SAMPLING DESIGN AND PROCEDURE

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Lecture 9

• SAMPLING DESIGN AND PROCEDURE

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Population and Sample

• Population
• The entire group that the researcher wishes to
  investigate
• Element
• A single member of the population

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Population and Sample

• Population (Sampling) Frame
• A listing of all the elements in the population
  from which the sample is drawn
• Sample
• A subset of the population
• Subject
• A single member of the sample

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CENSUS

• INVESTIGATION OF ALL INDIVIDUAL ELEMENTS THAT
  MAKE UP A POPULATION

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When Is A Census Appropriate?
Necessary
Feasible
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TARGET POPULATION

• RELEVANT POPULATION
• OPERATIONALLY DEFINE
• COMIC BOOK READER?

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Why Sample?
Availability of elements
Lower cost
Sampling provides
Greater accuracy
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SAMPLING FRAME

• A LIST OF ELEMENTS FROM WHICH THE SAMPLE MAY BE
  DRAWN
• WORKING POPULATION
• MAILING LISTS - DATA BASE MARKETERS
• SAMPLING FRAME ERROR

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Sampling

• Process of selecting a sufficient number of
  elements from the population
• Reasons for Sampling practicality (time and
  resources), destructive sampling
• Need for a representative sample

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SAMPLING UNITS

• GROUP SELECTED FOR THE SAMPLE
• PRIMARY SAMPLING UNITS (PSU)
• SECONDARY SAMPLING UNITS
• TERTIARY SAMPLING UNITS

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TWO MAJOR CATEGORIES OF SAMPLING

• PROBABILITY SAMPLING
• KNOWN, NONZERO PROBABLITY FOR EVERY ELEMENT
• NONPROBABLITY SAMPLING
• PROBABLITY OF SELECTING ANY PARTICULAR MEMBER IS
  UNKNOWN

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Probability and NonprobabilitySampling

• Probability Sampling
• Elements in the population have known chance of
  being chosen
• Used when the representativeness of the sample is
  of importance
• Nonprobability Sampling
• The elements do not have a known or predetermined
  chance of being selected as subjects

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Probability Sampling

• Unrestricted/Simple Random Sampling
• Every element in the population has a known and
  equal chance of being selected as a subject
• Has the least bias and offers the most
  generalizability
• Restricted/Complex Probability Sampling
• Systematic Sampling
• Stratified Random Sampling
• Cluster Sampling (USM, UM, etc)
• Area Sampling
• Double Sampling (USM and then grad students)

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PROBABLITY SAMPLING

• SIMPLE RANDOM SAMPLE
• SYSTEMATIC SAMPLE
• STRATIFIED SAMPLE
• CLUSTER SAMPLE
• MULTISTAGE AREA SAMPLE

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SIMPLE RANDOM SAMPLING

• a sampling procedure that ensures that each
  element in the population will have an equal
  chance of being included in the sample

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Simple Random

• Advantages
• Easy to implement with random dialing

• Disadvantages
• Requires list of population elements
• Time consuming
• Uses larger sample sizes
• Produces larger errors
• High cost

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SYSTEMATIC SAMPLING

• A simple process
• every nth name from the list will be drawn

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Systematic

• Advantages
• Simple to design
• Easier than simple random
• Easy to determine sampling distribution of mean
  or proportion

• Disadvantages
• Periodicity within population may skew sample and
  results
• Trends in list may bias results
• Moderate cost

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STRATIFIED SAMPLING

• Probability sample
• Subsamples are drawn within different strata
• Each stratum is more or less equal on some
  characteristic
• Do not confuse with quota sample

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Stratified

• Advantages
• Control of sample size in strata
• Increased statistical efficiency
• Provides data to represent and analyze subgroups
• Enables use of different methods in strata

• Disadvantages
• Increased error will result if subgroups are
  selected at different rates
• Especially expensive if strata on population must
  be created
• High cost

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CLUSTER SAMPLING

• The purpose of cluster sampling is to sample
  economically while retaining the characteristics
  of a probability sample.
• The primary sampling unit is no longer the
  individual element in the population.
• The primary sampling unit is a larger cluster of
  elements located in proximity to one another.

