109 Table 21 Classification result of Nested GLMM for all WCMs
group of ranking prediction Crosstabulation prediction
Total 1
2 3
The result of
groups of ranking
1 Count
42
8 50
within response
84.0
16.0 0.0
100.0 2
Count 12
20 5
37 within response
32.4
54.1
13.5 100.0
3 Count
3 3
35
41 within response
7.3 7.3
85.4 100.0
Total Count
57 31
40 128
within response 44.5
24.2 31.3
100.0
4.5 Conclusion
According to the standard errors analysis, it is believed the poverty data tends to have independent correlation structure. Random factors in Nested GLMM
controls the correlation structure, and Nested GLMM with exchangeable and unstructured WCM give the best model to fit the data, but it is not significantly
different from independent WCM. Through the data structure, it is suspicious the data is appropriate to the
exchangeable correlation structure, but ratio of standard error robust and model- based show the best model is Nested GLM with independent structure. The result
of Nested GLMM show ratios of exchangeable and unstructured are slightly smaller than independent. Because the differences are not significant,
exchangeable and unstructured WCM is not vehemently
3
rejected. All parameter estimates among exchangeable, unstructured, and independent
WCMs of Nested GLMs are different, while for Nested GLMM, all model parameter estimates are the same, except for the threshold parameter estimates of
independent WCM. Moreover, robust model is more sensitive to reject hypothesis
null for Nested GLM and GLMM. According to application result of Nested GLM, characteristic of Central
Java is different from West Java and East Java, which is appropriate to a research of history about
“Civilization Java” by Rahardjo 2011. Rahardjo said,
3
strong
110 characteristic of geographical areas in Central Java is more closed than in East
Java. In addition, number of farm worker families in Central Java is significant as a contribution to poverty level, but not significant in other provinces. Number of
schools in West Java is significant as a contribution to poverty level. There is no significant parameter in East Java.
According to the standard errors and classification result, Nested GLMM gives the best estimation for the poverty data with any working correlation
structure specifications. If Nested GLM is used, it is better to specify independent working correlation matrix.
111
Chapter 5 GENERAL DISCUSSION
Ranking, hotspot detection and modeling are important techniques for almost all fields of study. These three techniques have important roles for decision
makers, even in business, education, ecology, and socio economic, especially in government to increase the transparency of decision making. Every country in this
world has several policies to arrange for several affairs. Due to the limitation of the sources, the right and apt decision is very important and urgent. To support the
right decision in every area, the role of these techniques is needed. Optimistically, this dissertation is able to contribute ideas and thoughts to the government and
ministries in decision making process related to poverty reduction. Focus of study in this dissertation is modeling in Nested Generalized Linear
Model NGLM and Nested Generalized Linear Mixed Model NGLMM as an expansio
n of Zhang’s and Lin’s Model 2008, which is a GLMM as a strategy to detect hotspot through parameter estimates of spatial association in non-nested
study area using count response variable. Modeling in this study is GLM and GLMM with hotspot detection result as an explanatory variable, applied in nested
area using multinomial ordinal response variable. Before modeling, two studies, i.e
. a ranking method and 2 hotspot detection methods were studied. Ranking method was concentrated in Chapter 2, hotspot detection methods were studied in
Chapter 3, and model development was built and implemented in Chapter 4. ORDIT Ordering Dually in Triangle ranking method was studied and
implemented on poverty data in Chapter 2. Actually, this method was developed to handle ranking process of many individuals based on many indicators. It is not
easy to rank individuals with many indicators. This chapter explained how to rank many individuals based on many indicators using some mathematical concepts,
such as order theory, duality, and partial order set poset. Due to the limitation of the data, this method was implemented to order sub districts according to poverty
level based on only two indicators, i.e. surkin SKTM or poverty letters PL and askeskin
asuransi kesehatan untuk orang miskin or health insurance for the poor
