What Are The Advantages And Disadvantages Of Statistical Population

this study is the use of convenience sampling, as previously mentioned. Black et al. (2000) make

Luker describes sampling through canonical social science research as a “systemic random probability sample”, with the goal of producing results that can then be apply and a general way. For example, with a research question that focuses on voting in a election, A canonical social science research question may trying to determine how many voters are expected to vote a certain way in a election. By using randomly sampling a certain number of voters could be surveyed the result can provide a general description of the voting trend of an entire populations with similar characteristic as the sample group. While I agree with Luker that sampling is important to both canonical qualitative and qualitative social science research, in the case of qualitative research, the word has different meaning.

According to Schutt (2008), sampling is defined as a subset of population used in a study to be a representation of the population as a whole. My final project is a pre-hire assessment which analyzes potential risky pattern behaviors and emotions in the work place. One of the most important considerations related to sampling that will need to be addressed in my final project is defining the population that will be taking the assessment.

Probability sampling, also known as random sampling, requires that every member of the study population have an equal opportunity to be chosen as a study subject. For each member of the population to have an equal opportunity to be chosen, the sampling method must select members randomly. Probability sampling allows every facet of the study population to be represented without researcher bias. Four common sampling designs have been developed for selection of a random sample: simple random sampling, stratified random sampling, cluster sampling, and systematic sampling (Burns & Grove,

A population is the entire group to be studied and a sample is a portion of the population.

This sample may have a bias resulting from subjects that have a special interest in the subject being studied.

Random sampling: Operates on the principle that everybody should have an equal chance of being selected as part of the sample. This is significant because it ensures everyone has the same opportunity of being represented in a poll.

A sample is a group of people chosen from a population to embody the population in the experiment, whereas a population is the complete group of the people that pertain to the research subject (Gravetter & Wallnau, 2014). An example of this would be if a researcher was doing a study on children with no siblings in Michigan, the population would be every only child and the sample would be a smaller group of only children to represent the larger group. The reason why a person would not use the entire population is because, although you would get a more accurate result, it also would be too difficult to attain an answer from every only child on Michigan. It is therefore easier to get an estimate to gauge the closest accuracy with a smaller sample set.

When researchers select participants from several different parts of the population, they are selecting a

Random Sample: A sample in which every “person of interest” has an equal chance of being selected into your research study.

When conducting a study, it is impossible to collect data from the whole population, therefore it is important to select a representative sample because sampling makes it possible to select a representative for study and discover things that apply to many more people who are not studies (Maxfield & Babbie, 2012). The purpose of sampling is to generate a set of individuals or other entitles that give us a valid picture of all such individual or other entitles. It is important to generalise from a sample to unobserved population the sample in intended to represent. Therefore, when selecting a group of subjects for study, it is important to ensure that we represent some larger population.

There are 3000 students at my school. For this experiment, I decided that using a sample size of 40 students will be feasible and fairly representative of the entire population. In order to try and insure that this sample best represents the school, it is important to carefully choose a sampling method which decreases any chance of bias. Originally, I wanted to use a simple random sample which would give everyone in the school an equal chance of being chosen, therefore making the results fair and not directed towards a certain group of individuals. However, getting a hold of a list of all students in the school that I can use to randomly select my sample group was not realistic. So, I decided that a voluntary response sample would be more practice in this situation. Not only does using this sample make the process less time consuming and convenient, but it also solves the problem of not being

In a probability sample each element or unit of the population have a known chance of being selected for the sample study. Probability samples also permit a precise estimate of parameters. Since all elements have an equal chance of being selected for your survey, you can randomly select participants without missing portions of your population; you have a complete sampling frame. With probability samples you can generalize your results from this random sample. With Non-probability samples some people have a greater, but unknown, chance than others of selection into the sample study. Nonprobability samples are used when there is not a complete population list available. Some units are unable to be selected; therefore you have no way of knowing the size and effect of sampling error. Nonprobability samples are not random and often bias; you cannot generalize your results to an entire population with a high level of confidence. Probability samples are the best for the purpose of making valid

-The sampling distribution is a distribution of the results if all possible samples are selected, where as a population distribution is the distribution of a particular variable of interest.

According to Hair et al. (2003), in the research, the sampling process enables identifying, developing and understanding an interested object that need to be determined (p.333). Hence, in order for the researcher to carry out the sampling appropriately, advantages and disadvantages of the various sampling methods should be considered along with the theoretical component of the study (Hair et al. 2003, p. 368 f). Theoretically, the sampling procedure is divided into two major types which consist of probability and nonprobability sampling. In probability sampling, individuals have a known chance of being selected. While, in non-probability sampling, individuals do not have a known possibility to be selected (Sekaran 2003, p. 269 f). Also, the different sampling methods provide different advantages and disadvantages. Hence, the researcher should consider this point before choosing the sampling method for the