Sampling is that part of statistical practice concerned with the selection of an unbiased or random subset of individual observations within a population of individuals intended to yield some knowledge about the population of concern, especially for making predictions based on the statistical inference (Ader, Mellenberg & Hand: 2008). There are quite a number of sampling methods that can be employed in research and these include simple random sampling, systematic sampling, stratified sampling, cluster sampling, matched random sampling, quota sampling, convenience sampling, line intercept sampling, to mention just a few.

Simple Random Sampling:
In a simple random sample of a given size (elements are randomly chosen until a desired sample size is obtained), all such subsets of the frame are given an equal chance or probability. Each element of the population thus has an equal probability of selection: the frame is not subdivided or partitioned. Furthermore, any given pair of elements has the same chance of selection as any other pair and similarly for triples, quads and so on. This minimizes the bias and simplifies analysis of the results. However, simple random sampling method can be vulnerable to sampling error because the randomness of the selection may result in a sample that does not reflect the makeup of the population, for instance, a simple random of ten people from a given town will on average produce five men and five women, but any give trial is likely to over-represent one sex and under-represent the other thus leading to bias misrepresentation of what is actually happening on the ground. Simple random sampling may also be cumbersome and tedious when sampling from an unusually large target population…………(add)

Systematic Sampling:
Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a...

...Types of Sampling
In applications:
Probability Sampling: Simple RandomSampling, StratifiedRandomSampling, Multi-Stage Sampling
* What is each and how is it done?
* How do we decide which to use?
* How do we analyze the results differently depending on the type of sampling?
Non-probability Sampling: Why don't we use non-probabilitysampling schemes? Two reasons:
* We can't use the mathematics of probability to analyze the results.
* In general, we can't count on a non-probability sampling scheme to produce representative samples.
In mathematical statistics books (for courses that assume you have already taken a probability course):
* Described as assumptions about random variables
* Sampling with replacement versus sampling without replacement
What are the main types of sampling and how is each done?
Simple RandomSampling: A simple random sample (SRS) of size n is produced by a scheme which ensures that each subgroup of the population of size n has an equal probability of being chosen as the sample.
StratifiedRandomSampling: Divide the population into "strata". There can be any number of these. Then choose a simple...

...SAMPLING TECHINIQUE
PROBABILITY SAMPLING
Having chosen a suitable sampling frame and established the actual sample size required, you need to select the most appropriate sampling technique to obtain a representative sample. The basic principle of probability sampling is that elements are randomly selected in a population. This ensures that bias is avoided in the identification of the elements. It is an efficient method of selecting elements which may have varied characteristics, as the process allows for a fair representation of this variability. It also means the laws of probability and statistics apply, allowing us to make certain inferences.
FIVE MAIN TECHNIQUES THAT CAN BE USED TO SELECT A PROBABILITY SAMPLE
1. SIMPLE RANDOMSAMPLING
Simple randomsampling (sometimes called just randomsampling) involves you selecting the sample at random from the sampling frame. In this approach, all elements are given equal chance of being included in the sample. No one from the population is excluded from the pool.
RandomSampling could be implemented through:
* The lottery method. In the lottery method of sampling, the names of the entire population are written on pieces of paper and a given number of names (or sample elements) are drawn at...

...L. M.
INTERMEDIATE MICROECONOMICS
SAMPLING TECHNIQUES
INTRODUCTION
A sample is a unit or subset of selection from a larger population that is used in studying to draw conclusions regarding the whole population. A sample is usually selected from the population because it is not easy to study the entire population at once and the cost of doing so may be very high. The sample should be the best representation of the whole population to enable accurate outcomes and accurate decisions made from the findings. Before sampling, the population should be clearly defined to ensure that the correct sample is selected for accuracy.
Sampling, therefore, is the process used in statistics in which a predetermined number of observations will be taken from a large population. It is the selection of units from a population of interest so that by studying it, we may generalize the findings back to the population from which the sample was obtained. The methodology to be used in sampling will depend on the type of analysis being performed. The methods include the following:-
1. SIMPLE RANDOMSAMPLING
Simple RandomSampling is obtained in such a way that each element of the population is given equal probability of being selected as a subject. The process is done in a single step with each subject selected independently of the rest of the members of the...

