Longitudinal relationship between depression and antisocial behaviors in Korean adolescents.

Background: It is well known that depression and delinquency in adolescents are highly correlated, but longitudinal studies on the causal relationship between them are not active in East Asia compared to in Western culture. In addition, even the results of research on causal models and sex differences are inconsistent. Objectives: This study examines the longitudinal reciprocal effects between depression and delinquent behavior in Korean adolescents based on sex differences. Methods: We conducted multiple-group analysis by using an autoregressive cross-lagged model (ACLM). Longitudinal data from 2,075 individuals (2011-2013) were used for analysis. The longitudinal data are from the Korean Children and Youth Panel Survey (KCYPS), and data were used beginning with students at 14 years old (in the second grade of middle school) and tracked them until they were 16 (in the first grade of high school). Results: Boys' delinquent behaviors at 15 years (the third grade of middle school) affected their depression at 16 years (the first grade of high school). In contrast, girls' depression at 15 years (the third grade of middle school) influenced their delinquent behaviors at 16 years (the first grade of high school). Discussion: The findings support the failure model (FM) among adolescent boys and the acting-out model (ACM) among girls. The results imply that strategies to effectively prevent and treat delinquency and depression in adolescents must consider sex effects.

Mental Healthcare through Cognitive Emotional Regulation Strategies among Prisoners.

Prisoners are exposed to a deprived environment, which triggers mental illness and psychological problems. Abundant research has reported that mental illness problems, suicide, aggression, and violent behaviors occur in incarcerated people. Although the mental healthcare system for incarcerated people is emphasized, little research has been conducted due to their limited environment. In particular, the regulation of negative emotion is significantly associated with mental illness and anti-social and violent behaviors. However, mental healthcare through cognitive emotional regulation based on cognitive behavioral therapy has not been fully investigated. This study identified four different patterns in cognitive strategies for regulating negative emotions. Cognitive emotional regulation strategies (i.e., self-blame, other-blame, rumination, catastrophizing, putting into perspective, positive refocusing, positive reappraisal, acceptance, and refocus on planning) were examined and addressed their vulnerable psychological factors. We analyzed a total of 500 prisoners' responses to the cognitive emotional regulation questionnaire (CERQ) by latent class profiling analysis. A four-class model was identified based on the responses of CERQ. In addition, the significant effect of depression on classifying the four classes was found. Furthermore, differences in the average number of incarcerations were also shown across four classes. In conclusion, Class 2 (Negative Self-Blamer) uses dysfunctional/negative strategies that may place the group at a high risk of psychological disorder symptoms, including depression and post-traumatic stress. Class 3 (Distorted Positivity) uses positive/functional strategies but seems to utilize the positive strategies in distorted manners to rationalize their convictions. Class 1 (Strong Blamer) and Class 4 (Moderator Blamer) showed similar patterns focused on the "other-blame" strategy for regulating negative emotion, but they are at different levels, indicating that they attribute incarceration to external factors. These findings provide useful information for designing mental healthcare interventions for incarcerated people and psychological therapy programs for clinical and correctional psychologists in forensic settings.

Final validation of the mental health screening tool for depressive disorders: A brief online and offline screening tool for major depressive disorder.

