Ans.1:
Non-Probability Sampling:
When the units of a sample are chosen so that each unit in the population does not have a calculable non-zero probability of being selected in the sample, this is called Non-Probability Sampling. Also, Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected. In contrast with probability sampling, non-probability sample is not a product of a randomized selection processes. Subjects in a non-probability sample are usually selected on the basis of their accessibility or by the purposive personal judgment of the researcher.

Scope of Non-Probability Sampling:
•This type of sampling can be used when demonstrating that a particular trait exists in the population. •It can also be used when the researcher aims to do a qualitative, pilot or exploratory study. •It can be used when randomization is impossible like when the population is almost limitless. •It can be used when the research does not aim to generate results that will be used to create generalizations pertaining to the entire population. •It is also useful when the researcher has limited budget, time and workforce. •This technique can also be used in an initial study which will be carried out again using a randomized, probability sampling.

Advantages of Non-Probability Sampling:
•Cheaper
•Used when sampling frame is not available
•Useful when population is so widely dispersed that cluster sampling would not be efficient •Often used in exploratory studies, e.g. for hypothesis generation •Some research not interested in working out what proportion of population gives a particular response but rather in obtaining an idea of the range of responses on ideas that people have.

Types of Non-Probability Sampling:
There are five types of Non-Probability Sampling, they are: 1.Convenience Sampling.
2.Consecutive Sampling.
3.Quota...

...Concept and basics of probabilitysampling methods
One of the most important issues in researches is selecting an appropriate sample. Among sampling methods, probability sample are of much importance since most statistical tests fit on to this type of sampling method. Representativeness and generalize-ability will be achieved well with probable samples from a population, although the matter of low feasibility of a probable sampling method or high cost, don’t allow us to use it and shift us to the other non-probable sampling methods. In probabilitysampling we give known chance to be selected to every unit of the population. We usually want to estimate some parameters of a population by a sample. These parameters estimates when we don’t observe whole population usually have some errors. Fortunately in probabilitysampling it is possible that we know how much our estimates are trustable or close to the parameter value from population by computing standard errors of estimates. This is not easily possible in non-probabilitysampling methods.
Types of probabilitysampling methods
Simple Random Sampling
What is it?
Simple random sampling is selecting randomly some units from a known and well defined...

...Probability And NonProbabilitySampling Cultural Studies Essay
A probabilitysampling method is any method of sampling that utilizes some form of random selection. 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 various 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.
Probabilitysampling methods are those in which every item in the universe has a known chance, or probability of being chosen for sample. This implies that the selection of the sample items is independent of the person making the study that is the sampling operation is controlled so objectively that the items will be chosen strictly at random.
Types of probabilitysampling
Simple Random Sampling: The simplest form of random sampling is called simple random sampling. Neither of these mechanical procedures is very feasible and, with the development of inexpensive computers there is a much easier way. Simple random sampling is simple to...

...Purposive sampling
Purposive sampling, also known as judgmental, selective or subjective sampling, is a type of non-probabilitysampling technique. Non-probabilitysampling focuses on sampling techniques where the units that are investigated are based on the judgement of the researcher.
Purposive sampling explained
Purposivesampling represents a group of different non-probabilitysampling techniques. Also known as judgmental, selectiveor subjective sampling, purposive sampling relies on the judgement of the researcher when it comes to selecting the units(e.g., people, cases/organisations, events, pieces of data) that are to be studied. Usually, the sample being investigated is quite small, especially when compared with probabilitysampling techniques.
Unlike the various sampling techniques that can be used under probabilitysampling (e.g., simple random sampling, stratified random sampling, etc.), the goal of purposive sampling is not to randomly select units from a population to create a samplewith the intention of making generalisations (i.e., statistical inferences) from that sample to the population of interest....

...Sampling is the use of a subset of the population to represent the whole population. Probabilitysampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. Nonprobability sampling does not meet this criterion and should be used with caution. Nonprobability sampling techniques cannot be used to infer from the sample to the general population.
The advantage of nonprobability sampling is its lower cost compared to probabilitysampling. However, one can say much less on the basis of a nonprobability sample than on the basis of a probability sample. Of course, research practice appears to belie this claim, because many analysts draw generalizations (e.g., propose new theory, propose policy) from analyses of nonprobability sampled data. One must ask, however, whether those published works are publishable because tradition makes them so, or because there really are justifiable grounds for drawing generalizations from studies based on nonprobability samples.
Some embrace the latter claim, and assert that while probability methods are suitable for large scale studies concerned with representativeness, non-probability approaches are more suitable for in-depth qualitative research in which the focus is...

