Ex Post Facto Experiment Design

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					Ex Post Facto Experiment Design

         Ahmad Alnafoosi
         CSC 426 Week 6
        Ex Post Facto what???
• Webster Dictionary defines Ex Post Facto
  as:
   • after the fact : retroactively
   • Late Latin, literally, from a thing done
    afterward. First Known Use: 1621
            Explain More…
• In situations where it is not possible to
  manipulate variables.
• Ex Post Facto design provides an alternative
  to investigate how independent variables
  affect dependant variables.
• The researcher can observe the independent
  variables after the event.
  That sounds like Co-relational
            design?
• Co-relational design and Ex post facto
  design involve examining existing
  conditions.
• Ex Post Facto design has dependant and
  independent variables whereas Co-relational
  design does not.
     What about experimental
            Design?
• Both experimental design and Ex post facto
  design have independent and dependant
  variables.
• Ex Post Facto differs that it does not
  introduce the presumed producing cause.
• Thus in Ex Post Facto the researcher is
  NOT able to draw firm cause and effect.
• Both share similar designs.
What does Ex Post Facto Design
          Look like
• Similar to Experimental design, ex post
  facto design has multiple forms.
• These form involve variation of events
  (experience), Observations, Groups and
  combination of the above.
Simple Ex Post Facto Design
    Simple Ex Post Facto Design
• Similar to Static Group
  Comparison with the
  difference of the timing of
  the treatment
  (Experience).
• It is called Experience
  since the researcher can
  not control it.
• Association can be drawn
  from this study (NOT
  Cause and effect).
           Factorial Design
• In designs that involve multiple dependant
  variables with Ex Post Facto design,
  Factorial design is needed.
  Randomized Two Factor Design
• 2 variables tested by 4
  groups.
• Variable 1 effect can be
  studied by comparing
  group1 and group2 of that
  of group3 and group4.
• Variable 2 effect can be
  studied by comparing
  group 1 and group 3 of that
  of group 2 and group4
Randomized Two Factor Design
           - Cont
• This design is a generalized version of
  Solomon four group design. (event instead
  of experiment)
• This design allow to see the effect of each
  of the variables.
• It also can show the interaction effect of the
  variables.
 Combined Experimental and Ex
      Post Facto Design
• Combining experiment
  with Ex Post Facto
  Experience
• It has Ex Post Facto
  component by initially
  selecting groups that have
  that experience.
• Then there is experimental
  phase where where
  experiment is conducted.
 Combined Experimental and Ex
   Post Facto Design - Cont
• The results will be 4 groups all possible
  combinations of experience and experiment.
• This design enables the study of experiment
  effect the dependant variables
• Also it enables the study of how previous
  experience interact with the experiment.
 Sampling



Ahmad Alnafoosi
CSC 426 Week 6
         Choosing a Sample in
          Descriptive Study
• The purpose of descriptive study is to be able to
  determine and describe large population.
• In most instances surveying all the population is
  not possible because of the sheer size.
• On the other hand the sample needs to be large
  enough to be representative of the population and
  their characterizations that are relevant to the
  study.
           Sampling Design
• To achieve the aforementioned goals a
  sampling design is needed.
• The sampling design needs to take into
  consideration the actual traits of the
  population to apply the appropriate
  sampling design.
        Probability Sampling
• Researcher can specify that each segment of
  the population will be represented in the
  sample.
• The sample is chosen using Random
  Selection (each member of the population
  has equal chance to be picked)
    Simple Random Sampling
• Is a probability sampling design.
• Each member has equal chance to be
  picked.
• Used for small population where every
  member is know.
   Stratified Random Sampling
• Is a probability Sampling design.
• Is used in stratified population where there
  is multiple layers strata
• Guarantee that each of the identified strata.
• Is used when the stratum are equal in size.
Proportional Stratified Sampling
• is probability sampling design.
• When the population is stratified but where
  stratum are not equal in size.
• In this case the number of random sample
  of each strata taken is dependant
  proportionally to the strata population to the
  whole population.
             Cluster Sample
• is probability sampling design.
• Is used when the population is spread over
  large area.
• Clusters need to be similar to each other as
  much as possible.
• Each cluster has to have equal
  heterogeneous population.
        Systematic Sampling
• Is probability sampling design.
• Involve selecting individuals based on pre-
  determined sequence.
• The sequence needs to be random.
        Factors in determining
      Probability Sample Design
•   Population size
•   Stratification
•   Size of stratum
•   Clustering
     Non-Probability Sampling
• Does not guarantee that each element of the
  population will be represented in the
  sample.
• Some members of the population have no
  chance of being represented.
       Convenience Sampling
• Is non probability sampling design.
• AKA Accidental sampling.
• It sample available members of the
  population.
            Quota Sampling
• Is Non-probability Sampling
• It select individuals in the same proportion
  as they are found in the general population,
  but it is not random.
         Purposive Sampling
• Is Non-probability Sampling
• It select individuals for a particular purpose.
• Needs to be careful since it assume that the
  chosen sample is useful for the purpose.
 Sampling Surveys of very large
          population
• To tackle very large population multi-
  staging of sampling areas might be needed.
• This involves
   • Primary area selection
   • Sample location selection
   • Chunk selection
   • Segment selection
   • Housing selection
   What is the right sample size
• For population less than 100, sample the
  entire population.
• For population around 500, sample 50%
• For population around 1,500 , sample 20%
• For population larger than 5,000 sample
  size can be around 400.
              Sample Bias
• Sampling will introduce bias into the
  sample.
• Researcher need to acknowledge.

				
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posted:10/16/2011
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