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]]>You need to know the following before calculating sample size.

At what confidence level you want to test your hypothesis. Suppose you want to test the hypothesis at 95 % confidence level, it means that you are 95 % confident about your findings. The chances of error in your study are 0.5 %.

The population size needs to be known and it is known in majority of the studies if the population is small. In case the population is large the size is unknown. Sample size can be calculated even if the accurate population size is unknown. An approximation of the population can be made in case you do not know the exact number of the people in your study population.

You need to know how much variation there might exist among the values or responses that you will get for your questions.Since the study is still not done you do not know the variation or standard deviation. You can take a standard deviation of 0.5 or 0.1 for example. This is only a probabilistic deviation and not the actual one. A 0.5 deviation means that there will be great variation in the responses and data will be spread over a large range. A 0.1 deviation means that there will be less deviation in the responses and you will get a range of data that is closer to the mean.

Sample mean = X

Population mean = mu

Sample size = n

Standard Error = E

Standard Deviation = s

Critical Value = za/2

The formula for standard error is

By rearranging this equation and adding the value of critical value we can get the formula for sample size.

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]]>In an ideal situation the whole population should be taken to study any variable that you want to study. It is unfortunately impossible to study the whole population. In most of the cases population is large and cannot be studied as a whole. A sample is a part of the population that is drawn from the population. It makes the collection of data and its analysis easy and feasible. Sample size is denoted by lowercase letter “n”.

Population is the total entity of people upon which you want to generalize your research. In research population size is denoted by uppercase letter “N”.

Target population is different from the actual population in one way. Target population can be very large and the researcher might select a small group of that population or people living in one area to make the research easy. For example, the researcher wants to study the effect of one beauty product on elderly ladies. The target population in this case will be all ladies that use that product. This can be very large group spread over several states or even countries. He decides to select a population that is accessible or lives in his region. This will be called as sampling population or population. Hence target population can be very large as compared to the population that the researcher selects for generalizing his findings.

Sampling framework is the entire population of people, situation, incidents or households from which the researcher has to take the sample. There might be several sampling framework but it is not always possible to draw samples from them. Some frameworks are difficult to research because of social, moral or ethical issues. Sampling framework is that population from which you can draw sample feasibly.

Sampling design is a technique or a procedure to select samples from the target population. The sampling design ensures that each element in the population has an equal and unbiased chances of becoming part of the sample. Sampling design can be nonrandom and, in this case, some compromises are made to get the desired sample.

Standard error is the standard deviation of the sampling distribution. Standard error thus describes the chances of error that may occur in the statistic calculated from the sample. As sample could just estimate the characteristics of the population, hence standard error describes the possibility of error in the sampling distribution.

Standard deviation determines variability of the actual population from which the sample has been drawn. It is denoted by Greek letter sigma and often written as sd. Standard deviation is also the square root of the variance of the population. In statistical terms it can be defined as the deviation of the data points on each side from the mean.

Confidence interval and confidence level are the two terms that are constantly used in sampling and in analyzing the data. Confidence level is the degree or percentage of confidence that the researcher has on the estimates that he has drawn from the sample. It means that higher the percentage of confidence level, greater will be its reliability.

In statistics mean is the most common type of average that is used. It is used in sampling to describe the characteristics of sampling distribution.

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]]>Each of the above mentioned sampling types have several sampling techniques in them.

Random sampling is also called as probability sampling. In random sampling design every element of the population has an equal

and independent chance of being included in the sample. This is by far the best sampling design if the researcher knows each element in the population and he is able to choose any element from it. It improves the validity of the study because each element included in the sample have been selected randomly without any bias or personal preferences. There are several types of random sampling designs:

Simple random sampling as the name suggests is the most basic type of random sampling. It fulfills all the criteria of random sampling. Each element in the simple random sampling design is chosen using either table of random numbers or through draw. The researcher identifies each element in the population, list them down. He decides about the number of elements in the sample and pick out the required sample randomly from the population. Simple random sampling is easier to use if the population size is small, in case of large population like a community or a city population the researcher has to use other type of random sampling technique.

