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Sampling - Types, Steps in Sampling process.pdf
- 1. What is Sampling? Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time-convenient and cost-effective method and hence forms the basis of any research design. Sampling techniques can be used in research survey software for optimum derivation. For example, suppose a drug manufacturer would like to research the adverse side effects of a drug on the country’s population. In that case, it is almost impossible to conduct a research study that involves everyone. In this case, the researcher decides on a sample of people from each demographic and then researches them, giving him/her indicative feedback on the drug’s behavior. SAMPLING DESIGN A Sampling design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the business researches would adopt to select units for the sample. It will also indicate the number of units to be included in the sample also known as Sample size. Sampling design is determined before data are collected. While developing a sampling design, the management must pay attention to the following points. Type of Population: The first step in developing any sampling design is to clearly define the aggregate of sampling units, namely, the population. Sometimes the structure of the population is quite unambiguous such as the population of a city or the number of workers in a factory. However, there may be occasions where the composition of the population is not so simple. For example, in the context of advertising a company may like to collect information about the number of television viewers watching the advertisement. Sampling Unit: The sampling unit must be identified before selection of a sample. A sampling unit may be a natural geographical unit such as a state, a district, a village or a constructed unit such as a flat. It may be a social entity such as a family or a school. It may also be an individual. The management should be able to select appropriate unit so that its objective is achieved.
- 2. Types of Sampling: Sampling Methods Sampling in market action research is of two types – probability sampling and non-probability sampling. 1. Probability sampling: Probability sampling is a sampling technique where a researcher selects a few criteria and chooses members of a population randomly. All the members have an equal opportunity to participate in the sample with this selection parameter. 2. Non-probability sampling: In Non-Probability sampling, the researcher randomly chooses members for research. This sampling method is not a fixed or predefined selection process. This makes it difficult for all population elements to have equal opportunities to be included in a sample. 1. PROBABILITY SAMPLING Types of Probability Sampling with examples: Probability sampling is a technique in which researchers choose samples from a larger population based on the theory of probability. This sampling method considers every member of the population and forms samples based on a fixed process. For example, in a population of 1000 members, every member will have a 1/1000 chance of being selected to be a part of a sample. Probability sampling eliminates sampling bias in the population and allows all members to be included in the sample. There are four types of probability sampling techniques: Simple Random sampling: One of the best probability sampling techniques that helps in saving time and resources is the Simple
- 3. Random Sampling method. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the same probability of being chosen to be a part of a sample. For example, in an organization of 500 employees, if the HR team decides on conducting team-building activities, they would likely prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected. Systematic sampling: Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, this sampling technique is the least time-consuming. For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. He/she numbers each element of the population from 1-5000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10). Stratified random sampling: Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then draw a sample from each group separately. For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results. Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters representing a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inferences from the feedback. For example, suppose the United States government wishes to evaluate the number of immigrants living in the Mainland US. In that case, they can divide it into clusters based on states such as California, Texas, Florida, Massachusetts, Colorado, Hawaii, etc. This way of conducting a survey will be more effective as the results will be organized into states and provide insightful immigration data. Uses of probability sampling There are multiple uses of probability sampling: Reduce Sample Bias: Using the probability sampling method, the research bias in the sample derived from a population is negligible to non-existent. The sample selection mainly depicts the researcher’s understanding and inference. Probability sampling leads to higher-
- 4. quality data collection as the sample appropriately represents the population. Diverse Population: When the population is vast and diverse, it is essential to have adequate representation so that the data is not skewed toward one demographic. For example, suppose Square would like to understand the people that could make their point-of-sale devices. In that case, a survey conducted from a sample of people across the US from different industries and socio-economic backgrounds helps. Create an Accurate Sample: Probability sampling helps the researchers plan and create an accurate sample. This helps to obtain well-defined data. 2. NON-PROBABILITY SAMPLING Types of Non-Probability sampling with examples: The Non-Probability Method is a sampling method that involves a collection of feedback based on a researcher or statistician’s sample selection capabilities and not on a fixed selection process. In most situations, the output of a survey conducted with a non-probable sample leads to skewed results, which may not represent the desired target population. But, there are situations, such as the preliminary stages of research or cost constraints for conducting research, where non-probability sampling will be much more useful than the other type. Convenience sampling: This method depends on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as convenience sampling because of the researcher’s ease of carrying it out and getting in touch with the subjects. Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness. This non-probability sampling method is used when there are time and cost limitations in collecting feedback. In situations with resource limitations, such as the initial stages of research, convenience sampling is used. For example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly. Consecutive Sampling: Consecutive sampling is similar to convenience sampling with a slight variation. The researcher picks a single person or a group of people for sampling. Then the researcher researches for a period of time to analyze the result and move to another group if needed. Judgmental or purposive sampling: Judgmental or purposive samples are formed at the researcher’s discretion. Researchers purely consider the purpose of the study, along with the understanding of the target audience. For instance, when researchers want to understand the thought process of people interested in studying for their master’s degree. The selection criteria will be: “Are you interested in doing your masters in …?” and those who respond with a “No” are excluded from the sample.