...Simple random sample (SRS)
In statistics, a simple random sample from a population is a sample chosen randomly, so that each possible sample has the same probability of being chosen. One consequence is that each member of the population has the same probability of being chosen as any other. In small populations such sampling is typically done "without replacement", i.e., one deliberately avoids choosing any member of the population more than once. Although simple randomsampling can be conducted with replacement instead, this is less common and would normally be described more fully as simple randomsampling with replacement.
Conceptually, simple randomsampling is the simplest of the probability sampling techniques. It requires a complete sampling frame, which may not be available or feasible to construct for large populations. Even if a complete frame is available, more efficient approaches may be possible if other useful information is available about the units in the population.
Advantages are that it is free of classification error, and it requires minimum advance knowledge of the population. It best suits situations where the population is fairly homogeneous and not much information is available about the population. If these conditions are not true, stratifiedsampling may be a better...

... of condominiums built in Manila in the last five years.
No. of people who died of lung cancer in the Philippines
Continous Variable
A variable that can assume any numerical value over a certain interval/result of measurement.
A quantitative variable is continuous if its set of possible values is uncountable.
Ex. temperature, exact height, exact age
Amount of soda consumed by a student in a month.
Parameter
Description of a characteristic of a population.
Statistic
Description of a characteristic of a sample.
A number that can be computed from data, involving no unknown parameters. As a function of a random sample, a statistic is a random variable. Statistics are used to estimate parameters, and to test hypotheses.
Survey:
Study of only a portion of the population
Census Survey
Study of certain characteristics of every element of a population.
Sampling Survey
Making inferences with a sample.
Nominal Scale
Observed data are merely classified into various distinct categories in which no ordering is implied.
Ex. Gender, Ownership of a house
Ordinal Scale
Observed data are merely classified into distinct categories w/ ranking implied in which the difference in rank is consistent in direction but not in magnitude.
Ex.
Faculty rank (Lecturer / Instructor / Professor)
Year level (freshmen, sophomore, junior, senior)
Interval Scale
Observed data are put in an ordered scale in which the difference between the...

...
Sampling methodologies
Sampling
It may be defined as a process of selecting units that may be people, organizations etc, from a larger whole i.e. from a population of interest, so that by studying the sample we may come up with general characteristics of the entire population under consideration.
Types of sampling methods:
Probability sampling
Probability sampling is a type ofsampling that includes random selection. And in order to achieve random selection, it must be made sure that different units of population have equal probability of being chosen.
Some relevant terms:
N = the number of cases in the sampling frame
n = the number of cases in the sample
f = n/N = the sampling fraction
I] Simple RandomSampling
It is the simplest type of probability sampling, wherein the probability of an element getting selected is directly proportional to its frequency. It is equivalent to say that every element has the same probability of getting chosen if they have the same frequency. For example in a random number generator each element has the same frequency and hence the same probability i.e. f=n/N.
It may be the simplest method but it is not considered as the statistically efficient.
II] Systematic Randomsampling:...

...Face to face interview
d) Structured record review
e) Structured observation
7. Please write down four factors which affect sampling error and for each factor, write down the effect of sampling error (i.e., if a factor is up, then what happens to the sampling error)
8. Please write down four factors to decide sample size and for each factor, write down the effect of sample size (i.e., if a factor is up, then what happens to the sample size)
9. Please write down when the systematic sampling is better than simple randomsampling.
10. Please write down why we do validity tests for a survey instrument.
Please write down why we do reliability tests for a survey instrument.
11. Please explain the concept of precision and accuracy with respect to reliability and validity.
12. CEO of an Auto maker company is concerned about the recent report of an economic journal. The journal says that complaints of SUV customers in US are increasing and varied. As such, she likes to know about satisfaction and demographic information about her customers who bought SUV which her company made recently. Let us assume that satisfaction is the main variable. The sampling design is following: 100 customers were selected from a list of customers who had applied for auto loan through her company. A sampling technique was the stratified...

...Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population.
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let's begin by covering some of the key terms in sampling like "population" and "sampling frame."
Basic Concepts Of Sampling
With a single grain of rice, an Asian housewife tests if all the rice in the pot has boiled; from a cup of tea, a tea-taster determines the quality of the brand of tea; and a sample of moon rocks provides scientists with information on the origin of the moon. This process of testing some data based on a small sample is called sampling.
Definition :
Sampling is the process by which inference is made to the whole by examining a part.
Purpose of Sampling
The purpose of sampling is to provide various types of statistical information of a qualitative or quantitative nature about the...