1. Sampling Methods in Quantitative and Qualitative Research

2. Sampling
Sampling in Quantitative Research

3. Sampling in Quantitative Research
Population
The entire aggregation of cases that meets a specified set of criteria
Eligibility criteria determines the attributes of the target population
Sampling
The process of selecting a portion of the population to represent the entire population

4. Sampling in Quantitative Research
Accessible population
The population of people available for a study
Target population
The entire population in which the researcher is interested and to which he/she wants to generalize the results

5. Sampling Plans A sample is a subset of the population
A sample should be representative and similar to the population to be studied

6. Sampling Plans
Strata
Subdivisions of the population based on specific characteristics

7. Samples vs. the Population
More economical
More efficient
More practical

8. Problems Using Samples
Sampling bias
Over-representation or under-representation of some characteristic of the population
Not representative of the population being studied

9. Sampling Plans Types of sampling plans Nonprobability sample
Convenience sampling
Purposive sampling
Quota sampling
Probability sample
Random sampling
Cluster sampling
Systematic sampling

10. Sampling Plans Nonprobability sample
The selection of the sample from a population using non-random procedures
Convenience sampling
Purposive sampling
Quota sampling

11. Sampling Plans Nonprobability sample
Convenience sampling (accidental sampling)
Selection of the most readily available people as participants in a study
Risk of bias and errors as sample may be atypical of the population
Weakest form of sampling
Snowball sampling (network sampling)
The selection of participants by means of referrals from earlier participants

12. Sampling Plans Nonprobability sample Quota sampling
Researcher pre-specifies characteristics of the sample to increase its representativeness
This is used so sample includes an appropriate number of cases from each stratum (subpopulation)
Usually use age, gender, ethnicity, socioeconomic status, and medical diagnosis

13. Sampling Plans Nonprobability sample
Purposive sampling (judgmental sampling)
Researcher selects study participants on the basis of personal judgement about which ones will be most representative or productive
Handpick cases, very subjective

14. Sampling Plans Nonprobability Sample Problems
Are rarely representative of the target population
But are convenient and economical

15. Sampling Plans Probability sample
The selection of the sample from a population using random procedures
Random selection – each element in the population has an equal, independent chance of being selected
Should be representative of the population
Random sampling
Cluster sampling
Systematic sampling

16. Sampling Plans Probability sample Simple Random sampling
Listing the population elements
Elements are assigned a number
Table of random numbers is used to draw at random a sample

17. Sampling Plans Probability sample Stratified Random sampling
Population divided into homogenous subsets
Elements are selected at random
Increases representativeness of the final sample

18. Sampling Plans Probability sample Stratified Random sampling
Proportionate sample
a sample that results when the researcher samples from different strata of a population in direct proportion to their representation in the population

19. Sampling Plans Probability sample Stratified Random sampling
Disproportionate sample
a sample that results when the researcher samples differing proportions of study participants from different strata that are comparatively smaller
Used when comparison between strata of unequal membership size are desired

20. Sampling Plans Probability sample
Cluster sampling (multistage sampling)
A form of sampling in which large groupings are selected first, with successive subsampling of smaller units
Used for large scale sampling where it is impossible to have a listing of all elements

21. Sampling Plans Probability sample Systematic sampling
The selection of study participants such that every Xth person or element in a sampling frame or list is chosen
Population is divided by the size of desired sample to obtain a sampling interval
Sampling interval is the standard distance between the selected elements

22. Sampling Plans Sample Size (Quantitative Studies) Sample size
The number of participants in a sample
Use the largest sample possible
The larger the sample, the more representative it is likely to be
The larger the sample, the smaller the sampling error
Large samples counter balance atypical values

23. Critiquing the Sampling Plan
Did the researcher adequately describe the sampling plan
Type of sampling used
The population under study
Number of participants
Main characteristics of participants
Number and characteristics of potential subjects
Were good sampling decisions made
Was the sample representative of the population

24. Critiquing the Sampling Plan
Response rates
The number of people participating in a study relative to the number of people sampled
Nonresponse bias
Differences between participants and those who declined to participate
A bias that can result when a nonrandom subset of people invited to participate in a study fail to do so

25. Sampling in Qualitative Studies

26. Sampling in Qualitative Studies
Uses small samples
Non-random samples
Sample design is emergent

27. Sampling in Qualitative Studies
Types of Qualitative Sampling
Convenience sampling (volunteer sample)
Snowball sampling
Purposive sampling (theoretical sampling, purposeful sampling)
Researcher selects sample based on information needs which emerged from earlier findings

28. Sampling in Qualitative Studies
Sample Size
Sample size is based on informational needs
Data saturation is sought
Sampling to the point at which no new information is obtained and redundancy is achieved

29. Sampling in Qualitative Studies
Evaluating Sampling Plans Based on:
Adequacy
Sufficiency and quality of the data the sample yielded
Appropriateness
Using the best informants for the sample, those who will provide the best information

30. Reference
Loiselle, C. G., Profetto-McGrath, J., Polit, D. F., & Beck, C. T. (2011). Canadian essentials of nursing research. (Third Edition). Philadelphia: Lippincott, Williams & Wilkins.