Chapter 7: Sampling Flashcards
Population
- Population of Interest
- Focus on the population of the interest (the target population)
- Is the entire set of people or products in which you are interested
Sample
Smaller set of people taken from the population
Biased Sample
- Also called an unrepresentative sample
- Not all members of the group have a equal probability of being included in the study
Example: Recruiting people sitting in the front row at the Texas Democratic Convention
Unbiased Sample
- Also called Representative sample
- Each member of the population have a equal chance of being included in the study
- Only unbiased samples allow us to make inferences about the population of interest
Example: Obtaining a list of all registered Texas Democrats from public records, and calling a sample of them through randomized digit dialing
Convenience Sampling
- Its when researchers sample only the people that are the easiest to the group
- Online studies normally use convenience samples
- Researchers might also end up with a convenience sample if they are unable to contact an important subset of people
Example: People who participate in online research for payment are considered a convenience sample
Example: Not study those who love far away, who don’t show up to a study appointment or who don’t answer the phone
Self Selection
- Sampling those who are volunteering the study
- It is ubiquitous in online polls, and it can cause serious problems for external validity
- Not all internet - based surveys are subject to self - selection bias
Simple Random Sampling
- A good sampling frame, when you have a list of all the people in the population interest
- Researchers tend to use software for this sampling
- Most basic form of probability sampling
- It can surprisingly difficult and time consuming
Cluster Sampling
- Used when you have people belong in some groups but the groups are somewhat arbitrary
- Randomly pick people from the different groups that will be included in the study (everyone)
- Problem if a clusters is significantly different from each other
Multistage Sampling
When you use a select from the clusters, recruit people from the clusters
- Two random samples are selected: a random sample of clusters, then a random sample of people within those clusters
Stratified Random Sampling
- Groups are created to represent the characteristics of the population of interest
- Groups are created in a meaningful way
- The final sample sizes of the strata reflect their proportion in the population, whereas clusters are not selected with such proportions in mind
Oversampling
- Oversampling due to the group being too small
- Variations of stratified sampling
Systematic Sampling
- When you randomly select a interval and a starting point, and individual
- Using a computer or a random number table, the researcher starts by selecting two random numbers
Random Sampling
(probability sampling)
- Is to get representative sample to increase external validity for each individual in the study
- Researchers create a sample using some random method so that each member of the population has an equal change of bing in the sample
Random Assignment
Used only in experimental designs to assign participants to groups at random
- We do this to increases internal validity
- By helping ensure that the comparison group and the treatment group have the same kinds of people in them, thereby controlling for alternative explanations
- We are able to be more confident
Convenience Sampling
Uses samples that are chosen merely on the basis of who is easy to reach
