Judul: Comparison Of SimulationExtrapolation, Regression Calibration And Instrumental Variable In Reducing The Bias Due To Measurement Error
Abstrak: The inability to accurately measure important variables complicates the statistical analysis of data when the purpose of inference is for the model defined in terms of the unobserved variables. In such situation, fitting the model directly to the observed data induces the bias estimates. For reducing the bias, some methods have been proposed such as Simulation-Extrapolation, Regression Calibration and Instrumen1al Variable. In this paper, the effects of measurement error on estimation)n linear and logistic regression will be demonstrated and the methods for bias rcd.uction will be compared. via simulation. The measurement error of predictors reduces the parameter estimate i.e. problem of attenuation. The corre1a1ion in predictors or the correlation in errors influences the magnitude and the direction of the bias. In general, Simulation-Extrapolation, Regression Calibration and Instrumental Variable can reduce the bias except for the structure of complex error in simple linear regression lnstrumental Variable tends more relatively stable on estimation with the increasing of measurement error compared with SimulationExtrapolation or Regression Cahbration estimation.
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Judul: The Impact of Missing Data on Reliability and Accuracy in the Results of the Structural Equation Modeling Case Analysis.
Abstrak: Missing data is a problem that often become an obstacle for researcher in data analysis. Missing data can cause bias in a model which can lead to inaccuracies of estimation parameter and wrong conclusion (Chen et al. 2016). General countermeasure used to resolve the adverse of missing data is by removing certain respondent with missing data from dataset. However, wasting data on fillen entry will reduce research efficiency. Another method used to resolve missing data is by imputing data using various approaches, including serial mean, linear interpolation, linear trend. Using Structural Equation Modeling (SEM) analysis, the researcher can observe internal relationship of a large number of variable which can represent the level of data reability in estimating the real data. Missing data estimation method in the SEM produces different estimated parameter coefficient values even when using the same simulation characteristic data. The estimation results show that the linear trend method is optimal in resolving the adverse of missing data in missing precentages of 5%, 10% and 15%. Serial mean method optimal in missing precentages of 5% and 10%. Linear interpolation method optimal in missing precentages 5%. Listwise deletion method is not optimal enough to resolve the adverse of missing data in missing precentages of 5%, 10%, 15% and 20%. The result of goodness of fit model using chi-square test, p-value, RMSEA and the reability level using construct reliability (CR) and variance extracted (VE) shows that increasing the percentage of missing data will cause a decrease in the efficiency of estimation. But more than that, the compatibility of the missing data pattern with the estimated value of each method used will affect the level of efficiency and optimization of the estimation. The result of accuracy and precision level using MAPE shows that in resolving the adverse of missing data the serial mean, linear interpolation, and linear trend methods with a missing precentage 5% are feasible in estimating the parameters of the control SEM model. Meanwhile the listwise deletion method for each missing precentage is not accurate to estimate the parameters of the control SEM model., Missing data is a problem that often become an obstacle for researcher in data analysis. Missing data can cause bias in a model which can lead to inaccuracies of estimation parameter and wrong conclusion (Chen et al. 2016). General countermeasure used to resolve the adverse of missing data is by removing certain respondent with missing data from dataset. However, wasting data on fillen entry will reduce research efficiency. Another method used to resolve missing data is by imputing data using various approaches, including serial mean, linear interpolation, linear trend. Using Structural Equation Modeling (SEM) analysis, the researcher can observe internal relationship of a large number of variable which can represent the level of data reability in estimating the real data. Missing data estimation method in the SEM produces different estimated parameter coefficient values even when using the same simulation characteristic data. The estimation results show that the linear trend method is optimal in resolving the adverse of missing data in missing precentages of 5%, 10% and 15%. Serial
Keyword: missing data, serial mean, linear interpolation, linear trend, SEM

Judul: Lejeuneaceae Subfamily Ptychanthoideae (Hepaticae) in  West Java
Abstrak: Subfamily Ptychanthoideae of the family Lejeuneaceae (Hepaticae) in West Java is still poorly known. Publications on this subfamily are out of dated and written in the 19th or early of 20th century. The last taxonomic treatment of Ptychanthoideae of West Java was in 1933, many species names have changed since then. Studies on Ptychanthoideae of West Java are urgently needed. Studies on the Ptychanthoideae were based on the specimens collected from West Java. A total number of 361 of specimens deposited in the Herbarium Bogoriense (BO), Herbarium Biotrop (BIOT), Gottingen Herbarium (GOET) and Bangi Herbarium (UKMB) were examined, those are old and several recent specimens collected in West Java. Recent collections were made from Telaga Warna, Ciater, Gunung Halimun Salak National Park, Cibodas Botanical Garden, Bodogol Education Center and Conservation (Gunung Gede Pangrango National Park), University of Indonesia Campus Depok, including several collections from Banten Province. Twenty four species of Lejeuneaceae subfamily Ptychanthoideae are known from West Java, belonging to 8 genera namely Acrolejeunea (Spruce) Schiffn. (2 species), Archilejeunea (Spruce) Schiffn. (1 species), Lopholejeunea (Spruce) Schiffn. (10 species), Mastigolejeunea (Spruce) Schiffn. (3 species), Ptychanthus Nees (1 species), Schiffneriolejeunea Verd. (1 species), Spruceanthus Verd. (2 species) and Thysananthus Lindenb. (4 species). Two new records of Ptychanthoideae: Mastigolejeunea indica Steph. and Thysananthus minor Verdoorn are reported for Java. A determination key to the species is presented including their descriptions and illustrations.
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