Korean Journal of Psychology: General
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- P-ISSN1229-067X
- E-ISSN2734-1127
- KCI
ISSN : 1229-067X
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Vol.36 No.4
Abstract
This article describes the development and validation of the Zhongyong Questionnaire(ZQ), a self-report instrument for measuring Zhongyong attitude. Theoretically, Zhongyong includes three aspects: (a) dialectic thinking and cognitive flexibility; (b) emotion regulation and balanced affect; (c)internal motive to consistently choose Zhongyong behavior. In study 1, thirty-seven out of 99 preliminary items were selected by six scholars studying Confucianism and Zhongyong for the face validity and the 37-item ZQ was administered to 352 college students. Data suggest that the ZQ consists of 19-item with three-factor structure. In study 2, 368 people participated to investigate the instrument’s reliability and validity and the relations between the ZQ and related scales. Results from two studies indicate that the 19-item Zhongyong Questionnaire has a three-factor structure(harmony/regulation, balance/stability, empathy/ tolerance), good internal consistency and 2-week test-retest reliability. The ZQ was significantly correlated with other measures of cognitive flexibility, dichotomous thinking, emotion regulation and dysregulation, and basic psychological need satisfaction, confirming the scale’s convergent construct validity. The concurrent construct validity of the ZQ was demonstrated by its correlation with the Zhong-yong Thinking Style Scale. There was a significant correlation with age and Zyongyong attitude. When controlling for age, there was a significant difference in the level of Zhongyong attitudes based on marital status and religion. These results suggest that the Zhongyong attitude can be developed through diverse experience. In the discussion section, the implications and limitations of this study and suggestions for future research are discussed.
Abstract
Parallel analysis is a method of estimating the number of factors by comparing the eigenvalues of sample data with the eigenvalues of random data. This method is considered to be theoretically more valid and empirically more accurate in estimating the number of factors than other methods, such as Kaiser method and scree test, that estimate the number of factors based on the eigenvalues. However, several criticisms have been raised about the validity of the rationale for parallel analysis and various modifications have been proposed. There have also been concerns about the conditions under which parallel analysis shows relatively low accuracy. The current study examined the rationale and limitations of the use of eigenvalues and parallel analysis to estimate the number of factors, and based on this, we specified the conditions under which the accuracy of parallel analysis may be low. We also examined, through a simulation, the effects of various factors that may affect the accuracy of parallel analysis and confirmed the conditions where cautions are needed when applying parallel analysis. The results of the simulation show that the accuracy of estimating the number of factors in the parallel analysis is greatly influenced by the size of factor correlations, the magnitude of factor loadings, the number of factors, and the number of variables per factor. In addition, we confirmed that the accuracy of the parallel analysis is significantly lower when a factor model includes a weak factor with low factor loadings. Overall, the accuracy of the parallel analysis for the reduced correlation matrix (PA-PAF) was higher than the parallel analysis for the correlation matrix (PA-PCA), which in particular, PA-PAF showed high accuracy when factor correlations were high, and PA-PCA showed high accuracy when factor correlations were low. Based on the results of the simulation analyses, we proposed sample sizes required for parallel analysis to provide accuracy of 90% or higher under conditions with different levels of factor correlation, factor loading, and the number of factors.
Abstract
Bifactor modeling approach is increasingly being applied to the study of psychometric properties of psychological measures. A bifactor structure consists of a single general factor that is purported to explain co-variances of all the items and a set of group factors that are purported to explain residual co-variances of some items that cannot be accounted for by a general factor. The model assumes that the general and group factors are uncorrelated. Bifactor modeling approach enables researchers to test whether a given psychological scale that is originally designed to measure a theoretically unidimensional construct appears to be multidimensional due to nuisance factors such as method factors. In this article, we gave an overview of statistical indices such as omega coefficients and explained common variance(ECV) that can be effectively employed to investigate dimensionality of a given scale. We illustrated how to compute various types of omega coefficients and explained common variance(ECV) and interpret them using Rosenberg Self-esteem scale(RSES) and Emotional Approach Coping Scale(EAC).