Difference between Census and Sampling
Census and sampling are two methods of collecting survey data about the population that are used by many countries. Census refers to the quantitative research method, in which all the members of the population are enumerated. On the other hand, the sampling is the widely used method, in statistical testing, wherein a data set is selected from the large population, which represents the entire group
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Census implies complete enumeration of the study objects, whereas Sampling connotes enumeration of the subgroup of elements chosen for participation. These two survey methods are often contrasted with each other, and so this article makes an attempt to clear the differences between census and sampling, in detail; Have a look.
BASIS FOR COMPARISON | CENSUS | SAMPLING |
Meaning | A systematic method that collects and records the data about the members of the population is called Census. | Sampling refers to a portion of the population selected to represent the entire group, in all its characteristics. |
Enumeration | Complete | Partial |
Study of | Each and every unit of the population. | Only a handful of units of the population. |
Time required | It is a time consuming process. | It is a fast process. |
Cost | Expensive method | Economical method |
Results | Reliable and accurate | Less reliable and accurate, due to the margin of error in the data collected. |
Error | Not present. | Depends on the size of the population |
Appropriate for | Population of heterogeneous nature. | Population of homogeneous nature. |
SAMPLING
Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population. The methodology used to sample from a larger population depends on the type of analysis being performed but may include simple random sampling or systematic sampling.
In business, a CPA performing an audit uses sampling to determine the accuracy of account balances in the financial statements, and managers use sampling to assess the success of the firm’s marketing efforts.
The sample should be a representation of the entire population. When taking a sample from a larger population, it is important to consider how the sample is chosen. To get a representative sample, the sample must be drawn randomly and encompass the whole population. For example, a lottery system could be used to determine the average age of students in a university by sampling 10% of the student body.
Examples of Sample Tests for Marketing
Businesses aim to sell their products and/or services to target markets. Before presenting products to the market, companies generally identify the needs and wants of their target audience. To do so, they may employ using a sample of the population to gain a better understanding of those needs to later create a product and/or service that meets those needs. Gathering the opinions of the sample helps to identify the needs of the whole.
What is Non-Probability Sampling?
Non-probability sampling is a sampling technique where the odds of any member being selected for a sample cannot be calculated. It’s the opposite of probability sampling, where you can calculate the odds. In addition, probability sampling involves random selection, while non-probability sampling does not–it relies on the subjective judgement of the researcher.
The odds do not have to be equal for a method to be considered probability sampling. For example, one person could have a 10% chance of being selected and another person could have a 50% chance of being selected. It’s non-probability sampling when you can’t calculate the odds at all.
Types of Non-Probability Sampling
Types of Non-Probability Sampling
· Convenience Sampling: as the name suggests, this involves collecting a sample from somewhere convenient to you: the mall, your local school, your church. Sometimes called accidental sampling, opportunity sampling or grab sampling.
· Haphazard Sampling: where a researcher chooses items haphazardly, trying to simulate randomness. However, the result may not be random at all and is often tainted by selection bias.
· Purposive Sampling: where the researcher chooses a sample based on their knowledge about the population and the study itself. The study participants are chosen based on the study’s purpose. There are several types of purposive sampling. For a full list, advantages and disadvantages of the method, see the article: Purposive Sampling.
· Expert Sampling: in this method, the researcher draws the sample from a list of experts in the field.
· Heterogeneity Sampling / Diversity Sampling: a type of sampling where you deliberately choose members so that all views are represented. However, those views may or may not be represented proportionally.
· Modal Instance Sampling: The most “typical” members are chosen from a set.
· Quota Sampling: where the groups (i.e. men and women) in the sample are proportional to the groups in the population.
· Snowball Sampling: where research participants recruit other members for the study. This method is particularly useful when participants might be hard to find. For example, a study on working prostitutes or current heroin users.
How to Develop a Good Research Question:
· Researchers should begin by identifying a broader subject of interest that lends itself to investigation. For example, a researcher may be interested in childhood obesity.
· The next step is to do preliminary research on the general topic to find out what research has already been done and what literature already exists. How much research has been done on childhood obesity? What types of studies? Is there a unique area that yet to be investigated or is there a particular question that may be worth replicating?
· Then begin to narrow the topic by asking open-ended "how" and "why" questions. For example, a researcher may want to consider the factors that are contributing to childhood obesity or the success rate of intervention programs. Create a list of potential questions for consideration and choose one that interests you and provides an opportunity for exploration.
· Finally, evaluate the question by using the following list of guidelines:
· Is the research question one that is of interest to the researcher and potentially to others? Is it a new issue or problem that needs to be solved or is it attempting to shed light on previously researched topic.
· Is the research question researchable? Consider the available time frame and the required resources. Is the methodology to conduct the research feasible?
· Is the research question measureable and will the process produce data that can be supported or contradicted?
