Surveys and Population-Based Studies by 9riGqrvL

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									Surveys and Population-Based Studies
     Definition of a "Survey"
         A method of collecting information about a human
         population in which direct (or indirect) contact is made
         with the units of the study (e.g., individuals,
         organizations, communities, etc.) by using systematic
         methods of measurements like questionnaires and
         interview schedules. (Warwick and Lininger, 1975)
     Examples of well-known surveys:
       – U.S. Decennial Census
       – Current Population Survey (n=60,000 HHs/mo.)
       – Health Interview Survey (n=50,000 HHs/yr.)
       – Other Examples in Groves, et al. (2004)


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     Nonprobability Sampling
 Selection by nonrandom methods
 Membership in the sample is ultimately left
  to human judgment
 No basis for assuming stochastic behavior
  of sample estimates
 One method: quota sampling



                                                2
                    Quota Sampling
 Quota control/allocation for each interviewer:
                                            Interviewer
                 Category   Age    Gender   Assignment
                    1       <40      M          15

                    2       <40      F          15

                    3       >40      M          10

                    4       >40      F          10

                                   TOTAL        50




 Filling category is left to interviewer's discretion (i.e., judgment)




                                                                   3
        Probability Sampling
 Ultimate   selection left to some randomized
  (i.e., chance) mechanism
 Two types:
  – Random sampling
  – Survey sampling




                                                 4
         Random Sampling
 "Population"  is infinite and abstract;
  distribution of measurements follows some
  assumed form (e.g., a normal distribution)
 Sample is the result of independently
  selecting a measurement at random from the
  assumed distribution, with sample size as
  the number of selections


                                           5
                Random Sampling
 “Random sample” as defined by Hogg & Craig:
 "Let X1, X2, . . ., Xn denote n mutually statistically independent
  random variables, each of which has the same but possibly
  unknown probability density function, f(x). The random variables
  X1, X2, . . ., Xn are then said to constitute a random sample from
  a distribution that has pdf, f(x).
 Example: f(x) for the normal distribution:



  f (x) 
              1
                   exp 
                         L
                         ( x   )2
                         M            O
                                      P        x  
            2  2
                         N  2 2      Q

                                                                6
   Population-Based Sampling
 Population is finite (i.e., made up of a
  countable set of members)
 Distribution of measurements usually does
  not follow a neat mathematical form
  – Ex: Number of health care visits in the past 12
    months
 Randomization used but selections may not
  be made independently

                                                  7
          Probability Sampling
 Each population element has a known and nonzero
  probability of being selected into the sample
 EPSEM sample design:
   – Sample in which selection probability for each
     element is equal;
   – Stands for Equal Probability Selection Method.
   – Also use the term "self-weighting"



                                                      8
     Advantages of Probability
            Sampling
 Statistical theory (including sampling
  theory) assumes this method
 Not subject to biases of human judgment
 Can directly measure the precision (i.e.,
  statistical quality) of estimates produced
  from sample


                                               9
    Utility of Sampling Theory
 Basis for settling on ways to estimate
  population parameters and the precision of
  those estimates
 Basis for much of the decision making in
  designing the sample




                                           10
Inference in Population-Based Studies
 Circle    of inference:      Population:
                               Values to be
                                Estimated




       Analysis:                                   Sample Design
  (Population Values                           (Probability Sampling)
      Estimated)




                            Selected Sample:
                            (Data Collected)
                                                                   11
             Population Hierarchy
Population




 Member
                                    12
Population Hierarchy: Some Examples
   First   grade students in NC schools



   Residents   of the United States




                                           13
            Components of a
         Population-Based Study
 Planning
  – Study specifications
        Target population
            vs.
        Survey population
  – Budget considerations
  – Staff communication
  – Sample size

                                  14
            Components of a
         Population-Based Study
 Sampling
  – Preliminary activities
  – Search for sampling frame(s)
        List(s) of units to be sampled
  – Develop the sample design
      Plan to choose the sample
      Consists of a sequence of statistical issues and
       decisions
  – Select the sample


                                                          15
             Components of a
          Population-Based Study
   Data collection instrument
     – Design questionnaire and forms
     – Small-scale testing
     – Manuals for training
   Data collection
     – Preparation (e.g., hiring and training)
     – Field operations (e.g., monitoring and supervision)
   Manual editing/coding
     – Preparation
     – Operations

                                                             16
         Components of a
      Population-Based Study
 Data   entry
  – Preparation
  – Operations
 Machine    editing/coding and file processing
  – Preparation
  – Run edits
  – Prepare analysis work files
 Analysis   and dissemination

                                              17

								
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