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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...
Midrange01:07

Midrange

A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to outliers and...
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...
Trimmed Mean01:10

Trimmed Mean

While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

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Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
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Geographic Variation in Missing Race and Ethnicity Data in Minimum Data Set 3.0.

Jennifer Tjia1, Francesca L Troiani1, Anna Wyndham2

  • 1Department of Population and Quantitative Health Sciences, Division of Epidemiology, Worcester, MA.

Medical Care
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

Geographic variation exists in missing race and ethnicity data within US nursing homes. Discrepancies between Minimum Data Set (MDS) and Medicare data sources highlight data quality challenges.

Keywords:
Medicareadministrative datanursing homesrace

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Published on: January 8, 2020

Area of Science:

  • Gerontology
  • Health Services Research
  • Data Science

Background:

  • Administrative data on race and ethnicity in US nursing homes exhibit geographic variability.
  • The extent of missing race and ethnicity data in nursing home administrative records is not well-characterized.

Purpose of the Study:

  • To analyze geographic variations in missing race and ethnicity data within the Minimum Data Set (MDS) 3.0.
  • To compare discrepancies in race and ethnicity data between MDS 3.0 and Medicare claims.

Main Methods:

  • A cross-sectional study analyzed Medicare beneficiaries with MDS 3.0 records from 2014-2018.
  • Missingness of race and ethnicity data in MDS was assessed by state.
  • Misclassification rates of race and ethnicity variables from Medicare enrollment database (EDB) and Research Triangle Institute (RTI) were compared against MDS data.

Main Results:

  • Significant geographic variation in missing MDS race and ethnicity data was observed, ranging from 1.2% to 14.7%.
  • Misclassification rates varied substantially between MDS and Medicare data sources (EDB and RTI) for Hispanic, Asian American/Pacific Islander, and Black populations.
  • RTI variables demonstrated superior sensitivity and specificity compared to EDB when contrasted with MDS data.

Conclusions:

  • Geographic disparities in missing race and ethnicity data are evident in US nursing homes' MDS.
  • Discrepancies between MDS and Medicare data sources (EDB, RTI) also vary geographically.
  • Careful consideration of these data quality issues is crucial when utilizing MDS race and ethnicity information.