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Examples: some survey variables without demographics (e.g., commuting behaviors).
We also analyzed associations and correlations between all survey variables.
Correlations and predictive power Correlations with other survey variables may sometimes provide information about the soundness of expectation answers.
The third section describes the objective of the research and the survey variables, while the fourth explains the statistical test used for the empirical analysis.
The survey variables obtained from the responses to the questionnaires that were administered can be divided in two categories: (1) hard variables and (2) soft variables.
The variables are as follows: type of health problem, pain intensity, seniority, and number of workers are survey variables, and the other variables are based on register data.
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In this paper, we propose goodness-of-fit procedures for a survey variable.
Two or three units account for an important percentage, say 5 10%, of the population total of a survey variable.
Grouping criteria were according to the frequency distribution of the survey variable.
While the D estimator is used when an auxiliary variable (proxy) strictly resembles the survey variable, the GREG estimator is used when one or more auxiliary variables are strongly correlated with the survey variable.
When auxiliary information is exploited, the performance tends to improve as the correlation between auxiliary and survey variable increases.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com