Cheongtag Kim

Studying Multivariate Change Using Multilevel Models and Latent Curve Models

In longitudinal research investigators often measure multiple variables at multiple points in time and are interested in investigating individual differences in patterns of change on those variables. In the vast majority of applications, researchers focus on studying change in one variable at a time. In this article we consider methods for studying relations1.1ips between patterns of change on different variables. We show how the multilevel modeling framework, which is often used to study univariate change, can be extended to the multivariate case to yield estimates of covariances of parameters representing aspects of change on different variables. We illustrate this approach using data from a study of physiological response to marital conflict in older married couples, showing a substantial correlation between rate of linear change on different stress-related hormones during conflict. We also consider how similar issues can be studied using extensions of latent curve models to the multivariate case, and we show how such models are related to multivariate multilevel models.

Distinguishing Optimism From Pessimism in Older Adults: Is It More Important to Be Optimistic or Not to Be Pessimistic?

Confirmatory factor analysis revealed that the Life Orientation Test (LOT) consisted of separate Optimism and Pessimism factors among middle-aged and older adults. Although the two factors were significantly negatively correlated among individuals facing a profound life challenge (i.e., caregiving), they were only weakly correlated among noncaregivers. Caregivers also expressed less optimism than noncaregivers and showed a trend toward greater pessimism, suggesting that life stress may affect these dispositions. Pessimism, not optimism, uniquely predicted subsequent psychological and physical health; however, optimism and pessimism were equally predictive for stressed and nonstressed samples. By exploring optimism and pessimism separately, researchers may better determine whether the beneficial effects of optimism result from thinking optimistically, avoiding pessimistic thinking, or a combination of the two.

Choosing the Optimal Number of Factors in Exploratory Factor Analysis: A Model Selection Perspective

A central problem in the application of exploratory factor analysis is deciding how many factors to retain (m). Although this is inherently a model selection problem, a model selection perspective is rarely adopted for this task. We suggest that Cudeck and Henlys (1991) framework can be applied to guide the selection process. Researchers must first identify the analytic goal: identifying the (approximately) correct m or identifying the most replicable m. Second, researchers must choose fit indices that are most congruent with their goal. Consistent with theory, a simulation study showed that different fit indices are best suited to different goals. Moreover, model selection with one goal in mind (e.g., identifying the approximately correct m) will not necessarily lead to the same number of factors as model selection with the other goal in mind (e.g., identifying the most replicable m). We recommend that researchers more thoroughly consider what they mean by “the right number of factors” before they choose fit indices.

Toward an explanation of the power law artifact: Insights from response surface analysis

The power law (y =ax−b) has been shown to provide a good description of data collected in a wide range of fields in psychology. R. B. Anderson and Tweney (1997) suggested that the model’s data-fitting success may in part be artifactual, caused by a number of factors, one of which is the use of improper data averaging methods. The present paper follows up on their work and explains causes of the power law artifact. A method for studying the geometric relations among responses generated by mathematical models is introduced that shows the artifact is a result of the combined contributions of three factors: arithmetic averaging of data that are generated from a nonlinear model in the presence of individual differences.

Unrealistic optimism on information security management

Information security is a critical issue that many firms face these days. While increasing incidents of information security breaches have generated extensive publicity, previous studies repeatedly expose low levels of managerial awareness and commitment, a key obstacle to achieving a good information security posture. The main motivation of our study emanates from this phenomenon that the increased vulnerability to information security breaches is coupled with the low level of managerial awareness and commitment regarding information security threats. We report this dissonance by addressing a cognitive bias called optimistic bias. Using a survey, we study if MIS executives are subject to such a bias in their vulnerability perceptions of information security. We find that they demonstrate optimistic bias in risk perception on information security domain. The extent of this optimistic bias is greater with a distant comparison target with fewer information sharing activities. This optimistic bias is also found to be related to perception of controllability with information security threats. In order to overcome the effects of optimistic bias, firms need more security awareness training and systematic treatments of security threats instead of relying on ad hoc approach to security measure implementation.

