Table of Contents It is incumbent on the researcher to clearly define the target population.
In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced Stratified sampling in research forms of random selection, such as picking a name out of a hat, or choosing the short straw.
These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection. Some Definitions Before I can explain the various probability methods we have to define some basic terms.
With those terms defined we can begin to define the different probability sampling methods. Simple Random Sampling The simplest form of random sampling is called simple random sampling.
Here's the quick description of simple random sampling: To select n units out of N such that each NCn has an equal chance of being selected. Use a table of random numbers, a computer random number generator, or a mechanical device to select the sample.
A somewhat stilted, if accurate, definition. Let's see if we can make it a little more real. How do we select a simple random sample? Let's assume that we are doing some research with a small service agency that wishes to assess clients' views of quality of service over the past year.
First, we have to get the sampling frame organized. To accomplish this, we'll go through agency records to identify every client over the past 12 months.
If we're lucky, the agency has good accurate computerized records and can quickly produce such a list. Then, we have to actually draw the sample.
Decide on the number of clients you would like to have in the final sample. For the sake of the example, let's say you want to select clients to survey and that there were clients over the past 12 months.
Now, to actually draw the sample, you have several options. You could print off the list of clients, tear then into separate strips, put the strips in a hat, mix them up real good, close your eyes and pull out the first But this mechanical procedure would be tedious and the quality of the sample would depend on how thoroughly you mixed them up and how randomly you reached in.
Perhaps a better procedure would be to use the kind of ball machine that is popular with many of the state lotteries. You would need three sets of balls numbered 0 to 9, one set for each of the digits from to if we select we'll call that Number the list of names from 1 to and then use the ball machine to select the three digits that selects each person.
The obvious disadvantage here is that you need to get the ball machines. Where do they make those things, anyway? Is there a ball machine industry? Neither of these mechanical procedures is very feasible and, with the development of inexpensive computers there is a much easier way.
Here's a simple procedure that's especially useful if you have the names of the clients already on the computer. Many computer programs can generate a series of random numbers.
Then, sort both columns -- the list of names and the random number -- by the random numbers.It is incumbent on the researcher to clearly define the target population.
There are no strict rules to follow, and the researcher must rely on logic and judgment. Blend Uniformity (Chair)Tom Garcia. Boehm, vetconnexx.com, “Results of Statistical Analysis of Blend and Dosage Unit Content Uniformity Data Obtained from the Product Quality Research Institute Blend Uniformity Working Group Data-Mining Effort,” PDA Journal of Pharmaceutical Science and .
Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. Stratified random sampling intends to guarantee that the sample represents specific sub-groups or strata.
The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does.
Does that mean that nonprobability samples aren't representative of the population? Not necessarily. This article throws light on the eleven important steps involved in the process of social research, i.e, (1) Formulation of Research Problem, (2) Review of Related Literature, (3) Formulation of Hypotheses, (4) Working Out Research Design, (5) Defining the Universe of Study, (6) Determining Sampling.
How big should a sample be? Sample size is an important consideration in qualitative research. Typically, researchers want to continue sampling until having achieved informational redundancy or saturation -- the point at which no new information or themes are emerging from the data.