Ratio imputation method for handling item-nonresponse in Eichhorn model

Missing data is often a problem in social science data. Imputation methods fill in the missing responses and lead, under certain conditions, to valid inference. This paper proposes an investigation procedure concerning the Eichhorn and Hayre (6)'s quantitative sensitive characteristic randomized response procedure in the presence of missing data. For the missing randomized responses, their values are imputed in the ratio form. The estimator for the population mean of sensitive characteristic variable is addressed and its nature is discussed in detail. A simulation study evaluates the proposed imputation methods, and this procedure is found to perform well with regards to bias and mean square error.

[1]  Bernard G. Greenberg,et al.  Randomized Response: a Data-Gathering Device for Sensitive Questions , 1976 .

[2]  G. Kalton,et al.  The treatment of missing survey data , 1986 .

[3]  Changbao Wu,et al.  Estimation of Variance of the Ratio Estimator. , 1982 .

[4]  Naurang Singh Mangat,et al.  An alternative randomized response procedure , 1990 .

[5]  Arijit Chaudhuri,et al.  Using randomized response from a complex survey to estimate a sensitive proportion in a dichotomous finite population , 2001 .

[6]  S L Warner,et al.  Randomized response: a survey technique for eliminating evasive answer bias. , 1965, Journal of the American Statistical Association.

[7]  Wayne A. Fuller,et al.  Fractional hot deck imputation , 2004 .

[8]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[9]  N. S. Mangat,et al.  An Improved Randomized Response Strategy , 1994 .

[10]  R. Fay.  Alternative Paradigms for the Analysis of Imputed Survey Data , 1996 .

[11]  Raghunath Arnab.  Randomized response surveys: Optimum estimation of a finite population total , 1998 .

[12]  Housila P. Singh,et al.  Estimation of population mean when coefficient of variation is known using scrambled response technique , 2005 .

[13]  J. Rao.  On Variance Estimation with Imputed Survey Data , 1996 .

[14]  A. Chaudhuri,et al.  Randomized Response: Theory and Techniques , 1987 .

[15]  B. V. Sukhatme,et al.  On the Bias and Mean Square Error of the Ratio Estimator , 1974 .

[16]  Lakhbir S. Hayre,et al.  Scrambled randomized response methods for obtaining sensitive quantitative data , 1983 .

[17]  Zaizai Yan,et al.  Ratio method of estimation of population proportion using randomized response technique , 2006, Model. Assist. Stat. Appl..