Expected Years Of Schooling Research Articles

Academic Resilience Profile: Study in Sampang District with the Lowest Human Development Index in East Java

Sampang Regency has the lowest Human Development Index (HDI) in East Java. Education indicators show an Expected Years of Schooling (EYS) of 12.38 and a Mean Years of Schooling (MYS) of 4.86. The low educational conditions in Sampang Regency require students to have high academic resilience. The purpose of this study was to determine the level of academic resilience and differences in academic resilience based on gender. This study used a non-experimental quantitative design. The subjects of the study were 308 students (47.7% male) with an average age of 16.67 years (SD = 1.10). The research instrument used is the Academic Resilience Scale-30 (ARS-30). Data were analyzed descriptively and using independent sample t-test. The results of the analysis showed that most of the students’ academic resilience in general was in the moderate category (75.65%). The academic resilience of both male (74.15%) and female (78.88%) students were the most in the moderate category. The results of the difference test showed that there were differences in students’ academic resilience based on gender (t = –2.4; P < 0.05). Different tests of three aspects of academic resilience showed differences based on gender, except for aspects of negative effect and emotional response. Gender has an influence on academic resilience in areas with low HDI.
 Keywords: academic resilience, gender, Human Development Index, Sampang district

Determining the Optimum Number of Clusters in Hierarchical Clustering Using Pseudo-F

Poverty refers to the condition where a person cannot meet the basic necessities based on the minimum living standards. Statistics Indonesia proxied an increase in the poverty rate in North Sumatra Province in 2021 from 8.75% to 9.01%. However, this increase is exclusive to North Sumatra Province, which has Indonesia's 3rd largest number of districts/cities. This study discussed mapping the North Sumatra Province region based on 10 poverty factor variables. The 10 variables are life expectancy, health complaints, poverty line, Gross Regional Domestic Product (GRDP), population growth rate, Expected Years of Schooling (EYS), Human Development Index (HDI), labor force participation rate, open unemployment rate, and district/city minimum wage. The Hierarchical Clustering analysis was employed to compare single, complete, and average linkage methods. The best method was determined based on the pseudo-F statistic value. 4 clusters had complete linkage methods, each of which possessed varied characteristics. Cluster 1 contains cities with the lowest poverty rate, including Medan City and Pematang Siantar City. Cluster 2 consists of cities with low poverty rates, while Cluster 3 consists of cities with high poverty rates. Cities that are included in Cluster 4 have very high poverty rates, including South Nias District and Pakpak Bharat District. The clusters present significant poverty rate gaps among North Sumatra Province regions.

HIERARCHICAL CLUSTER ANALYSIS OF DISTRICTS/CITIES IN NORTH SUMATRA PROVINCE BASED ON HUMAN DEVELOPMENT INDEX INDICATORS USING PSEUDO-F

Human development is needed to create prosperity and assist development in a country. In realising this, it is necessary to first look at the quality of human resources in the country, so that its use is more targeted. The measure used as a standard for the success of human development in a country is the Human Development Index (HDI). HDI figure are calculated from the aggregation of three dimensions, namely longevity and healthy living, knowledge, and decent standard of living. The longevity and healthy living dimension is represented by the Life Expectancy. Average Years of Schooling (AYS) and Expected Years of Schooling (EYS) are indicators representing the knowledge dimension. Meanwhile, the decent standard of living dimension is represented by the Expenditure per Capita indicator. The purpose of this study is to explain the characteristics of each cluster obtained from Hierarchical Cluster Analysis of districts/cities in North Sumatra Province based on HDI indicators in 2022 using Pseudo-F. The methods used are Hierarchical Cluster Analysis and Calinski-Harabasz Pseudo-F Statistic. The main concept of this method is to determine the optimum number of groups. This research uses secondary data obtained from BPS. The sample size in this study are 33 districts/cities and the number of variables are 4 variables. The results of the analysis of this study are the formation of 4 clusters with the best method is Ward. Cluster 1 consists of four members, namely Medan City, Pematang Siantar City, Binjai City, and Padang Sidempuan City, where this cluster has a very high HDI level. Meanwhile, Cluster 4 is a cluster that has a very low HDI level with four cluster members, namely Nias District, South Nias District, North Nias District, and West Nias District. Thus, it can be seen that there is a gap between regions in North Sumatra Province.

