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 predetermined 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 multistaging 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.