Bài giảng 15b - Phân tích dữ liệu CVM pptx

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. TWTP V σ = 2       = δ ZV N α δ 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). C 0 is always zero and C J is largest in the sample  Let h j 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 ∑ += = J jk kj hn 1 N n CS j j =)( [ ] ∑ = + −= J j jjj CCCSC 0 1 )( Estimating mean and median WTP: non-parametric  Binary data  Total number of respondents is N  The sun-sample facing Bj is Nj.  The number of respondents saying “Yes” to amount Bj is nj.  Survivor function:  Mean WTP is j j j N n BS =)( [ ] ∑ = − −= J j jjj BBBSC 0 1 )( Estimating mean and median WTP: non-parametric  Binary data – increasing survivor function  Calculate  Beginning with the first bid level, compare S(Bj) with S(Bj+1)  If S(Bj+1) is less than or equal S(Bj), continue  If S(Bj+1) > S(Bj), pool the observations of the two bid levels and recalculate the survivor function:  Continue until survivor function is non-increasing  Mean WTP is j j j N n BS =)( [ ] ∑ = − −= J j jjj BBBSC 0 1 )( 1 1 )( + + + + = jj jj j NN nn BS Estimating mean and median WTP: non-parametric  Interval data: WTP lies in a range lower B L and upper B H  Example intervals – non-overlapping resp lower upper 1 0.5 1 2 0 0.5 3 1 4 4 4 10 5 1 4 [...]...Estimating mean and median WTP: non-parametric  Interval data:   Non-overlapping: use the lower bound and calculate as continuous data Overlapping: may occur in double-bounded dichotomous choice    Resp is offered an initial bid If yes, follow up with a higher amount If no, lower amount Estimating mean and median WTP: non-parametric  Interval data:  WTP will fall in ranges:... median WTP: non-parametric  Break overlapping intervals into basic ints  Starting from: 1 = S ( B0 ) ≥ S ( B1 ) ≥ S ( B2 ) ≥ ≥ S ( B j ) ≥ S ( B j +1 ) = 0  Using basic intervals only, the probability of lying in basic interval j from Bj-1 to Bj is: S ( B j −1 ) − S ( B j )  Consider overlapping interval of Bi to Bk that spans the basic interval j Estimating mean and median WTP: non-parametric ...   Income Socio-economic characteristics Attitudinal variables Attitude toward CV program design Knowledge on the good provided Proximity to the site of provision Testing validity of WTP values  To test:  Regress WTP on variables  Test for significance of coefficients (t-test can be used)  Examine the sign of coefficients Are they consistent with economic theory?  Look at pseudo-R2 Should not... ( Bi ) − S ( Bk )  Multiply this probability by number of resp whose WTP is in interval Bi to Bk to obtain estimated number of resp falling in the basic interval j Estimating mean and median WTP: non-parametric  Break overlapping intervals into basic ints   Continue the process for all overlapping, we obtain the survivor function for basic intervals only Then estimate WTP:    Total number of . Bài giảng 15b: Phân tích dữ liệu CVM Trương Đăng Thụy Sampling techniques  Non-Probabilistic  Convenient sample: asembles sample at. B H  Example intervals – non-overlapping resp lower upper 1 0.5 1 2 0 0.5 3 1 4 4 4 10 5 1 4 Estimating mean and median WTP: non-parametric  Interval data:  Non-overlapping: use the lower. 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