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Analytical results derived from the multiply imputed data were compared with results obtained with other methods for dealing with missing data (including complete case analyses and with missing data indicators with single-value imputation).
Respondents were excluded if they were missing data on baseline covariates (2,013 cases), particularly birth region (9 % of age-eligible respondents missing data, including some born abroad), maternal education (9 % missing data), race/ethnicity (2 % missing data), and income or wealth (1 % missing data).
Blanks indicate missing data, including data withheld for confidentiality reasons, and groups for which no data was available.
Blanks indicate missing data, including data withheld for confidentiality, and groups for which no data were available.
Analysis of the 66 characters and 27 taxa (including Electrorana) based on the matrix from Henrici et al.30 resulted in four equally parsimonious trees (score = 243); the matrix contains 20% missing data, including 36 of 66 characters for Electrorana.
Analysis of the 97 characters and 52 taxa (including Electrorana) based on the matrix from Gao & Chen29 resulted in 598,547 equally parsimonious trees (score = 381); the matrix contains 28% missing data, including 47 of 97 characters for Electrorana.
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But the fact that the missing data included intelligence information increased the level of concern among law enforcement officials.
The reasons for missing data included late arrival to the clinic (n = 4), difficulty logging onto the system (n = 1), and being too ill to report (n = 1).
Reasons for missing data include; RCS (RIP = 13, LITU care ongoing at time of analysis = 5, not recorded = 2), ICUMS and CPAx (RIP= 13, no therapy input required = 9, LITU care ongoing at time of analysis = 5, not recorded = 18).
Factors were coded as categorical variables with missing data included as a category.
* The 29 missing data included all the 23 older people living alone.
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