1. 1 For-Hire Survey Survey Design Recommendations Presented by Jim Chromy jrc@rti.org

2. 2 NRC 2006 “For-Hire” Concerns  More like commercial sector  Estimation does not recognize design  Physical, financial, and operational constraint biases  Fish caught and not brought to dock  Cover small and private landing points  Dual frame to reduce bias: logbooks

3. 3 Themes  Survey design is often intuitive.  Theoretically sound design depends on specific procedures for sampling and estimation  Many acceptable solutions  None will be perfect

4. 4 Topics  General survey vs. fisheries survey terms  Probability sampling procedures at all stages  Sample size to meet analytic needs  Sample allocation to control sampling error  Estimation based on sample design, including appropriate weighting.  Coverage and response issues

5. 5 Before Sampling  Conceptual population –Points of departure or area fished –Vessels –Anglers –Catch  Conceptual domains –Region –Catch species –Time periods

6. 6 Sampling Frames  Try to cover conceptual population  List of labels and rules –Labels are unique and of finite number –Rules are links to actual population elements— e.g., names and contact information for vessels  Labels can be selected using probability sampling.  Rules permit identification of the sample.

7. 7 Frame Examples  Directory of for-hire vessels operating from NC coast during a specified period  List of for-hire fishing trips returning to a single landing during a specified time period  List of anglers participating in a vessel trip; stringer tags plus list of unsuccessful anglers  Order number for fish landed by an angler: could be ordered by size

8. 8 Frame Structure  Simplest: list –Example: for-hire vessel directory for NC –Used for telephone survey component  Multi-stage or nested lists –Landing area by time period –Vessel trips ending in above –Anglers aboard a vessel trip –Fish landed by an angler  Crossed frames: spatial vs. temporal

9. 9 Temporal Frame Structure  Year  Month: 1, 2,…,12  Week ending on Sunday: 1, 2,…,52  Kind of day:1=weekend, 2=weekday  Day: (Sat, Sun) (M, T, W, Th, F)  Hourly periods including night time:(

10. 10 Sample Size  Must be adequate to meet analytic needs  No 10 or 20 percent sampling unless those rates are justified by need and adequacy to meet that need.  May be limited by budget

11. 11 Stratification of Frames (1)  For administrative control  For workload distribution  For analytic purposes—match domains  To allow different sampling rates  To identify certain exclusions—reduce coverage in a controlled manner

12. 12 Stratification of Frames (2)  To increase efficiency, reduce sampling error and control costs!  To permit different sampling and data collection methods by strata: e.g., dockside vs. at-sea.

13. 13 Example: For-Hire Directory  State  Region within state  Headboat vs. charter  Capacity in anglers  Active status for survey period: e.g., active, verified as inactive, not sure.  Need to know number of vessels in each stratum and their labels.

14. 14 Example: Vessel Trips  Landing area  Time period of landing  Order of landing  Vessel capacity  Need to know number of vessel trips in each stratum and their labels  Label could be order of landing during specified time period

15. 15 Example: Anglers  May take all on small vessels: each angler selected with probability 1.0  Large vessels intercepted at dock (sample size may be determined by time available)  At-sea observation on large vessels

16. 16 Intercepted At Dock  Frame problem—list or order of departure  If time permits, pre-identify some anglers for sampling with probability 1.00 (based on species caught, size, or other factors)  Sample remainder at lower rate or rates  Include all anglers in an assigned stratum  For each stratum, know N, n, and probability of selection (n/N).

17. 17 At-Sea  Sampling for discard observation  Frame in time and location on vessel –Mark locations (areas along rail) and sample by time period once fishing begins. Observe and record all discards.  Sampling for retained catch at completion of fishing –Similar to intercept problem –More time to obtain data

18. 18 Stratified Sample of Angler’s Landed Catch  Classify fish into groups/strata –Rare species –Size  Record number of fish in each stratum  Select probability sample by stratum  For each stratum, record N, n, and sampling rate (n/N)  Simplest case: “take all”

19. 19 Probability Sampling Methods  Simple Random Sampling  Systematic Random Sampling  PPS Sampling  All can be applied within strata  All can be applied at various stages of sampling

20. 20 Estimation  General topic for another team  Must be based on design  General form: weight inversely to selection probability  Weights may be adjusted for nonresponse or undercoverage

21. 21 Selecting a Simple Random Sample  All samples of size n have equal probability  Each unit is selected with probability n/N  Estimation weight: W=N/n.  Random permutation is easy to apply: currently used for telephone survey samples

22. 22 Random Permutation Example 31210176 14251516 18214924 81372319 122252011

23. 23 Simple Random Sample of 7 of 25 31210176 14251516 18214924 81372319 122252011

24. 24 Random Sample of 7 out of 12 31210176 14251516 18214924 81372319 122252011

25. 25 Systematic Random Sampling  Select a random number between 1 and k for first sample label  Then select every k-th label to end of list  Probability of selection is 1/k. W=k.  Nice if N=nk.  Alternatives for non-integer k, i.e. k=N/n.

26. 26 PPS Sampling  Many acceptable methods  SAS/STAT Proc Surveyselect –Several methods available –My favorites: Method=Chromy  Output provides probability of selection, P  Weight = 1/P.

27. 27 Sample Allocation  Achieved through stratification and sample allocation  Can also be achieved through PPS sampling.  Improve precision  Control costs  Fishing pressure is a natural size measure or basis for sample allocation.

28. 28 Form of Estimates  Total effort  Average CPUE

29. 29 Nonresponse Adjustments  Weight adjustment for unit level nonresponse  Imputation for partial nonresponse

30. 30 Poststratification  Ratio-type adjustments to incorporate known (or better) data for related statistics  Can help adjust for undercoverage  Basis for adjustment should be justified and re-evaluated on a regular basis.  Can also adjust for unusual sample outcome.  After sampling stratification and adjusted estimation

31. 31 Double Sampling  Technique for adjusting biased estimates perhaps based on low cost approach  Uses smaller (high cost) sample to fine- tune.  Example: 100 percent logbook data could be adjusted based on dockside or at-sea samples for a sample of vessel-trips.

32. 32 Many Techniques Available  Ultimate approach will be a mix of methods  Tough problems remain.  Continuous improvement plant should begin.

33. 33 Thank You