ex post facto experiment design ahmad alnafoosi csc 426 week 6
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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.