for hire survey l.
Skip this Video
Loading SlideShow in 5 Seconds..
For-Hire Survey PowerPoint Presentation
Download Presentation
For-Hire Survey

Loading in 2 Seconds...

play fullscreen
1 / 33

For-Hire Survey - PowerPoint PPT Presentation

  • Uploaded on

For-Hire Survey Survey Design Recommendations Presented by Jim Chromy 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

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'For-Hire Survey' - sherlock_clovis

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
for hire survey

For-Hire Survey

Survey Design Recommendations

Presented by Jim Chromy

nrc 2006 for hire concerns
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
  • Survey design is often intuitive.
  • Theoretically sound design depends on specific procedures for sampling and estimation
  • Many acceptable solutions
  • None will be perfect
  • 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
before sampling
Before Sampling
  • Conceptual population
    • Points of departure or area fished
    • Vessels
    • Anglers
    • Catch
  • Conceptual domains
    • Region
    • Catch species
    • Time periods
sampling frames
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.
frame examples
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
frame structure
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
temporal frame structure
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:(
sample size
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
stratification of frames 1
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
stratification of frames 2
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.
example for hire directory
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.
example vessel trips
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
example anglers
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
intercepted at dock
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).
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
stratified sample of angler s landed catch
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”
probability sampling methods
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
  • 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
selecting a simple random sample
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
systematic random sampling
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.
pps sampling
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.
sample allocation
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.
form of estimates
Form of Estimates

Total effort

Average CPUE

nonresponse adjustments
Nonresponse Adjustments
  • Weight adjustment for unit level nonresponse
  • Imputation for partial nonresponse
  • 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
double sampling
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.
many techniques available
Many Techniques Available
  • Ultimate approach will be a mix of methods
  • Tough problems remain.
  • Continuous improvement plant should begin.