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The logistic regression eliminated missing data.
This maximized efficiency, minimized respondent burden, eliminated missing data, and allowed subject control of time management during data collection.
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The separate quantitative data analysis involved eliminating missing data by using listwise deletion of questionnaire and classroom observation items and then generating descriptive statistics (mean, standard deviation, and frequency) using SPSS Statistics 20 software within each data source and terms.
The dataset, retrieved from the descriptive data and the results of the test for MSLQ factors, consisted of 121 learners' items after eliminating the missing data.
Because particle species were not measured every day, we computed the mean monthly ratios of the elemental concentrations to the total PM2.5 mass for each month therefore eliminating the missing data issue.
Imputation thereby eliminates bias from missing data per se (i.e. there is no longer missing data), but is highly dependent on the validity of deriving the missing HRQL survey data from the existing dataset.
Three procedures were adopted to deal with missing and anomalous coverage data. 1) Interpolation was performed to eliminate inconsistent and missing data for any one quarter.
For the present study, a set of 1352 subjects was available after eliminating those with missing data on dietary habits or adiposity.
Eliminating observations with missing data in one or more study variables resulted in a final study sample of 5453 unique respondents, who contributed of 11869 person-wave data records.
After eliminating samples with missing data or the lowest parasitaemia levels (<0.01%), 36 isolates per site were randomly chosen for genotyping, a sample size considered adequate for the planned population genetics analyses.
Evolutionary distances were computed using the JTT matrix-based method with all positions containing alignment gaps and missing data eliminated in pair-wise sequence comparisons.
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