• 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.

• Journal of Digital Convergence
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• v.15 no.10
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• pp.103-111
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• 2017

This study was performed with a view to examine the nature and difference of EFA(Exploratory Factor Analysis) and CFA(Confirmatory Factor Analysis), and to compare the analysis process and result of EFA and CFA with the same data. The result of empirical analysis was as follows. Meanwhile, p.1, p.3 was removed owing to hampering the convergent validity in EFA, p.3 was removed owing to hampering the discriminent validity in CFA. EFA was reduction process of muti measurement variables to a few factor, but CFA was understanding and confirmatory process of measurement and latent variables' relation. Eventually, this study showed that EFA and CFA used different methology, thus the different outcomes appeared although using the same data, and implicated resonable application of methology according to given data.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.84-93
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• 2012

The study was conducted to analyze the spatio-temporal changes in water quality of the major 36 sampling stations of Nakdong River, depending on each station, season using the 17 water quality variables from 2000 to 2010. The result was verified to interpret the characteristics of water quality variables in a more accurate manners. According to the Principal component analysis (PCA) and Exploratory factor analysis (EFA) results; the results of these analyses were identified 4 factors, Factor 1 (nutrients) included the concentrations of T-N, T-P, $NO_{3}-N$, $PO_{4}-P$, DTN, DTP for sampling station and season, Factor 2 (organic pollutants) included the concentrations of BOD, COD, Chl-a, Factor 3 (microbes) included the concentrations of F.Coli, T.Coli, and Factor 4 (others) included the concentrations of pH, DO. The results of a Cluster analysis indicated that Geumhogang 6 was the most contaminated site, while tributaries and most of the down stream sites of Nakdong River were mainly affected by each nutrients (Factor 1) and organic pollutants (Factor 2). The verification consequence of Confirmatory factor analysis (CFA) from Exploratory factor analysis (EFA) result can be summarized as follows: we could find additional relations between variables besides the structure from EFA, which we obtained through the second-order final modeling adopted in CFA. Nutrients had the biggest impact on water pollution for each sampling station and season. In particular, It was analyzed that P-series pollutant should be controlled during spring and winter and N-series pollutant should be controlled during summer and fall.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.26 no.3
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• pp.226-235
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• 2015

Objectives : The purpose of this study was to examine the factor structure of the Adolescent Personality Assessment Inventory (PAI-A) in a standardized adolescent sample using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Methods : For this purpose, three models about factor structure of the PAI-A were explored with EFA in 490 adolescents and then were evaluated with CFA in 268 young offenders. Results : The results showed that the five factor model was considered to be most appropriate for factor structures of the PAI-A in EFA. However, none of the factor models were appropriate for the factor structures of the PAI-A in CFA. Conclusion : These findings suggest that the "five factor model" is thought to explain the PAI-A the best, but further studies are needed.

• Korean Security Journal
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• no.43
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• pp.99-120
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• 2015

This research aimed at analyzing health behavior of private security guards applying planned behavioral theory. In order to achieve the above purpose, this research conducted purposive sampling on the security guards who live in Seoul Gyeonggi region. Excluding unfaithful response and abnormal outlier, material of 187 persons was used for analysis. As the concrete analysis method, multiple regression analysis and logistic regression analysis to presume exploratory factory analysis(EFA), Polyserial Exploratory Factor Analysis(EFA), Polyserial correlation analysis, and causal relationship between each variable. The result can be summarized as follows. First, attachment, attitude subjective standard on behavior, perceived behavioral control appeared to positively influence affirmative(+) effect on health behavior continuance will. Second, attachment had no meaningful influence attitude toward behavior. Third, attachment had affirmative(+) influence on health behavior continuance will. Fourth, perceived behavioral control had affirmative(+) influence on realization of health behavior, possibility of practising health behavior increased by about 62.9% when perceived behavioral control increased by 1 unit.

