What is the difference between internal validity and external validity quizlet?

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A. how to take statements, some have an assumption, a # have a qualification, they all have strengths and/or an importance statements, and they also typically have weakness statement
*understand how you apply each of these probability sample design
Simple random samples (most basic, list of people)
- find SF from population that you want to generalize
- find a table of random #'s
- label each element on the SF with #'s from 1 to N in order they appear on the list
-go to table and randomly select a # which represents a person in SF
- continue to write more #'s from the sample that your consistent from fashion from the table
- each # picked represents a person of the SF to be
Systematic samples (list of people)
- pick a # between 1 and K as the 1st SF members to be selected for inclusion in the sample
- then pick every Kth case/element after that until you go to the end of the list

K=1/sampling ratio
K=1/(sampling size/population size)
5% sample of 100 ---K=1/(5/100)=1(1/20)=20=K
10% sample of 100 ---K 1/(10/100)=1(1/10)=10=K
20% sample of 100 ---K 1/(20/100)=1(1/5)=5=K
*the larger the designed sample the smaller K will be (the smaller K is, the bigger the sample)
**Sample of 160 ---- SF=16000
K= 1/(sample size/population size)=1(160/16000)=100=K

Stratified samples (list of people, time & effort)
- you would take a simple (or systematic) sample within each of the strata. easiest way to think about this is to divide the SF into its relevant strata and then assign a sampling ration(%) that will be randomly taken from each category of the stratification variable
Cluster samples (list of grouping)
*randomly selet a # of grouping (called cluster) from a list with groups on it
*randomly select smaller sampling elements from the chosen group (clusters)
*they solve the problem of not having a list of elements for the population that you want to generalize to

LIST OF GROUPING
Probability Proportionate to Size (PPS) Samples
*probability cluster picked = size of the cluster/size of population
*randonly chose the same # of elements from each of the chosen clusters
**you can use a computer generated formula to do this
Disproportionate Sampling
*you could analyze the small group data separately and then analyze large group data separately and compare the results then it doesn't matter if the small group is disproportionate or you could use statistical weighting to weight down the disproportionate back to the correct % distribution found in the population.

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