Bshs 435 - Sampling and Data Collection in Research Paper

Sampling and Data Collection in Research Paper

Melinda Cafarelli

BSHS/435

April 10, 2017

Chris Sites

Sampling and Data Collection in Research Paper

Sampling gives researches the ability to study a usable number of cases from a big group to come up with findings that are applicable to all group members. The act of sampling also allows for better information when precisely are drawn from a whole group.

Samples

Population is one of the fundamentals of sampling. Population refers to the entire project. According to "Monette, Sullivan, & DeJong"(2011), "the definition of a population should specify four things: (1) content, (2) units, (3) extent, and (4) time"(Applied Social Research. A Tool for the Human Services (8th ed.)  Frames are another fundamental of sampling. Frames are the basis of a group unit being formed. The sampling frame comprises a list of items where the sample is to be drawn from.  ("Winstudent", 2017).  

There are probability and non-probability samples. Probability samples are picked in a way that are representative of the population. Providing the biggest amount of validation or credibility in the results due to their reflection of the characteristics of the population used. This can be students or inmates. There are also two types of portability samples: stratified and random. The "University Of California, Davis" (2017) states that "The term random has a very precise meaning. Each individual in the population of interest has an equal likelihood of selection. This is a very strict meaning -- you can't just collect responses on the street and have a random sample. The assumption of an equal chance of selection means that sources such as a telephone book or voter registration lists are not adequate for providing a random sample of a community. In both these cases there will be a number of residents whose names are not listed. Telephone surveys get around this problem by random-digit dialing -- but that assumes that everyone in the population has a telephone. The key to random selection is that there is no bias involved in the selection of the sample. Any variation between the sample characteristics and the population characteristics is only a matter of chance." (Types of samples)

To avoid any sort of bias when sampling you need to make sure that you’re using a sample that has every characteristic of the subject that you are researching. Random does not always mean there will be no bias so you need to make sure that you do extensive research on what you actually want to gain so that you are surveying and studying the correct target population. The best way to avoid bias is by using probability sampling. Also doing small test sampling of subgroups if you will, can give you more information on a larger group that could give you the information needed and more statistical accuracy than using simple random sampling.