• Culinary science and hospitality research
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• v.18 no.2
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• pp.82-90
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• 2012

This study was conducted to evaluate a take-out food safety perception instrument that could be used by foodservice establishments. A total of 324 responses was collected via online survey, and 299 responses (92.3%) were used for the statistical analysis. Data was randomly split into two groups. Exploratory Factor Analysis (EFA) was performed on the first split-half sample (n=150) to identify a factor structure using standard principal component analysis. EFA revealed three dimensions, titled "Consumer food safety perception," "Take-out food handling," and "Elements impacting on purchase decisions." Confirmatory Factor Analysis (CFA) was performed on the remaining half sample (n=149) using Structural Equation Modeling (SEM). CFA revealed acceptable absolute model fits for three dimensions and excellent comparative model fits for the instrument. These findings propose standardized measures that can be useful in assessing the take-out food safety perception.

• Tunnel and Underground Space
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• v.25 no.1
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• pp.86-96
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• 2015

In this study, the performance-based evaluation factors for rock slopes have been deducted using Delphi-method. Validity of the result was verified through factor analysis. Performance of rock slope is classified as soundness, stability and durability. Through the Delphi survey, 17 factors including discontinuity orientation are deducted for soundness, 4 factors and 3 factors are selected for stability and durability, respectively. Validation is conducted using Exploratory Factor Analysis (EFA) for 24 factors, and all factors are found to be valid. As a result of Exploratory Factor Analysis (EFA), 3-types of performance were subdivided into internal soundness, external soundness, risk, damage and durability of slopes and protection (reinforcement) facilities.

• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.160-169
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• 2016

Self-Compassion(SCS) Scale is developed by Neff(2003a) and translated by Kim, Lee, Cho, Chae, Lee(2008). But, there is the limitation with validation SCS and Korean version Self-Compassion(K-SCS) performed in college students and the incoherence for the results of the scale's factor analysis in other countries. Therefore, this study examined the validity of factor structure in SCS based on data in 435 adult aged from 18 to 79. For this, we conducted exploratory factor analysis(EFA) and confirmatory factor analysis(CFA), and we examined the each adequacy of two-factor, tree-factor and six-factor model. The result of EFA supported six-factor and the result of CFA was the six factor model best as well.

• The Journal of Korean Association of Computer Education
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• v.9 no.3
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• pp.67-74
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• 2006

The purpose of this study is identifying variables that affect to learners' preference toward specific e-learning programs, using an exploratory factor analysis(EFA) method. We extract common factors that explain the correlations among variables. In the result, 8 factors were identified as main influential factors: e-learning program design(1st factor), the purpose of e-learning use(2nd factor), social and cultural issues(3rd factor), demographics(4th factor), organizational needs(5th factor), impacts of e-learning(6th factor), e-learning management(7th factor), and technical issue(8th factor).

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.4
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• pp.168-177
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• 2019

Objectives: We examined the factor structure of the Adolescent version of the General Behavior Inventory (A-GBI) for Koreans. Methods: We retrospectively reviewed the medical records of 220 adolescents (age, 12-18 years) who completed the A-GBI through the Department of Psychiatry at Asan Medical Center, Seoul, Korea, from October 2011 to December 2018. Caregivers of the study participants completed the Parent version of the GBI (P-GBI) 10-item Mania Scale. The adolescents were evaluated based on the A-GBI, Children's Depression Inventory (CDI), and Revised-Children's Manifest Anxiety Scale (RCMAS). Subsequently, an exploratory factor analysis (EFA) using the maximum likelihood method with direct oblimin rotation and correlation analyses with other scales were performed. Results: The EFA identified a two-factor structure as having the best fit: factor I included depressive symptoms and factor II included hypomanic/biphasic symptoms. Factor I was very strongly correlated with the A-GBI depressive subscale (r=0.990, p<0.001) and strongly correlated with CDI (r=0.764, p<0.001) and RCMAS (r=0.666, p<0.001). Factor II was also very strongly correlated with the A-GBI hypomanic/biphasic subscale (r=0.877, p<0.001) and weakly correlated with CDI (r=0.274, p<0.001) and RCMAS (r=0.332, p<0.001). Conclusion: The above findings support a two-dimensional model of mood symptoms in Korean youth.