Early screening for depressive disorders is crucial given that major depressive disorder (MDD) is one of the main reasons of global burden of disease, and depression is the underlying cause for 60% of suicides. The need for an accurate screening for depression with high diagnostic sensitivity and specificity in a brief and culturally adapted manner has emerged. This study reports the final stage of a 3-year research project for the development of depression screening tool. The developed Mental Health Screening Tool for Depressive Disorders (MHS:D) was designed to be administered in both online and offline environments with a high level of sensitivity and specificity in screening for major depressive disorder. A total of 527 individuals completed two versions (online/offline) of the MHS:D and existing depression scales, including the BDI-II, CES-D, and PHQ-9. The Mini International Neuropsychiatric Interview (MINI) for diagnostic sensitivity/specificity was also administered to all participants. Internal consistency, convergent validity, factor analysis, item response theory analysis, and receiver operating characteristics curve (ROC) analysis were performed. The MHS:D showed an excellent level of internal consistency and convergent validity as well as a one-factor model with a reasonable level of model fit. The MHS:D could screen for major depressive disorder accurately (0.911 sensitivity and 0.878 specificity for both online and paper-pencil versions). Item response theory analysis suggested that items from the MHS:D could provide significantly more information than other existing depression scales. These statistical analyses indicated that the MHS:D is a valid and reliable scale for screening Korean patients with MDD with high diagnostic sensitivity and specificity. Moreover, given that the MHS:D is a considerably brief scale that can be administered in either online or paper-pencil versions, it can be used effectively in various contexts, particularly during the pandemic.

Evidence centered design framework and dynamic bayesian network for modeling learning progression in online assessment system.

An overarching mission of the educational assessment community today is strengthening the connection between assessment and learning. To support this effort, researchers draw variously on developments across technology, analytic methods, assessment design frameworks, research in learning domains, and cognitive, social, and situated psychology. The study lays out the connection among three such developments, namely learning progressions, evidence-centered assessment design (ECD), and dynamic Bayesian modeling for measuring students' advancement along learning progression in a substantive domain. Their conjunction can be applied in both formative and summative assessment uses. In addition, this study conducted an application study in domain of beginning computer network engineering for illustrating the ideas with data drawn from the Cisco Networking Academy's online assessment system.

Assessing quality of life among elementary school students: Validation of the Korean version of the Meaning in Life in Children Questionnaire.

Meaning in life (MIL) has been widely recognized as a hallmark of psychological well-being and positive youth development. The goal of this study was to validate the Korean version of the Meaning in Life in Children Questionnaire (K-MIL-CQ) utilizing the framework suggested by the Standards for Educational and Psychological Testing. Data were obtained from 277 fifth graders aged 10-11 in three elementary schools in Seoul and Gyeonggi through a paper-and-pencil survey (55.2% boys). We translated the MIL-CQ, a 21-item self-report measure developed based on Frankl's "meaning triangle," into Korean. Psychological well-being measures were also assessed. Validity and reliability data were collected. (1) The content of domains and items was appropriate for measuring MIL among children. (2) A three-factor model consisting of attitude, creativity, and experience pathways was extracted via exploratory factor analysis, and a three-factor hierarchical model including attitude, creativity, and experience as first-order factors and MIL as a second-order factor was confirmed via confirmatory factor analysis. (3) Higher levels of MIL were related to higher levels of satisfaction with life, self-esteem, positive affectivity, and lower levels of negative affectivity. (4) All item fit statistics were acceptable based on the Rasch model. (5) The analysis of the measurement invariance of each item showed that the responses to one item varied by gender, suggesting that additional items might facilitate better measurement of MIL in children. This study provides validity and reliability evidence that K-MIL-CQ is appropriate for measuring MIL among South Korean elementary school students.

Psychometric Properties for Multidimensional Cognitive Load Scale in an E-Learning Environment.

