• The Korean Journal of Applied Statistics
• /
• v.27 no.7
• /
• pp.1077-1095
• /
• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• Journal of Digital Convergence
• /
• v.12 no.10
• /
• pp.405-414
• /
• 2014

This research aims to strengthen the ethical decision-making capability of nursing students, and involves 82 fourth-year undergraduate nursing students in a nonequivalent control group pre-post quasi experimental research design from March 4 to June 3, 2013. Experimental group took a discussion-based class and control group took a traditional lecture-based class and we identified the differences in ethical knowledge, class satisfaction and ethical values between the two groups. Experimental group had higher scores for ethical knowledge after the class. There was no significant change in ethical values after the class in the two groups. The experimental group achieved significantly higher scores for the comprehension of class contents and practical application within the class satisfaction criteria. So we propose to use this model as an effect teaching method to apply ethical principles in nursing practice.

• Journal of The Korean Association of Information Education
• /
• v.22 no.3
• /
• pp.297-305
• /
• 2018

Physical computing is the concept of expanding computing to humans, environments, and objects. It draws attention as a programming learning medium based on physical outputs in integration of hardware and software. This study developed a programming learning model based on interactive prototyping using the characteristics of physical computing with a high degree of technical freedom and analyzed its learning effect in an experiment. To examine the effect of the experimental treatment, this researcher divided fifty nine 5th-grade elementary students into an experimental group and into a control group. the interactive prototyping programming learning model was applied to the experimental group, and a linear sequential programming learning model was applied to the control group. Information Science Creative Personality Test was conducted before and after the experimental treatment. Analysis of Covariance was conducted with the pre-test scores of the two groups. As a result, it was proved that there was the effect of learning at the significance level of .05. It indicates that the physical computing based interactive prototyping programming learning model is applicable to the programming learning for 5th-grade elementary students.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.2
• /
• pp.27-43
• /
• 2024

Recently, the importance of trademarks as a core resource of corporate competitiveness to protect and differentiate products and services is increasing. Global companies are focusing hard to secure trademark rights to manage brands that reflect their core values and to respond to increasingly frequent trademark disputes, while start-ups and individuals are working hard to secure trademark to run stable businesses and attract investment funds. Meanwhile, this study conducts an empirical analysis to identify the relationship between the brand and business strategy of domestic venture startups. The analysis data used was the response data of 2,230 corporate companies from the 2021 Venture Business Precision Survey, and the propensity score matching method, structural equation model analysis, and binomial logit analysis were used as analysis methods. As a result of the analysis, it was confirmed that domestic venture startups' trademark ownership does not make a significant difference in terms of the level of business strategy. This was confirmed to be because the brands of domestic venture start-ups mainly advance their business strategies only through the internal competency process, while the advancement of business strategies through the external competency process is very minimal. Meanwhile, it was confirmed that the level of cost advantage strategy among the business strategy levels of venture start-ups strengthens the tendency to hold trademarks, indicating that the higher the completeness of the cost advantage level, the more likely it is to expand trademark ownership for stable sales and supply of products and services through trademark ownership and to convert to high value-added in the future.

• The Korean Journal of Applied Statistics
• /
• v.34 no.6
• /
• pp.923-936
• /
• 2021

A large number of non-responses are occurring in the sample survey, and various methods have been developed to deal with them appropriately. In particular, the bias caused by non-ignorable non-response greatly reduces the accuracy of estimation and makes non-response processing difficult. Recently, Chung and Shin (2017, 2020) proposed an estimator that improves the accuracy of estimation using parametric super-population model and response rate model. In this study, we suggested a bias corrected non-response mean estimator using a nonparametric function generalizing the form of a parametric super-population model. We confirmed the superiority of the proposed estimator through simulation studies.

• The Korean Journal of Applied Statistics
• /
• v.32 no.2
• /
• pp.173-185
• /
• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• 한국사회정책
• /
• v.19 no.3
• /
• pp.85-117
• /
• 2012

This study attempts to examine the relationship between giving & volunteering behavior and charitable bequest intention. For the examination, this study used '2011 Korean National Social Survey' that was randomly sampled from the population of Korean in 2011. In examining the relationship, this study employed the method of Propensity Score Matching that permits the comparisons between experimental group and control group. In this study, the experimental groups consist of six different combinations of philanthropic behaviors as follows: donating only; volunteering only; participating both; regular donating only; regular volunteering only; and doing both regularly. The results show that all the types of philanthropic behaviors have statistically significant positive effect on charitable bequest intention. First, more active philanthropic behavior such as regular behavior causes higher charitable bequest intention. Second, those who participate in both philanthropic behaviors (combined behavior of donating and volunteering) have stronger effect on charitable bequest intention in comparison to participating only one philanthropic behavior (either donating or volunteering). Third, giving have relatively stronger relationship with charitable bequest intention than volunteering. Throughout more careful examination of the causal relationship from philanthropic behavior to charitable bequest intention, this study provides new understanding on the effect of daily philanthropic behavior on charitable bequest and practical implication to nurture charitable bequest. Indeed, this study contributes to building a knowledge foundation for future research on charitable bequest.

• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.615-632
• /
• 2017

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

• Journal of Technology Innovation
• /
• v.31 no.3
• /
• pp.201-230
• /
• 2023

The Innovation Procurement Policy was introduced as part of the strategic public procurement policy to improve firms' innovation capabilities and enhance the public sector's ability to solve social problems by designating and purchasing so-called 'innovative products.' The pilot procurement project for innovative products was first introduced in 2019, and the policy system for designating and discovering innovative products by government departments, as well as the priority purchase system, was established in 2020. Hence, this study conducted a quantitative analysis focusing on the effectiveness of the innovation procurement system in supporting firms after it was fully implemented. For this purpose, corporate financial and employment data from 2017 to 2021 were used, and propensity score matching(PSM) and difference-in-difference(DID) methods were utilized as analytical tools. The study found that the innovation procurement system contributed to corporate growth and employment and created additional public and private sales channels. Moreover, it is necessary to enhance the innovation procurement system, such as matching innovative product-producing companies with existing SME support policies, for companies to become self-sustaining after the innovative product designation ends.

• Proceedings of the Korean Association for Survey Research Conference
• /
• 2005.12a
• /
• pp.169-183
• /
• 2005

This paper considers formation of nonresponse weighting adjustment cell for handling unit nonresponse in sample surveys. We propose a multivariate regression tree mehtod for segmentation using the variable of interest and the estimated response probability simultaneously to construct effective nonresponse adjustment cell. One is using only response data and the other is using response and nonresponse data. These two cases are compared in terms of bias.