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Bài giảng 15b - Phân tích dữ liệu CVM

Tham khảo tài liệu 'bài giảng 15b - phân tích dữ liệu cvm', công nghệ thông tin, cơ sở dữ liệu phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Bài giảng 15b: Phân tích dữ liệu CVM Trương Đăng Thụy Sampling techniques Non-Probabilistic Convenient sample: asembles sample at the convenience of researcher Judgement sample: a panel of respondents judged to be representative of the target population is assembled. Quota sample: Selection is controlled by interviewer, ensuring that sample contain given proportion of various types of respondents. Sampling techniques Probabilistic Simple random sampling: every respondents in the sample frame has the same chance of being selected. Systematic sampling: select every kth respondent from a randomly-ordered population frame. Stratified sampling: sampling frame is divided into sub-populations (strata), using random sampling for each stratum. Clustered sampling: population is divided into a set of groups (clusters), and clusters are randomly selected. All elements in the chosen clusters will be included. Multi-stage sampling: random sample of elements within the randomly-chosen clusters. . | Bài giảng 15b: Phân tích dữ liệu CVM Trương Đăng Thụy Sampling techniques Non-Probabilistic Convenient sample: asembles sample at the convenience of researcher Judgement sample: a panel of respondents judged to be representative of the target population is assembled. Quota sample: Selection is controlled by interviewer, ensuring that sample contain given proportion of various types of respondents. Sampling techniques Probabilistic Simple random sampling: every respondents in the sample frame has the same chance of being selected. Systematic sampling: select every kth respondent from a randomly-ordered population frame. Stratified sampling: sampling frame is divided into sub-populations (strata), using random sampling for each stratum. Clustered sampling: population is divided into a set of groups (clusters), and clusters are randomly selected. All elements in the chosen clusters will be included. Multi-stage sampling: random sample of elements within the randomly-chosen clusters. Sample size Coefficient of variation: Necessary sample size: If V=1, =.05 (for Z=1.96), =.1. Then sample size must be 385. In this session Data of WTP Estimating mean and median WTP Non-parametric Parametric Testing validity of WTP values Exercise Data of WTP Three types of CV data: Continuous data (results from open-ended or bidding game questions) Binary data (response “yes” or “no” to a bid level) Interval data (payment card or double-bounded choice) Estimating mean and median WTP: non-parametric Continuous data Imagine a dataset of max WTP of HH/ind Total number of HH is N There are J diferent values of WTP. J might be smaller than N for there could be several HH/ind reporting the same WTP Order the values of WTP Cj from lowest to highest (J=0,J). C0 is always zero and CJ is largest in the sample Let hj is the number of HH/ind in the sample with WTP of Cj Total number of HH/ind with a WTP greater than Cj will be The survivor function is Mean WTP is Estimating mean and median WTP: .