1. MR2300: Marketing Research Paul Tilley
Unit 9: Sampling Designs, Sampling Procedures & Sample Size.

2. In THIS Video WE WILL:
Define a sample; a population; a population element and a census
Explain why researchers use samples.
Design an appropriate sample.
Use appropriate statistical tools to extract a useful sample from a population.
Identify the key concepts in a sampling plan
Control for errors that can occur in sampling
Illustrate the distinctive features of probability and non-probability samples
Calculate and interpret the Mean, Median, Mode and Standard Deviation of data.
Develop frequency distributions for data
Calculate sample size and the sample size of a proportion.

3. Population Target Population: Canada? NL? Target Population
Any complete group
Usually people
Target Population: Canada? NL?
Target Population
Cda Population= 35,540,400
NL Population= 526,977

4. Investigation of all individual elements that make up a population
difficult, slow and very expensive to measure
Census

5. Sample A sample is a subset of a larger target population
The Sampling process involves drawing conclusions about an entire population by taking a measurement from only a portion of all the population elements
Taking samples of populations is easier, faster and cheaper than taking a census of the population. Sample size relative to the population size will determine how accurately the sample results will mirror the population results. The difference is known as Error.
Samples may have to be used if testing results in destruction of the test unit

6. Stages in the Selection of a Sample Define the target population
Select a sampling frame
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure
for selecting sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork

7. Sampling Frame A list of elements from which the sample may be drawn
Working population
Mailing lists - data base marketers
Sampling frame error

8. Sampling Units Group selected for the sample
Primary Sampling Units (PSU)
Secondary Sampling Units
Tertiary Sampling Units

9. Random Sampling Error
The difference between the sample results and the result of a census conducted using identical procedures
Statistical fluctuation due to chance variations

10. Systematic Errors Nonsampling errors Unrepresentative sample results
Not due to chance
Due to study design or imperfections in execution

11. Errors Associated with Sampling
Sampling frame error
Random sampling error
Nonresponse error

12. Two Major Categories of Sampling
Nonprobability sampling
Probability of selecting any particular member is unknown
Convenience Sample
Judgment Sample
Quota Sample
Snowball Sample
Probability sampling
Known, nonzero probability for every element
Simple Random Sample
Stratified Sample
Cluster Sample
Multistage Area Sample

13. Nonprobability Sampling
Convenience Sampling - (also called haphazard or accidental sampling) refers to the sampling procedure of obtaining the people who are most conveniently available.
Judgment - is a nonprobability technique in which an experienced individual selects the sample upon his or her judgment about some appropriate characteristic required of the sample members
Quota - In quota sampling, the interviewer has a quota to achieve. to ensure that the various subgroups in a population are represented on pertinent sample characteristics to the exact extent that the investigators desire.
Snowball - refers to a variety of procedures in which initial respondents are selected by probability methods, but additional respondents are then obtained from information provided by the initial respondents. This technique is used to locate members of rare populations by referrals.

14. Probability Sampling Simple random sample Systematic sample
Stratified sample
Cluster sample
Multistage area sample

15. Simple Random Sampling
A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample
A simple random sample of 10 students is to be selected from a class of 50 students. Using a list of all 50 students, each student is given a number (1 to 50), and these numbers are written on small pieces of paper. All the 50 papers are put in a box, after which the box is shaken vigorously to ensure randomisation. Then, 10 papers are taken out of the box, and the numbers are recorded. The students belonging to these numbers will constitute the simple random sample.

16. Systematic Sampling
A simple process
Every nth name from the list will be drawn
Systematic sampling works well when the individuals are already lined up in order. In the past, students have often used this method when asked to survey a random sample of CNA students. Since we don't have access to the complete list, just stand at a corner and pick every 3rd person walking by.

