ICT role in 21st century education and its challenges
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8. 1.3 Types of Surveys & Sampling Methods non-probabilistic Quota sample : elements are chosen in the field to meet predetermined number of cases in different categories (e.g. 40% men, 60% women) Expert sample : elements chosen on the basis of informed opinion that they are representative probabilistic Inferences about the underlying population cannot be made Probability of obtaining each sample can be computed, confidence intervals can be developed, bounds on sampling errors, etc. Simple Random Sampling Stratified Random Sampling Cluster Sampling Systematic Sampling
Difference between target population and sample population may arise and is problematic, under certain circumstances. Selection bias in trade analysis for example. Example of the poll in the US. If having a phone or not is a systematic process or a random process, then, the implications are different.
Random NE haphazard Typically don’t enumerate samples, then inclusion probs Populations usually too big More likely to assign the elements selection probs and proceed from there
Start on most difficult part of this class Essential to understand material in Ch2, especially early sections/this week’s lecs Possible to by chance select an unrep. sample using a stat design (e.g., SRS of class)
Start on most difficult part of this class Essential to understand material in Ch2, especially early sections/this week’s lecs Possible to by chance select an unrep. sample using a stat design (e.g., SRS of class)
SAMPLING FRAME EXAMPLE Target population = Ames households OU = household There is no list of households, but we can list out telephone numbers Frame = list of all possible (land line) telephone numbers in the Ames area SU = telephone number Frame includes non-working and business numbers that do not correspond to households Frame excludes households who have no land line phone
What disinguishes SS from the rest of your statistics classes We usually start with a regression model or AOV model that assumes errors are normal (write these models and the error assumptions on the board) OLD NOTES Finite Infinite Land in US Yield of corn variety (inf. # conditions) People in CA Impact of chemical on pests Strata blocks Clusters split-plots Means Percentiles Randomization Model based, e.g., resids ~ normal (Model assisted)
Sampling counties Sampling states, then counties
SAMPLING FRAME EXAMPLE Target population = Ames households OU = household There is no list of households, but we can list out telephone numbers Frame = list of all possible (land line) telephone numbers in the Ames area SU = telephone number Frame includes non-working and business numbers that do not correspond to households Frame excludes households who have no land line phone
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