Ex Post Facto Designs

January 9, 2018 | Author: Anonymous | Category: Social Science, Psychology, Experimental Psychology
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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.


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.

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