Figure 1 - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
Source publication
The Generalized linear mixed model (GLMM) is an extension of the generalized linear model by adding random effects to linear predictors to accommodate clustered or over dispersion. Severe computational problems in the GLMM modelling cause its use restricted for only a few predictors. When many predictors are available, the estimators become very un...
Context in source publication
Context 1
... this applied study, a total of 4,605 mean IPA scores of Indonesian students used as response variables. The student's science scores are data with a ratio scale Fig. 1 below shows a scatter diagram of the standardized science scores of Indonesian students. The range of normalized response values ranges from -3 to +3. The corresponding coefficient is buildups for glmmLasso, shown in Figure 2. The cutting offline in Figure 2 shows the value of s, which is the opposite of the value of λ. Based on Figure ...
Citations
... Didapatkan juga bahwa presentase kemiskinan juga mempengaruhi hasil UN dan nilai akreditasi sekolah. Penelitian terdahulu yang juga menggunakan linear mixed model adalah [11,12]. [11] menemukan bahwa kualitas sekolah berpengaruh terhadap hasil ujian nasional sedangkan [12] menggunakan nilai Program for International Student Assessment (PISA) menemukan bahwa sekolah sebagai variabel acak berdampak signifikan terhadapa nilai literasi membaca. ...
... Penelitian terdahulu yang juga menggunakan linear mixed model adalah [11,12]. [11] menemukan bahwa kualitas sekolah berpengaruh terhadap hasil ujian nasional sedangkan [12] menggunakan nilai Program for International Student Assessment (PISA) menemukan bahwa sekolah sebagai variabel acak berdampak signifikan terhadapa nilai literasi membaca. Penelitian lain terkait kebijakan UN pada SMK oleh [13] dan [14]. ...
... Several researchers have researched PISA, such as [7][8][9][10]. In Pakpahan's study in 2016, only one response variable was used, namely mathematical literacy, and assumed all explanatory variables were fixed and did not involve the school of origin of each student [7]. ...
... [8] used one response variable, namely mathematical literacy in PISA, and used the LASSO, MCP, and Ridge penalty functions in modeling PISA data without considering random effects in the model. [9] used a response variable, namely scientific literacy in Indonesian students using the Generalized Linear mixed models with a penalized Lasso approach, (10) used three response variables, namely mathematical literacy, scientific literacy, and reading literacy, and used Multivariate Linear mixed models in modeling PISA data containing multicollinearity. ...
... Namely education level, father's education, internet access at home, the presence of dictionaries at home, the number of TVs, the number of cellphones, the number of computers, the number of e-book tabs, the number of books at home, truant behavior at school, the behavior of being late for school, not listening to teacher's explanations, the age of entering kindergarten and elementary school, and having stayed in class during elementary school. This study's findings align with [7][8][9][10] research. ...
... The Generalized Linear Mixed Model with a penalized Lasso method (known as GLMM-LASSO) was used to identify significant features. This penalized Lasso method simultaneously selects variables and estimates coefficients 104 . The tuning parameter, lambda, was meticulously chosen using the Bayesian Information Criterion (BIC) and cross-validation techniques. ...
Cognition, defined as the ability to learn, remember, sustain attention, make decisions, and solve problems, is essential in daily activities and in learning new skills. The purpose of this study was to develop cognitive workload and performance evaluation models using features that were extracted from Electroencephalogram (EEG) data through functional brain network and spectral analyses. The EEG data were recorded from 124 brain areas of 26 healthy participants conducting two cognitive tasks on a robot simulator. The functional brain network and Power Spectral Density features were extracted from EEG data using coherence and spectral analyses, respectively. Participants reported their perceived cognitive workload using the SURG-TLX questionnaire after each exercise, and the simulator generated actual performance scores. The extracted features, actual performance scores, and subjectively assessed cognitive workload values were used to develop linear models for evaluating performance and cognitive workload. Furthermore, the Pearson correlation was used to find the correlation between participants’ age, performance, and cognitive workload. The findings demonstrated that combined EEG features retrieved from spectral analysis and functional brain networks can be used to evaluate cognitive workload and performance. The cognitive workload in conducting only Matchboard level 3, which is more challenging than Matchboard level 2, was correlated with age (0.54, p -value = 0.01). This finding may suggest playing more challenging computer games are more helpful in identifying changes in cognitive workload caused by aging. The findings could open the door for a new era of objective evaluation and monitoring of cognitive workload and performance.
... The research on PISA survey data that has been carried out by [12], [13], [14], and [15] can only find out the student factors that have a significant effect on PISA scores. So it is necessary to do further analysis to find out not only student factors but also school factors that have a significant effect on PISA scores. ...
