The multiplicative standard errors for the replacement approaches (i.e., inserting DL/2, E[ Z|LB < Z < UB], or a fill-in value) are smaller than standard errors from the multiple imputation approach and direct estimation.
Factor loadings, squared multiple correlations, standard errors and t values were inspected for appropriate sign and/or magnitude.
In this multiple imputation procedure, standard errors were decomposed to the variability across and within the five imputations (Enders [2010]; OECD [2005]; Wu [2005]).
For countries with data from multiple years, robust standard errors were calculated to account for within-country time dependencies and the longitudinal facet of the data.
Multiple R 0.960 Squared multiple R 0.921 Adjusted squared multiple R 0.779 Standard error of estimate 0.474.
The values so obtained for the vat dyed fabric were, Multiple R 0.965 Squared multiple R 0.931 Adjusted squared multiple R 0.805 Standard error of estimate 0.459.
L treatment parameters when studied on the K/S value of sulphur black dyed denim fabric, the values obtained are Multiple R 0.954 Squared multiple R 0.910 Adjusted squared multiple R 0.747 Standard error of estimate 1.202.
In multiple regression analyses, adjusting standard errors for the complex survey design of the IHS3 and controlling for the effects of several covariates on household dietary diversity, farm production diversity was consistently positively associated with dietary diversity (P < 0.0001).
Equally important, using multiple estimates will bias standard errors of summary statistics, with understatement of errors being the generally expected outcome [[7], p. 226].
The k statistics for multiple observers and the standard errors for were calculated by using the method reported by Fleiss [23] [25]: k values range between +1 (perfect agreement) and −1 (perfect disagreement); values greater than 0.75 represent an excellent agreement; values lower than 0.40 a poor agreement; and values between 0.40 and 0.75 a fair to good agreement beyond chance.
