception in early adolescence Specifically, using self-reported weight and height data as compared with actual weight and height data may result in lower occurrences of underestimation and higher occurrences of overestimation
  • Survey Weights as Indicators of Data Quality
    Weight variance depends on the discrepancy between the achieved sample and the population, or the extent to which the raw data need to be corrected to represent the population Thus, in some sense, the weight variance can be assumed as a rough indicator of the quality of the sample
  • What happens when a survey estimate doesn’t match a known . . .
    Several factors could result in such discrepancies — in some countries respondents may try to meet social and legal expectations when answering questions about civic behaviors like paying taxes (what survey researchers refer to as “social desirability bias”), or respondents may be unable to accurately recall a specific behavior or action





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