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EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
Malaysian adult population States Districts
Metropolitan Statistical Area Census
tracts Blocks Households
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EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
College seniors Colleges Manufacturing
firms Districts Metropolitan Statistical
Areas Localities Plants
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EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
Airline travelers Airports Planes Sports
fans Football stadia Basketball
arenas Baseball parks
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Cluster

• Advantages
• Provides an unbiased estimate of population
  parameters if properly done
• Economically more efficient than simple random
• Lowest cost per sample
• Easy to do without list

• Disadvantages
• Often lower statistical efficiency due to
  subgroups being homogeneous rather than
  heterogeneous
• Moderate cost

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Stratified and Cluster Sampling

• Stratified
• Population divided into few subgroups
• Homogeneity within subgroups
• Heterogeneity between subgroups
• Choice of elements from within each subgroup

• Cluster
• Population divided into many subgroups
• Heterogeneity within subgroups
• Homogeneity between subgroups
• Random choice of subgroups

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Double

• Advantages
• May reduce costs if first stage results in enough
  data to stratify or cluster the population

• Disadvantages
• Increased costs if discriminately used

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Nonprobability Samples
No need to generalize
Feasibility
Limited objectives
Issues
Cost
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Nonprobability Sampling Methods
Convenience
Judgment
Quota
Snowball
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NONPROBABLITY SAMPLING

• CONVENIENCE
• JUDGMENT
• QUOTA
• SNOWBALL

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Nonprobability Sampling

• Convenience Sampling
• Based on availability, e.g. students in a
  classroom
• Purposive Sampling
• Specific targets, because they posses the desired
  info
• Judgement sampling
• Quota sampling

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CONVENIENCE SAMPLING

• also called haphazard or accidental sampling
• the sampling procedure of obtaining the people or
  units that are most conveniently available

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QUOTA SAMPLING

• ensures that the various subgroups in a
  population are represented on pertinent sample
  characteristics
• to the exact extent that the investigators desire
• it should not be confused with stratified sampling

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JUDGMENT SAMPLING

• also called purposive sampling
• an experienced individual selects the sample
  based on his or her judgment about some
  appropriate characteristics required of the
  sample member

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SNOWBALL SAMPLING

• a variety of procedures
• initial respondents are selected by probability
  methods
• additional respondents are obtained from
  information provided by the initial respondents

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Area Sampling
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Sample Size

• Factors Determining Sample Size
• Homogeneity of population
• Level of confidence
• Precision
• Cost, Time and Resources

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Larger Sample Sizes
Population variance
Number of subgroups
Desired precision
When
Small error range
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Roscoes Rule of Thumb

• gt30 and lt500 appropriate for most research
• Not less than 30 for each sub-sample
• In multivariate analysis, 10 times or more the
  number of variables
• Simple experiment with tight controls, 10-20
  quite sufficient

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WHAT IS THE APPROPRIATE SAMPLE DESIGN

• DEGREE OF ACCURACY
• RESOURCES
• TIME
• ADVANCED KNOWLEDGE OF THE POPULATION
• NATIONAL VERSUS LOCAL
• NEED FOR STATISTICAL ANALYSIS

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What Is A Good Sample?
Precise
Accurate
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AFTER THE SAMPLE DESIGN IS SELECTED

• DETERMINE SAMPLE SIZE
• SELECT ACTUAL SAMPLE UNITS
• CONDUCT FIELDWORK

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SYSTEMATIC ERRORS

• NONSAMPLING ERRORS
• UNREPRESENTATIVE SAMPLE RESULTS
• NOT DUE TO CHANCE
• DUE TO STUDY DESIGN OR IMPERFECTIONS IN EXECUTION

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ERRORS ASSOCIATED WITH SAMPLING

• SAMPLING FRAME ERROR
• RANDOM SAMPLING ERROR
• NONRESPONSE ERROR

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RANDOM SAMPLING ERROR

• THE DIFFERENCE BETWEEN THE SAMPLE RESULTS AND THE
  RESULT OF A CENSUS CONDUCTED USING IDENTICAL
  PROCEDURES
• STATISTICAL FLUCTUATION DUE TO CHANCE VARIATIONS

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Stages in the Selection of a Sample
Define the target population
Select a sampling frame
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure for selecting sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork
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End of lesson