Hypothesis & sampling
- 1. HYPOTHESIS & SAMPLING Dr. S. BELLARMIN DIANA ASSISTANT PROFESSOR DEPARTMENT OF MANAGEMENT STUDIES BON SECOURS COLLEGE FOR WOMEN, THANJAVUR
- 2. HYPOTHESIS Hypothesis is the composition of some variables which have some specific position or role of the variables i.e., to be verified empirically. It is a proposition about the factual and conceptual elements. Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of a problem. Hypothesis is the composition of some variables which have some specific position or role of the variables i.e. to be verified empirically. It is a proposition about the factual and conceptual elements. Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of a problem. Hypothesis is the composition of some variables which have some specific position or role of the variables i.e. to be verified empirically. It is a proposition about the factual and conceptual elements. Hypothesis is called a leap into the dark. It is a brilliant guess about the solution of a problem. ASSUMPTION, POSTULATE AND HYPOTHESIS Assumption: Assumption means restrictive conditions before the argument can become valid. Assumptions are made on the basis of logical insight and their truthfulness can be observed on the basis of data or evidences. Postulate: Postulates are the working beliefs of most scientific activity. A postulate is a statement assumed to be true without need of proof of any kind. Hypothesis: A hypothesis is different from both of these. It is the presumptive statement of a proposition which the investigator seeks to prove. It is a condensed generalization. This generalization requires knowledge of principles of things or essential characteristics which pertain to entire class of phenomena.
- 3. NATURE OF HYPOTHESIS The hypothesis is a clear statement of what is intended to be investigated. It should be specified before research is conducted and openly stated in reporting the results. This allows to – Identify… ▪ the research objectives; ▪ the key abstract concepts involved in the research; and ▪ its relationship to both the problem statement and the literature review. The following are the main features of a hypothesis – It… Is conceptual in nature. Is a verbal statement in a declarative form. Has the empirical referent. Indicates the tentative relationship between two or more variables. Is a powerful tool of advancement of knowledge, consistent with existing knowledge and conducive to further enquiry Can be tested, verifiable or falsifiable. Is not moral or ethical questions. Is neither too specific nor to general. Is a prediction of consequences Is considered valuable even if proven false. FUNCTIONS / ROLES OF HYPOTHESIS A hypothesis serves as a sound guide to:
- 4. (i) the kind of data that must be collected in order to answer the research problem; (ii) (ii) the way in which the data should be organized most efficiently and meaningfully, and (iii) (iii) the type of methods that can be used for making analysis of the data. A hypothesis also performs the following significant functions – Test theories: A hypothesis, when empirically proved, helps us in testing an existing theory. A theory is not a mere speculation, but it is built upon facts. It is a set of inter-related propositions or statements organized into a deductive system that offers an explanation of some phenomenon. Facts constitute a theory when they are assembled, ordered and seen in a relationship. Therefore, when a hypothesis is ‘tested’, it not only supports the existing theory that accounts for description of some social phenomenon but also in a way ‘tests’ it. Suggest new theories: A hypothesis, even though related to some existing theory, may, after tested, reveal certain ‘facts’ that are not related to the existing theory or disclose relationships other than those stated in the theory. It does not support the existing theory but suggests a new theory. Describe social phenomenon: A hypothesis also performs a descriptive function. Each time a hypothesis is tested empirically, it tells us something about the phenomenon it is associated with. If the hypothesis is empirically supported, then our information about the phenomenon increases. Even if the hypothesis is refuted, the test tells us something about the phenomenon we did not know before. Suggest social policy: A hypothesis, after its testing, may highlight such ‘ills’ of the existing social or legislative policy. In such a situation, the tested hypothesis helps us in formulating (or reformulating) a social policy. It may also suggest or hint at probable solutions to the existing social problem(s) and their implementation. The hypotheses play significant role in the scientific studies. The following are some of the important role and functions of the hypothesis -
- 5. Helps in the testing of the theories. Serves as a great platform in the investigation activities. Provides guidance to the research work or study. Hypothesis sometimes suggests theories. Helps in knowing the needs of the data. Explains social phenomena. Develops the theory. Also acts as a bridge between the theory and the investigation. Provides a relationship between phenomena in such a way that it leads to the empirical testing of the relationship. Helps in knowing the most suitable technique of analysis. Helps in the determination of the most suitable type of research. Provides knowledge about the required sources of data. Research becomes focused under the direction of the hypothesis. It is very helpful in carrying out an enquiry of a certain activity. Helps in reaching conclusions, if it is correctly drawn. IMPORTANCE OF HYPOTHESIS Hypothesis as the Investigator’s ‘Eyes’: By guiding the investigator in further investigation it serves as the investigator’s ‘Eyes’ in seeking answers to tentatively adopted generalization. It Focuses Research: Without it, research is unfocussed research and remains like a random empirical wandering. It serves as necessary link between theory and the investigation. It Places Clear and Specific Goals: A well thought out set of hypothesis is that they place clear and specific goals before the research worker and provide researcher with a basis for selecting sample and research procedure to meet these goals. It Links Together: It serves the important function of linking together related facts and information and organizing them into wholes.
