yer i did but i didint understand the answers there
Ok, well, this is an explanation I typed out for someone a while back:
Stratified sampling: Involves dividing the target population into important subcategories (or strata. They may be divided according to gender, age, income, IQ, socioeconomic background etc) and then selecting members of these subcategories in the proportion that they occur in the target population.
This might be a bit confusing, so for example, just say we were doing some research on individuals with Asperger’s syndrome (a type of Autism). The target population is individuals with Asperger’s. However, Asperger’s is a lot more common in men.
So, just say in the actual Asperger’s population, 75% who have the condition are men and 25% are women (this probably isn't statistically correct btw). So, in a sample of 20 participants in our investigation, we would replicate the target population so that 15 participants are men and 5 are women (or 75% are men and 25% are women) to represent the actual population better.
Strengths:
-A deliberate effort is made to identify the characteristics of a sample most important to be representative of the target population. (In other words, stratified sampling gives you a representative sample of your population on the basis of those identified characteristics you want to investigate... in the example above, researchers replicated the Aspergers population according to gender.)
Weaknesses:
-Stratified sampling is time consuming because characteristics in the population have to be identified, and a calculation of their ratio of occurrence worked out. This is to ensure the correct ratios in your stratified sample.
Hope that makes sense