ABSTRACT
Objective To explore the sampling method in China National Human Biomonitoring Program (HBP) and the related errors,so as to calculate and evaluate the study design in sampling.Methods The sampling method of HBP is of multistage nature.Taking the results of sampling method from Guizhou province as an example,results related to sampling error and variation coefficient were calculated,using the multistage unequal probability sampling error method.Results The HBP covered 152 monitoring sites in 31 provinces (autonomous regions and municipalities) and with 21 888 residents selected.The replacement rates at various stages were 5.26％,6.35％ and 40.6％ respectively.The sampling error in Guizhou province was 3 207 594,and the coefficient of variation was 0.097.Conclusions According to the multi-stage unequal probability sampling method,the sampling coefficient variability appeared small with high precision,in Guizhou province.However,this method did not consider the weight adjustment of non-sampling errors such as population missing rate and response rate.Methods related to the calculation on multi-stage sampling error among large-scale public health monitoring projects need to be further studied.
ABSTRACT
Objective To explore the sampling method in China National Human Biomonitoring Program (HBP) and the related errors,so as to calculate and evaluate the study design in sampling.Methods The sampling method of HBP is of multistage nature.Taking the results of sampling method from Guizhou province as an example,results related to sampling error and variation coefficient were calculated,using the multistage unequal probability sampling error method.Results The HBP covered 152 monitoring sites in 31 provinces (autonomous regions and municipalities) and with 21 888 residents selected.The replacement rates at various stages were 5.26％,6.35％ and 40.6％ respectively.The sampling error in Guizhou province was 3 207 594,and the coefficient of variation was 0.097.Conclusions According to the multi-stage unequal probability sampling method,the sampling coefficient variability appeared small with high precision,in Guizhou province.However,this method did not consider the weight adjustment of non-sampling errors such as population missing rate and response rate.Methods related to the calculation on multi-stage sampling error among large-scale public health monitoring projects need to be further studied.
ABSTRACT
Background & objectives: Physicians’ satisfaction/dissatisfaction from their job is an important factor associated with health service that deals with human life. This study was conducted to ascertain overall level and proportion of physicians’ satisfaction from their job as well as to identify those components that influenced it. Method: A comprehensive customized questionnaire was used with Section A to assess demographic profile of physicians and Section B to assess satisfaction. Response to each question was devised using Likert scale. Likert scale responses were converted to normal scale so that statistical procedures could be naturally developed. A total of 170 physicians were selected using multistage sampling. Questionnaire was administered on one to one basis to avoid non-response. Precise and contextualized descriptive and inferential statistical procedures were used for analysis. Result: Of the 140 physicians, 103 (74%) were satisfied from their job with average score of 19.15 ± 11.46 while 37 (26%) were dissatisfied with average score -09.27 ± 06.30. Nine out of 15 components were found significant (P<0.05). Conclusion: Comparative assessment of the present results with those of other studies revealed that satisfaction percentage of Indian physicians and those of the developed countries were almost the same. Perhaps, magnitude of satisfaction level (average score) of the Indian physicians were towards the lower side. Nine determinants, identified in this study can be used safely to assess any professionals’ satisfaction.
ABSTRACT
Multistage sampling techniques are widely applied in the cross-sectional study of epidemiology, while methods based on independent assumption are still used to analyze such complex survey data. This paper aims to introduce the application of weighted estimation methods for the complex survey data. A brief overview of basic theory is described, and then a practical analysis is illustrated to apply to the weighted estimation algorithm in a stratified two-stage clustered sampling data. For multistage sampling survey data, weighted estimation method can be used to obtain unbiased point estimation and more reasonable variance estimation, and so make proper statistical inference by correcting the clustering, stratification and unequal probability effects.