Sampling Design

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.

• Provide at least two examples or problem situations in which statistics was used or could be used.

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.

Explain the importance of random sampling. What problems/limitations could prevent a truly random sampling and how can they be prevented?

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

In Greyber, L. , Dulmus, C. , & Cristalli, M. (2012) article, it indicated that the sample size consisted of 59 children participates ages 6-16. Non-Probability sampling was used in this study, as most of the children sampled were referred from a child psychiatric outpatient clinic. According to Rubin and Babbie (2014), this type of convenience or purposive sampling is the

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.

Lind, D. A. (2005). Statistical Techniques in Business & Economics (12 ed.). New York: The

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.

A. Sampling is a selection of a smaller percentage of individuals from within a larger population that is used to estimate the attitudes and characteristics of the whole population. Sampling saves time and money which is always a good thing, and allows for more manageable research. It may be almost impossible to identify every member of a population/group some may be too large, for example, if you were conducting a study on high school students as to whether they bring lunch to school or eat at the cafeteria, and this research was conducted for the entire state of Texas. Well, it would take an enormous amount of manpower and money to survey every single high school student in the state plus you would have other factors to consider as well. Then you would run into managing issues with such a large endeavor, so choosing a sample or small representation of individuals from the larger population is more

-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