• Journal of the Korean Data and Information Science Society
• /
• v.26 no.6
• /
• pp.1547-1555
• /
• 2015

We study the problem of nonignorable nonresponse in a two-way contingency table and there may be one or two missing categories. We describe a nonignorable nonresponse model for the analysis of two-way categorical table. One approach to analyze these data is to construct several tables (one complete and the others incomplete). There are nonidentifiable parameters in incomplete tables. We describe a hierarchical Bayesian model to analyze two-way categorical data. We use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. To reduce the effects of nonidentifiable parameters, we project the parameters to a lower dimensional space and we allow the reduced set of parameters to share a common distribution. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data to obtain the finite population proportions.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.4
• /
• pp.899-910
• /
• 2016

Asymptotic tests are often not suitable for the analysis of sparse ordered contingency tables as asymptotic p-values may either overestimate or underestimate the true pvalues. In this pater, we describe permutation procedures in which we compute exact or resampling p-values for a weighted specific-category agreement in ordered $k{\times}k$ contingency tables. We use the weighted specific-category kappa proposed by $Kv{\dot{a}}lseth$ to measure the extent to which two independent raters agree on the specific categories. We carried out comparison studies between exact p-values, resampling p-values and asymptotic p-values using $3{\times}3$ contingency data (real and artificial data sets) and $4{\times}4$ artificial contingency data.

• Journal of Information Technology Applications and Management
• /
• v.16 no.2
• /
• pp.1-21
• /
• 2009

The balanced scorecard(BSC) framework is a tool for strategic decision making and task support that aims for efficiency in performance management and strategy execution. BSC evolved from an old performance management practice, which tended to be limited to a finance-only perspective, to a new system of corporate management looking at corporate tasks from a multi-dimensional, future-inclined value perspective. This form of BSC amounts to a framework capable of driving management innovation and renewing the ways in which companies conceive their strategy and perform their operations. Since BSC draws integrally on user participation, it can be expected that users' satisfaction with BSC systems is an important factor in systems' success or failure. However, previous studies of the BSC system have not yet considered it as a theoretical model, specifically examining BSC system and task characteristics. To date, only a few studies have put forward plans for the implementation and use of BSC systems, and these studies have the common limitation of failing to consider the circumstances or theoretical structure of the companies for which a BSC system is being proposed. This paper then begins to fill some of this gap by characterizing the BSC system from the perspective of contingency theories. Contingency theories can be particularly useful in the Korean context in exploring how different companies use the BSC system in ways determined by their unique environmental characteristics, which may also determine the performance factors behind the application of a company's particular BSC system. In order to provide concrete suggestions for implanting and using the BSC system from a contingency theory perspective, this study sets out to determine the relationships between the contingency variables affecting BSC system performance and BSC system property variables(in given cases) through an empirical analysis. The study takes into account the perspective from which contingency theory is to be applied in individual cases, sets contingency and BSC property variables with reference to the BSC system user's environment and BSC system's character, and frames initial hypotheses concerning corporate structure and environmental variables and BSC system performance variables with reference to previous studies. A survey was then conducted on users in Korean companies that have implemented the BSC system in order to verify the research model and understand results.

• Proceedings of the KIEE Conference
• /
• 1986.07a
• /
• pp.300-304
• /
• 1986

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.4
• /
• pp.993-1002
• /
• 2004

Imputation procedures such as fully efficient fractional imputation(FEFI) or multiple imputation(MI) can be used to construct complete contingency tables from samples with partially classified responses. Variances of FEFI estimators of population proportions are derived. Simulation results, when data are missing completely at random, reveal that FEFI provides more efficient estimates of population than either multiple imputation(MI) based on data augmentation or complete case analysis, but neither FEFI nor MI provides an improvement over complete-case(CC) analysis with respect to accuracy of estimation of some parameters for association between two variables like $\theta_{i+}\theta_{+i}-\theta_{ij}$ and log odds-ratio.

• Proceedings of the Safety Management and Science Conference
• /
• 2012.04a
• /
• pp.293-300
• /
• 2012

Reliability measures of questionnaire and ${\chi}^2$ contingency tests of categorized responses are most practical tools to analyze the characteristics of subjects of survey study. This research evaluates the Cronbaha's reliability measures by using Repeated Measure Design (RMD) with illustrated MINITAB examples. In addition, ${\chi}^2$ statistics of each cell of categorized tables can be effectively interpreted with the symmetric plot of correspondence analysis. The practical example is also discussed to provide comprehensive understanding of topic.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.1
• /
• pp.245-254
• /
• 2016

Many surveys provide categorical data and there may be one or more missing categories. We describe a nonignorable nonresponse model for the analysis of two-way contingency tables from small areas. There are both item and unit nonresponse. One approach to analyze these data is to construct several tables corresponding to missing categories. We describe a hierarchical Bayesian model to analyze two-way categorical data from different areas. This allows a "borrowing of strength" of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the small areas. Also we use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data on thirteen states to obtain the finite population proportions.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.919-925
• /
• 2006

Maximum likelihood estimate(MLE) is obtained from the partial log-likelihood function for the cell probabilities of two way incomplete contingency tables proposed by Chen and Fienberg(1974). The partial log-likelihood function is modified by adding lagrangian multiplier that constraints can be incorporated with. Variances of MLE estimators of population proportions are derived from the matrix of second derivatives of the loglikelihood with respect to cell probabilities. Simulation results, when data are missing at random, reveal that Complete-case(CC) analysis produces biased estimates of joint probabilities under MAR and less efficient than either MLE or MI. MLE and MI provides consistent results under either the MAR situation. MLE provides more efficient estimates of population proportions than either multiple imputation(MI) based on data augmentation or complete case analysis. The standard errors of MLE from the proposed method using lagrangian multiplier are valid and have less variation than the standard errors from MI and CC.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.29-40
• /
• 2019

Categorical data with non-responses are frequently observed in election poll surveys, and can be represented by incomplete contingency tables. To estimate supporting rates of candidates, the identification of the missing mechanism should be pre-determined because the estimates of non-responses can be changed depending on the assumed missing mechanism. However, it has been shown that it is not possible to identify the missing mechanism when using observed data. To overcome this problem, sensitivity analysis has been suggested. The previously proposed sensitivity analysis can be applicable only to two-way incomplete contingency tables with binary variables. The previous sensitivity analysis is inappropriate to use since more than two of the factors such as region, gender, and age are usually considered in election poll surveys. In this paper, sensitivity analysis suitable to an multi-dimensional incomplete contingency table is devised, and also applied to the 19th Korean presidential election poll survey data. As a result, the intervals of estimates from the sensitivity analysis include actual results as well as estimates from various missing mechanisms. In addition, the properties of the missing mechanism that produce estimates nearest to actual election results are investigated.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.173_174
• /
• 2009

K-EMS(한국형에너지관리시스템)용 상정고장해석(Contingency Analysis) 프로그램에 대해 설명한다. 상정고장해석 프로그램은 가상의 고장에 따른 계통의 상태변화 판정 기능과 심각한 상정고장 목록을 선별하는 기능, 그리고 선별된 고장목록의 완전해석 기능으로 구성된다. 본 논문에서는 그간의 연구에서 개발된 고장에 따른 계통의 상태변화를 판정하는 방법을 설명하며, 그러한 방법에 적용한 사례연구결과와 개발 프로그램의 성능을 기술한다.