Description
techniques used in market research. It explains various sampling methods like random sampling, stratified sampling, cluster sampling, systematic sampling, convenience sampling, quota sampling, judgement sampling and snowball sampling.
SAMPLING DESIGN
1
Basic definitions and concepts
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Population – A predefined set of potential respondents in geographical area. The entire aggregation of items/objects from which samples can be drawn is known as population. ‘N’ represents the population size. Census – A complete study of all the elements present in the population is known as Census. The national population survey is an example of census survey. (Collection of demographic information) (Its usually a complete count of population). It is a procedure that records information about the members of population.
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2
Basic definitions and concepts…
Sample – Sample represents a proportion of population. Sample size is represented by ‘n’. Sampling – Sampling is the process of selecting a representative part of the population for the purpose of determining the characteristics of the whole population. Bias – Bias is term used to describe a tendency or preference towards a particular perspective, ideology or result. In statistics, there is a sampling bias in which some members of the population are more likely to be included than others.
3
SAMPLING METHODS
Sampling methods are divided into two broad types:
I)
II)
Probability sampling – A sampling process in which every member of the population has the probability of being included in the sample. Non-probability sampling – This involves selection of units based on the factors other than random chance. (Also called as deliberate or purposive sampling).
NON-PROBABILITY SAMPLING
1) Convenience sampling 2) Quota sampling 3) Judgment sampling
PROBABILITY SAMPLING
1) Simple random sampling 2) Stratified sampling 3) Cluster sampling
4
4) Systematic sampling
4) Snowball sampling
PROBABILITY SAMPLING
1. SIMPLE RANDOM SAMPLING
?
A sampling process where each element in the target population has an equal chance or probability of being included in the sample is known as simple random sampling. Every element is selected independently of every other element and the sample is drawn by a random procedure from a sampling frame. For example, selecting 5 students randomly from a class of 20 students by making chits of their names.
?
?
5
PROBABILITY SAMPLING
2. STRATIFIED RANDOM SAMPLING
?
Stratification is the process of grouping members of the population into relatively homogenous subgroups before sampling. It should be ensured that each element in the population is assigned a particular stratum only. Then random sampling is applied within each stratum independently. This often improves the representativeness of the sample by reducing sampling error. Stratified sampling with its potential for greater statistical efficiency scores over simple random sampling.
?
6
PROBABILITY SAMPLING
3. CLUSTER SAMPLING
?
Cluster sampling is a process of grouping the elements in a population into various clusters and then selecting few clusters randomly for further study. It has to be ensured that clusters are homogenous. Cluster sampling is suitable for covering larger geographical areas having respondents scattered all over. It differs from stratified sampling in the sense that sampling is done on clusters in contrast to elements within the strata as in case of stratified sampling, Elements are randomly chosen from each stratum in case of stratified sampling whereas only selected clusters are studied in cluster sampling.
?
7
PROBABILITY SAMPLING
4. SYSTEMATIC SAMPLING
?
Systematic sampling involves the selection of every ‘n’th element from a sampling frame. A random starting point should be selected. ‘n’ represents the skip interval and skip interval = Population size / sample size. For example, if you want to sample 8 houses out of 120 houses then 120 / 8 = 15, so every 15th house is chosen after a random starting point between 1 and 15. This method is more economical and less time consuming than simple random sampling.
?
?
8
NON-PROBABILITY SAMPLING
1. CONVENIENCE SAMPLING
?
The selection of units from the population based on their easy availability and accessibility to the researcher is known as convenience sampling. This process includes the respondent whoever happens to be available at the time.
?
?
Its main disadvantage is the difficulty in determining how much of the effect (dependent variable) results from the cause (independent variable).
9
NON-PROBABILITY SAMPLING
2. QUOTA SAMPLING
?
In quota sampling, the entire population is segmented into mutually exclusive groups or categories.
?
