• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.71-84
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• 2017

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.365-377
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• 2020

We discuss latent factor regression when constructing a common structure inherent among explanatory variables to solve multicollinearity and use them as regressors to construct a linear model of a response variable. Bayesian estimation with LASSO prior of a large penalty parameter to construct a significant factor loading matrix of intrinsic interests among infinite latent structures. The estimated factor loading matrix with estimated other parameters can be inversely transformed into linear parameters of each explanatory variable and used as prediction models for new observations. We apply the proposed method to Product Service Management data of HBAT and observe that the proposed method constructs the same factors of general common factor analysis for the fixed number of factors. The calculated MSE of predicted values of Bayesian latent factor regression model is also smaller than the common factor regression model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.633-636
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• 2006

In order to determine new settings of key process variables optimally, a new rule induction method through a historical data is proposed without using an explicit functional model between process and quality variables. First, a partial least square is used to reduce the dimensionality of the process variables. Then new process settings that yield the best quality variable are identified by sequentially partitioning the reduced latent variable space using a patient rule induction method. The proposed method is illustrated with a case study obtained from steel-making processes. We also show, through simulation, that the proposed method gives more stable results than estimating an explicit function even when the form of the function is known in advance.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.2
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• pp.137-143
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• 2003

In the forming of an integrated system of radiant floor cooling and dehumidifying, chilled coil can be used for cooling and dehumidification. Therefore, it is necessary to find the efficient control method which can eliminates latent load efficiently. This study has been conducted to find this method by dividing the dehumidification system into 3 types according to the control variables and analyzing characteristics of each system. To prevent the floor surface condensation, the amount of condensation can be manipulated by water temperatures, water flow rates in chilled coil, and air flow rates passing by it. So dehumidification system control can be divided into constant air flow control and variable air flow control. Regarding dehumidification control, variable air flow control, which eliminates latent load rather than sensible load, is preferable to constant flow control.

• International Journal of Human Ecology
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• v.8 no.2
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• pp.75-83
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• 2007

Korean secondary school students (n=263) responded to surveys measuring their family contexts and school adjustment during the time period August-September 2004. Structure Equation Modeling tests were conducted to identify the nested model on school adjustment, a latent variable constructed with peer relations, teacher-adolescent relations, and academic attitude. In the nested model, parental involvement was a powerful predictor for school adjustment. Family conflict had a negative impact on school adjustment and was statistically significantly when correlated with the other predictors in the model. These finding suggested that family contexts play an important role in Korean adolescents' school adjustment. Hence, adolescents' perceived GPA level and satisfaction for school were important predictors for school adjustment.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.609-622
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• 2011

The pattern of methylation draws significant attention from cancer researchers because it is believed that DNA methylation and gene expression have a causal relationship. As the interest in the role of methylation patterns in cancer studies (especially drug resistant cancers) increases, many studies have been done investigating the association between gene expression and methylation. However, a model-based approach is still in urgent need. We developed a finite mixture model in the Bayesian framework to find a possible relationship between gene expression and methylation. For inference, we employ Expectation-Maximization(EM) algorithm to deal with latent (unobserved) variable, producing estimates of parameters in the model. Then we validated our model through simulation study and then applied the method to real data: wild type and hydroxytamoxifen(OHT) resistant MCF7 breast cancer cell lines.

• Speech Sciences
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• v.11 no.4
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.662-667
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• 2022

Computing music similarity is an indispensable component in developing music search service. This paper focuses on learning a music similarity function in order to boost cover song identification performance. By using the probabilistic linear discriminant analysis, we construct a latent music space where the distances between cover song pairs reduces while the distances between the non-cover song pairs increases. We derive a music similarity function by testing hypothesis, whether two songs share the same latent variable or not, using the probabilistic models with the assumption that observed music features are generated from the learned latent music space. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• Korean Journal of Culture and Social Issue
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• v.28 no.4
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• pp.577-595
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• 2022

The purposes of the study is to classify the psychological characteristics of gamblers using by latent profile analysis and to identify the consequences according toof the latent profiles. The subjects of the study are 473 adults gamblers who responded to a online survey about gambling patterns, basic psychological needs(BPNs), and mental health status(MHS) such as stress, depression, and anxiety. Using latent profile analysis known as the person-centered analysis, the results showed that psychological characteristics of gamblers were classified into three groups: 'Lower MHS-BPNs', 'Middle MHS-BPNs', and 'Upper MHS-BPNs'. Also, the as outcome variable, levels of problematic gambling(KCPGI) showed significant differences across the latent profiles such as Problem gambling(M=11.393) on 'Lower MHS-BPNs', Moderate-risk gambling(M=4.277) on 'Middle MHS-BPNs' and Low-risk gambling (M=1.718) on 'Upper MHS-BPNs'. Overcoming the limitations of variable-centered analysis in the existing studies, this study providesreveals new insights onin the psychological characteristics of gamblers and how different latent profiles of gamblers may be in theirdistinct levels of problematic gambling. Finally, limitations of the study and future directions for research on gambling problems are discussed.

• The Mathematical Education
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• v.50 no.1
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• pp.103-118
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• 2011

The study examined whether the relation between mathematics self-efficacy and mathematics achievement was partially mediated by the learning strategies, using latent growth model analyses. It was also examined the auto-regressive, cross-lagged (ARCL) panel model for testing the stability and change in the relation of mathematics self-efficacy and learning strategy over time. The study analyzed the first-year to the third-year data of the Korean Educational Longitudinal Survey (KELS). The result of ARCL panel model analysis showed that earlier mathematics self-efficacy could predict later learning strategy use. There were linear trends in mathematics self-efficacy, learning strategy, and mathematics achievement. Specifically, mathematics achievement was increased over the three time points, whereas mathematics self-efficacy and learning strategies were significantly decreased. In the analyses of latent growth models, the mediating effects of learning strategies were overall supported. That is, both of initial status and change rate of rehearsal strategy partially mediated the relation of mathematics self-efficacy and mathematics achievement. However, in elaboration and meta-cognitive strategies, only the initial status of each variable showed the indirect relationship.