(1) Background: A learner's cognitive load in a learning system should be effectively addressed to provide optimal learning processing because the cognitive load explains individual learning differences. However, little empirical research has been conducted into the validation of a cognitive load measurement tool (cognitive load scale, i.e., CLS) suited to online learning systems within higher education. The purpose of this study was to evaluate the psychometric properties of the CLS in an online learning system within higher education through the framework suggested by the Standards for Educational and Psychological Testing. (2) Methods: Data from 800 learners were collected from a cyber-university in South Korea. The age of students ranged from 20 to 64. The CLS was developed, including three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. Then, psychometric properties of the CLS were evaluated including reliability and validity. Evidence relating to content validity, construct validity, and criterion validity were collected. The response pattern of each item was evaluated on the basis of item response theory (IRT). Cronbach's α was computed for reliability. (3) Results: The CLS presented high internal consistency. A three-factor model with extraneous cognitive load, intrinsic cognitive load, and germane cognitive load was suggested by exploratory and confirmatory factor analysis. This three-factor model is consistent with the previous research into the cognitive load in an offline learning environment. Higher levels of the extraneous cognitive load and intrinsic cognitive load were related to lower levels of academic achievement in an online learning environment, but the germane cognitive load was not significantly positively associated with midterm exam scores, though it was significantly related to the final exam scores. IRT analysis showed that the item-fit statistics for all items were acceptable. Lastly, the measurement invariance was examined through differential item functioning analysis (DIF), with the results suggesting that the items did not contain measurement variance in terms of gender. (4) Conclusions: This validation study of the CLS in an online learning environment within higher education assesses psychometric properties and suggests that the CLS is valid and reliable with a three-factor model. There is a need for an evaluation tool to take into account the cognitive load among learners in online learning system because the characteristics of learners within higher education were varied. This CLS will help instructional/curriculum designers and educational instructors to provide more effective instructions and identify individual learning differences in an online learning environment within higher education.

A Brief Online and Offline (Paper-and-Pencil) Screening Tool for Generalized Anxiety Disorder: The Final Phase in the Development and Validation of the Mental Health Screening Tool for Anxiety Disorders (MHS: A).

Generalized anxiety disorder (GAD) can cause significant socioeconomic burden and daily life dysfunction; hence, therapeutic intervention through early detection is important. This study was the final stage of a 3-year anxiety screening tool development project that evaluated the psychometric properties and diagnostic screening utility of the Mental Health Screening Tool for Anxiety Disorders (MHS: A), which measures GAD. A total of 527 Koreans completed online and offline (i.e., paper-and pencil) versions of the MHS: A, Beck Anxiety Inventory (BAI), Generalized Anxiety Disorder-7 (GAD-7), and Penn State Worry Questionnaire (PSWQ). The participants had an average age of 38.6 years and included 340 (64.5%) females. Participants were also administered the Mini-International Neuropsychiatric Interview (MINI). Internal consistency, convergent/criterion validity, item characteristics, and test information were assessed based on the item response theory (IRT), and a factor analysis and cut-off score analyses were conducted. The MHS: A had good internal consistency and good convergent validity with other anxiety scales. The two versions (online/offline) of the MHS: A were nearly identical (r = 0.908). It had a one-factor structure and showed better diagnostic accuracy (online/offline: sensitivity = 0.98/0.90, specificity = 0.80/0.83) for GAD detection than the GAD-7 and BAI. The IRT analysis indicated that the MHS: A was most informative as a screening tool for GAD. The MHS: A can serve as a clinically useful screening tool for GAD in Korea. Furthermore, it can be administered both online and offline and can be flexibly used as a brief mental health screener, especially with the current rise in telehealth.

A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis.

With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions' variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.

Estimation of item parameters and examinees' mastery probability in each domain of the Korean medical licensing examination using deterministic inputs, noisy and gate(DINA) model.

PURPOSE: Deterministic inputs, noisy and gate (DINA) model is one of the promising statistical means for providing useful diagnostic information about a student' level of achievement. Diagnostics information is core element for improving learning instead of selection. Educators often want to be provided with diagnostic information which how a given examinees did on each content strand, called diagnostic profiles. The purpose of this paper is to classify examinees in different content domains using the DINA model. METHODS: This paper analyzed data from the Korean medical licensing examination (KMLE) with 360 items and 3259 examinees. The application study estimate examinees parameters as well as item characteristics. The guessing and slipping parameters of each item were estimated. DINA model was conducted as a statistical analysis. RESULTS: The output table shows the examples of some items, which can be used for the check of item quality. In addition, the probabilities of being mastery at each content domain were estimated, which indicates the mastery profile of each examinee. Classifications accuracy for 8 contents ranged from .849 to .972 and classification consistency for 8 contents ranged from .839 to .994. As a result, classification reliability in a CDM was very high for 8 contents in KMLE. CONCLUSION: This mastery profile can be useful diagnostic information for each examinee in terms of the content domains of KMLE. The master profile from KMLE provides each examinee's mastery profile in terms of each content domain. The individual mastery profile allows educators and examinees to understand that which domain(s) should be improved for mastering all domains in KMLE. In addition, the results found that all items are reasonable level with respect to item parameters character.