- 6. It Prevents Blind Research: The use of hypothesis prevents a blind search and indiscriminate gathering of masses of data which may later prove irrelevant to the problem under study. As a Sort of Guiding Light: A hypothesis serves as a powerful beacon that lights the way for the research work. Van Dalen advocates the importance of hypothesis in the following ways - Hypotheses are indispensable research instrument, for they build a bridge between the problem and the location of empirical evidence that may solve the problem. A hypothesis provides the map that guides and expedites the exploration of the phenomena under consideration. A hypothesis pin points the problem. The investigator can examine thoroughly the factual and conceptual elements that appear to be related to a problem. Using hypothesis determines the relevancy of facts. A hypothesis directs the researcher’s efforts into a productive channels. The hypothesis indicates not only what to look for is an investigation but how to obtain data. It helps in deciding research design. It may suggest what subjects, tests, tools, and techniques are needed. The hypothesis provides the investigator with the most efficient instrument for exploring and explaining the unknown facts. A hypothesis provides the framework for drawing conclusions. These hypotheses simulate the investigator for further research studies. CHARACTERISTICS OF A GOOD HYPOTHESIS A ‘workable’ or ‘usable’ hypothesis would be the one that satisfies many of the following criteria. Hypothesis should be conceptually clear: The concepts used in the hypothesis should be clearly defined, not only formally but also, if possibly, operationally. Formal definition of the concepts will clarify what a particular concept stands for, while the operational definition will
- 7. leave no ambiguity about what would constitute the empirical evidence or indicator of the concept on the plane of reality. Obviously, an undefined or ill-defined concept makes it difficult or rather impossible for the researcher to test hypothesis as there will not be any standard basis for researcher to know the observable facts. However, a researcher, while defining concepts, should use, as far as possible, the terms that are communicable or definitions that are commonly accepted. It should be stated as far as possible in most simple terms so that it can be easily understandable all concerned. Researcher should not create ‘a private world of words’. Hypothesis should be specific: No vague or value-judgmental terms should be used in formulation of a hypothesis. It should specifically state the posited relationship between the variables. It should include a clear statement of all the predictions and operations indicated therein and they should be precisely spelled out. Specific formulation of a hypothesis assures that research is practicable and significant. It helps to increase the validity of results because the more specific the statement or prediction, the smaller the probability that it will actually be borne out as a result of mere accident or chance. A researcher, therefore, must remember that narrower hypothesis is generally more testable and s/he should develop such a hypothesis. Hypothesis should be empirically testable: It should have empirical referents so that it will be possible to deduce certain logical deductions and inferences about it. Therefore, a researcher should take utmost care that his/her hypothesis embodies concepts or variables that have clear empirical correspondence and not concepts or variables that are loaded with moral judgments or values. Such statements as ‘criminals are no worse than businessmen’, ‘capitalists exploit their workers’, ‘bad parents beget bad children’, ‘bad homes breed criminality’, or ‘pigs are well named because they are so dirty’ can hardly be usable hypotheses as they do not have any empirical referents for testing their validity. In other words, a researcher should avoid using terms loaded with values or beliefs or words having moral or attitudinal connotations in his hypothesis. Hypothesis should be related to available techniques: Researcher may ignorance of the available techniques, makes him/her weak in formulating a workable hypothesis. A
- 8. hypothesis, therefore, needs to be formulated only after due thought has been given to the methods and techniques that can be used for measuring the concepts or variables incorporated in the hypothesis. Hypothesis should be related to a body of theory or some theoretical orientation: A hypothesis, if tested, helps to qualify, support, correct or refute an existing theory, only if it is related to some theory or has some theoretical orientation. A hypothesis imaginatively formulated does not only elaborate and improve existing theory but may also suggest important links between it and some other theories. Thus, exercise of deriving hypothesis from a body of theory may also be an occasion for scientific leap into newer areas of knowledge. A hypothesis derived from a theory invests its creator with the power of prediction of its future. The potency of hypothesis in regard to predictive purpose constitutes a great advancement in scientific knowledge. A genuine contribution to knowledge is more likely to result from such a hypothesis. A hypothesis, it is said, to be preferred is one which can predict what will happen, and from which we can infer what has already happened, even if we did not know (it had happened) when the hypothesis was formulated. SOURCES OF HYPOTHESIS A hypothesis or a set of hypotheses may originate from a variety