• Korean Journal of Childcare and Education
• /
• v.9 no.2
• /
• pp.117-141
• /
• 2013

The purpose of this study is to investigate the young children's 'more means more' bias and knowledge change process regarding a lever phenomenon, especially the relationship between the weight of an object and the strength of force and between the length of a lever and the strength of force. Subjects, who were presented with the tasks, were eight young children 5 years of age. Major findings were as followings. First, most of the subjects have a 'more means more' bias about the relationship between the weight of an object and the strength of force and between the length of a lever and the strength of force regarding a lever phenomenon. This meant that young children have similar concepts about a lever phenomenon regardless of whether it is right or wrong physically. Second, young children tried to make sure of their knowledge during experiments. They chose the evidence which confirmed their knowledge. But they tried to change their knowledge, when the evidence presented did not correspond to their knowledge. These findings contribute to understanding young children's 'more means more' bias and knowledge change process about a lever phenomenon and can be used in preschool science education programs and curriculums.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.125-134
• /
• 2021

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.

• The Journal of the Korea Contents Association
• /
• v.21 no.5
• /
• pp.1-10
• /
• 2021

This study empirically confirmed 'the political bias of the YouTube recommendation algorithm' and 'the selective exposure of user' to verify the Filter Bubble phenomenon of YouTube. For the experiment, two new YouTube accounts were opened and each account was trained simultaneously in a conservative and a liberal account for a week, and the "Recommended" videos were collected from each account every two days. Subsequently, through the text mining method, the goal of the research was to investigate whether conservative videos are more recommended in a righties account or lefties videos are more recommended in a lefties account. And then, this study examined if users who consumed political news videos via YouTube showed "selective exposure" received selected information according to their political orientation through a survey. As a result of the Text Mining, conservative videos are more recommended in the righties account, and liberal videos are more recommended in the lefties account. Additionally, most of the videos recommended in the righties/lefties account dealt with politically biased topics, and the topics covered in each account showed markedly definitive differences. And about 77% of the respondents showed selective exposure.

• Korean Journal of Cognitive Science
• /
• v.18 no.4
• /
• pp.399-415
• /
• 2007

Many objects are always present in a visual scene. Since the visual system has limited capacity to process multiple stimuli at a time, how to cope with this informational overload is one of the important problems to solve in visual perception. This study investigated the suppressive interactions among multiple stimuli when attention was directed to either one of the stimuli or all of them. The results indicate that suppressive interactions among multiple circles were reduced in V4 when subjects paid attention to one of the four locations, as compared to the unattended condition. However, suppressive interactions were not reduced when they paid attention to all four items as a set, in order to compute their mean size. These results suggest that whereas focused attention serves to later out irrelevant information, distributed attention provides an average representation of multiple stimuli.

• Proceedings of the Korean Association for Survey Research Conference
• /
• 2002.06a
• /
• pp.157-162
• /
• 2002

패널 무응답자(panel nonrespondent)란 처음 조사에서는 응답을 하였으나 나중 조사에서는 응답을 하지 않은 사람을 의미한다. 패널조사에서는 앞 단계에서의 응답으로부터 뒷 단계의 무응답에 대한 정보를 얻을 수 있다. 무응답에 대한 수정 방법은 어떤 보조 변수들을 선택하고, 그 변수들이 수정하는 데 어떻게 사용하는 가를 결정하는 것이다. 우리는 가중 수정을 패널 무응답자에 대해서만 생각한다. 이러한 가중은 패널 무응답자에 대하여 보상하기 위하여 패널 무응답의 가중값을 수정한다. 종속 변수로서 패널응답 상태(status)는 로지스틱 회귀분석으로 패널 무응답에 대한 모형을 선택하는 방법이다. 로지스틱 회귀분석에서 패널무응답과 상관이 있는 변수들은 패널무응답 편향을 감소시키기 위하여 가중 수정에서 사용하기 위한 변수들이다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.8
• /
• pp.5288-5293
• /
• 2014

This paper presents the characterization of the wavelength selective switch using a LCOS display instead of a MEMS device, which is a core technology for the next generation ROADM. We constructed a five-port WSS, and examined the basic characteristics of the WSS, such as the response time, beam steering, insertion loss, and channel isolation. The response time of WSS was 11.6 mS and the beam steering characteristics of LCOS was well performed. The measured insertion loss at 40 channels in the c-band were 5.5~12.7 dB and channel isolation was 16~18 dB. Although the characteristics of LCOS-based WSS are inferior to the conventional MEMS-based WSS, it can be improved by additional experiments that secure the technology competitiveness.

• Analyses & Alternatives
• /
• v.6 no.2
• /
• pp.79-115
• /
• 2022

This study aims to account for electoral choice in the 2020 presidential election by focusing on social identity which forms the basis for core partisan groups. Two views compete to explain the origins of polarization, policy versus party. One emphasizes policy as more influential in choosing presidential candidates. This follows the tradition of retrospective voting theory in which voters' choice rely on government performance. Incumbent president whose performance proves well are rewarded to be reelected. Policy performance is based on measures around distinctive preferences for government spending. Republican Individuals prefer individual responsibility to government support, while Democratic counterparts support government support. Another perspective put an emphasis on the role partisanship which favors in-party members and disfavors partisan out-groups. Interparty animosity plays the key role in determining electoral behavior. This study relies on the Views of the Electorate Research (VOTER) Survey which provides a panel data of several waves from 2011 to 2020. A comparative evaluation of two views highlights three findings. First, policy matters. Policy preferences of voters are the primary drives of political behavior. Electoral outcomes in 2020 turned out to be the results of policy considerations of voters. 53.7 percent of voters tilted toward individual responsibility voted for Trump, whereas 70.4 percent of those favorable views of government support than individual responsibility voted for Biden. Thus effects of policy correspond to a positive difference of 26.4 percent points. Second, partisanship effects are of similar extent in influencing electoral choice of candidates: Democrats are less likely to vote for Trump by 42.4 percent points, while Republicans are less likely to vote for Biden by 48.7 percent points. Third, animosity of Republicans toward Democrat core groups creates 26.5 percent points of favoring Trump over Biden. Democrat animosity toward Republican core groups creates a positive difference of 13.7 percent points of favoring Biden.

• The Korean Journal of Applied Statistics
• /
• v.31 no.6
• /
• pp.733-749
• /
• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1361-1371
• /
• 2016

Quantile regression provides a variety of useful statistical information to examine how covariates influence the conditional quantile functions of a response variable. However, traditional quantile regression (which assume a linear model) is not appropriate when the relationship between the response and the covariates is a nonlinear. It is also necessary to conduct variable selection for high dimensional data or strongly correlated covariates. In this paper, we propose a penalized quantile regression tree model. The split rule of the proposed method is based on residual analysis, which has a negligible bias to select a split variable and reasonable computational cost. A simulation study and real data analysis are presented to demonstrate the satisfactory performance and usefulness of the proposed method.

• Journal of the Korean Data and Information Science Society
• /
• v.9 no.2
• /
• pp.179-188
• /
• 1998

Local polynomial regression estimation is the most popular one among kernel type regression estimator. In local polynomial regression function esimation bandwidth selection is crucial problem like the kernel estimation. When the regression curve has complicated structure variable bandwidth selection will be appropriate. In this paper, we propose a variable bandwidth selection method fully data driven. We will choose the bandwdith by selecting minimising estiamted MSE which is estimated by the pilot bandwidth study via croos-validation method. Monte carlo simulation was conducted in order to show the superiority of proposed bandwidth selection method.