The Poverty Measurement Analysis of Three Districts in Terengganu, Malaysia using Principal Component Analysis

被引:0
|
作者
Zakaria, Syerrina [1 ]
Hwei, Ng Qin [1 ]
Rahman, Nuzlinda Abdul [2 ]
机构
[1] Univ Malaysia Teregganu, Sch Informat & Appl Math, Kuala Terengganu 21300, Malaysia
[2] Univ Sains Malaysia, Pusat Pengajian Sains Matemat, George Town 11800, Malaysia
关键词
Deprivation index; Factor analysis; Poverty; DEPRIVATION INDEX;
D O I
10.1063/1.5041679
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Deprivation indices are widely used in public health study. These indices are also referred as the index of inequalities or disadvantage and poverty index that commonly referred to the quality of life. Even though, there are many indices that have been built before, it is believed to be less appropriate to use the existing indicesto be appliedin other countries or areas which had different socio-economic conditions and different geographical characteristics. The objectives of this study are to investigate the relationship between social economic well-being characteristics variables using correlation analysis and to develop a suitable multidimensional deprivation indexinSetiu Wetlands, Terengganu which are Besut, Setiu and Merang to reflect poverty disparity using factor analysis. In this study, factor analysis using principal component extraction method is applied to carry out factor reduction. Dimensionality of several observed variables data with similar patterns of responses will be reduced using factor analysis approach. As a result, a new measurement of poverty in Terengganu will be developed using the chosen factors. The analysis based on the new measurements could show the poverty incidence and disparity among people with relevant measures. This research is suitable for policymakers to refer and understand what local residents need and lack in order to design better plan for development in the areas.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Technique analysis in elite athletes using principal component analysis
    Gloersen, Oyvind
    Myklebust, Havard
    Hallen, Jostein
    Federolf, Peter
    JOURNAL OF SPORTS SCIENCES, 2018, 36 (02) : 229 - 237
  • [42] Analysis of an European union election using principal component analysis
    Rodrigues, Paulo C.
    Lima, Ana T.
    STATISTICAL PAPERS, 2009, 50 (04) : 895 - 904
  • [43] Reionization constraints using principal component analysis
    Mitra, Sourav
    Choudhury, T. Roy
    Ferrara, Andrea
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2011, 413 (03) : 1569 - 1580
  • [44] Principal component analysis using neural network
    Yang, Jian-Gang
    Sun, Bin-Qiang
    Journal of Zhejinag University: Science, 2002, 3 (03): : 298 - 304
  • [45] Spectrum Sensing using Principal Component Analysis
    Bhatti, Farrukh Aziz
    Rowe, Gerard B.
    Sowerby, Kevin W.
    2012 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2012,
  • [46] Classification of Wines Using Principal Component Analysis
    Barth, Jackson
    Katumullage, Duwani
    Yang, Chenyu
    Cao, Jing
    JOURNAL OF WINE ECONOMICS, 2021, 16 (01) : 56 - 67
  • [47] Wind forecasting using Principal Component Analysis
    Skittides, Christina
    Frueh, Wolf-Gerrit
    RENEWABLE ENERGY, 2014, 69 : 365 - 374
  • [48] Principal component analysis using LISREL 8
    Department of Developmental Psychology, Psychology Faculty, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, Netherlands
    Struct. Equ. Model., 4 (307-322):
  • [49] Penalized principal component analysis using smoothing
    Rebecca Hurwitz
    Georg Hahn
    Statistics and Computing, 2025, 35 (3)
  • [50] Principal component analysis using neural network
    Jian-gang Yang
    Bin-qiang Sun
    Journal of Zhejiang University-SCIENCE A, 2002, 3 (3): : 298 - 304