17. Stratified Sampling
Probability sample
Subsamples are drawn within different strata
Each stratum is more or less equal on some characteristic
Do not confuse with quota sample
One easy example using a stratified technique would be a sampling of people at CNA. To make sure that a sufficient number of students, faculty, and staff are selected, we would stratify all individuals by their status - students, faculty, or staff. (These are the strata.) Then, a proportional number of individuals would be selected from each group.

18. Cluster Sampling
The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample.
The primary sampling unit is no longer the individual element in the population
The primary sampling unit is a larger cluster of elements located in proximity to one another
Suppose your company makes light bulbs, and you'd like to test the effectiveness of the packaging. You don't have a complete list, so simple random sampling doesn't apply, and the bulbs are already in boxes, so you can't order them to use systematic. And all the bulbs are essentially the same, so there aren't any characteristics with which to stratify them.
To use cluster sampling, a quality control inspector might select a certain number of entire boxes of bulbs and test each bulb within those boxes. In this case, the boxes are the clusters.

19. What is the Appropriate Sample Design?
Degree of accuracy
Resources
Time
Advanced knowledge of the population
National versus local
Need for statistical analysis

20. After the Sample Design is Selected
Determine sample size
Select actual sample units
Conduct fieldwork

21. Sample Statistics Variables in a sample Measures computed from data
English letters for notation

22. Frequency Distribution of Deposits
Frequency (number of
people making deposits
Amount in each range)
less than $3,
$3,000 - $4,
$5,000 - $9,
$10,000 - $14,
$15,000 or more
3,120

23. Percentage Distribution of Amounts of Deposits
Amount Percent
less than $3,
$3,000 - $4,
$5,000 - $9,
$10,000 - $14,
$15,000 or more
100

24. Measures of Central Tendency
Mean - arithmetic average
µ, Population; , sample
Median - midpoint of the distribution
Mode - the value that occurs most often

25. Number of Sales Calls Per Day by Salespersons
Salesperson Sales calls
Mike
Patty
Billie
Bob
John
Frank
Chuck
Samantha 26

26. Measures of Dispersion or Spread
Range - the distance between the smallest and the largest value in the set.
Variance - measures how far a set of numbers is spread out.
Standard deviation - square root of the variance

27. The Normal Distribution
Normal curve
Bell shaped
Almost all of its values are within plus or minus 3 standard deviations
I.Q. is an example

28. Normal Distribution 13.59% 13.59% 34.13% 34.13% 2.14% 2.14%
Conventional Product Adoption Life Cycle:
Five types of customers who will end up adopting a product
INNOVATORS (2.5%): People who are the first to adopt a product. They are trend-setting, risk-taking, and are not typical consumers. Example: See a movie first weekend it’s out or in a preview.
EARLY ADOPTERS (13.5%): People who are among the first but not as risk-taking. They adopt ideas early but with consideration, and they enjoy roles as opinion leaders. They spread the word about the product. Example: See a movie the first week of its release.
EARLY MAJORITY (34%): Deliberate customers; adopt earlier than most customers but are not leaders. Example: See a movie after a few weeks, after reading all the reviews and getting recommendations from early adopters.
LATE MAJORITY (34%): Skeptical customers, will only adopt an idea if the majority of people have tried it. Example: See a movie after it has been nominated for an Oscar.
LAGGARDS (16%): Tradition-bound, suspicious of change; will adopt an idea only after it has been around long enough. Example: See a movie after it has come out on video.
2.14%
2.14%

29. Ingredients in determining Sample Size
Estimated standard deviation of population
Magnitude of acceptable sample error
Confidence level

30. Sample size calculation for questions involving means2 E
Z S n =
Where:
n = Number of items in samples
Z = Standard Deviation Confidence interval
S = Standard Deviation Estimate for Population
E = Acceptable error

31. Sample size calculation For a Proportion2 E pq z n =
Where:
n = Number of items in samples
Z2 = The square of the confidence interval
in standard error units.
p = Estimated proportion of success
q = (1-p) or estimated the proportion of failures
E2 = The square of the maximum allowance for error
between the true proportion and sample proportion
or zsp squared.