of sources. The source of hypothesis, however, has an important bearing on the nature of contribution in the existing body of knowledge. A few prominent sources of hypothesis are discussed here below. Hunch or intuition: A hypothesis may be based simply on hunch or intuition of a person. It is a sort of virgin idea. Such a hypothesis, if tested, may ultimately make an important contribution to the existing science or body of knowledge. However, when a hypothesis is tested in only one study, it suffers from two limitations. First, there is no assurance that the relationship established between the two variables incorporated in the hypothesis will be found in other studies. Secondly, the findings of such a hypothesis are likely to be unrelated to, or unconnected with other theories or body of science. They are likely to remain isolated
- 9. bits of information. Nevertheless, these findings may raise interesting questions of worth pursuing. They may stimulate further research, and if substantiated, may integrate into an explanatory theory. Findings of other: A hypothesis may originate from findings of other study or studies. A hypothesis that rests on the findings of other studies is obviously free from the first limitation, i.e. there is no assurance that it may relate with other studies. If such a hypothesis is proved, it confirms findings of the earlier studies though it replicates earlier study conducted in different concrete conditions. A theory or a body of theory: A hypothesis may stem from existing theory or a body of theory. A theory represents logical deductions of relationship between inter-related proved facts. A researcher may formulate a hypothesis, predicting or proposing certain relationship between the facts or propositions interwoven in a theory, for verifying or reconfirming the relationship. A theory gives direction to research by stating what is known. Logical deductions from these known facts may trigger off new hypotheses. General social culture: General social culture furnishes many of its basic hypotheses. Particular value-orientation in the culture, if it catches attention of social scientists for their careful observation, generates a number of empirically testable propositions in the form of hypotheses. Analogy: Analogies may be one of the fertile sources of hypothesis. Analogies stimulate new valuable hypotheses. They are often a fountainhead of valuable hypotheses. Even casual observation in the nature or in the framework of another science may be a fertile source of hypotheses. A proved particular pattern of human behavior, in a set of circumstances or social settings, may be a source of hypothesis. A researcher may be tempted to test these established co-relations with similar attributes in different social settings. Researcher may be interested to test these analogies in a sort of different settings and circumstances. Researcher seeks inspiration for formulating the hypothesis from analogies of others. However, a researcher, when s/he uses analogy as a source of his/her hypothesis, needs to carefully appreciate the
- 10. theoretical framework in which the analogy was drawn and its relevancy in the new frame of reference. Personal experience: Not only do culture, science and analogy, among others, affect the formulation of hypotheses. The way in which an individual reacts to each of these is also a factor in the statement of hypotheses. Therefore, individual experience of an individual contributes to the type and the form of the questions researcher asks, as also to the kinds of tentative answers to these questions (hypotheses) that s/he might provide. Some scientists may perceive an interesting pattern from merely seem a ‘jumble of facts’ to a common man. The history of science is full of instances of discoveries made because the ‘right’ individual happened to make the ‘right’ observation because of researcher particular life history, personal experience or exposure to a unique mosaic of events. Researcher personal experience or life history may influence his/her perception and conception and in turn direct quite readily to formulate certain hypothesis. Thus, a hypothesis may originate from a variety of sources, in isolation or in combination with another. However, in spite of these fertile sources of hypotheses, it is not easy to formulate a usable or workable hypothesis. It is often more difficult to find and formulate a problem than to solve it. If a researcher succeeds in formulating a hypothesis, s/he can assure that it is half-solved. While formulating a hypothesis, researcher has to keep reminding that s/he has to formulate tentative proposition in such a way that it becomes usable in systematic study. TYPES OF RESEARCH HYPOTHESIS Before researchers can begin working on a question that interests them, they need to formulate a research hypothesis. This is an important step in the scientific method because this determines the direction of the study. Scientists need to scrutinize previous work in the area and select an experimental design to use that helps them find data that either supports or rejects their hypothesis. Research hypotheses are of different types: simple, complex,