... The results of testing the significance of parameters partially using the t-test showed that there are 12 explanatory variables level-1 that significantly affect the reading literacy score of students, namely, gender (X1), grade level (X2), mother education (X3), study desk at home (X5), many mobile phones with internet access at home (X7), many computers at home (X8), many books at home (X9), age of entry to early childhood education (X10), age of entry to elementary school (X11), not listening to teachers (X12), skipping school (X13), and failing grade (X15) and there are 2 explanatory variables level-2 that significantly affect the reading literacy score of students, namely, the type of school (Z1) and the location of school (Z2). Based on the results of this study are in line with research conducted by [12], [13], [14], and [15]. Based on Table 5, estimation variance components for every level in a random intercept model with explanatory variables (model 2), where the estimate of level-1 (students) residual variance shows the diversity of reading literacy scores between students in schools (σ e 2 = 2.757,299), while the estimation level-2 (school) residual variance shows the diversity of the average reading literacy scores between schools (σ u o 2 = 1.270,383). ...
The multilevel regression model is a development of the linear regression model that can be used to analyze data that has a hierarchical structure. The problem with this data structure is that individuals in the same group tend to have the same characteristics, so the observations at lower levels are not independent. Education research often produces a hierarchical structure, one of which is PISA data, where students as level-1 nested within schools as level-2. In the PISA 2018 survey, reading literacy is the main focus. The data are sourced from the Organisation for Economic Co-operation and Development (OECD). The survey results show that the reading literacy scores of Indonesian students have decreased, thus placing Indonesia at 74th out of 79 countries. However, it is still very rare to research the reading literacy of Indonesian students' using a multilevel regression model. This study aims to apply a multilevel regression model to determine the factors influencing Indonesian reading literacy scores in PISA 2018 survey data. The results of this study indicate that the factors that influence response variable are gender, grade level, mother's education, facilities at home, age at school entry, student discipline behavior at school, and failing grade, while at the school level are the type of school and school location. The magnitude variance of student reading literacy scores can be explained by the explanatory variables the student level is 11,42% and the school level is 60,66%, while the rest is explained by another factor outside the study.
... Meanwhile, Santi et al. [9] produced 11 factors that significantly influenced the scientific literacy score. Santi et al. [10] modeled PISA data using the Generalized Linear Mixed Model (GLMM) involving random effects on univariate response variables. Until now, studies on quantitative PISA data scores have been extremely rare. ...
... with a p-value of 0.00 <0.05, so that it can be said that there is a significant effect of all model parameters simultaneously on the three response variables, including father's education, internet access, facilities at home, and the age of entering kindergarten (TK). These findings are in line with research conducted by Pakpahan [8], Santi et al. [9], and Santi et al. [10]. Based on Table 4 above, the estimation of the variance of random effects obtained from the three responses, the literacy scores of mathematics, science, and reading are 1548.12, ...
The Program for International Student Assessment (PISA), becomes one of the references or indicators used to assess the development of students' knowledge and skills in each member country of the Organization for Economic Cooperation and Development (OECD). The results of the PISA survey in 2018 placed Indonesia in the bottom 10, indicating that the implementation of the national education system has not been successful. This underlies the need for a more in-depth study of the factors that influence PISA data scores not only statistically qualitatively but also quantitatively which is still very rarely done. The data structure of the PISA survey results is complex, which involves multicollinearity, multivariate response variables, and random effects. Thus, it requires an appropriate statistical analysis method such as the multivariate mixed linear regression (MLMM) model. In this study, secondary data from the results of the 2018 PISA survey with Indonesian students as the smallest unit of observation were used as sample. School is used as an intercept random effect which is assumed to be normally distributed. Multicollinearity is overcome by selecting independent variables based on AIC and BIC values. Estimation of variance and random effect parameters was performed using the restricted maximum likelihood (REML) method. Based on the estimator of the variance of random effects for the response variables of mathematics, science, and reading literacy, it was obtained 1548.12, 1359.39, and 1082.48, respectively, which explains the significant effect of each school as a random effect on the three response variables.
The linear mixed models is a development of the linear model which includes both fixed and random effects in the model. Random effect in the model is used to model complex data that has a grouping structure. The grouping structure can occur because the same observations are measured repeatedly or each observation is measured only once but these observations have some form of group structure. Students who participate in the Program for International Student Assessment (PISA) are nested in several schools, so the PISA data structure is quite complex and requires a more in-depth analysis. Quantitative studies on PISA, especially in reading literacy, are still rarely done. The purpose of this study is to determine what factors effect the Indonesian student’s PISA reading literacy scores using a linear mixed model approach with school being used as a random effect in the model. The findings of the study are that the factors that affects Indonesian student’s PISA reading literacy scores are the class being taken, gender, mother's highest education, facilities at home, school entry age, student discipline and failed a grade. The result of the estimation of random effect variance which is not equal to zero indicates that there is a random effect from the student’s school on PISA reading literacy scores. Based on model diagnostics and parameter testing, it was concluded that the model obtained is fitted in modeling Indonesian student’s PISA reading literacy scores.