The Effects of Acupuncture Stimulation for Brain Activation and Alcohol Abstinence Self-Efficacy: Functional MRI Study

We attempted to investigate whether acupuncture stimulation at HT7 can have an effect on brain activation patterns and alcohol abstinence self-efficacy. Thirty-four right-handed healthy subjects were recruited for this study. They were randomly assigned into two groups: the HT7 (Shenmen) group and the LI5 (Yangxi) group. Acupuncture stimulation was performed using a block paradigm during fMRI scanning. Additionally, the Korean version of Alcohol Abstinence Self-Efficacy Scale (AASES) was used to determine the effect of acupuncture stimulation on self-efficacy to abstain from alcohol use. According to the result of fMRI group analysis, the activation induced by HT7 stimulation was found on the bilateral postcentral gyrus, inferior parietal lobule, inferior frontal gyrus, claustrum, insula, and anterior lobe of the cerebellum, as well as on the left posterior lobe of the cerebellum (p < 0.001, uncorrected). According to the AASES analysis, the interaction effect for gender and treatment was marginally significant (F(1, 30) = 4.152, p = 0.050). For female group, the simple main effect of treatment was significant (F(1, 11) = 8.040, p = 0.016), indicating that the mean change score was higher in the HT7 stimulation than in the LI5 stimulation. Therefore, our study has provided evidence to support that HT7 stimulation has a positive therapeutic effect on the alcohol-related diseases.

Acupuncture for Alcohol Use Disorder: A Meta-Analysis

Empirical research has produced mixed results regarding the effects of acupuncture on the treatment of alcohol use disorder in humans. Few studies have provided a comprehensive review or a systematic overview of the magnitude of the treatment effect of acupuncture on alcoholism. This study investigated the effects of acupuncture on alcohol-related symptoms and behaviors in patients with this disorder. The PubMed database was searched until 23 August 2016, and reference lists from review studies were also reviewed. Seventeen studies were identified for a full-text inspection, and seven (243 patients) of these met our inclusion criteria. The outcomes assessed at the last posttreatment point and any available follow-up data were extracted from each of the studies. Our meta-analysis demonstrated that an acupuncture intervention had a stronger effect on reducing alcohol-related symptoms and behaviors than did the control intervention (g = 0.67). A beneficial but weak effect of acupuncture treatment was also found in the follow-up data (g = 0.29). Although our analysis showed a significant difference between acupuncture and the control intervention in patients with alcohol use disorder, this meta-analysis is limited by the small number of studies included. Thus, a larger cohort study is required to provide a firm conclusion.

Molecular learning with DNA kernel machines.

We present a computational learning method for bio-molecular classification. This method shows how to design biochemical operations both for learning and pattern classification. As opposed to prior work, our molecular algorithm learns generic classes considering the realization in vitro via a sequence of molecular biological operations on sets of DNA examples. Specifically, hybridization between DNA molecules is interpreted as computing the inner product between embedded vectors in a corresponding vector space, and our algorithm performs learning of a binary classifier in this vector space. We analyze the thermodynamic behavior of these learning algorithms, and show simulations on artificial and real datasets as well as demonstrate preliminary wet experimental results using gel electrophoresis.

Developing a Domain-Referenced Test given a Selection Ratio and a Cut-Off Score: An application of Item Response Theory

In the present study we developed a domain-referenced test consisting of four subtests with the application of one parameter logistic model of item response theory. Some conditions are required: 10%–15% of the test takers pass the test given a cut-off score of 80% correct. Two of the four subtests satisfied the above requirements. One of the other two showed a passing ratio higher than specified. The other showed a low degree of classification consistency. In regards to validity, we attempted to secure content-related validity. Also there is an evidence of supporting construct-related validity. The correlations among the four subtests were small enough to show adequate discrimination of constructs.