- 5. Snowball sampling: Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace. For example, surveying shelterless people or illegal immigrants will be extremely challenging. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method when the topic is highly sensitive and not openly discussed—for example, surveys to gather information about HIV Aids. Not many victims will readily respond to the questions. Still, researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information. Quota sampling: In Quota sampling, members in this sampling technique selection happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples. Uses of Non-probability sampling Non-probability sampling is used for the following: Create a hypothesis: Researchers use the non-probability sampling method to create an assumption when limited to no prior information is available. This method helps with the immediate return of data and builds a base for further research. Exploratory research: Researchers use this sampling technique widely when conducting qualitative research, pilot studies, or exploratory research. Budget and time constraints: The non-probability method when there are budget and time constraints, and some preliminary data must be collected. Since the survey design is not rigid, it is easier to pick respondents randomly and have them take the survey or questionnaire. How do you decide on the type of sampling to use? For any research, it is essential to choose a sampling method accurately to meet the goals of your study. The effectiveness of your sampling relies on various factors. Here are some steps expert researchers follow to decide the best sampling method. Jot down the research goals. Generally, it must be a combination of cost, precision, or accuracy. Identify the effective sampling techniques that might potentially achieve the research goals. Test each of these methods and examine whether they help achieve your goal. Select the method that works best for the research.
- 6. Difference between Probability Sampling and Non-probability sampling methods The significant differences between probability sampling methods and non- probability sampling methods are as below: Probability Sampling Methods Non-Probability Sampling Methods Definition Probability Sampling is a sampling technique in which samples from a larger population are chosen using a method based on the theory of probability. Non-probability sampling is a sampling technique in which the researcher selects samples based on the researcher’s subjective judgment rather than random selection. Alternatively Known as Random sampling method. Non-random sampling method Population selection The population is selected randomly. The population is selected arbitrarily. Nature The research is conclusive. The research is exploratory. Sample Since there is a method for deciding the sample, the population demographics are conclusively represented. Since the sampling method is arbitrary, the population demographics representation is almost always skewed. Time Taken Takes longer to conduct since the research design defines the selection parameters before the market research study begins. This type of sampling method is quick since neither the sample nor the selection criteria of the sample are undefined.