Micmac Analysis of Improving Social Variables of Expected Years of Schooling (EYS) in Coastal and Non Coastal Areas in Pasangkayu Regency, West Sulawesi

The condition of coastal communities, including the Pasangkayu area, in terms of the Expected Years of Schooling (EYS), is still very vulnerable. This study aims to analyze the key factors of increasing the EYS of the community in the coastal countryside of Pasangkayu Regency. The Matrix of Cross Impact Multiplications Applied to a Classification (MICMAC) methods were used in this study. Respondents are experts sourced from the government, educational institutions, non-governmental organizations and communities in the Coastal Village of Pasangkayu Regency. The results showed that the key factor in increasing the EYS in Coastal Village, Pasangkayu Regency was helping parents. The economic condition of coastal rural communities is still vulnerable, so helping people and being able to go to school is a key factor in increasing the EYS. Institutions related to coastal villages are advised to develop work programs that support research results, namely by increasing the old expectations of coastal community schools through these key factors. One way is to develop a school model for fishermen's children. The strategy is carried out by mobilizing teachers to teach non-formal schools for fishermen's children. Supported by the Education Office in equivalence of diplomas with formal education. EYS, can be increased through the increasing number of elderly school-age population who continue to higher education.

Worldwide incidence and mortality of ovarian cancer and Human Development Index (HDI): GLOBOCAN sources and methods 2018.

SummaryObjectiveOvarian cancer is known as the seventh most common cancer among women, accounting for about 4% of all cancers associated with the females.MethodThis is a descriptive cross-sectional study based on cancer incidence data and cancer mortality rates from the Global Cancer Data in 2018. The incidence and mortality rates were estimated and ovarian cancer distribution maps for world countries were drawn. To analyze data, correlation and regression tests were used to evaluate association between its incidence and mortality with human development index (HDI)ResultsResults revealed a direct and significant correlation between ovarian cancer incidence (R = 0.409, P < 0.0001) and mortality (R = 0.193, P < 0.05) with HDI. It also projected a direct and significant correlation between incidence with Gross National Income per 1,000 capita (GNI), mean years of schooling (MYS), life expectancy at birth (LEB) and expected years of schooling (EYS) (P < 0.0001). The findings also demonstrated a direct and significant correlation between mortality and GNI, MYS, LEB as well as EYS (P < 0.05). The linear regression model showed that a higher MYS [B = 0.2, CI 95%: (-0.03, 0.5)] can significantly augment the incidence of ovarian cancer while an increased MYS [B = 0.2, CI 95% (0.03, 0.4)] can induce mortality.ConclusionsGiven the direct and significant correlation between ovarian cancer incidence and mortality with HDI, attention to risk factors in these countries can be effective in curbing its incidence and mortality.

Regresi Logistik Biner dalam Penentuan Ketepatan Klasifikasi Tingkat Kedalaman Kemiskinan Provinsi-Provinsi di Indonesia

This study aims to find out the variables that affect the poverty depth index of provinces in Indonesia as well as the level of classification produced by logistic regression analysis method. This analysis was chosen because the variables tied to this study had values of 0 and 1 (categorically). The variable tied to this research is the level of poverty depth of provinces in Indonesia in 2019. While the free variables are PPP (adjusted per capita expenditure), MYS (Mean Years School) and EYS (Expected Years of Schooling) of provinces in Indonesia in 2019. The population used in this study is all provinces in Indonesia with sampling techniques is census because the population is less than 100. The samples in this study are the same as the population of all provinces in Indonesia. The results showed that MYS and EYS influenced the level of poverty depth with the accuracy of the overall prediction is 85.3% which indicates that there are still differences in classification results obtained from the original data with the logit regression model obtained.

The association between incidence and mortality of brain cancer and human development index (HDI): an ecological study

