1. Sampling Methods

2. Definitions of Important Terms
Population
The total group of people from whom information is needed.
Census
Data obtained from or about every member of the population of interest.
Sample
A subset of the population of interest.
Sampling Units (elements) –
Person/object of interest for study
Sampling Frame –
All the elements that are available for selection

3. Parameter vs Statistic
Parameter: The “true” population data.
Statistic: The sample data.

4. Sampling Designs Nonprobability Probability Convenience Judgment Quota
snowball
Probability
Simple Random
Systematic Random
Stratified
Cluster

5. Probability Sampling
Each unit in the sampling frame has an equal, non-zero chance of being selected for the study. Implies random selection.
You can then say that the sample of representative of the target population (of interest)

6. Nonprobability Sampling
Nonprobability sampling the probability of selection of each sampling unit is unknown.
Data results can’t be used to make predictions about the defined target population; it is limited to just the people who provided the raw data in the survey.

7. Types of Probability Sampling Designs
Simple Random Sampling
Types of Probability Sampling Designs
Systematic Random Sampling
Every sampling unit making up the
defined target population has a known
equal, non-zero, chance of being selected into the sample.
Stratified Random Sampling
Similar to simple random sampling, but requires that the defined target population be naturally ordered in some way, i.e customer list.
Requires the separation of the defined target population into different subgroups (strata), and the selecting of samples from each stratum.

8. Types of Nonprobability Sampling Designs
Convenience Sampling
(Accidental Samples)
Types of Nonprobability Sampling Designs
Judgment Sampling
Drawn based on the convenience of the researcher or interviewer of when and where the study is being conducted.
Quota Sampling
Participants are selected based on an experienced individual’s belief that the prospective respondent will meet the requirements of the study.
Selection of prospective participants based on pre-specified quota requirements. Ie., males and females

9. Total Error
the difference between the true value in the population of interest (the parameter) and the observed value in the sample (the statistic).
sources of error:
Sampling error, sampling bias
Non-sampling error

10. Total Error – when a sample is not representative
Poor Logic
Improper Use of Statistics
Inadequate sample
size
design
Poor data collection
Improper design
Poor problem
formulation
Poorly written
report

11. Controlling Sampling Error
Error due to sampling problems or sampling size.
controlled by increasing the sample size.
However, a larger sample can result in less quality control…in other words, more non-sampling error

12. Controlling Sampling Bias - systematic error
Sample differs from the population in a systematic way
not controlled by sample size but by better sampling controls (ie., defining population better).

13. Controlling Non-sampling Error
All other error sources such as measurement error
controlled by using good measurement principles and good data entry/analysis
a census and a sample both have non-sampling error