• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1409-1419
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• 2013

We analyzed the data from KYPS using the latent growth model which has been widely studied as an analysis method of longitudinal data. In this study, we applied repeated measures ANOVA to unconditional model in order for faster decision of the unconditional model of the latent growth model. Also, we compared the six-type models, the quadratic model and the model of which repeated measures ANOVA is applied.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.413-420
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• 2008

This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.577-585
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• 2008

This paper suggests a marginal probability model for analyzing repeated polytomous response data when some factors are nested in others in treatment structures on a larger experimental unit. As a repeated measures factor, time is considered on a smaller experimental unit. So, two different experiment sizes are considered. Each size of experimental unit has its own design structure and treatment structure, and the marginal probability model can be constructed from the structures for each size of experimental unit. Weighted least squares(WLS) methods are used for estimating fixed effects in the suggested model.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.187-191
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• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.1-9
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• 2010

This paper suggests a mixed-effects model for analyzing split-plot data when there is a repeated measures factor that affects on the response variable. Covariance structures are discussed among the observations because of the assumption of a repeated measures factor as one of explanatory variables. As a plausible covariance structure, compound symmetric covariance structure is assumed for analyzing data. The restricted maximum likelihood (REML)method is used for estimating fixed effects in the model.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.1-12
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• 2004

In this paper, we consider choosing the appropriate covariance structure for analyzing repeated measures data with three repeat factors from a study of blood pressure data, which is collected from the local residents of Yangpyeong, Gyeonggi-do (2001) and fitted linear mixed models to find the significant covariates on outcome variable(Blood Pressure)

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.621-630
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• 2008

This paper discusses how to construct a generalized marginal logit model for analyzing repeated polytomous response data when some factors are applied to larger experimental units as treatments and time to a smaller experimental unit as a repeated measures factor. So, two different experimental sizes are considered. Weighted least squares(WLS) methods are used for estimating fixed effects in the suggested model.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.525-534
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• 2007

It is a common practice in many research areas that multiple range tests for a between subject factor such as Tukey are applied to a within subject factor in a repeated measures design. Tukey procedure, however, sometimes detects no pairs with different means even when the hypothesis of all equal level means is rejected. This study attempts to provide a rationale for the proposition that Tukey is inappropriate post hoc procedure for a within subject factor in which the observations are correlated. We introduce two multiple range tests, Bonferroni and Scheffe, for a within subject factor and show that Bonferroni is more appropriate than Scheffe for pairwise multiple comparisons. Subsequent simulation study indicates that Tukey has significantly less power than Bonferroni in detecting actual difference between means of some pairs when the observations of a within subject factor are highly correlated.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.205-219
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• 1998

Statistical analyses have been perf7rm7d to find factors affecting chronic renal allograft dysfunction for 114 renal transplanted patients. Renal function was evaluated using serum creatinine values every three months during 1 year to 5 years after transplantation. Statistical models for the repeated measures were considered to evaluate factors affecting the reciprocal of serum creatinine values. This paper focuses on some common problems on the choice of correlation matrices occurred in the analysis of repeated measures.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.39 no.2
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• pp.105-115
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• 2013

In Korea, the human skin tests to evaluate the anti-wrinkles and whitening effect have been accomplished in accordance with the KFDA guideline. Regarding the data of the visual assessment and machinery evaluation of the results for the human skin test, unpaired t-test have been used in order to compare between the test and the control groups and paired t-test for the comparison of effects for before and after. Descriptive statistics such as frequency analyses was used for the questionnaire evaluation data. In many cases of the European and American clinical test centers, the methodology and the statistical analysis were similar to ours. But, the documentation obtained by repeated application from identical individual has high relation. For this reason, it is desirable to apply RM ANCOVA and RM ANOVA to a visual assessment and machinery evaluation. We suggested that RM ANCOVA and RM ANOVA is the new approach to statistical analysis of human test data of functional cosmetics.