Sampling and Data Collection in Research Paper
Sampling and data collection is made up of many things and is used in many things. The one main thing is research and in research it helps to obtain information about groups or individuals without being bias. Along with the research, making sure that it is valid and reliable is very important and knowing the ways that research can be done surveys via online or telephone.
The purpose of sampling is that if a researcher decides to research a group, a group can be very large that the information obtained may not be completely accurate. A researcher can get better information by breaking down the groups into smaller groups and researching them (Monette, 2011). An example of sampling in this case is the large group would be people with PTSD and it can be broken down smaller like researching veterans with PTSD. By breaking the group down for sampling, the data can be obtained more quickly and it is a feasible way of collection. There are types of sampling called probability and nonprobability.
Probability sampling is basically randomly picking information, whereas nonprobability sampling is not randomly selecting information. A type of probability sampling is simple random sampling is when the population that is being researched is treated equally as a whole. An example of this is researching a group of college students. A type of nonprobability is snowball sampling which is taking one case of study and that case leads into more cases of study (Monette, 2011). An example of snowball sampling is child abuse because that can lead to more cases not found. When it comes to bias the way to avoid it in research and sampling is to make sure that inclusion of all races, cultures, and sexes are provided when picking areas of sampling. On top of sampling there is data collection in research.
Data collection is also known as the scales of measurement in research. The scales of measurement are broken into different levels. The first level is nominal measure which is observations into different categories and an example of this is a favorite hockey player number 11. The second level is ordinal measures which is a category of relationships or ranks. An example of this is like the ranks of the armed forces where each branch has their own rankings. The next level is an interval measure represents numbers and units. An example of this is hockey scores or even the temperature because it can go below zero. The last level is a ratio measure which is the same as the interval measures but it does not go below zero. An example of this is measuring a piece of wood in inches (Monette, 2011).
In the levels of measurement there are types of validity which are construct, face, and criterion. Construct validity represents reality like role playing scenarios as training. Face validity is when something is being testing like children being tested on math and asking parents if the test if working is an example. Criterion validity is showing a relationship between a measurement device and standard like the risk of pedophilia there would be signs of child abuse. It is about the relationship between them (Monette, 2011).
There are types of reliability just like validity which are test-retest, multiple forms, and internal consistency approach. The test-retest is basically is when people take a test and decide to take a retake at a different time. It is the same people just at a different time. Multiple forms are when there are different sets of things like either question to a test or problems to solve. The last one is internal consistency approach which is taking a question and asking it to individuals and comparing it with another individuals answer (Monette, 2011). The reason why it is important to make sure that data collection is valid and reliable is so that there are no errors and that the information that is being collected is not falsely given...
References: Monette, D., Sullivan, T.J., & DeJong, C.R.(2011). Applied Social Research (8th ed.). Retrieved
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