For example, if a researcher wants to segment the entire population based on gender, then he would have two categories of the respondents i.e. males and females. If he plans to select a sample of 30, he may allot a quota of 50% for each category that is, 15 male and 15 female respondents.
?
10
NON-PROBABILITY SAMPLING
3. JUDGEMENT SAMPLING
?
The selection of units from the population based on the judgement of an experienced researcher or an expert is known as judgement or purposive sampling. For example, a company prefers certain cities / shopping malls during test marketing their products. It depends on the personal judgement or previous experience of the researcher. Its disadvantage is that there is a chance for inaccuracy in the researcher’s criteria and resulting sample selections.
?
?
11
NON-PROBABILITY SAMPLING
4. SNOWBALL SAMPLING
?
?
?
12
Sampling procedure involving the selection of additional respondents based on the referrals of initial respondents are known as snowball sampling. This sampling technique is used against low incidence populations or rare populations. This involves selecting a few individuals who can identify other individuals who can identify still other individuals who might be good participants for a study. For example, SG sports pvt ltd – a manufacturer of sports equipment plans to survey 100 senior squash players for getting their feedback on the quality of its products. Respondent may go to one squash player and get the information on how to contact another squash player.
STEPS INVOLVED IN SAMPLING PROCESS
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1. DEFINE THE TARGET POPULATION 2. SPECIFY THE SAMPLING FRAME 3. SPECIFY THE SAMPLING UNIT 4. SELECTION OF SAMPLING METHODS
?
?
?
13 ? 5. DETERMINATION OF SAMPLING SIZE
1. Define the target population
?
Target population should be defined in line with the objectives of the research study. For example, a kitchen appliances firm wants to conduct a survey to estimate the demand for its micro ovens. It may define the population as “all women above the age of 20 years who can cook, whose monthly household income exceeds Rs. 20,000 and residing in Mumbai. A well defined population reduces the probability of including the respondents who do not fit the research objectives of the company.
?
?
14
2. Specify the sampling frame
?
Sampling frame is the list of elements from which sample may be drawn. Sampling frame for microwave oven would be the database that contains all the households having a monthly income above Rs 20,000. Researchers generally use easily available sampling frames like telephone directories and list of credit card users and mobile phone users. Some private players provide databases developed along various demographic and economic variables.
?
?
15
3. Specify the sampling unit
?
A sampling unit is a basic unit a single unit or group of elements of the population to be sampled.
?
For example, blocks / sectors in an area can be described as the sampling unit and households can be described as the sampling elements. Sampling element is an exact target audience.
?
16
4. Selection of sampling method
?
The choice of sampling methods is influenced by the following factors –
– – – –
The objectives of the research study Availability of financial resources Time constraints Nature of the problem to be investigated
17
5. Determination of sample size
?
In case of probability sampling, formulas are used to calculate the sample size after the levels of acceptable error and level of confidence are specified. In case of non – probability sampling, allocation of budget, thumb rules, number of variables, nature of analysis play a major role in the sample size determination.
?
18
Criteria for selecting appropriate sample design
?
Degree of accuracy – It has to ensured that sample is representative of the target population.
?
Resources – Resources in the form of budget allocation and manpower has to be decided.
Time – Time is another important criteria. Researchers are likely to opt for simple, less time consuming sampling designs in case of time constraint. Prior knowledge of the population – A researcher should have the prior knowledge in terms of population characteristics and the availability of the respondents.
?
?
19
Sample size determination when population mean/SD is known
When population mean or population standard deviation is known, the sample size is calculated by using the following formula – n = [ ZS / E ] 2 Where,
?
Z = standardized value corresponding to the confidence level S = sample standard deviation E = acceptable level of error, plus or minus an error factor
20
Sample size determination when population proportion is known
?
When population proportion is known, the sample is calculated by using the following formula –
n=pq[Z/E]2
Where, p = probability of success q = probability of failure = 1 – p Z = standardized value corresponding to the confidence level E = acceptable level of error, plus or minus an error factor
21
doc_940375730.pptx
techniques used in market research. It explains various sampling methods like random sampling, stratified sampling, cluster sampling, systematic sampling, convenience sampling, quota sampling, judgement sampling and snowball sampling.
