• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.29-40
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• 2018

There are various reasons for missing values when collecting data. Missing values have some influence on the analysis and results; consequently, various methods of processing missing values have been studied to solve the problem. It is thought that the later point of view may be affected by the initial time point value in the repeated measurement data. However, in the existing method, there was no method for the imputation of missing values using this concept. Therefore, we proposed a new missing value imputation method in this study using clustering in initial time point of the repeated measurement data and the measure of property proposed by Kim and Kim (The Korean Communications in Statistics, 30, 463-473, 2017). We also applied the Monte Carlo simulations to compare the performance of the established method and suggested methods in repeated measurement data.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.185-196
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• 2004

It is an important research area in Integrated Pest Management System to estimate the pest density within plants, because the artificial controls such as spraying pesticides or biological enemies depend on the information of pest density. This paper studies the population density distribution of two-spotted spider mite in glasshouse roses. As the data were collected repeatedly on the same subject, Subject-Specific and Population Averaged approaches are used and compared.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.377-385
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• 2009

Twenty five female students in Seoul participated and were divided into two group in the experiment of weight loss effect of two treatments. Fourteen students(Treatment A group), randomly chosen from the students, had fed on diet foods and exercised over 8 weeks, and the remaining students(Treatment B group) had fed on diet foods only for the same periods. Weights of 25 students had been measured repeatedly four times at an interval of two weeks during 8 weeks, It resulted from mixed model analysis of repeated measurements data that separate Toeplitz pattern for each treatment group was selected as the optimal covariance pattern. Based upon the optimal covariance pattern model, the baseline effect and time effect were found to be highly significant, but the treatment-time interaction effect was found to be insignificant. Finally, the students with diet foods and exercises were more effective in losing weight than the students with only diet foods were.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.641-650
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• 2010

This study was conducted to investigate whether Mulligan manual therapy and Physical therapy have effectiveness on the pain and muscle assessment questionnaire in female elders with osteoarthritis of the knee. Thirty subjects were participated in this study. And they were all randomly divided into Mulligan manual therapy and Physical therapy group. To evaluate the effects of Mulligan manual therapy and Physical therapy, subjects were evaluated by using visual analogue scale and muscle assessment questionnaire. The assessment parameters were evaluated before, after 2 weeks, and after 4 weeks treatments. And we received a consent form from Mulligan manual therapy subjects. The results of repeated measures analysis of variance showed that pain, strength, endurance, coordination/balance were significantly improved after than before therapy in Mulligan manual therapy group. So we conclude that Mulligan manual therapy has effectiveness on the pain and muscle assessment questionnaire in female elders with osteoarthritis of the knee.

• Journal of the Korean School Mathematics Society
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• v.24 no.1
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• pp.59-81
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• 2021

This study aimed to investigate how the learners' mathematics learning processes change with repeatedly reading mathematical text. As a way to teach and learn mathematics, we also wanted to examine the effect of repeated reading and to explore the implications for a more efficient teaching and learning strategy. To help us with this study, we mainly used eye tracking and heart rate (HR) measurement. There were four cycles in a cycle of repeated reading, and the number of repeated readings for all cycles was fixed to three times. Eight prospective mathematics teachers in the Department of Mathematics Education of a National University in South Korea participated. Data were analyzed in five aspects: (1) the total reading time per round, the total reading time per slide; (2) the change trends of total reading time per round and slide; (3) the order of slides read; (4) the change trends of HR per round. We found that most participants read in a similar pattern in the first reading, but the second and third reading patterns appeared more diverse for each learner. Also, the first reading required the most time regardless of the repeat cycle, and the time it took to repeatedly read afterward varied depending on the individual. Based on the findings of this study, the most primary conclusion is that self-directed mathematics learning by using repeated reading is effective regardless of cycle. In addition, we suggested four strategies to improve the efficiency of this teaching and learning method.

• 한국지구물리탐사학회:학술대회논문집
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• 2010.10a
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• pp.189-190
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• 2010

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.811-824
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• 2005

The ADCP is an instrument based on Doppler effect, which measures discharge of a river in a short time while crossing it. In this study we aim to make a comparison of the discharge results from a moving-vessel ADCP with those measured by velocity-area method at the same cross-section, and to investigate the characteristics of velocity and discharge data using ADCP. Bathymetry measured by ADCP almost coincides with that by direct depth measurements. Because velocity data from ADCP are essentially instantaneous, individual velocity profiles obtained by ADCP are rather different from time-averaged velocity profiles. But spatially averaged velocity profiles of the individual ADCP data near the comparable verticals have similar vertical velocity pattern with the time-averaged ones. The average velocity profile from repeatedly crossed data is also similar with the time-averaged one. In case of the velocity distribution, individual and spatially averaged data for the sub-width of mid-section method Have good agreement with those by velocity-area method. Discharge data determined by averaging several ADCP measurement transects have $0.1\%{\~}9.3\%$ of difference with those from velocity-area method, and as the number of measurement increases, the relative difference to the velocity-area method decreases.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.111-116
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• 2015

The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

• Survey Research
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• v.9 no.2
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• pp.1-27
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• 2008

Problems of incomplete data are pervasive in statistical analysis. In particular, incomplete data have been an important challenge in repeated measures studies. The objective of this study is to give a brief introduction to missing data mechanisms and conventional/recent missing data methods and to assess the performance of various missing data methods under ignorable and non-ignorable missingness mechanisms. Given the inadequate attention to longitudinal studies with missing data, this study applied recent advances in missing data methods to repeated measures models and investigated the performance of various missing data methods, such as FIML (Full Information Maximum Likelihood Estimation) and MICE(Multivariate Imputation by Chained Equations), under MCAR, MAR, and MNAR mechanisms. Overall, the results showed that listwise deletion and mean imputation performed poorly compared to other recommended missing data procedures. The better performance of EM, FIML, and MICE was more noticeable under MAR compared to MCAR. With the non-ignorable missing data, this study showed that missing data methods did not perform well. In particular, this problem was noticeable in slope-related estimates. Therefore, this study suggests that if missing data are suspected to be non-ignorable, developmental research may underestimate true rates of change over the life course. This study also suggests that bias from non-ignorable missing data can be substantially reduced by considering rich information from variables related to missingness.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.