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For-Hire SurveyFor-Hire Survey

Survey Design RecommendationsSurvey Design Recommendations

Presented by Jim ChromyPresented by Jim Chromy

jrc@rti.orgjrc@rti.org

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NRC 2006 “For-Hire” ConcernsNRC 2006 “For-Hire” Concerns

More like commercial sectorMore like commercial sector Estimation does not recognize designEstimation does not recognize design Physical, financial, and operational Physical, financial, and operational

constraint biases constraint biases Fish caught and not brought to dockFish caught and not brought to dock Cover small and private landing pointsCover small and private landing points Dual frame to reduce bias: logbooksDual frame to reduce bias: logbooks

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ThemesThemes

Survey design is often intuitive.Survey design is often intuitive. Theoretically sound design depends on Theoretically sound design depends on

specific procedures for sampling and specific procedures for sampling and estimationestimation

Many acceptable solutionsMany acceptable solutions None will be perfectNone will be perfect

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TopicsTopics General survey vs. fisheries survey termsGeneral survey vs. fisheries survey terms Probability sampling procedures at all stagesProbability sampling procedures at all stages Sample size to meet analytic needsSample size to meet analytic needs Sample allocation to control sampling errorSample allocation to control sampling error Estimation based on sample design, Estimation based on sample design,

including appropriate weighting.including appropriate weighting. Coverage and response issuesCoverage and response issues

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Before SamplingBefore Sampling

Conceptual populationConceptual population– Points of departure or area fishedPoints of departure or area fished– VesselsVessels– AnglersAnglers– CatchCatch

Conceptual domainsConceptual domains– RegionRegion– Catch speciesCatch species– Time periodsTime periods

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Sampling FramesSampling Frames

Try to cover conceptual populationTry to cover conceptual population List of labels and rulesList of labels and rules

– Labels are unique and of finite numberLabels are unique and of finite number– Rules are links to actual population elements—Rules are links to actual population elements—

e.g., names and contact information for vesselse.g., names and contact information for vessels Labels can be selected using probability Labels can be selected using probability

sampling.sampling. Rules permit identification of the sample.Rules permit identification of the sample.

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Frame ExamplesFrame Examples

Directory of for-hire vessels operating from NC Directory of for-hire vessels operating from NC coast during a specified periodcoast during a specified period

List of for-hire fishing trips returning to a single List of for-hire fishing trips returning to a single landing during a specified time periodlanding during a specified time period

List of anglers participating in a vessel trip; stringer List of anglers participating in a vessel trip; stringer tags plus list of unsuccessful anglerstags plus list of unsuccessful anglers

Order number for fish landed by an angler: could Order number for fish landed by an angler: could be ordered by size be ordered by size

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Frame StructureFrame Structure

Simplest: listSimplest: list– Example: for-hire vessel directory for NCExample: for-hire vessel directory for NC– Used for telephone survey componentUsed for telephone survey component

Multi-stage or nested listsMulti-stage or nested lists– Landing area by time periodLanding area by time period– Vessel trips ending in aboveVessel trips ending in above– Anglers aboard a vessel tripAnglers aboard a vessel trip– Fish landed by an anglerFish landed by an angler

Crossed frames: spatial vs. temporalCrossed frames: spatial vs. temporal

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Temporal Frame StructureTemporal Frame Structure

YearYear Month: 1, 2,…,12Month: 1, 2,…,12 Week ending on Sunday: 1, 2,…,52 Week ending on Sunday: 1, 2,…,52 Kind of day:1=weekend, 2=weekdayKind of day:1=weekend, 2=weekday Day: (Sat, Sun) (M, T, W, Th, F)Day: (Sat, Sun) (M, T, W, Th, F) Hourly periods including night time:(Hourly periods including night time:(

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Sample SizeSample Size

Must be adequate to meet analytic needsMust be adequate to meet analytic needs No 10 or 20 percent sampling unless those No 10 or 20 percent sampling unless those

rates are justified by need and adequacy to rates are justified by need and adequacy to meet that need.meet that need.

