Template:Pilot Testing
Pilot Testing
Description
- Pilot testing is an often overlooked but extremely important part of the research process!
- helps detect potential problems in research design and/or instrumentation (e.g., whether the questions asked is intelligible to the targeted sample)
- ensures that the measurement instruments used in the study are reliable and valid measures of the constructs of interest
- pilot sample = small subset of the target population
Examples
Further Readings
[1] Bhattacherjee, A. (2012). "Social Science Research: Principles, Methods, and Practices", Textbooks Collection. 3. Available at: https://scholarcommons.usf.edu/oa_textbooks/3
Sampling Strategy
Description
choose target population and a strategy of how to choose samples related to the unit of analysis in a research problem avoid a biased sample
Sampling approaches:
Probability sampling:
- technique in which every unit in the population has a chance (non-zero probability) of being selected
- chance to be selected can be accurately determined
- sampling procedure involves random selection at some point
methods: random sampling, systematic sampling, stratified sampling, cluster sampling, matched pairs sampling, multi-stage sampling
Non-probability sampling:
- some units of the population have either zero chance of selection or the probability of selection cannot be determined accurately
- selection criteria are non-random (e.g. quota, convenience)
- estimation of sampling errors is not allowed
- information from a sample cannot be generalized back to the population
methods: convenience sampling, snowball sampling, quota sampling, expert sampling
Examples
Probability sampling:
Cluster sampling:
- divide population into clusters
- randomly sample a few clusters and measure all units within that cluster
Matched pairs sampling:
- two subgroups of population
- compare two individual units from subgroups with other
- ideal way to understand bipolar differences
Multi-stage sampling:
- combine the previously described sampling technique
- e.g. combine cluster and random sample
Random sampling:
- all subsets are given equal probability of being selected
- unbiased estimates of population parameters
Stratified sampling:
- divided into homogeneous and non-overlapping subgroups
- simple random sample within each subgroup
Systematic sampling:
- sampling frame is ordered according to some criteria
- elements are selected at regular intervals
Non-probability sampling:
Convenience sampling:
- take a sample from a population that is close to hand
- e.g. outside of shopping mall
- may not be representative, therefore limited generalization
Expert sampling:
- choose respondents in a non-random manner based on their expertise on the phenomenon
- findings are still not generalizable to a population
- e.g. in-depth study on an institutional factor such as Sarbanes-Oxley Act
Quota sampling:
- segment population into mutually exclusive subgroups
- take a non-random set of observations to meet pre-defined quota
- pre-defined quota can either proportional (as the overall population) or non-proportional (less restrictive)
- both are not representative of the population
Snowball sampling:
- start criteria-based
- ask respondents for further potential participants
Further Readings
[1] Bhattacherjee, A. (2012). "Social Science Research: Principles, Methods, and Practices", Textbooks Collection. 3. Available at: https://scholarcommons.usf.edu/oa_textbooks/3