Survey sampling Sampling Theory Concepts by wmo26898

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									                     ADMS 3352 3.0 Sampling Technique and Survey Studies




Survey sampling
Many of our behaviours and action are based on samples (could be of size one),
for incidence, our like or dislike of a foreign dish. Would such a sample be
representative of the whole population? How many should we try /samples to
be drawn?

Sample design determines the precision of estimates. It consists of both a sample
selection plan and an estimation procedure.

Population:
      The entire set of persons, objects, or events, which the researcher intends
      to study.
      Must specify the inclusion and exclusion criteria that define a pop’s
      characteristics

Sample: A subset of the pop, serves as the ref group for drawing inference about pop
     e.g. for quality control: a sample of items from the entire inventory
           In a survey: a sample of households

Sampling: involves the selection of the sample from the population
      A good sample reflects the relevant characteristics and variations of the
      population
      No guarantee that a sample represents the pop
      Probability sampling procedures minimize bias and error in choosing a
      representative sample

Sampling Theory Concepts

population

Target Population:
      The universe of interest, or reference population e.g. all learning disabled

Accessible/experimental Population
      A population very close to the target population and can be assessed e.g.
      learning-disabled in a given city’s school system
      Validity of the accessible pop is not readily testable, require good
      judgement and expertise

Elements of a Population
     Individual units of a population
     When elements are persons, they are referred to as subjects



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                   ADMS 3352 3.0 Sampling Technique and Survey Studies



Sampling Criteria
     Characteristics essential for membership in the target population

Representativeness
     An objective plan of selection, minimize bias
     Drop out
     Non-response

Sampling Error
  Discrepancy between the true population parameter and the sample statistic.

   •   Random Variation
         Differences are due to chance, not human bias

   •   Systematic Variation
          Sampling bias occurs when individuals selected for a sample over-
          represent or under-represent the population attributes that are related
          to the phenomenon under study, e.g. random sampling at the corner of
          a street (unconscious bias: haphazard sample)

Randomization
  • Obtain samples to represent the population
  • Permits valid generalization of the findings of an investigation to
        the population (external validity : population validity) or
        other situations/settings (external validity : ecological validity)
  • Random sample affords the greatest possible confidence in the sample’s
     validity because in the long run, it will produce samples that most
     accurately reflect the population’s characteristics


Sampling Frame
     A listing of all members in the target (accessible) pop
     Subjects are selected from the sampling frame using a sampling plan
     Accessible population is usually defined according to available listing(s)

Sampling Plans
     Define the process / strategies of making a sample selection




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                    ADMS 3352 3.0 Sampling Technique and Survey Studies




Sampling techniques

Probability sampling methods
Samples are created through a process of random selection
Every element has a chance to be selected
The sample is considered representative of the population
Provides a mechanism to estimate sampling distribution and error

Nonprobability sampling methods
Degree of sampling error cannot be estimated


Probability (Random) Sampling Methods / Schemes

•   Simple random sampling
    Sampling without replacement
    Each selection is independent
    Each possible sample of a specified size of the population has equal chance of
    being selected
    The accessible pop is organized as a finite, pre-numbered list
    Blind draw, use of dice, random numbers

•   Systematic sampling
    1. Divide the total number of elements in the accessible pop by the number
       of elements to be selected: sampling interval (n)
    2. Determine a starting point on the list at random
    3. Now pick every nth element on the list from this starting point
       Considered equivalent to random sampling, as long as no recurring
       pattern or particular order exists in the listing

•   Stratified random sampling
    Identify relevant population characteristics, then
    Partition members of a population into homogeneous non-overlapping strata
    (subsets) based on the identified characteristics
    Random or systematic samples are then drawn from each stratum
    Proportional stratified samples could be drawn to reflect pop composition
    Stratification increases the precision of estimates only when the stratification
    variable is closely related to the variables of experimental / study interest




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                    ADMS 3352 3.0 Sampling Technique and Survey Studies



•   Disproportional sampling
    Select random samples of adequate size from each category (for comparison)
    This may lead to over-representation of the characteristics of one group
    (stratum) in the pop
    Control this error by calculating proportional weights for strata

•   Cluster sampling
    If it is impractical or impossible to obtain a complete listing of a large
    dispersed pop, then use cluster / multi-stage sampling
    For example, a random selection of province; within selected province,
    random selection of hospitals; within each selected hospital, a random
    selection of therapists
    Advantages: convenience and efficiency (time-wise)
    Price paid: increased sampling error because of the number of samples
    drawn, each subjected to error

    Examples used in survey:
    Area probability sampling (sampled geographically-> districts->households)
    Random-digit dialing (sample area-code->telephone exchanges);
       bias: can only reach those with phones; timing of calls


Nonprobability (Nonrandom) Sampling Methods
  q Generalization of data collected from nonrandom samples must be made
     with caution

    q   Keppel suggests that researchers can distinguish between
          o statistical (require random sampling and based on the validity of
             representativeness) and
          o nonstatistical generalization (justified on the basis of knowledge of
             the research topic, the logic of the study, and consistency in
             replicated outcomes).

•   Convenience (Accidental) sampling
    Chosen on the basis of availability
    Potential bias of self-selection
    Not possible to assess the attributes that are present in those who offer
    themselves
    Unclear how these attributes affect the ability to generalize the study
    /experimental outcomes




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                   ADMS 3352 3.0 Sampling Technique and Survey Studies



•   Quota sampling
    A convenience sample with added feature:
    maintain a balance of specific characteristics
    For example: maintain a certain proportion of each gender

•   Purposive sampling
    Researcher handpicks subjects OR use of groups of elements as sampling unit
    on the basis of specific criteria
    Generalization of results is limited to those who have these characteristics

Snowball / Network sampling
  1. When subjects with specific characteristics are hard to locate, a few
     subjects are identified
  2. Interview /test the few subjects
  3. These subjects further id others who have the requisite characteristics
  4. A chain referral / snowballing / network referral until an adequate
     sample is obtained
  5. Researcher must verify the eligibility of each respondent to ensure a
     representative group


Sample Surveys
    Ø Descriptive:
      For example, study the proportion of pop watching a certain TV program
    Ø Analytical: for example, compare groups and employ stat techniques in
      order to estimate pop parameters


Factors influencing Sample Sizes
•   Sampling technique
•   Estimation procedure
•   Measurement sensitivity: precision
•   Effect size: the extent of the presence of a phenomenon
•   Study design influences power
•   Number of variables
•   Data analysis techniques
    (Chi-square test on association between categorical variables have weak
    power)
•   Significance level




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                   ADMS 3352 3.0 Sampling Technique and Survey Studies



Conducting a Survey
(from the perspective of sampling for estimation with specified precision)

   1.  Make a clear statement of objectives
   2.  Define the population to be sampled
   3.  List the relevant data to be collected
   4.  Specify the required precision of estimates
   5.  Determine well-defined sampling units
       (The list of sampling units is called a frame)
   6. Determine the sampling scheme – method of selecting the sample
   7. Plan ahead how to handle non-response
   8. Collect data
   9. Summarize the data
           a. Take into consideration if there was large non-response
           b. If sample size is large, may apply central limit theorems
           c. If sample size is small, may wish to apply distribution free
              techniques
   10. Proceed with sample estimation procedure (if appropriate)
   11. Identify mistakes in the present survey for the benefit of future work.

Reference:

Govindarajulu, Zakkula (1999), Elements of Sampling Theory and Methods,
Prentice-Hall, New Jersey.




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