- 7. Results This type of sampling is entirely unbiased; hence, the results are also conclusive. This type of sampling is entirely biased, and hence the results are biased, too, rendering the research speculative. Hypothesis In probability sampling, there is an underlying hypothesis before the study begins, and this method aims to prove the hypothesis. In non-probability sampling, the hypothesis is derived after conducting the research study. STEPS IN SAMPLING PROCESS An operational sampling process can be divided into seven steps as given below: 1. Defining the target population. 2. Specifying the sampling frame. 3. Specifying the sampling unit. 4. Selection of the sampling method. 5. Determination of sample size. 6. Specifying the sampling plan. 7. Selecting the sample. 1. Defining the Target Population: Defining the population of interest, for business research, is the first step in sampling process. In general, target population is defined in terms of element, sampling unit, extent, and time frame. The definition should be in line with the objectives of the research study. For ex, if a kitchen appliances firm wants to conduct a survey to ascertain the demand for its micro ovens, it may define the population as ‘all women above the age of 20 who cook (assuming that very few men cook)’. However this definition is too broad and will include every household in the country, in the population that is to be covered by the survey. Therefore the definition can be further refined and defined at the sampling unit level, that, all women above the age 20, who cook and whose monthly household income exceeds Rs.20,000. This reduces the target population size and makes the research more focused. The population definition can be refined further by specifying the area from where the researcher has to draw his sample, that is, households located in Hyderabad. A well defined population reduces the probability of including the respondents who do not fit the research objective of the company. For ex, if the population is defined as all women above the age of 20, the researcher may end up taking
- 8. the opinions of a large number of women who cannot afford to buy a micro oven. 2. Specifying the Sampling Frame: Once the definition of the population is clear a researcher should decide on the sampling frame. A sampling frame is the list of elements from which the sample may be drawn. Continuing with the micro oven ex, an ideal sampling frame would be a database that contains all the households that have a monthly income above Rs.20,000. However, in practice it is difficult to get an exhaustive sampling frame that exactly fits the requirements of a particular research. In general, researchers use easily available sampling frames like telephone directories and lists of credit card and mobile phone users. Various private players provide databases developed along various demographic and economic variables. Sometimes, maps and aerial pictures are also used as sampling frames. Whatever may be the case, an ideal sampling frame is one that entire population and lists the names of its elements only once. A sampling frame error pops up when the sampling frame does not accurately represent the total population or when some elements of the population are missing another drawback in the sampling frame is over —representation. A telephone directory can be over represented by names/household that have two or more connections. 3. Specifying the Sampling Unit: A sampling unit is a basic unit that contains a single element or a group of elements of the population to be sampled. In this case, a household becomes a sampling unit and all women above the age of 20 years living in that particular house become the sampling elements. If it is possible to identify the exact target audience of the business research, every individual element would be a sampling unit. This would present a case of primary sampling unit. However, a convenient and better means of sampling would be to select households as the sampling unit and interview all females above 20 years, who cook. This would present a case of secondary sampling unit. 4. Selection of the Sampling Method: The sampling method outlines the way in which the sample units are to be selected. The choice of the sampling method is influenced by the objectives of the business research, availability of financial resources, time constraints, and the nature of the problem to be investigated. All sampling methods can be grouped under two distinct heads, that is, probability and non-probability sampling. 5. Determination of Sample Size: The sample size plays a crucial role in the sampling process. There are various ways of classifying the techniques used in determining the sample size. A couple those hold primary importance and are worth mentioning are whether the technique deals with fixed or sequential sampling and whether its logic is based on traditional or Bayesian methods. In non-probability sampling
- 9. procedures, the allocation of budget, thumb rules and number of sub groups to be analyzed, importance of the decision, number of variables, nature of analysis, incidence rates, and completion rates play a major role in sample size determination. In the case of probability sampling, however, formulas are used to calculate the sample size after the levels of acceptable error and level of confidence are specified. The details of the various techniques used to determine the sample size will be explained at the end of the chapter. 6. Specifying the Sampling Plan: In this step, the specifications and decisions regarding the implementation of the research process are outlined. Suppose, blocks in a city are the sampling units and the households are the sampling elements. This step outlines the modus operandi of the sampling plan in identifying houses based on specified characteristics. It includes issues like how is the interviewer going to take a systematic sample of the houses. What should the interviewer do when a house is vacant? What is the recontact procedure for respondents who were unavailable? All these and many other questions need to be answered for the smooth functioning of the research process. These are guide lines that would help the researcher in every step of the process. As the interviewers and their co-workers will be on field duty of most of the time, a proper specification of the sampling plans would make their work easy and they would not have to revert to their seniors when faced with operational problems. 7. Selecting the Sample: This is the final step in the sampling process, where the actual selection of the sample elements is carried out. At this stage, it is necessary that the interviewers stick to the rules outlined for the smooth implementation of the business research. This step involves implementing the sampling plan to select the sampling plan to select a sample required for the survey.