SAMPLING DESIGN
1
Basic definitions and concepts
?
Population – A predefined set of potential respondents in geographical area. The entire aggregation of items/objects from which samples can be drawn is known as population. ‘N’ represents the population size. Census – A complete study of all the elements present in the population is known as Census. The national population survey is an example of census survey. (Collection of demographic information) (Its usually a complete count of population). It is a procedure that records information about the members of population.
?
2
Basic definitions and concepts…
Sample – Sample represents a proportion of population. Sample size is represented by ‘n’. Sampling – Sampling is the process of selecting a representative part of the population for the purpose of determining the characteristics of the whole population. Bias – Bias is term used to describe a tendency or preference towards a particular perspective, ideology or result. In statistics, there is a sampling bias in which some members of the population are more likely to be included than others.
3
SAMPLING METHODS
Sampling methods are divided into two broad types:
I)
II)
Probability sampling – A sampling process in which every member of the population has the probability of being included in the sample. Non-probability sampling – This involves selection of units based on the factors other than random chance. (Also called as deliberate or purposive sampling).
NON-PROBABILITY SAMPLING
1) Convenience sampling 2) Quota sampling 3) Judgment sampling
PROBABILITY SAMPLING
1) Simple random sampling 2) Stratified sampling 3) Cluster sampling
4
4) Systematic sampling
4) Snowball sampling
PROBABILITY SAMPLING
1. SIMPLE RANDOM SAMPLING
?
A sampling process where each element in the target population has an equal chance or probability of being included in the sample is known as simple random sampling. Every element is selected independently of every other element and the sample is drawn by a random procedure from a sampling frame. For example, selecting 5 students randomly from a class of 20 students by making chits of their names.
?
?
5
PROBABILITY SAMPLING
2. STRATIFIED RANDOM SAMPLING
?
Stratification is the process of grouping members of the population into relatively homogenous subgroups before sampling. It should be ensured that each element in the population is assigned a particular stratum only. Then random sampling is applied within each stratum independently. This often improves the representativeness of the sample by reducing sampling error. Stratified sampling with its potential for greater statistical efficiency scores over simple random sampling.
?
6
PROBABILITY SAMPLING
3. CLUSTER SAMPLING
?
Cluster sampling is a process of grouping the elements in a population into various clusters and then selecting few clusters randomly for further study. It has to be ensured that clusters are homogenous. Cluster sampling is suitable for covering larger geographical areas having respondents scattered all over. It differs from stratified sampling in the sense that sampling is done on clusters in contrast to elements within the strata as in case of stratified sampling, Elements are randomly chosen from each stratum in case of stratified sampling whereas only selected clusters are studied in cluster sampling.
?
7
PROBABILITY SAMPLING
4. SYSTEMATIC SAMPLING
?
Systematic sampling involves the selection of every ‘n’th element from a sampling frame. A random starting point should be selected. ‘n’ represents the skip interval and skip interval = Population size / sample size. For example, if you want to sample 8 houses out of 120 houses then 120 / 8 = 15, so every 15th house is chosen after a random starting point between 1 and 15. This method is more economical and less time consuming than simple random sampling.
?
?
8
NON-PROBABILITY SAMPLING
1. CONVENIENCE SAMPLING
?
The selection of units from the population based on their easy availability and accessibility to the researcher is known as convenience sampling. This process includes the respondent whoever happens to be available at the time.
?
?
Its main disadvantage is the difficulty in determining how much of the effect (dependent variable) results from the cause (independent variable).
9
NON-PROBABILITY SAMPLING
2. QUOTA SAMPLING
?
In quota sampling, the entire population is segmented into mutually exclusive groups or categories.
?
