Korean Journal of Psychology: General
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- P-ISSN1229-067X
- E-ISSN2734-1127
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ISSN : 1229-067X
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Vol.37 No.1
Abstract
The purpose of this study was to develop and validate the Growth Orientation Scale(GOS). 53 items and 6 factors of the GOS were obtained based on questionnaires from expert. the preliminary on-line surveys from persons was carried to analyze factor structure of the GOS. The final result showed that the 5 factor model with 34 items was appropriate. Finally, to test validity of the GOS, the main on-line survey was carried that the questionnaires were collected from 986 persons across a wide divided into two sub-groups (each group with 493 persons). The results of factor analyses with group 1 showed that the 5-factor model with 28 items was appropriate. Also, the results of confirmatory factor analysis with group 2 showed that the 5-factor model fit the data well. Final 5 factors were as follows: 1) intelligent belief 2) process-oriented performance attitude 3) resilience 4) fate belief 5) talent belief. The GOS was significantly correlated with various criteria such as life satisfaction, subjective happiness, self-efficacy, mindsets and learning goal orientation. Finally, implications and limitations of this study and the directions for future study were discussed.
Abstract
Creativity is a competence to produce useful and novel results in uncertain environment. Creativity is an essencial factor for college students who will lead the society in the near future. In the present study we developed and validated CPAS-K(Creative Product Assessment Scale-K) to investigate college students' everyday problem solving creativity. CPAS-K was developed through theoretical and experiential approach, and validated using empirical data of three different creative products collected from 436 adults. We split the data into two sets. One set of 300 observations was analyzed by exploratory factor analysis using ESEM(exploratory structural equation modeling), and the other set of 136 observations was analyzed by confirmatory factor analysis using MTMM(Multitrait-Multimethod) approach. Then, the total data were used in a multi-group analysis. exploratory and confirmatory factor analyses yielded two factors that were interpreted as Originality and Appropriateness/Resolution. As a result of the multi-group analysis, factor means of the three products were compared. There were some differences between factor means in the Originality and the Appropriateness/Resolution dimensions so that the degree of creativity of products could be differentiated. Finally, implications, limitations, and potential future research directions were discussed.
Abstract
The purpose of this study is to investigate the relationship between the level of ageism and depressive mood with examining serial multiple moderating effect of self-perception of physiological and psychosocial aging in three generation groups. Data of the male babyboomer group(N=69), the male young-old group(N=151) and the male old-old group(N=63) were analysed. They were asked to fill out a set of questionnaire which includes Ageism Survey, short form of CES-D, Aging Perceptions Questionnaire, and Self-Perceived Adverse Age-Change Scale. The results of this study are as follows: (1) Significant group differences were found on perception of physiological aging, psychosocial aging and depressive mood. (2) Expect for relationships between generation groups & ageism, and ageism & perception of physiological aging, all variables were significantly positively related. (3) In the male babyboomer generation, indirect effect of ‘physiological aging → psycho-social aging → depressive mood’ and direct effect of ‘physiological aging → depressive mood’ were significant. In the male young-old and the old-old generations, indirect effect of ‘ageism → psycho-social aging → depressive mood’ were significant. Also, physiological aging showed tendencies with different patterns in these two generation groups. This study provides implications for socio-historical generational differences in the face of existential threat.
Abstract
This study was to investigate the effects of psychological factors on the experience of subjective time, and to propose an explanation framework of subjective time based on the implications of the results. Two experiments were conducted with an operational concept of ‘time judgment ratio’(objective time/subjective time). In Experiment 1, participants judged subjective time in either a loss(negative emotion) or gain(positive emotion) situation. The results were that while the objective time was shorter than the subjective time in the loss situation(time judgment ratio < 1.0), the reverse was true in the gain situation(time judgment ratio > 1.0). In Experiment 2, the present time and a certain future time(1 month later) were manipulated as either a loss or a gain situation, and participants were asked to judge their subjective temporal distances to the future time. The results were as follows. When the present was positive and the future was negative, and when both the present and the future were positive, the future looked closer and came faster than the objective time. On the other hand, when the present was negative and the future was positive, and when both the present and the future were negative, the future looked farther and came slower than the objective time. One notable result was that the subjective temporal distance to a certain future was influenced by the positivity/negativity of the present more than by those of the future. An explanation framework of subjective time was proposed based on the operational concepts of ‘time of perseverance’ and ‘time of availability’.
Abstract
Recently, applications of structural equations and meta-analysis are increasing rapidly in many areas. The two methods have been used for different purposes in different fields. That is, structural equation modeling has been used for evaluating theoretical models, while meta-analysis has been applied to integrating individual studies. However, combing the two separate methods, meta-analysis can be done using structural equation modeling. In this study, SEM based meta-analysis was introduced and applied to empirical studies to demonstrate an example of analysis. We focused on the SEM based meta-analysis, which Cheung (2008, 2015) presented, and explained the method using Mplus. Our presentation was as follows: first, advantages of SEM-based meta-analysis were explained in detail in comparison with conventional meta-analysis. Next, SEM-based meta-analysis model(fixed-effects, random-effects, mixed-effects model) and analysis method were presented. For a real-data example, the effect sizes of empirical studies on the relationship between procrastination and academic self-efficacy were integrated and the effects of research characteristics as predictors were investigated. Finally, the implications of this study and future research directions were discussed.
Abstract
The multiple-indicator latent growth model (MI-LGM) is a second-order confirmatory factor model that analyzes latent trajectories of a factor measured by multiple indicators over time. Although MI-LGM can test the factorial invariance of indicators and estimate trajectories of a latent variable controlling measurement error, model fit and parameter estimates of the model may vary depending on factor scaling methods. The purpose of this study is to investigate how factor scaling methods, given a specified level of factorial invariance, change the meaning of the factor mean and thus affects the model fit and parameter estimates of MI-LGM. The authors first explored how factorial invariance and factor scaling affect the definition of factor means and the model fit in longitudinal factor analysis models. Next, they showed that constraining the sum of the indicator’ intercepts to zero creates a clear definition of the factor mean and the constraint provides consistent results and interpretation of the means of growth factors in the MI-LGM even under the weak factorial invariance. An analysis of actual panel data then illustrated such characteristics of the MI-LGM. Finally, the authors discussed the importance of factorial invariance and factor scaling in the analysis of mean and covariance structure models and that of using the strong factorial invariance when modeling the MI-LGM.