Arezoo Bagheri ¹, Mahsa Saadati ¹

Affiliations
1 National Population Studies and Comprehensive Management Institute, Tehran, IR

Abstract

Respondent driven sampling as a chain referral approach has been utilized for finding a sample of people from a hidden or a hard to reach population by following social links from sample individuals to detect additional members of the target population to form final sample. This sampling method has been applied successfully in several countries for sampling in networks such as injecting drug users, female sex workers, men who have sex with men and immigrants. The aim of this article is to explore the problems involved in conducting research with hidden populations and to suggest two chain referral strategies to recruit sample participants. Finally, the merit of respondent driven sampling comparing to the most applicable chain referral sampling method, snowball sampling, is discussed.

Keywords

Chain Referral Sampling, Conventional Sampling, Hard to Reach Population, Respondent Driven Sampling, Snowball Sampling

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Mahsa Saadati ¹, Arezoo Bagheri ¹

Affiliations
1 National Population and Comprehensive Management Institute, Tehran, IR

Abstract

Classification is an important problem in many fields of science. In this problem the goal is classification of categorical variable as a response based on any input variables as covariates. For years Logistic regression and discriminant analysis were used as statistical methods for classification. Nowadays by developing data base storage and computer programming some new methods such as classification trees are applied to classify data. The aim of this article is introducing CART algorithm as a classification tree rule to analyze demographic data. CARTs generate binary decision tree and have simple interpretations. We use CART to classify children ever born as an important phenomenon in demographic research.

Keywords

Children Ever Born, Chi-Square Test, Classification Tree, CART, Data Mining, Gini Index.

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