1. Taejin Jung, Ph.D. Week 8: Sampling Messages and People
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Taejin Jung, Ph.D.
Week 8: Sampling Messages
and People

2. Sampling Definition A major difference between
The science of systematically drawing a valid group of objects from a population reliably
A major difference between
formal and informal methodology
Ability to generalize to a larger population
Types of sampling
Census
Nonprobability sampling
Probability sampling
Critical Issues in Sampling
Who/what should be questioned/ observed?
What demographic, psychographic, or behavioral traits should be used to identify population membership?
How many population elements must be selected to ensure representativeness?
How reliable does the information have to be for the decision maker? What are the data quality factors and acceptable levels of sampling error?
What sampling techniques should be used?
What are the time and cost constraints?
1.Given the stated decision problem, research obj, and specific info requirements, who would be the best type of person or object to question or to observe.

3. Sampling Terminology Population
- Any complete group of interest in which members share some common characteristic
- The complete set of subjects, variables, or concepts under consideration
- e.g., registered voters, press release for a particular product, households with satellite dishes
Sample
-A subset of the population
Sampling
- Using a subset of the population to make conclusions about the entire population
Census
- Investigating every item or person in the population

4. Sampling Why Sample? - Cost and time to census is great
If it's not representative, it's not generalizable to the larger population
In other words, to be generalizable, a sample should "look" like the population as a whole.
Why Sample?
- Cost and time to census is great
- Sampling can give accurate results
- Some tests destroy the sampled units (or sensitizes them)

5. Sampling Process 1. Determine the population 2. Select sampling frame
- Who is the target of interest
- Must be operationally defined
2. Select sampling frame
- A listing of the all available sampling units
- Used to draw the sample
- Sampling frame error
: when the frame is not identical to the population
3. Select sampling design
- Probability sample: every element in the population has a equal chance of selection
- Non-probability sample
Example: For nail polish, females between the ages of 18 and 34 who purchased at least one brand of nail polish during the past 30 days living in cities with populations between 100,000 and 1 million people

6. Sampling Elements and Definitions
Universe
- The general concept of who or whom will be sampled
(e.g., all college & university students)
Population
- The message types or the people to be sampled, as defined and described (e.g., Those that are private)
Sampling frame
- A list of all the messages or people to be surveyed
(e.g., most current list of private college students)
Sample
- The actual messages or people chosen for inclusion in the research
(e.g., available list of private college students)
Completed sample
- Messages selected and analyzed and the people who actually responded to the survey (e.g., sample college students who responded to the survey)

7. Sampling Elements and Definitions
Coverage error
- Error produced in not having an up-to-date sampling framing from which to sample
Sampling error
- Error produced when you do not sample from all the members of the sampling frame
Measurement error
- Error found when people misunderstand or incorrectly respond to questions (found primarily when sampling people)

8. Types of Probability Samples
Simple Random Sample
- Each element has an equal chance of selection
- You must identify all population members
- e.g., bucket selection
Stratified Random Sample
-Population is divided into two or more groups (strata) and a random sample is selected from each group (strata)
- Proportional sample - The size of each group in the sample is proportional to the size of the group in the population
- Disproportional - Sample size for each group is not proportional to the population.

9. Types of Probability Samples
Systematic
- A random starting point is selected
- Every Nth item is selected
(skip interval: population size/sample size)
Cluster
- When do not have a complete sampling frame but know that population consists of relatively easily identifiable subgroups
- Primary sampling unit is not the individual element in the population but a large cluster of elements
Multistage area sampling
- Uses a combination of two or more probability sampling techniques
Suppose the Department of TV Violence wants to investigate the use of obscenities by teens in Florida. A cluster sample could be taken by identifying the different counties in FL as clusters. A sample of these counties (clusters) would then be chosen at random, so all teens in those counties selected would be included in the sample. It can be seen here then that it is easier to visit several teens in the same county than it is to travel to each school in a random sample to observe the use of obscenities.

10. Nonprobability Sampling
The chance that any particular item being selected is unknown
No methods for determining random sampling error
Results can not be generalized to the population.

11. Types of Nonprobability Samples
Judgment/Purposive sample
-Researcher selects subjects based upon opinion of some characteristic
Quota sample
- Subgroups identified
- Items/subjects selected non-randomly to match subgroups
Snowball sample
- Initial subjects selected randomly
- Those subjects identify next subjects
Convenience sample
- Subjects who are convenient are selected

12. Distributions Population distribution
Normal Distribution
A symmetrical bell shaped distribution
Almost all (99%) of its values are within ± 3 standard deviations from the mean
Standardized Normal Distribution.
A special normal distribution
Mean = 0, Std. Dev. = 1
Z table
Used in inferential statistics
Population distribution
: a frequency distribution of the elements of a population
Sample distribution
: a frequency distribution of a sample
Sampling distribution
: a theoretical probability distribution of sample means for all possible samples of a certain size drawn from a particular population

13. Sample Size and The Normal Curve

14. Central Limit Theorem
As a sample size increases, the distribution of sample means of size n approaches a normal distribution.
The central limit theorem works regardless of the shape of the original population distribution.

15. Sample Size for Probability Samples (How large a sample depends on…)
Sampling Error (Confidence)
- Sampling error is typically set at 5%, or 95% confidence interval
- When we set a 95% confidence interval, no more than 5 units (people or messages) will be missampled)
Measurement Error (Accuracy)
The amount of random error found in any measure
How much accuracy at a minimum we are willing to accept or tolerate
The nominal standard is 95% confidence in measurement

16. Factors to Specify Sample Size
Population size (Q)
Expected Outcome (p)
Measurement Error (E)
Sampling Confidence (S)
N = (Q)(p)(1-p)/
(Q-1)(E/C2) + (p)(1-p)

17. Determining Factors for Sample Size
Budgetary consideration
- The larger the sample, the more it will cost
Time constraints
- In PR, you do not have the luxury of time
Resource constraints
- Unlike marketing and advertising, you may have smaller necessary staffs
Prior research
- May provide clues as to how large the sample size needed
Precision or accuracy required for the research