Sampling Method






MKTG1418 Marketing Intelligence
Semester 1, 2023 
Seven steps in the sampling process 
Step  1: Define  the target population




•	We first answer the question: “What is the population we are trying to project?”



•	In other words, what is the larger group that is intended to be represented by using the sample?



•	In answering this question, refer to the “unit of analysis” as determined from Stage 1 of Problem Definition 
Key concepts




•	Unit of analysis: the unit around which you collect the data for your research project i.e., the subject of research




•	If there are different units of analysis, you might need to define different target populations and build separate sampling plans for each target population. 
Some other important  concepts



•	Census: a research approach that gathers the attitudes and opinions of every  unit of analysis in a target population e.g., national survey of households in Victoria state



•	Sample: a subset of elements/units of the target population that are selected to do the research upon.



•	The sample should be as representative of the target population as possible so that the research could be conclusive

•	i.e., generalizations of knowledge about the sample can only be made/projected onto the target population if the sample is representative enough. 
Relating back to marketing research




•	For marketing research, a very common subject of research or unit of analysis is the target customer.

•    Different segmentation variables can help you with this task. 
Step  2: Select a sampling frame




•	A sampling frame is a list of all units in the target population.
•    It is the operational target population.




•	Why “operational”? It shows the actual way to access to the population. 
Sampling frame –  Example




•	Target population: all people living in HCM city with a monthly income higher than VND 15 million.
•    Where can you find them?




•	Sampling frame: the list of all people with a monthly income that is higher than VND 15 million from the Tax Office of HCMC. 
Sampling frame –  Considerations




•	Note that there would always be a difference between the sampling frame and the target population.

•	In the previous example, who would we miss out in the list of people acquired from the Tax Office of HCMC?



•	That difference between the target population and the sampling frame is called “sampling frame error”. 
Sampling frame –  Considerations




•	There is not always a sampling frame that you can access to for usage in your project.




•	We will need to select relevant sampling methods in the next step based  on whether we have access to a sampling frame. 
Step  3: Selecting the sampling methods




Access to a sampling frame and use it?

 
NO
Non-probability sampling
 
YES Probability sampling 

	
		
 

Convenience            Judgement               Snowball                        Quota
 
Simple
Random
 

Systematic                Stratified                            Cluster 
Probability vs.  non-probability 
Probability methods 
Example: simple random  sampling




•    There is a list of people in HCMC with income higher than
VND15 mil/month from the Tax Office.




•	Researchers randomly select 1000 names from that list (if the sample size is 1000). 
Example: systematic sampling





•	There is the list of people in HCMC with income higher than VND15 mil/month from the Tax Office.



•    The list contains the names of 2 million people.


•	The sample size is 1000. We  divide 2 mil. by 1000 to get an sampling interval of 2000


•    We choose every 2000th name  on the list as the sample element. 
Example: cluster sampling





•	There is a list of people in HCMC with income higher than VND15 mil/month from the Tax Office.



•	The list also provides information about the districts these people live in. Each district  can be considered as  a cluster (a group of elements that are close in terms of geographic location).


•    There are 24 districts (both urban and rural) in HCMC.


•	10 districts are randomly selected from these 24 districts, and then researchers select totally 1000 people from these 10 districts. 
Example: Stratified  Sampling





•	There is a list of people in HCMC with income higher than VND15 mil/month from the Tax Office.



•	The list also provides information about their job positions in their workplace such as staff or manager.



•	Since the job position would be related to income, we divide the list of people into to sub-groups of staff and manager.


•    From these two subgroups we select the sample. 
Non-probability Methods 
Example: Convenient sampling





•	To select the sample from the target population of people who live in HCMC with an income above VND 15 mil, the researcher reaches out to friends and family members who are most conveniently available to meet for interview, or to join a focus group, or to complete a survey. 
Example: Judgment sampling




•	To select the sample from the target population of people who live in HCMC with an income above VND 15 mil, the researcher stands in front of shopping malls and reaches out to people seem to have such income level, by looking at their clothes, shoes, and accessories.

•	This selection is entirely based on the judgment of the researcher. 
Example: Snowball sampling





•	To select the sample from the target population of people who live in HCMC with an income above VND 15 mil, the researcher reaches out to people who have such income.

•	These people (could be friends and family members of the researcher) introduce the researcher to their contacts who, they know, would have such income. 
Example: Quota sampling



•	To select the sample from the target population of people who live in HCMC with an income above VND 15 mil, the researcher first checks the sub-groups within the population based on some demographics (e.g., gender).