When the population is large and heterogeneous the researcher cannot use simple random sampling. There is another sampling design that is called as stratified random sampling and it is best suited to situations where the population has greater variation. The researcher identifies the population in this sampling design. He then decides about the number of units in the sample. The researcher makes strata of the population. The reason for dividing the population in strata is that the population is too diverse and cannot be treated in a simple manner. The researcher can make strata according to any special characteristic of the population. From each strata the researcher chooses units in the sample. The number of sample from each strata can be chosen in two ways. One the researcher can take proportionate units from each stratum, the other way is to take disproportionate, equal elements, from each stratum. It depends on the type of research and its requirements.

In systematic probability random sampling the researcher selects every kth element in the population. The value of k can be taken by dividing the number of units in the population by the number of units in the sample. For example there are 40 units in the population and the sample size is taken to be 20 so 40/20 is equal to 2 and hence every 2nd unit in the population will be taken as a sample. In household surveys this technique is most commonly used for sampling.

In stratified random sampling the researcher divides the population in strata but in cluster sampling the researcher identifies clusters or groups in the population. Units are selected from each cluster and taken in the sample. These clusters or groups are usually naturally found in the population and the researcher does not divide the population himself.

Non-random sampling designs are also known as non-probability sampling designs. These samples are not taken on the principles of probability. It does not mean, however, that these samples are not representable, valid or generalizable to the whole population. There are times when the researcher cannot take random sample from the population and he is forced to select a non-random sample. For example, the research is conducted on the behavior of transsexual people in a community. Taking a random sample is difficult as not all the units or members of that population will be ready to share their views or fill questionnaires. The researcher has to ask them and if they will be willing he can only then take observations or interviews. In psychology, social sciences and behavioral sciences there is always a posed risk of not getting a random sample and hence non-random sampling techniques has to be used. There are different types of sampling designs in this type of sampling:

In quota sampling the investigator or the researcher decides about the number of samples to be taken and then he can freely choose any sample from the population. There is no distinction and he can choose any unit.

The investigator in this type of sampling selects the units from the population according to his own judgement. The reason might be that the investigator thinks certain elements in the population to be more fit for the survey than other.

In snowball sampling the investigator selects one element in the population randomly or non randomly to ask questions. The investigator asks that individual to identify another element of the population that can be taken in the sample.

In convenience sampling the researcher or the investigator selects samples according to his convenience. He selects sample that are easily available or more easy to ask questions. These are only few of the sampling types in research and there can be many more depending on the type of study.

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]]>The post Sampling Principles in Research appeared first on Reading Craze.

]]>The first sampling principles says that there will be a difference between the population mean and parameters and sample statistics. The reason for this difference is that the sample is only part of the population but not the whole population itself. The selection of units through random sampling can help minimize this differences. There are several other ways that can be used to get the most representable sample. Different samples drawn for the same population may vary in their sample statistics. The difference that exists between population and sample characteristics is known as the sampling error. Sampling error can be minimized but it cannot be avoided completely.

The second principle of sampling states that greater the sample size and the more accurate and generalizable the sample will be of population mean. The difference between the population mean and sample statistics can be reduced greatly if the sample size is taken large. Hence sampling error can be reduced if the sample size is large. Taking large sample means greater effort and time to process the data but the sampling error is reduced.

The third sampling principles is very important it states that greater is the difference in the population characteristics, greater will be difference between population mean and sample statistics. When the population is varied and widespread the sample taken also contains greater variability and the results obtained may not be representable and generalizable of the population mean. The sampling error can result but it can be reduced by making clusters or strata of the population. From each strata sample can be taken and the variability in the population characteristics can be reduced in this way.

The fourth principle of sampling is about the nonsampling errors and biases. In sampling from any population any other error that occurs that has been invested because of the researcher is known as researcher bias. Faulty estimates and poor use of techniques can also result in errors and these errors are known as nonsampling errors. Before selecting sample from the target population sampling principles should be understood to make the process of sampling valid and reliable.

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