Review of Machine Learning Algorithms for Diagnosing Mental Illness.

OBJECTIVE: Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. METHODS: Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. RESULTS: Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. CONCLUSION: Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Screening Tool for Anxiety Disorders: Development and Validation of the Korean Anxiety Screening Assessment.

OBJECTIVE: This study evaluated the psychometric properties of the Korean Anxiety Screening Assessment (K-ANX) developed for screening anxiety disorders. METHODS: Data from 613 participants were analyzed. The K-ANX was evaluated for reliability using Cronbach's alpha, item-total correlation, and test information curve, and for validity using focus group interviews, factor analysis, correlational analysis, and item characteristics based on item response theory (IRT). The diagnostic sensitivity and specificity of the K-ANX were compared with those of the Beck Anxiety Inventory (BAI) and Generalized Anxiety Disorder 7-item scale (GAD-7). RESULTS: The K-ANX showed excellent internal consistency (α=0.97) and item-total coefficients (0.92-0.97), and a one-factor structure was suggested. All items were highly correlated with the total scores of the BAI, GAD-7, and Penn State Worry Questionnaire. IRT analysis indicated the K-ANX was most informative as a screening tool for anxiety disorders at the range between 0.8 and 1.6 (i.e., top 21.2 to 5.5 percentiles). Higher sensitivity (0.795) and specificity (0.937) for identifying anxiety disorders were observed in the K-ANX compared to the BAI and GAD-7. CONCLUSION: The K-ANX is a reliable and valid measure to screen anxiety disorders in a Korean sample, with greater sensitivity and specificity than current measures of anxiety symptoms.

Usefulness of the DETECT program for assessing the internal structure of dimensionality in simulated data and results of the Korean nursing licensing examination.

PURPOSE: The dimensionality of examinations provides empirical evidence of the internal test structure underlying the responses to a set of items. In turn, the internal structure is an important piece of evidence of the validity of an examination. Thus, the aim of this study was to investigate the performance of the DETECT program and to use it to examine the internal structure of the Korean nursing licensing examination. METHODS: Non-parametric methods of dimensional testing, such as the DETECT program, have been proposed as ways of overcoming the limitations of traditional parametric methods. A non-parametric method (the DETECT program) was investigated using simulation data under several conditions and applied to the Korean nursing licensing examination. RESULTS: The DETECT program performed well in terms of determining the number of underlying dimensions under several different conditions in the simulated data. Further, the DETECT program correctly revealed the internal structure of the Korean nursing licensing examination, meaning that it detected the proper number of dimensions and appropriately clustered the items within each dimension. CONCLUSION: The DETECT program performed well in detecting the number of dimensions and in assigning items for each dimension. This result implies that the DETECT method can be useful for examining the internal structure of assessments, such as licensing examinations, that possess relatively many domains and content areas.

Cultural variances in composition of biological and supernatural concepts of death: a content analysis of children's literature.

Children's reasoning about the afterlife emerges naturally as a developmental regularity. Although a biological understanding of death increases in accordance with cognitive development, biological and supernatural explanations of death may coexist in a complementary manner, being deeply imbedded in cultural contexts. This study conducted a content analysis of 40 children's death-themed picture books in Western Europe and East Asia. It can be inferred that causality and non-functionality are highly integrated with the naturalistic and supernatural understanding of death in Western Europe, whereas the literature in East Asia seems to rely on naturalistic aspects of death and focuses on causal explanations.