Child Care and Early Education Research Connections

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Research Glossary

The research glossary defines terms used in conducting social science and policy research, for example those describing methods, measurements, statistical procedures, and other aspects of research; the child care glossary defines terms used to describe aspects of child care and early education practice and policy.

A B C D E F G H I J K L M N O P Q R S T U V W Z
Variance
A commonly used measure of dispersion for variables. The variance is calculated by squaring the standard deviation. The variance is based on the square of the difference between the values for each observation and the mean value.
Weighted Score
A score adjusted by such factors as the importance of the attribute assessed or the reliability and validity of the assessment from which the score was derived, or a combination of such factors.
Weighting
A process used to ensure that statistics produced from a sample are representative of the population from which the sample was drawn. In survey research, weights are used to adjust for differences in the probabilities of selection (i.e., members of the sample did not have an equal chance of being selected) and differences in the rates of nonresponse (i.e., some groups of respondents had higher rates of non-response than others). Most large surveys, and especially those that are national in scope, include one or more weights and statistics produced from such surveys should be based on weighted data. Using weights is one way to help reduce the risk of nonresponse bias in the survey findings.
Z Score
A score that is produced by subtracting the mean value from an individual data value and dividing by the standard deviation. This standardizes data values and allows for individual data values from different distributions (distributions with different means and standard deviations) to be compared.
Z Test
A statistical test that is used to compare the means of two independent samples or the mean of one sample with some fixed value. The test assumes that the populations from which the samples are drawn are normally distributed. It is used when testing differences for large samples (over 30 observations) and for smaller samples in which the variance of the population is known.