Chapter 7 - Sampling

Question 1

What is the difference between random (probability) and non-random sampling?

Answer 1

probability
- a mathematical process that makes sure elements in the population will have an equal probability of being selected
- saves time and money
- more accuracy
non-probability
- sampling elements are selected not using mathematic processes
- purpose is to develop an in-depth understanding of a phenomenon

Question 2

What are the non-random sampling type?

Answer 2

1. Hazard sampling
   - research selects anyone they come across
2. Quota sampling
   - identifies general categories into which people will be selected and selects a number of cases for each category
3. Purposive sampling
   - uses judgement to select cases with a specific purpose in mind
4. Snowball Sampling
   - identifying and sampling the cases in a network
5. Sequential sampling
   - gathering cases until no new information is discovered or the diversity of cases are filled

Question 3

What are the random sampling types?

Answer 3

1. Stratified sampling
   - often used when researcher wants to ensure a particular subpopulation is represented
2. Cluster sampling
   - researchers sample a dispersed population and mitigate the high cost to reach a sampled element
3. Random-digit dealing
   - uses all possible telephone numbers as a sampling frame
4. Systematic sampling
   - research selects every 5th case in the sampling frame using a sampling interval
5. Simple random sampling
   - researcher uses a sampling frame and a pure random process to select cases

Question 4

What is Population Sampling?

Answer 4

• researchers draw samples from a larger pool of cases

Sampling element
- unit of analysis or case in population
Population
- large pool
Sampling ratio
- ratio of the size of the sample compared to the size of the target population

Question 5

What is a Sampling frame?

Answer 5

• a list of all cases in a population
• always have some degree of inaccuracy

Question 6

What is the difference between statistics and parameters?

Answer 6

Parameters
- exact characteristic of a population
Statistic
- a numeric estimate of population parameter computed from a sample

Question 7

Whaat are the different types of error?

Answer 7

Sampling error
- how much a sample deviates from being representative of the population
Margin of error
- estimate about the amount of sampling error that exists in a survey’s results

Question 8

What is the central limit theorem?

Answer 8

states that whenever many random samples are drawn from a population and plotted a normal distribution is formed

Question 9

What is a Confidence interval?

Answer 9

• range of values
• a higher and lower than a specific value found in a sample

Question 10

What are inferential statistics?

Answer 10

• based on random sampling
• cannot infer to a larger population without a random sample