Importance of Sampling design in market research
What is the importance of sampling in research? What are the sampling methods?
Sampling is a crucial and indispensable part of market research. This blog is to understand the importance of sample design in market research
Introduction to sampling design
You can study the entire population, which will be hard when the population is large.
If there are only two occurrences of the phenomenon then you can do it easily
But you can learn about the population by sampling design.
Surprisingly small sample sizes can give a good reflection of a population parameter.
There are numerous consumers for the products and services.
Each consumer has their own preference for brands.
They also vary in terms of their perception of a brand on functional and emotional aspects.
Now, if you as a marketer, desire to reach out to each of these consumers, it is a huge task.
More so ever, the efforts in time and money need to be justified in terms of the benefits of conducting the exercise.
Sampling design Framework
Sampling design helps us to conduct a survey over a smaller sample compared to all eligible respondents.
Sample design is important due to the following aspects:
- Conducting a survey among all eligible respondent/household is a challenge
- The time involved in the survey
- Expenses incurred for a large survey
Let’s understand some important terminologies in sample design perspective
1. Population
The population is all the eligible consumers for a particular product or service.
The population will comprise the universe for the purpose of the marketing research problem.
Some examples are:
- Bathing soap bar is used by most of the households. But if there are households which use liquid bathing soap, then they will not be part of a survey for bathing soap bar.
- There are numerous consumers for chocolate. However, if you desire to conduct a survey among Cocoa powder chocolate your population will be smaller than chocolate consumers.
2. Census
If all the eligible respondents in the population or study object are considered, it is called census.
- Government of India conducts a survey of the population periodically to understand the demographic changes in the country. In India, it is conducted during the 1st year of the decade. Ie. census 1991, census 2001, census 2011, etc
- Some companies conduct a census of the retail shops selling certain product categories
Census is an elaborate exercise to understand the population, universe, etc.
These studies form a benchmark for few years.
In the case of many industrial products, however, the population is small, making a census feasible as well as desirable.
Key features of a census are:
- Large budget
- More time to complete the survey
- Variance in characteristic is large
- The cost of sampling errors is high ie. if the sample omitted a major respondent group/area/ manufacturer, the results could be the misleading
- The cost of non-sampling errors is low (ie. interviewers incorrectly questioning target respondents) as a large number of interviewers are involved.
However, when you are conducting a survey for your business or marketing objectives it becomes unviable to conduct it for a large sample.
3. Sample
A subgroup of the elements of the population selected for participation in the study.
Respondents are selected using a probability or non-probability sampling technique.
Key features of a sample are:
- Small budget
- Less time to complete the survey
- Variance in characteristic is small
- The cost of sampling errors is low
- The cost of non-sampling errors is high (ie. interviewers incorrectly questioning target respondents) as a limited number of interviewers are involved.
Sampling design – Key elements
You will need to define the following while planning your sampling design:
- Objectives
- Periodicity of survey
- Geographic coverage
- Research Instrument
- Sampling Design/Methodology used
- Stratification (if any)
- Target respondent
- Number of variables
- Nature of analysis
- Incidence rates
- Completion rate
- Resource constraints (if any)
- Sample Size
- Data Collection
Now let us understand the elements in the sample design.
1. Objectives
Example
- To collect data on the subjects of Household Consumer Expenditure and Employment and Unemployment
- On the basis of complete enumeration in a sample of 20 percent of the villages, to provide: (i) Estimates of the principal crop areas and production; and (ii) Information on irrigated area, un-irrigated area and also area under high yielding and local varieties of the various crops.
- To understand the brand awareness of Bathing soap bar
- To understand the consumption behaviour of chocolates
2. Periodicity of survey
One time or once a month or once a quarter or once a year, or once in 2 years, once in 5 years, etc
3. Geographic coverage
- All states in the country or
- Specific states in the country or
- Metros and Tier 1 cities or
- Urban and rural areas, etc
4. Research Instrument
Primary research can be Qualitative or Quantitative or both
Qualitative research is an unstructured, primarily exploratory design based on small samples, intended to provide insight and understanding.
