• Proceedings of the Korean Society of Crop Science Conference
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• 2005.10b
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• pp.272-273
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• 2005

• Journal of Haehwa Medicine
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• v.12 no.2
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• pp.177-197
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• 2004

1. The values of Cronbach's alpha for the Taeyang, Taeum, Soyang and Soeum questionnaire were 0.7955, 0.7776, 0.8545, and 0.8601 respectively. These results indicate a highly satisfactory level of internal consistency for the questionnaire. 2. If the deletion of an item increases Cronbach's alpha then what that means is that the deletion of that item improves reliability. Therefore, any items that result in substantially greater values of alpha than the overall alpha may need to be deleted from the questionnaire to improve its reliability. 3. Factor analysis was performed on the 81 questionnaires. Based on the scree plot and the number and decrement of eigen values greater than one, three to four factor solution was most significant. 4. The hierarchical cluster analysis was performed on the 81 Sasang constitution questionnaire. These results suggested that two or four clusters identified with homogeneous groups 5. The hierarchical cluster analysis was performed on the 1046 responders. These results suggested that two, three, or four clusters might identified with homogeneous groups. Furthemore, there were statistically significant difference among the each group by ANOVA(P<0.0001).

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.192-197
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• 2020

Foreign students in South Korea face important challenges when they try to maintain their health. As a measure of their motivation to actively build skills for overcoming those challenges, we evaluated the 10-item Consumer Health Activation Index (CHAI), testing its feasibility, dimensionality, and reliability. There were no missing data, there was no floor effect, and for the total scores the ceiling effect was trivial (< 2%). Results of the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity indicated that the data were suitable for the detection of structure by factor analysis. The results of parallel analysis and the shape of the scree plot supported a two-factor solution. One factor had 3 items concerning "my doctor" and the other factor had the 7 remaining items. Reliability was high for the 10-item CHAI (alpha = 0.856), for the 3-item subscale (alpha = 0.838), and for the 7-item subscale (alpha = 0.857). Reliability could not be improved by deletion of any items. Use of the CHAI to gather data from these foreign students is feasible, and reliable results can be obtained whether one uses the total score from all 10 items or scores from the proposed 7-item and 3-item subscales.

• Journal of Korean Critical Care Nursing
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• v.8 no.1
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• pp.50-61
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• 2015

Purpose: The purpose of this study was to develop a valid tool for measuring the work environment of Korean nurses and test the validity and reliability of the instrument. Methods: This is a methodological study for developing and testing a measurement tool. The conceptual framework for the tool was formed through a literature review and in-depth interviews with clinical nurses. 260 meaningful statements were derived through this process, and 59 of the primary items were generated out of these statements. On the basis of the content validity test, 41 items were selected for the preliminary tool. A total of 350 nurses from 7 hospitals responded to the preliminary questionnaire in October 2011. Results: After the item analysis and the principal component analysis using varimax rotation methods, 11 items were deleted. Considering the conceptual framework and the scree plot, we extracted 4 factors from the final 30 items. These factors were organizational support, head nurse's leadership, support system, and relationship with coworkers, and their total variance was 54.4%. The value of Cronbach's ${\alpha}$ of the final tool was 0.92. Conclusion: The above findings revealed that the Korean Nursing Work Environment Scale (K-NWES) is an appropriate, valid, and reliable tool for evaluating the work environment of Korean nurses. Further research to perform a criterion validity test of the K-NWES is needed.

• Journal of Distribution Science
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• v.13 no.9
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• pp.103-112
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• 2015

Purpose - This paper aims to provide a step-by-step approach to factor analytic procedures, such as principal component analysis (PCA) and exploratory factor analysis (EFA), and to offer a guideline for factor analysis. Authors have argued that the results of PCA and EFA are substantially similar. Additionally, they assert that PCA is a more appropriate technique for factor analysis because PCA produces easily interpreted results that are likely to be the basis of better decisions. For these reasons, many researchers have used PCA as a technique instead of EFA. However, these techniques are clearly different. PCA should be used for data reduction. On the other hand, EFA has been tailored to identify any underlying factor structure, a set of measured variables that cause the manifest variables to covary. Thus, it is needed for a guideline and for procedures to use in factor analysis. To date, however, these two techniques have been indiscriminately misused. Research design, data, and methodology - This research conducted a literature review. For this, we summarized the meaningful and consistent arguments and drew up guidelines and suggested procedures for rigorous EFA. Results - PCA can be used instead of common factor analysis when all measured variables have high communality. However, common factor analysis is recommended for EFA. First, researchers should evaluate the sample size and check for sampling adequacy before conducting factor analysis. If these conditions are not satisfied, then the next steps cannot be followed. Sample size must be at least 100 with communality above 0.5 and a minimum subject to item ratio of at least 5:1, with a minimum of five items in EFA. Next, Bartlett's sphericity test and the Kaiser-Mayer-Olkin (KMO) measure should be assessed for sampling adequacy. The chi-square value for Bartlett's test should be significant. In addition, a KMO of more than 0.8 is recommended. The next step is to conduct a factor analysis. The analysis is composed of three stages. The first stage determines a rotation technique. Generally, ML or PAF will suggest to researchers the best results. Selection of one of the two techniques heavily hinges on data normality. ML requires normally distributed data; on the other hand, PAF does not. The second step is associated with determining the number of factors to retain in the EFA. The best way to determine the number of factors to retain is to apply three methods including eigenvalues greater than 1.0, the scree plot test, and the variance extracted. The last step is to select one of two rotation methods: orthogonal or oblique. If the research suggests some variables that are correlated to each other, then the oblique method should be selected for factor rotation because the method assumes all factors are correlated in the research. If not, the orthogonal method is possible for factor rotation. Conclusions - Recommendations are offered for the best factor analytic practice for empirical research.