- 11. directional, non-directional, associative, causal, inductive & deductive, null, and alternative or research. Simple Hypothesis: This predicts the relationship between a single independent variable (IV) and a single dependent variable (DV). For example: Lower levels of exercise postpartum (IV) will be associated with greater weight retention (DV). Complex Hypothesis: This predicts the relationship between two or more independent variables and two or more dependent variables. Example of a complex multiple independent variable hypothesis - low risk pregnant women (IV) who value health highly; believe that engaging in health promoting behaviours will result in positive outcomes; perceive fewer barriers to health promoting activities; are more likely than other women to attend pregnancy- related education programs (DV). Another example of a complex multiple dependent variable hypothesis - the implementation of an evidence based protocol for urinary incontinence (IV) will result in (DV) decreased frequency of urinary incontinence episodes; decreased urine loss per episode; decreased avoidance of activities among women in ambulatory care settings. Directional Hypothesis: This may imply that the researcher is intellectually committed to a particular outcome. They specify the expected direction of the relationship between variables i.e. the researcher predicts not only the existence of a relationship but also its nature. Scientific journal articles generally use this form of hypothesis. The investigator bases this hypothesis on the trends apparent from previous research on this topic. Considering the example, a researcher may state the hypothesis as, ‘High school students who participate in extracurricular activities have a lower GPA than those who do not participate in such activities.’ Such hypotheses provide a definite direction to the prediction. Non-directional Hypothesis: This form of hypothesis is used in studies where there is no sufficient past research on which to base a prediction. Do not stipulate the direction of the relationship. Continuing with the same example, a non-directional hypothesis would read, ‘The academic performance of high school students is related to their participation in extracurricular activities.’
- 12. Associative Hypothesis: Associative hypotheses propose relationships between variables, when one variable changes, the other changes. Do not indicate cause and effect. Causal Hypothesis: Causal hypotheses propose a cause and effect interaction between two or more variables. The independent variable is manipulated to cause effect on the dependent variable. The dependent variable is measured to examine the effect created by the independent variable. For the example mentioned, the causal hypothesis will state, ‘High school students who participate in extracurricular activities spend less time studying which leads to a low GPA.’ When verifying such hypotheses, the researcher needs to use statistical techniques to demonstrate the presence of a relationship between the cause and effect. Such hypotheses also need the researcher to rule out the possibility that the effect is a result of a cause other than what the study has examined. Inductive and Deductive Hypotheses: Inductive hypotheses are formed through inductively reasoning from many specific observations to tentative explanations. Deductive hypotheses are formed through deductively reasoning implications of theory. Null Hypothesis: This is a hypothesis that proposes no relationship or difference between two variables. This is the conventional approach to making a prediction. It involves a statement that says there is no relationship between two groups that the researcher compares on a certain variable. The hypothesis may also state that there is no significant difference when different groups are compared with respect to a particular variable. For example, ‘There is no difference in the academic performance of high school students who participate in extracurricular activities and those who do not participate in such activities’ is a null hypothesis. It asserts that there is no true difference in the sample statistic and population parameter under consideration (hence the word ‘null’ which means invalid, void, or a mounting to nothing) and that the difference found is accidental arising out of fluctuations of sampling. It is denoted as H0. What is sampling?
- 13. Sampling definition: Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate 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 a cost-effective method and hence forms the basis of any research design. Sampling techniques can be used in a research survey software for optimum derivation. For example, if a drug manufacturer would like to research the adverse side effects of a drug on the country’s population, it is almost impossible to conduct a research study that involves everyone. In this case, the researcher decides a sample of people from each demographic and then researches them, giving him/her indicative feedback on the drug’s behavior. Sample A sample is a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population. Samples are used in statistical testing when population sizes are too large for the test to include all possible members or observations. A sample should represent the whole population and not reflect bias toward a specific attribute. Characteristics of a Good Sample (1) Goal-oriented: A sample design should be goal oriented. It is means and should be oriented to the research objectives and fitted to the survey conditions. (2) Accurate representative of the universe: A sample should be an accurate representative of the universe from which it is taken. There are different methods for selecting a sample. It will be truly representative only when it represents all types of units or groups in the total population in fair proportions. In brief sample should be selected carefully as improper sampling is a source of error in the survey.
