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Visualization in the assessment of construct validity
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 Title & Authors
Visualization in the assessment of construct validity
Noh, Hohsuk; Song, Ji Na; Cho, Hyeyoon;
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 Abstract
It is common to quantify the concept of interest in the social and human sciences to test a research hypothesis. In such a case, it is strongly recommended to investigate if the procedure is appropriately designed and implemented according the research purpose since the quantification procedure highly affects the result of statistical analysis. In this work, we propose a visualization tool which enables us to check the construct validity of a measurement tool (such a questionnaire) in a concise and convenient way based on a penalized factor analysis model. We illustrate our method with numerical simulation and real data analysis.
 Keywords
construct validity;penalized factor analysis;quantification;
 Language
Korean
 Cited by
 References
1.
Campbell, D. T. and Fiske, D. W. (1959) Convergent and discriminant validation by the multitraitmultimethod matrix, Psychological Bulletin, 56, 81-105. crossref(new window)

2.
Choi, J., Zou, H., and Oehlert, G. (2011). A penalized maximum likelihood approach to sparse factor analysis, Statistics and Its Interface, 3, 429-436.

3.
Hirose, K. and Yamamoto, M. (2014). Estimation of an oblique structure via penalized likelihood factor analysis, Computational Statistics & Data Analysis, 79, 120-132. crossref(new window)

4.
Hirose, K. and Yamamoto, M. (2015). Sparse estimation via nonconcave penalized likelihood in factor analysis model, Statistics and Computing, 25, 863-875. crossref(new window)

5.
Kang, H., Han, S., Kim, K., and Jhun, M. (2005). Multivariate Data Analysis using SAS by Examples, Freedom Academy, Paju.

6.
Zhang, C.-H. (2010). Nearly unbiased variable selection under minimax concave penalty, Annals of Statistics, 38, 894-942. crossref(new window)