· Is the research question too broad or too narrow?
Types of sampling design in Research Methodology
There are different types of sample designs based on two factors viz., the representation basis and the element selection technique. On the representation basis, the sample may be probability sampling or it may be non-probability sampling. Probability sampling is based on the concept of random selection, whereas non-probability sampling is ‘non-random’ sampling. On element selection basis, the sample may be either unrestricted or restricted. When each sample element is drawn individually from the population at large, then the sample so drawn is known as ‘unrestricted sample’, whereas all other forms of sampling are covered under the term ‘restricted sampling’. The following chart exhibits the sample designs as explained above.
Thus, sample designs are basically of two types viz., non-probability sampling and probability sampling. We take up these two designs separately.
Non-probability sampling: Non-probability sampling is that sampling procedure which does not afford any basis for estimating the probability that each item in the population has of being included in the sample. Non-probability sampling is also known by different names such as deliberate sampling, purposive sampling and judgement sampling. In this type of sampling, items for the sample are selected deliberately by the researcher; his choice concerning the items remains supreme. In other words, under non-probability sampling the organizers of the inquiry purposively choose the particular units of the universe for constituting a sample on the basis that the small mass that they so select out of a huge one will be typical or representative of the whole. For instance, if economic conditions of people living in a state are to be studied, a few towns and villages may be purposively selected for intensive study on the principle that they can be representative of the entire state. Thus, the judgement of the organizers of the study plays an important part in this sampling design.
Probability sampling: Probability sampling is also known as ‘random sampling’ or ‘chance sampling’. Under this sampling design, every item of the universe has an equal chance of inclusion in the sample. It is, so to say, a lottery method in which individual units are picked up from the whole group not deliberately but by some mechanical process. Here it is blind chance alone that determines whether one item or the other is selected. The results obtained from probability or random sampling can be assured in terms of probability i.e., we can measure the errors of estimation or the significance of results obtained from a random sample, and this fact brings out the superiority of random sampling design over the deliberate sampling design. Random sampling ensures the law of Statistical Regularity which states that if on an average the sample chosen is a random one, the sample will have the same composition and characteristics as the universe. This is the reason why random sampling is considered as the best technique of selecting a representative sample. In such a design, personal element has a great chance of entering into the selection of the sample. The investigator may select a sample which shall yield results favorable to his point of view and if that happens, the entire inquiry may get vitiated. Thus, there is always the danger of bias entering into this type of sampling technique. But in the investigators are impartial, work without bias and have the necessary experience so as to take sound judgment, the results obtained from an analysis of deliberately selected sample may be tolerably reliable. However, in such a sampling, there is no assurance that every element has some specifiable chance of being included. Sampling error in this type of sampling cannot be estimated and the element of bias, great or small, is always there. As such this sampling design in rarely adopted in large inquires of importance. However, in small inquiries and researches by individuals, this design may be adopted because of the relative advantage of time and money inherent in this method of sampling. Quota sampling is also an example of non-probability sampling. Under quota sampling the interviewers are simply given quotas to be filled from the different strata, with some restrictions on how they are to be filled. In other words, the actual selection of the items for the sample is left to the interviewer’s discretion. This type of sampling is very convenient and is relatively inexpensive. But the samples so selected certainly do not possess the characteristic of random samples. Quota samples are essentially judgement samples and inferences drawn on their basis are not amenable to statistical treatment in a formal way.
What is Sample design in Research Methodology ?
A sample design is made up of two elements. Random sampling from a finite population refers to that method of sample selection which gives each possible sample combination an equal probability of being picked up and each item in the entire population to have an equal chance of being included in the sample. This applies to sampling without replacement i.e., once an item is selected for the sample, it cannot appear in the sample again (Sampling with replacement is used less frequently in which procedure the element selected for the sample is returned to the population before the next element is selected. In such a situation the same element could appear twice in the same sample before the second element is chosen). In brief, the implications of random sampling (or simple random sampling) are:
· It gives each element in the population an equal probability of getting into the sample; and all choices are independent of one another.
· It gives each possible sample combination an equal probability of being chosen.
Keeping this in view we can define a simple random sample (or simply a random sample) from a finite population as a sample which is chosen in such a way that each of the NCn possible samples has the same probability, 1/NCn, of being selected. To make it more clear we take a certain finite population consisting of six elements (say a, b, c, d, e, f ) i.e., N = 6. Suppose that we want to take a sample of size n = 3 from it. Then there are 6C3 = 20 possible distinct samples of the required size, and they consist of the elements abc, abd, abe, abf, acd, ace, acf, ade, adf, aef, bcd, bce, bcf, bde, bdf, bef, cde, cdf, cef, and def. If we choose one of these samples in such a way that each has the probability 1/20 of being chosen, we will then call this a random sample.
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