Quantitative Research: Sampling, Data, Collection, Measurement and Data Quality Flashcards
What is “N”?
N=population
-Entire aggregation of cases the researcher is interested
What is “n”?
n=subset of population
What are 2 key considerations of a representative sample?
- Representation
2. Size
Describe eligibility criteria
- Specifies population characteristics
- Cost
- Practical constraints
- People’s ability to participate
- Design considerations
Probability Sampling
- Random sampling
- Estimates probability that an element will be included in sample
Non-probability Sampling
- Elements selected by non-random methods
- No way to estimate the probability each element will be included in sample
Convenience Sampling
Non-probability Sampling
-Using those who are the most available as participants
Snowball Sampling
Non-probability Sampling
-Network or chain sampling by referral
Consecutive Sampling
Non-probability Sampling
-Recruiting all from accessible population over specified time/size
Purposive Sampling
Non-probability Sampling
-Researcher uses knowledge about population to select sample
Simple Random Sampling
Probability Sampling
-Establishing a sampling frame-Using random numbers to draw sample
Stratified Random Sampling
Probability Sampling
- Subdivide population into homogenous subsets then randomly select sample
- Proportionate vs. disproportionate
Multistage Cluster Sampling
Probability Sampling
-Selecting broad groups in stages then randomly selecting sample
Systematic Sampling
Probability Sampling
- Selecting every kth case from a list
- Sampling interval
- Need a large population size to pick from
Sampling Bias
- Systematic over-representation or under representation of population segment
- Based on population’s homogeneity