May be limited by budgetMay be limited by budget

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Stratification of Frames (1)Stratification of Frames (1)

For administrative controlFor administrative control For workload distributionFor workload distribution For analytic purposes—match domainsFor analytic purposes—match domains To allow different sampling ratesTo allow different sampling rates To identify certain exclusions—reduce To identify certain exclusions—reduce

coverage in a controlled mannercoverage in a controlled manner

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Stratification of Frames (2)Stratification of Frames (2)

To increase efficiency, reduce sampling To increase efficiency, reduce sampling error and control costs!error and control costs!

To permit different sampling and data To permit different sampling and data collection methods by strata: e.g., dockside collection methods by strata: e.g., dockside vs. at-sea.vs. at-sea.

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Example: For-Hire DirectoryExample: For-Hire Directory

StateState Region within stateRegion within state Headboat vs. charterHeadboat vs. charter Capacity in anglersCapacity in anglers Active status for survey period: e.g., active, Active status for survey period: e.g., active,

verified as inactive, not sure.verified as inactive, not sure. Need to know number of vessels in each Need to know number of vessels in each

stratum and their labels.stratum and their labels.

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Example: Vessel TripsExample: Vessel Trips

Landing areaLanding area Time period of landingTime period of landing Order of landingOrder of landing Vessel capacityVessel capacity Need to know number of vessel trips in each Need to know number of vessel trips in each

stratum and their labelsstratum and their labels Label could be order of landing during Label could be order of landing during

specified time periodspecified time period

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Example: AnglersExample: Anglers

May take all on small vessels: each angler May take all on small vessels: each angler selected with probability 1.0selected with probability 1.0

Large vessels intercepted at dock (sample Large vessels intercepted at dock (sample size may be determined by time available)size may be determined by time available)

At-sea observation on large vesselsAt-sea observation on large vessels

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Intercepted At DockIntercepted At Dock

Frame problem—list or order of departureFrame problem—list or order of departure If time permits, pre-identify some anglers for If time permits, pre-identify some anglers for

sampling with probability 1.00 (based on sampling with probability 1.00 (based on species caught, size, or other factors)species caught, size, or other factors)

Sample remainder at lower rate or ratesSample remainder at lower rate or rates Include all anglers in an assigned stratumInclude all anglers in an assigned stratum For each stratum, know N, n, and probability For each stratum, know N, n, and probability

of selection (n/N).of selection (n/N).

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At-Sea At-Sea

Sampling for discard observationSampling for discard observation Frame in time and location on vesselFrame in time and location on vessel

– Mark locations (areas along rail) and sample by Mark locations (areas along rail) and sample by time period once fishing begins. Observe and time period once fishing begins. Observe and record all discards.record all discards.

Sampling for retained catch at completion of Sampling for retained catch at completion of fishingfishing– Similar to intercept problemSimilar to intercept problem– More time to obtain dataMore time to obtain data

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Stratified Sample of Angler’s Landed Stratified Sample of Angler’s Landed CatchCatch

Classify fish into groups/strataClassify fish into groups/strata– Rare speciesRare species– SizeSize

Record number of fish in each stratumRecord number of fish in each stratum Select probability sample by stratumSelect probability sample by stratum For each stratum, record N, n, and sampling For each stratum, record N, n, and sampling

rate (n/N)rate (n/N) Simplest case: “take all”Simplest case: “take all”

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Probability Sampling MethodsProbability Sampling Methods

Simple Random SamplingSimple Random Sampling Systematic Random SamplingSystematic Random Sampling PPS SamplingPPS Sampling All can be applied within strataAll can be applied within strata All can be applied at various stages of All can be applied at various stages of

samplingsampling

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EstimationEstimation

General topic for another teamGeneral topic for another team Must be based on designMust be based on design General form: weight inversely to selection General form: weight inversely to selection

probabilityprobability Weights may be adjusted for nonresponse Weights may be adjusted for nonresponse

or undercoverageor undercoverage

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Selecting a Simple Random SampleSelecting a Simple Random Sample