BackgroundBrain cancer is a rare and deadly malignancy with a low survival rate. The present study aims to evaluate the epidemiology of brain cancer and its relationship with the human development index (HDI) worldwide.MethodsThis is an ecological study. The data on cancer incidence and cancer mortality was extracted from the World Bank for Cancer in 2018 (GLOBOCAN 2018). The incidence, mortality rate, and brain cancer distribution maps were drawn for different countries. We used correlation and regression tests to examine the association of incidence and mortality rates of brain cancer with HDI. The statistical analysis was carried out by Stata-14 and a significance level of 0.05 was considered.ResultsAccording to the results of Global Cancer Registry in 2018, there were 18,078,957 registered cases of cancer in both sexes, of which 29,681 were related to brain cancer. The highest incidence (102,260 cases, 34.4%) and mortality (77,815 cases, 32.3%) belonged to very high HDI regions. Results showed that incidence (r = 0.690, P < 0.0001) and mortality rates (r = 0.629, P < 0.001) of brain cancer are significantly correlated with HDI. We also observed a positive correlation between brain cancer incidence and Gross National Income (GNI) (r = 0.346, P < 0.001), Mean Years of Schooling (MYS) (r = 0.64, P < 0.001), TABLE (LEB) (r = 0.66, P < 0.001) and Expected Years of Schooling (EYS) (r = 0.667, P < 0.001). Results also revealed that mortality rate was significantly correlated with GNI (r = 0.28, P < 0.01), MYS (r = 0.591, P < 0.01), LEB (r = 0.624, P < 0.01), and EYS (r = 0.605, P < 0.01).ConclusionThe results of the study showed that the incidence and mortality of brain cancer in countries with higher HDI levels is higher than countries with lower HDI levels, so attention to risk factors and action to reduce it in countries with higher HDI levels in controlling this cancer in this Countries are effective.

Earmarking Tax for Indonesia's Economic Growth through the Education and Health Sector in the Long and Short Term Period

The earmarking tax policy is expected to provide quality improvements as a result of the certainty of fund allocation. There are two examples of the government undertaking an earmarking tax approach in Indonesia, where 20% of funds are allocated for education, and 5% of funds are allocated for health. This measure of quality improvement can be seen from the improvement of the Human Development Index (HDI), Expected Years of Schooling (EYS), and Life Expectancy at Birth (LEB) that in turn impacts increasing economic growth (EG) during the period of earmarking tax policy in Indonesia. This research uses Path Analysis and Error Correction Model. Earmarking tax cause a negative indirect effect on EG through the education and health sector. However, in the long term, it has a positive effect on both sectors, while EYS and LEB has a negative effect. In the short term, only the health sector can increase EG.

A Simple Measure of Human Development: The Human Life Indicator.

A Simple Measure of Human Development: The Human Life Indicator.

Analisis Angka Harapan Lama Sekolah di Indonesia Timur Menggunakan Weighted Least Squares Regression

Human Development Index (HDI) is one of the data and information used by the local government to measure the achievement of human development with the basic components of quality of life that is life expectancy that represents health dimension, expected years of schooling (EYS) and mean years of schooling (MYS) represents the educational dimension, and purchasing power parity that represents decent living dimension. HDI especially in eastern Indonesia in the last three years has continued to increase but the figure is always below from the national figure even left behind if compared with West Indonesia. One dimension that is still low achievement is the educational dimension. EYS is one of the indicators on the educational dimension that is still a low achievement. Therefore, this research would like to know the influence of percentage of poor people, Gross Regional Domestic Product (GRDP) per capita, net enrollment rate (NER) of junior high school, and a ratio of educational facilities to EYS in eastern Indonesia. Using Weighted Least Squares (WLS) method concluded that the four predictor variables used were able to influence EYS in Eastern Indonesia.

Education sector optimization for improving human development index

The research was intended for determining: (1) the influence of Mean Years of Schooling (MYS) to Human Development Index (HDI) and describe the strategies for increasing MYS in Klungkung regency; (2) the influence of Expected Years of Schooling (EYS) to HDI and describe the strategies to improving EYS in Klungkung regency; (3) the influence of MYS and EYS simultaneously to HDI and describe the strategies to increasing it in Klungkung regency. In order to achieve the above objectives, the research was conducted on HDI in Klungkung using Mixed Methods Study, the population was the society in Klungkung with purposive sampling, snowball, and random sampling technique. Quantitative data were analyzed using the Eviews 7.0. The qualitative data were analyzed using Miles and Huberman model. The result was obtained that: (1) MYS influence to HDI on the t-statistic was 4.09, probability 0.0264, and R-square 0.97; (2) EYS influence of HDI on t-statistic 11.83, probability 0.0013, and R-square was 0.96; (3) MYS and EYS simultaneously influence of HDI with the t-statistic 29.07, probability 0.0012, and R-square was 0.99. The strategies that can be conducted to increase MYS included: (a) increasing the education budget that amount in accordance with the laws and regulations, (b) launching a program of compulsory education to 12 years, (c) revitalizing of local genius educations, (d) developing schools models, (e) establishing the integrity of the community through character education and anti-corruption education, (f) developing entrepreneurship education, and (g) developing models of learning and computer-based testing (ICT). The rise of EYS could be conducted by: (a) reduce poverty, and (b) overcome the problems due to geography and population distribution was uneven.