• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.291-302
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• 2023

Objectives: Disability weights require regular updates, as they are influenced by both diseases and societal perceptions. Consequently, it is necessary to develop an up-to-date list of the causes of diseases and establish a survey panel for estimating disability weights. Accordingly, this study was conducted to calculate, assess, modify, and validate disability weights suitable for Korea, accounting for its cultural and social characteristics. Methods: The 380 causes of disease used in the survey were derived from the 2019 Global Burden of Disease Collaborative Network and from 2019 and 2020 Korean studies on disability weights for causes of disease. Disability weights were reanalyzed by integrating the findings of an earlier survey on disability weights in Korea with those of the additional survey conducted in this study. The responses were transformed into paired comparisons and analyzed using probit regression analysis. Coefficients for the causes of disease were converted into predicted probabilities, and disability weights in 2 models (model 1 and 2) were rescaled using a normal distribution and the natural logarithm, respectively. Results: The mean values for the 380 causes of disease in models 1 and 2 were 0.488 and 0.369, respectively. Both models exhibited the same order of disability weights. The disability weights for the 300 causes of disease present in both the current and 2019 studies demonstrated a Pearson correlation coefficient of 0.994 (p=0.001 for both models). This study presents a detailed add-on approach for calculating disability weights. Conclusions: This method can be employed in other countries to obtain timely disability weight estimations.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1179-1189
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• 2007

In this paper, we estimate the proportion of individuals having health insurance in a given year for several small domains cross-classified by age, sex and other demographic characteristics using the data provided by the National Center for Health Statistics(NCHS). We employ Bayesian as well as frequentist methodology to obtain small domain estimates and the associated measures of precision. One of the new features of our study is that we utilize the survey weights along with the model to derive the small domain estimates.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.3
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• pp.85-89
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• 2013

In the current domestic, many industrial accidents have happened. And these are analyzed according to several factors. But it is difficult that they evaluate their business safety. Thus, we conducted a study on business specific safety assessment techniques in order that business know their safety level and perform appropriative safety activities. Study methods are survey and AHP (Analytic Hierarchy Process). Each specific weighting factors to calculate the survey was conducted for safety and consulting experts (20 persons). Weight factor was used to AHP decision support as one of the ways through a number of alternatives to the ratings for the Minesota Multiphasic. Factors are each type of industry, specific industrial scale, disaster type and strength, worker age and tenure period, and region. First, survey was conducted with 20 professionals to estimate the weighting factors. Weights between factors using the AHP analysis tool based on the mean values were calculated. Second, last 3 years between the industrial accidents statistics were used to calculate the weights for each of the rating factors in the Occupational Safety and Health Agency. Grade weights between each factor which was based on the rating of each factor was calculated as the average of three years. Finally, the weights between each factor and the grade weights for each factor using the safety level of the enterprise were calculated so that you can evaluate the weighting.

• Survey Research
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• v.12 no.3
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• pp.173-186
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• 2011

We introduced the methodologies used to construct the longitudinal weights and cross-sectional weight that are required for the analysis of Korea Youth Panel Survey. To analyze the longitudinal dynamic change of the population, we derived the longitudinal weight through nonresponse adjustment based on logistic regression and post-stratification. Cross-sectional weights that are necessary to produce an asymptotically unbiased estimator of the population parameter were constructed through simple nonresponse adjustment based on overall response rate and post-stratification.

• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• Proceedings of the Korean Association for Survey Research Conference
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• 2005.12a
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• pp.143-157
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• 2005

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.173-185
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• 2006

In this paper, we propose two methods for setting initial weights in the PLS algorithm which is employed to measure the customer satisfaction in SEM. Using data from the survey of the students conducted with the questionnaire of the ACSI survey, we evaluate the education service in terms of the satisfaction level of the students and compare our proposed methods with the previous method.

• Survey Research
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• v.12 no.2
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• pp.89-112
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• 2011

This study shows the results of constructing panel data using Farm Household survey and presents some examples of empirical application. This study shows that ex post constructed panel data using repeated cross-sectional survey can be used in various dynamic analyses. This paper also shows that the well known difficult problem of longitudinal weights can be easily solved by using the existing cross-sectional weights in original cross-section data. Based on these results, we propose that the National Statistical Office not only try to construct panel data, but also construct panel data by using existing repeated cross-section data. The benefits of this approach seems to be very big in establishment survey.

• Journal of the Korean housing association
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• v.20 no.2
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• pp.69-76
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• 2009

The evaluation of the quality of residential environment, in the planning process, is very important. If the environmental quality can be measured on the design scheme itself, it would be possible not only to estimate the residential environment, but also to apply the results of measuring in the planning and design of new residential projects. This study aimed at estimating the relative weights of the constituents of a residential environment by AHP (Analytic Hierarchy Process). For the detailed analysis of relative weights, the respondent of survey were composed of two different groups-dwellers and experts. The findings are as follows (:) 1) the relative weights of residential constituents were different between the respondent groups; 2) the relative weights of residential constituents were different according to socio-demographic characteristics; and 3) for the practical application of estimation results of the relative weights, the relation between residential performance constituents and physical design elements should be considered.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.19-28
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• 2003

Weighting adjustment is a method of improving the efficiency of the estimator by incorporating auxiliary variables at the estimation stage. One commonly used method of weighting adjustment is the poststratification, which is a special case of regression estimation but is relatively feasible in terms of actual implementation. If too many auxiliary variables are used in the poststratification, the bias of the resulting point estimator is no longer negligible and the final weights may have extreme weights. In this study, we propose a method of weight ing adjustment that compromises the efficiency and the bias of the point estimator. A limited simulation study is also presented.