Research techniques that seek to quantify data and, typically, apply some form of statistical analysis are called Quantitative research
5. Sampling Design
Sampling techniques are broadly classified into:
- Probability sampling – A sampling procedure in which each element of the population has a fixed probabilistic chance of being selected for the sample
- Non-probability sampling – Sampling techniques that do not use chance selection procedures but rather rely on the personal judgment of the researcher.
Sample size refers to the number of elements to be included in the study. Determining the sample size involves several qualitative and quantitative considerations.
Types of Probability sampling
- Simple random sampling (SRS) – A probability sampling technique in which each element has a known and equal probability of selection. Every element is selected independently of every other element, and the sample is drawn by a random procedure from a sampling frame.
- Systematic sampling – A probability sampling technique in which the sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame
- Stratified sampling – A probability sampling technique that uses a two-step process to partition the population into subsequent subpopulations, or strata. Elements are selected from each stratum by a random procedure sampling
- Cluster sampling – A two-step probability sampling technique where the target population is first divided into mutually exclusive and collectively exhaustive subpopulations called clusters, and then a random sample of clusters is selected based on a probability sampling technique such as SRS.
Types of Non-Probability sampling
- Convenience sampling: A non-probability sampling technique that attempts to obtain a sample of convenient elements.
- Judgemental sampling: A form of convenience sampling in which the population elements are purposely selected based on the judgment of the researcher.
- Quota sampling: A non-probability sampling technique that is a two-stage restricted judgemental sampling. The first stage consists of developing control categories or quotas of population elements. In the second stage, sample elements are selected based on convenience or judgment.
- Snowballing sampling: A non-probability sampling technique in which an initial group of respondents is selected randomly.
6. Stratification (if any)
Stratification pertains to the levels of strata.
Level 1 – Stratum formed at district level.
Level 2 – Within each district of a State/UT, two basic strata have been formed:
(i) rural stratum comprising of all rural areas of the district and
(ii) urban stratum comprising of all the urban areas of the district.
7. Target respondent
You need to define the respondent criteria ie. who should be interviewed – Male / Female, Age group, Socio economic classification, etc
8. Number of variables
Urban & Rural, Male & Female, Socio economic class – A, B, C and other
9. Nature of analysis
- Segmentation
- Conjoint
- Regression-based on certain criteria
- Any other statistical analysis, etc
10. Incidence rates
- Incidence rate is based on the rate of the last screening question in the survey (when there is more than one).
- If your survey is too targeted (e.g., with 4 screening questions), it may result in a lower incidence rate.
- We require surveys to meet a minimum incidence rate of 5% to provide valid data and enough responses.
11. Completion rate
If the survey is long, there will be incomplete surveys. Completion rates are the number of respondents completing the survey.
12. Resource constraints (if any)
Some surveys may seek to have a sample of users of new or niche brands.
Such users may be difficult to get in the random survey.
This sample can be covered through non-probability sampling techniques
13. Sample Size
Sample size considerations will be different for the qualitative and quantitative surveys.
In a Qualitative survey, fewer participants are interviewed as the goal is to achieve data saturation, i.e., when themes begin to repeat within each subsequent participant response during the data analysis phase.
Non-probability sampling applies to qualitative research.
A quantitative survey requires the use of probability or non-probability sampling based upon research objectives and methodology.
For a quantitative survey – the sample size should:
- Adequately represent the diversity
- Need to understand the use to be made of the results
- What margin of error is needed to enable sound research conclusions?
14. Data Collection
Data collection methodology can be Online, Face-to-Face, Telephonic, etc.
Interviews in rural areas cannot be conducted through web-based surveys.
Face-to-Face is most ideal for fieldwork in rural areas if the length of the interview is more than 10~15 minutes.
Summary
We sample primarily to facilitate data collection that we use for research analysis particularly when the population being studied is larger.
Sampling enables you to collect and analyze data for a smaller portion of the population (sample) which must be representative of the entire population and then apply the results to the whole population.
Sampling permits you to draw conclusions about very complex situations.
Sampling permits you to do your research faster and at lesser costs.
In fact, sampling is very important in facilitating the research process but adequate care needs to be taken when selecting the sample because if the sample is not representative, the results being applied to the entire population will be misleading.
Hence, have a proper sampling framework is the most crucial part of the survey.
Moreover, the execution needs to happen as per defined guidelines to ensure consistency and minimizing sampling errors.