...Evaluation
Professor: Dr. Elidio T. Acibar
Reporter: Evelyn L. Embate
Topic: SamplingSAMPLING
Measuring a small portion of something and then making a general statement about the whole thing.
Advantages of samplingSampling makes possible the study of a large, heterogeneous population
It is almost impossible to reach the whole population to be studied. Thus, sampling makes possible this kind of study because in sampling only a small portion of the population may be involved in the study, enabling the researcher to reach all through this small portion of the population.
* Sampling is for economy
A research without sampling may be too costly. For an instance if you are to take the whole population, it will take you an expensive cost because of the number of questionnaire copies.
* Sampling is for speed
A research without sampling might be too time consuming. If a research takes a long time to finish, there may be many intervening factors that deter the researcher from finishing his research.
* Sampling is for accuracy
A time too long to cover the whole study population, may ne inaccurate. By the time the last person is interviewed, the data gathered from the first interviewees may be obsolete already so that the conclusions are no longer accurate. It is important that the research must be finished...

...SAMPLING AND ITS CHARACTERISTICS
INTRODUCTION:
For studying any problem it is impossible to study the entire population. It is therefore convenient to pick out a sample out of the population proposed to be covered by the study. Sampling method is an important tool in the realm of social science researches. It was first introduced and used in social research in 1754 A.D. by Bowley.
DEFINITION:
“A sample is finite part of a statistical population whose properties are studied to gain information about the whole.” – Webster Dictionary, 1985
“a smaller representation of large whole.” – Goode and Hatt
‘a subject of cases from the population chosen to represent it.” – Nan Lin
“Sampling method is the process or the method of drawing a definite number of the individuals, cases or the observations from a particular universe, selecting part of a total group for investigation.” – Mildred Parton
Therefore, Sampling can be defined as the method, or the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. For this, population is divided into a number of parts called Sampling Units.
Or, we can say, that when a small group is selected as representative of the whole it is known as sample.
Or, a sample size is a small percentage of a population that is used for statistical...

...Rights Reserved.
Sampling
We are now putting all of the pieces together. Considering each observation xi as an outcome from a random variable Xi , we have that functions g(x1 ; x2 ; :::; xn ) are draws from the random variable
Pn g(X1 ; X2 ; :::; Xn ):
For 120a the function we are interested in is the sample mean — g(x1 ; x2 ; :::; xn ) = n1 i=1 xi : In this chapter
we work with this function for distributions with many random variables.
1
From the Text
Question 1. Problem 6-10, p.206
Question 2: Problems 6-16,6-22.
Question 3: Problem 6-36.
2
Sampling Questions
Question 1. A study suggests that in a trial of 50 people the average decrease in cholestorol from using a
particular drug is 18 points with a standard deviation of 10 points.
(a) What is the chance that, given the true e¤ect is a reduction of 20 points, that a particular person
has no decrease in cholestorol when they take this drug?
(b) A reporter writes a newspaper article discussing the drug. They report that although the study claims
that the reduction is 18 points, they have found a user that actually had no change to their cholestorol. They
use this persons story to argue that the results are misleading. What is the chance that, if the real e¤ect
was zero, that the average in a trial might be 18 just by chance?
(c) Is the counter example found by the reporter useful in understanding the results for the study?
Question 2. In the questions on normal...

...of observations, which gives each observation equal weight, the mean of a random variable weights each outcome xi according to its probability, pi. The mean also of a random variable provides the long-run average of the variable, or the expected average outcome over many observations.The common symbol for the mean (also known as the expected value of X) is , formally defined by
Variance - The variance of a discrete random variable X measures the spread, or variability, of the distribution, and is defined by
The standard deviation is the square root of the variance.
Expectation - The expected value (or mean) of X, where X is a discrete random variable, is a weighted average of the possible values that X can take, each value being weighted according to the probability of that event occurring. The expected value of X is usually written as E(X) or m.
E(X) = S x P(X = x)
So the expected value is the sum of: [(each of the possible outcomes) × (the probability of the outcome occurring)].In more concrete terms, the expectation is what you would expect the outcome of an experiment to be on average.
2. Define the following;
a) Binomial Distribution - is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Therewith the probability of an event is defined by its binomial...

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