•	From here, it is known that the percentage of male and female in the target population would be 51% and 49% respectively.



•	The researcher then collects enough data for the sample based on that ratio e.g., if the sample size is 1000, then data from 510 males and 490 females would be required. 
What can go wrong?





•	In sampling, what researchers are trying to achieve is to have the highest possible level of representativeness.


•    However, there are errors that are parts of the process. 
Errors associated with sampling































Source: Zikmund et al. 2010 
Sampling frame & random  sampling errors




•	Sampling frame error: the difference between total population and the actual sampling frame. If there is no sampling frame accessible, we cannot say anything about sampling frame error.



•    Random sampling error: the error created by the process of sampling itself.

•	Sampling means we only select representative units of the population rather than observing the whole population, so what we find out about that sample cannot be 100% true when we project it to the population.

•	In short, random sampling error is defined as the error created by random variation among the elements/units of the population. 
Sampling frame & random  sampling errors





•	Besides random sampling error, we have systematic sampling errors such as non-response error.



•	Non-response error: error created by the fact that a certain number of chosen sample elements will not respond to the survey due  to the wrong-timing, reluctance, etc. 
Systematic sampling errors (bias)


 
•     Non-response error



•     Response bias
•    Acquiescence
•    Extremity
•    Interviewer
•    Auspices
•    Social desirability
 
•     Administrative error
•    Data processing error
•    Sample selection error
•    Interviewer error
•    Interviewer cheating 
Step  4: Selecting the sampling units




•     Sampling unit is usually one unit of the target population.

•	However, we will need to note that in the case of multi-stage sampling, we might look at more than one level of sampling unit.

•	Multi-stage sampling: the sampling process in which the population is divided into smaller sub-groups in a few stages with different levels of sampling unit. A combination of cluster sampling, or stratified sampling and simple random sampling or systematic random sampling is usually applied.

•	For multi-stage sampling to be called probability sampling, there must be probability methods used in all steps. 
Example: Multi-stage sampling




•	Researchers want to measure the IQ of high school students in Vietnam. It would be unrealistic to try to have the list of all students in Vietnam.

•	In the first stage, researchers will get a list of all provinces and cities in Vietnam and select randomly from the list a certain number of provinces and cities. In this first stage, the sampling unit is geographic administrative unit i.e., province or city.

•	In the second stage, researchers will get a list of schools in each of the chosen provinces and cities, and randomly select a certain number of schools in those provinces and cities. In this 2nd stage, the sampling unit is school.

•	In the third stage, researchers get a list of all class groups in each school, and randomly choose the classes. In this 3rd stage, the sampling unit is class group.

•	In the fourth stage, researchers get a list of students from these classes, and randomly select students from class list. In this 4th stage, the sampling unit is student. 
Step  5: Determine the sample size




The sample size will be determined based on a few factors as follows:

1.	How much variation is expected between individual survey responses – greater expected variation requires larger sample size

2.    How precise must the outcomes be (margin of error, or p statistic)
– many surveys present numeric results ± 5%)

3.	How much error will be acceptable (confidence level) – many surveys claim to be 95% confident that their results are accurate



Note that the size of the population of interest has minimal impact on the sample size needed 
The Yamane  Formula 
Sample sizes required



p= 0.05, 95% confidence level
Size  of Population	Sample Size  Required
500	222
1000	286
3000	353
5000	370
10000	385
20000	392
30000	395
50000	397
	
At higher population numbers, impact on sample size is negligible
 
Practical considerations




•   Can we actually identify and research a sample?

•   Can we afford to do so?

•   Do we have time to do so?

•   What, if any, are the industry-specific influences?

•   How important is the resulting decision?




The best sample size in marketing research is the sample size that offers an optimal  balance between quality of data and the cost of obtaining it. 
Some important  notes





Most of the times in practical cases, the sampling process might require multi-stage sampling, in which there will be different units of sampling used in different stages, and different methods of sampling used in each of these stages. 
Calculating errors





Random sampling error:


Simple formula for 𝑒 = 𝑧 ∗  ! 
"

•    𝑒 = margin of error

•	z-score relevant to the level of confidence (z =1.96 if level of confidence is 95%

•    𝑛 = sample size

•    𝜎 = population standard deviation 
Calculating errors




Sampling frame error = #$#%&'()$" *)+, -*'.#&)"/ 01'., *)+,
#$#%&'()$" *)+,




•	Note that usually the population size is estimated and  not available accurately







Non-response error = #&'"",2 *'.#&, *)+, -'3(%'& *'.#&, *)+,
#&'"",2 *'.#&, *)+,