- 14. (3) Proportional: A sample should be proportional. It should be large enough to represent the universe properly. The sample size should be sufficiently large to provide statistical stability or reliability. The sample size should give accuracy required for the purpose of particular study. (4) Random selection: A sample should be selected at random. This means that any item in the group has a full and equal chance of being selected and included in the sample. This makes the selected sample truly representative in character. (5) Economical: A sample should be economical. The objectives of the survey should be achieved with minimum cost and effort. (6) Practical: A sample design should be practical. The sample design should be simple i.e. it should be capable of being understood and followed in the fieldwork. (7) Actual information provider: A sample should be designed so as to provide actual information required for the study and also provide an adequate basis for the measurement of its own reliability. In brief, a good sample should be truly representative in character. It should be selected at random and should be adequately proportional. These, in fact, are the attributes of a good sample. Essentials of a Sample Investigation
- 15. Some critical essentials of sampling include: 1. Representativeness – You must select the sample in a manner which represents the universe in its truest sense. Further, if you fail to do so, then you might get misleading results. 2. Adequacy – You should also select the size of the sample adequately which represents the parametric characteristics of the population. 3. Independence – When you select a sample, you must ensure that you select the items independently and also randomly. 4. Homogeneity – This is another important element of a sample investigation. Homogeneity means that there is no basic difference in the nature of the units in the sample and the universe. Merits of a Sample Investigation Here are some important merits of sampling:
- 16. 1. Cost-efficient – In a sample investigation, the costs associated with the collection of data are less. This is because you collect data only from a fraction of the entire population. Therefore, it is cost-efficient. 2. Time-efficient – In sampling, you require less time to collect, analyze, and interpret the data since you are working only on a fraction of the population. Hence, it is time-efficient too. 3. Reliable – Usually, the data collected under a sample investigation is reliable because of the use of well-trained and experienced investigators or experts. 4. Flexible – When you collect data through sampling, you have a greater scope of flexibility. 5. Detailed Information – Since sampling is cost-efficient and also time-efficient, you can collect detailed information about the sample in your survey. Demerits of a Sample Investigation While sampling has many merits, there are some demerits associated with it too. Here is a quick look: 1. It is impossible to attain a 100 percent accuracy using this process. This is because the investigator draws conclusions about the characteristics of the population using the results that he obtains from the selected sample. 2. The results are prone to a sampling error or a random error. 3. Experts are required to ensure that the results of a sample investigation are satisfactory. 4. Sometimes, the sample does not represent the population correctly. This is because it depends on the attitude and mindset of the investigators. 5. If the population has a heterogeneous character, then you cannot use this method. Types of sampling: sampling methods
- 17. Sampling in market research is of two types – probability sampling and non-probability sampling. Let’s take a closer look at these two methods of sampling. 1. Probability sampling: Probability sampling is a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. All the members have an equal opportunity to be a part of the sample with this selection parameter. 2. Non-probability sampling: In non-probability sampling, the researcher chooses members for research at random. This sampling method is not a fixed or predefined selection process. This makes it difficult for all elements of a population to have equal opportunities to be included in a sample. Types of probability sampling with examples: Probability sampling is a sampling technique in which researchers choose samples from a larger population using a method 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 bias in the population and gives all members a fair chance to be included in the sample.
- 18. 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 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, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 employees has an equal opportunity of being selected. • Cluster sampling: Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent 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 inference from the feedback. For example, if the United States government wishes to evaluate the number of immigrants living in the Mainland US, 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. • Systematic sampling: Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires the selection of a starting point for the sample and sample size that can be repeated at regular intervals. This type of sampling method has a predefined range, and 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
- 19. 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. Uses of probability sampling There are multiple uses of probability sampling. They are: • Reduce Sample Bias: Using the probability sampling method, the bias in the sample derived from a population is negligible to non-existent. The selection of the sample mainly depicts the understanding and the inference of the researcher. Probability sampling leads to higher 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 towards one demographic. For example, if Square would like to understand the people that could make their point-of-sale devices, 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. 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. Four types of non-probability sampling explain the purpose of this sampling method in a better manner:
- 20. • Convenience sampling: This method is dependent 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 where there are 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. • Judgmental or purposive sampling: Judgmental or purposive samples are formed by the discretion of the researcher. 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. • Snowball sampling: Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace. For example, it will be extremely challenging to survey shelter less people or illegal immigrants. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method in situations where 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, the selection of members in this sampling technique 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:
- 21. • 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 at random 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 in achieving your goal. • Select the method that works best for the research. Difference between Probability Sampling and Non-Probability Sampling Methods We have looked at the different types of sampling methods above and their subtypes. To encapsulate the whole discussion, though, 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.
- 22. 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 or selection criteria of the sample are undefined. Results This type of sampling is entirely unbiased and hence the results are unbiased too and 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 the objective of this method is to prove the hypothesis. In non-probability sampling, the hypothesis is derived after conducting the research study.