For example, if a researcher wants to segment the entire population based on gender, then he would have two categories of the respondents i.e. males and females. If he plans to select a sample of 30, he may allot a quota of 50% for each category that is, 15 male and 15 female respondents.
?
10
NON-PROBABILITY SAMPLING
3. JUDGEMENT SAMPLING
?
The selection of units from the population based on the judgement of an experienced researcher or an expert is known as judgement or purposive sampling. For example, a company prefers certain cities / shopping malls during test marketing their products. It depends on the personal judgement or previous experience of the researcher. Its disadvantage is that there is a chance for inaccuracy in the researcher’s criteria and resulting sample selections.
?
?
11
NON-PROBABILITY SAMPLING
4. SNOWBALL SAMPLING
?
?
?
12
Sampling procedure involving the selection of additional respondents based on the referrals of initial respondents are known as snowball sampling. This sampling technique is used against low incidence populations or rare populations. This involves selecting a few individuals who can identify other individuals who can identify still other individuals who might be good participants for a study. For example, SG sports pvt ltd – a manufacturer of sports equipment plans to survey 100 senior squash players for getting their feedback on the quality of its products. Respondent may go to one squash player and get the information on how to contact another squash player.
STEPS INVOLVED IN SAMPLING PROCESS
?
1. DEFINE THE TARGET POPULATION 2. SPECIFY THE SAMPLING FRAME 3. SPECIFY THE SAMPLING UNIT 4. SELECTION OF SAMPLING METHODS
?
?
?
13 ? 5. DETERMINATION OF SAMPLING SIZE
1. Define the target population
?
Target population should be defined in line with the objectives of the research study. For example, a kitchen appliances firm wants to conduct a survey to estimate the demand for its micro ovens. It may define the population as “all women above the age of 20 years who can cook, whose monthly household income exceeds Rs. 20,000 and residing in Mumbai. A well defined population reduces the probability of including the respondents who do not fit the research objectives of the company.
?
?
14
2. Specify the sampling frame
?
Sampling frame is the list of elements from which sample may be drawn. Sampling frame for microwave oven would be the database that contains all the households having a monthly income above Rs 20,000. Researchers generally use easily available sampling frames like telephone directories and list of credit card users and mobile phone users. Some private players provide databases developed along various demographic and economic variables.
?
?
15
3. Specify the sampling unit
?
A sampling unit is a basic unit a single unit or group of elements of the population to be sampled.
?
For example, blocks / sectors in an area can be described as the sampling unit and households can be described as the sampling elements. Sampling element is an exact target audience.
?
16
4. Selection of sampling method
?
The choice of sampling methods is influenced by the following factors –
– – – –
The objectives of the research study Availability of financial resources Time constraints Nature of the problem to be investigated
17
5. Determination of sample size
?
In case of probability sampling, formulas are used to calculate the sample size after the levels of acceptable error and level of confidence are specified. In case of non – probability sampling, allocation of budget, thumb rules, number of variables, nature of analysis play a major role in the sample size determination.
?
18
Criteria for selecting appropriate sample design
?
Degree of accuracy – It has to ensured that sample is representative of the target population.
?
Resources – Resources in the form of budget allocation and manpower has to be decided.
Time – Time is another important criteria. Researchers are likely to opt for simple, less time consuming sampling designs in case of time constraint. Prior knowledge of the population – A researcher should have the prior knowledge in terms of population characteristics and the availability of the respondents.
?
?
19
Sample size determination when population mean/SD is known
When population mean or population standard deviation is known, the sample size is calculated by using the following formula – n = [ ZS / E ] 2 Where,
?
Z = standardized value corresponding to the confidence level S = sample standard deviation E = acceptable level of error, plus or minus an error factor
20
Sample size determination when population proportion is known
?
When population proportion is known, the sample is calculated by using the following formula –
n=pq[Z/E]2
Where, p = probability of success q = probability of failure = 1 – p Z = standardized value corresponding to the confidence level E = acceptable level of error, plus or minus an error factor
21
doc_940375730.pptx