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SAMPLING DESIGN AND PROCEDURE

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Lecture 9 SAMPLING DESIGN AND PROCEDURE Population and Sample Population The entire group that the researcher wishes to investigate Element A single member of the ... – PowerPoint PPT presentation

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Title: SAMPLING DESIGN AND PROCEDURE


1
Lecture 9
  • SAMPLING DESIGN AND PROCEDURE

2
Population and Sample
  • Population
  • The entire group that the researcher wishes to
    investigate
  • Element
  • A single member of the population

3
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

4
CENSUS
  • INVESTIGATION OF ALL INDIVIDUAL ELEMENTS THAT
    MAKE UP A POPULATION

5
When Is A Census Appropriate?
Necessary
Feasible
6
TARGET POPULATION
  • RELEVANT POPULATION
  • OPERATIONALLY DEFINE
  • COMIC BOOK READER?

7
Why Sample?
Availability of elements
Lower cost
Sampling provides
Greater accuracy
8
SAMPLING FRAME
  • A LIST OF ELEMENTS FROM WHICH THE SAMPLE MAY BE
    DRAWN
  • WORKING POPULATION
  • MAILING LISTS - DATA BASE MARKETERS
  • SAMPLING FRAME ERROR

9
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

10
SAMPLING UNITS
  • GROUP SELECTED FOR THE SAMPLE
  • PRIMARY SAMPLING UNITS (PSU)
  • SECONDARY SAMPLING UNITS
  • TERTIARY SAMPLING UNITS

11
TWO MAJOR CATEGORIES OF SAMPLING
  • PROBABILITY SAMPLING
  • KNOWN, NONZERO PROBABLITY FOR EVERY ELEMENT
  • NONPROBABLITY SAMPLING
  • PROBABLITY OF SELECTING ANY PARTICULAR MEMBER IS
    UNKNOWN

12
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

13
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)

14
PROBABLITY SAMPLING
  • SIMPLE RANDOM SAMPLE
  • SYSTEMATIC SAMPLE
  • STRATIFIED SAMPLE
  • CLUSTER SAMPLE
  • MULTISTAGE AREA SAMPLE

15
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

16
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

17
SYSTEMATIC SAMPLING
  • A simple process
  • every nth name from the list will be drawn

18
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

19
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

20
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

21
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.

22
EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
Malaysian adult population States Districts
Metropolitan Statistical Area Census
tracts Blocks Households
23
EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
College seniors Colleges Manufacturing
firms Districts Metropolitan Statistical
Areas Localities Plants
24
EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
Airline travelers Airports Planes Sports
fans Football stadia Basketball
arenas Baseball parks
25
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

26
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

27
Double
  • Advantages
  • May reduce costs if first stage results in enough
    data to stratify or cluster the population
  • Disadvantages
  • Increased costs if discriminately used

28
Nonprobability Samples
No need to generalize
Feasibility
Limited objectives
Issues
Cost
29
Nonprobability Sampling Methods
Convenience
Judgment
Quota
Snowball
30
NONPROBABLITY SAMPLING
  • CONVENIENCE
  • JUDGMENT
  • QUOTA
  • SNOWBALL

31
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

32
CONVENIENCE SAMPLING
  • also called haphazard or accidental sampling
  • the sampling procedure of obtaining the people or
    units that are most conveniently available

33
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

34
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

35
SNOWBALL SAMPLING
  • a variety of procedures
  • initial respondents are selected by probability
    methods
  • additional respondents are obtained from
    information provided by the initial respondents

36
Area Sampling
37
Sample Size
  • Factors Determining Sample Size
  • Homogeneity of population
  • Level of confidence
  • Precision
  • Cost, Time and Resources

38
Larger Sample Sizes
Population variance
Number of subgroups
Desired precision
When
Small error range
39
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

40
WHAT IS THE APPROPRIATE SAMPLE DESIGN
  • DEGREE OF ACCURACY
  • RESOURCES
  • TIME
  • ADVANCED KNOWLEDGE OF THE POPULATION
  • NATIONAL VERSUS LOCAL
  • NEED FOR STATISTICAL ANALYSIS

41
What Is A Good Sample?
Precise
Accurate
42
AFTER THE SAMPLE DESIGN IS SELECTED
  • DETERMINE SAMPLE SIZE
  • SELECT ACTUAL SAMPLE UNITS
  • CONDUCT FIELDWORK

43
SYSTEMATIC ERRORS
  • NONSAMPLING ERRORS
  • UNREPRESENTATIVE SAMPLE RESULTS
  • NOT DUE TO CHANCE
  • DUE TO STUDY DESIGN OR IMPERFECTIONS IN EXECUTION

44
ERRORS ASSOCIATED WITH SAMPLING
  • SAMPLING FRAME ERROR
  • RANDOM SAMPLING ERROR
  • NONRESPONSE ERROR

45
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

46
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
47
End of lesson
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