All samples of size n have equal probabilityAll samples of size n have equal probability Each unit is selected with probability n/NEach unit is selected with probability n/N Estimation weight: W=N/n.Estimation weight: W=N/n. Random permutation is easy to apply: Random permutation is easy to apply:

currently used for telephone survey samplescurrently used for telephone survey samples

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Random Permutation ExampleRandom Permutation Example

3 12 10 17 614 2 5 15 1618 21 4 9 24

8 13 7 23 191 22 25 20 11

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Simple Random Sample of 7 of 25Simple Random Sample of 7 of 25

3 12 10 17 614 2 5 15 1618 21 4 9 24

8 13 7 23 191 22 25 20 11

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Random Sample of 7 out of 12Random Sample of 7 out of 12

3 12 10 1717 614 2 5 15 1618 21 4 9 24

8 13 7 23 191 22 25 20 11

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Systematic Random SamplingSystematic Random Sampling

Select a random number between 1 and k Select a random number between 1 and k for first sample labelfor first sample label

Then select every k-th label to end of listThen select every k-th label to end of list Probability of selection is 1/k. W=k.Probability of selection is 1/k. W=k. Nice if N=nk.Nice if N=nk. Alternatives for non-integer k, i.e. k=N/n.Alternatives for non-integer k, i.e. k=N/n.

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PPS SamplingPPS Sampling

Many acceptable methodsMany acceptable methods SAS/STAT Proc SurveyselectSAS/STAT Proc Surveyselect

– Several methods availableSeveral methods available– My favorites: Method=ChromyMy favorites: Method=Chromy

Output provides probability of selection, POutput provides probability of selection, P Weight = 1/P.Weight = 1/P.

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Sample AllocationSample Allocation

Achieved through stratification and sample Achieved through stratification and sample allocationallocation

Can also be achieved through PPS Can also be achieved through PPS sampling. sampling.

Improve precisionImprove precision Control costsControl costs Fishing pressure is a natural size measure Fishing pressure is a natural size measure

or basis for sample allocation.or basis for sample allocation.

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Form of EstimatesForm of Estimates

Total effortTotal effort

Average CPUEAverage CPUE

n

iiiYWE

1

n

ii

n

iii

W

XWCPUE

1

1

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Nonresponse AdjustmentsNonresponse Adjustments

Weight adjustment for unit level Weight adjustment for unit level nonresponsenonresponse

Imputation for partial nonresponseImputation for partial nonresponse

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PoststratificationPoststratification

Ratio-type adjustments to incorporate Ratio-type adjustments to incorporate known (or better) data for related statisticsknown (or better) data for related statistics

Can help adjust for undercoverageCan help adjust for undercoverage Basis for adjustment should be justified and Basis for adjustment should be justified and

re-evaluated on a regular basis.re-evaluated on a regular basis. Can also adjust for unusual sample Can also adjust for unusual sample

outcome.outcome. After sampling stratification and adjusted After sampling stratification and adjusted

estimationestimation

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Double SamplingDouble Sampling

Technique for adjusting biased estimates Technique for adjusting biased estimates perhaps based on low cost approachperhaps based on low cost approach

Uses smaller (high cost) sample to fine-Uses smaller (high cost) sample to fine-tune.tune.

Example: 100 percent logbook data could Example: 100 percent logbook data could be adjusted based on dockside or at-sea be adjusted based on dockside or at-sea samples for a sample of vessel-trips.samples for a sample of vessel-trips.

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Many Techniques AvailableMany Techniques Available

Ultimate approach will be a mix of methodsUltimate approach will be a mix of methods Tough problems remain.Tough problems remain. Continuous improvement plant should Continuous improvement plant should

begin.begin.

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Thank You Thank You