Korean Journal of Psychology: General
- P-ISSN : 1229-067X
- E-ISSN : 2734-1127
- Publisher : The Korean Psychological Association
- CCL :
Korean Journal of Psychology: General
- KOREAN
- P-ISSN1229-067X
- E-ISSN2734-1127
- KCI
Latest Articles
Vol.45 No.1
5papers in this issue.
PDF
Citaion
A Systematic Literature Review and Methodological Recommendations for Studies Evaluating the Effectiveness of Mental Health Intervention Programs in Korea
Soyeon Shin ; Eungi Jeon ; Hanjoe Kim
pp.1-29
https://doi.org/10.22257/kjp.2026.3.45.1.1
초록보기
Abstract
With growing attention to mental health promotion and prevention interventions, the systematic examination of research methodologies used to evaluate their effectiveness has become a fundamental task for both researchers and practitioners. This study conducted a methodological review of effectiveness studies on mental health promotion and prevention programs implemented in South Korea. A total of 177 peer-reviewed articles published over the past decade in journals affiliated with the Korean Psychological Association were analyzed, focusing on research design and statistical analysis methods. The review revealed two notable findings: first, over 90% of the studies had fewer than 30 participants per group; second, nearly half (48.5%) of the studies employed nonequivalent group designs. Based on these findings, this study proposes several methodological recommendations and practical guidelines to enhance program evaluation practices. The results provide foundational insights for improving the scientific rigor of future evaluations of mental health promotion and prevention programs.
PDF
Citaion
Bayes Factors for Independent Samples t-Test Under Equal and Unequal Variance Assumptions
Chaereen Kang ; Seungmin Jahng
pp.31-54
https://doi.org/10.22257/kjp.2026.3.45.1.31
초록보기
Abstract
Heterogeneity of variance is a persistent concern in independent-samples t tests, raising questions about the robustness of Bayesian hypothesis testing when the equal-variance assumption is violated. The Jeffreys-Zellner-Siow (JZS) prior, commonly used as the default in Bayesian t tests, inherently assumes homoscedasticity. The present study examines the implications of this assumption by comparing a homoscedastic Bayesian t test based on the JZS prior with a heteroscedastic alternative that allows group-specific variances, the Girón-del Castillo (BFGC) model. An extensive simulation study was conducted to investigate how Bayes factors behave across varying combinations of variance ratios, sample size ratios, standardized effect sizes, and total sample sizes. Particular attention was given to conditions in which sample size imbalance interacted with variance heterogeneity. The results showed that the two models exhibit qualitatively different patterns of evidence accumulation under heteroscedasticity. Specifically, the JZS-based Bayes factor tended to provide weaker support for the true hypothesis when the group with the larger variance also had the larger sample size, whereas the BFGC-based Bayes factor showed the opposite pattern, yielding weaker support when the larger-variance group had the smaller sample size. These findings highlight that variance assumptions in Bayesian t tests can systematically influence the interpretation of Bayes factors, especially in the presence of sample size imbalance. When heteroscedasticity is plausible, adopting a heteroscedastic Bayesian model such as BFGC may therefore lead to more reliable Bayesian inference than reliance on the default JZS specification.
Youngju Kim
pp.55-82
https://doi.org/10.22257/kjp.2026.3.45.1.55
초록보기
Abstract
Trust is a crucial component of social capital that enables individuals and groups to cooperate effectively. Amid a global decline in social trust, growing attention has turned to the role of economic inequality. Focusing on perceived economic inequality, the present research examines whether its impact on trust varies across different social class contexts. Across three studies (N = 1,133) using samples from South Korea and the United States, I tested whether income moderates the association between perceived economic inequality and two forms of trust (i.e., generalized trust and relational trust). Results showed that higher perceived inequality predicted lower generalized trust (trust in most people) among low-income participants (Studies 1 and 2). Extending prior work, I also assessed relational trust (trust in familiar others) and found that higher perceived inequality predicted greater relational trust, with this effect again most pronounced among low-income participants (Studies 2 and 3). Collectively, these findings suggest a narrowing of the radius of trust among disadvantaged groups: trust becomes increasingly concentrated in known others, while trust in unknown others declines. More broadly, the results imply that the social crisis of inequality is asymmetrically experienced across income groups and may contribute to the persistence of socioeconomic inequality by reshaping trust structures among those at the disadvantaged end of the income distribution.
Jaehee Jung ; Hee Eun Rim ; Yena Kim ; Kyunghun Kim ; Kee-Hong Choi
pp.83-117
https://doi.org/10.22257/kjp.2026.3.45.1.83
초록보기
Abstract
Gambling problems constitute a major public health concern that threatens individuals’psychological and social well-being, and the rapid expansion of illegal online gambling and the diversification of gambling models have intensified the need for more multifaceted intervention strategies. In this context, we focused on digital health, which has been recognized for its advantages in accessibility and cost-effectiveness. We examined international literature on web- and app-based programs with a focus on intervention content, as well as chatbot-based interventions, immersive technologies, and machine learning-based prediction and assessment as key drivers of changing intervention paradigms. Web- and app-based interventions were largely grounded in cognitive behavioral therapy and motivational interviewing, with recent developments incorporating integrated interventions addressing emotional and gambling problems, location-based approaches, and just-in-time adaptive interventions. In addition, chatbot-based, immersive, and machine learning–based approaches demonstrated potential to support continuous bidirectional interaction, enable ecologically valid intervention environments, and enhance the objectivity of clinical decision-making based on big data. A review of domestic trends reveals that Korea’s digital health applications for gambling remain in the early stages with limited empirical evidence. Finally, we discussed key issues surrounding the application of digital health and future directions for research and practice.
PDF
Citaion
Comparison and Evaluation of Fit Indices, ΔBIC, and the Vuong Test for Nonnested Model Selection in Structural Equation Modeling
INHYE KIM ; MIRIM KIM ; SOONMOOK LEE
pp.119-146
https://doi.org/10.22257/kjp.2026.3.45.1.119
초록보기
Abstract
This study aims to comprehensively examine the characteristics and limitations of various indices applicable to non-nested model selection in structural equation modeling (SEM). Monte Carlo simulations were conducted using fit indices (CFI, TLI, RMSEA, SRMR), the information-based index (ΔBIC), and the Vuong test. The simulation design employed a 3×3×3×4 factorial structure varying factor loading differences, error correlations, factor correlations, and sample sizes, yielding 108 conditions with 1,000 replications each. The findings indicate that CFI and TLI were particularly sensitive to structural misspecifications and demonstrated stable discriminative power with larger sample sizes. In contrast, RMSEA was substantially influenced by sample size and degrees of freedom, tending to impose stricter thresholds, while SRMR showed limitations in distinguishing models when factor correlations were high. BIC provided a quantitative measure of model differences but was constrained in interpretation under equal degrees of freedom. The Vuong test supplemented BIC by offering statistical significance, thereby reinforcing the basis for model selection. Overall, this study provides practical recommendations for comparison strategies, emphasizing that researchers should avoid reliance on a single index and instead adopt complementary interpretations across indices to achieve more robust and reliable model selection.