All people in sampling frame are divided into groups or categories called strata. Within each group, a simple random sample or systematic sample is selected. I have decided to do a simple random sample. Example of stratified sampling:

If I want to ensure that a sample of 10 students from a group of 100 contains both male and female students in same proportions as in the full population, first divide that population into male and female. In this example, there are 54 male students and 46 females. To work out the number of males and females in the sample i will need to do the following calculations: No. of males in sample = (10/100) x 54 = 5.4

No. of females in sample = (10/100) x 46 = 4.6
I obviously can't have.4 of a person or .6 of a person in my sample, and so have to "round" the numbers. Therefore i would choose 5 males and 5 females in the sample. I will then use the random sampling technique to get my sample. I will put the females and males in seperate groups, and number them in order. I will then use the random number generator on my calculator and which ever number comes up multiply it by the size of the population and then i will use the corresponding person from the group. I will do this until i have the amount of each that i had worked out earlier using my stratified sampling method.

...ANSWER:
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 randomsampling, systematic sampling, stratifiedsampling, 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...

...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 random sampling.
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
School of Nursing
The Hong Kong Polytechnic University
Sept., 16, 05; 6:30p.m. Y302
2014/9/27
1
Outline
Introduction
Select a Random Sample
Types of Sampling
• Systematic Sampling
• StratifiedSampling
• Cluster Sampling
Power Analysis
Type I and Type II Errors
Determine Sample Size – Examples
2014/9/27
2
Introduction
Sample
In statistics we always assume that a
sample is representative of the population
and that it has been selected at random.
Therefore, a sample is a part of a
population and it is important to report in
detail the methods and criterions by which
the subjects were selected.
Population
A population of entities is defined as the
largest collection of entities for which we
have an interest at a particular time. We
are not interested in the individual sample
who are included in a research, but in the
general outcome of the research. We want
the results to be generalized thus they can
be applied to all subjects.
2014/9/27
It is rare to have access to
all the information that we
would like to know.
Usually, we need to
examine some portion of
the total system, then
extend our knowledge of
that portion to the total
system. Such a portion is
said to be a sample, and
the total system is referred
to as the population.
3
Introduction (cont.)
Sampling error: Even if a...

...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 RANDOM SAMPLING
Simple random sampling (sometimes called just random sampling) 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.
Random Sampling 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 random.
* Consulting the table of random numbers. If you already...

...Sampling Procedures
There are many sampling procedures that have been developed to make sure that a sample really represents the target population.
Simple Random Sampling
In simple random sampling, every individual in the target population has an equal chance of being part of the sample. This requires two steps:
1. Obtain a complete list of the population.
2. Randomly select individuals from that list for the sample.
In a study where the unit of analysis is the student, the researcher must obtain a complete list of every student in the target population to achieve simple random sampling. This is rarely possible, so very few, if any, educational studies use simple random sampling.
Stratified Random Sampling
In stratified random sampling, the researcher first divides the population into groups based on a relevant characteristic and then selects participants within those groups. In educational research, stratified random sampling is typically used when the researcher wants to ensure that specific subgroups of people are adequately represented within the sample. Stratified random sampling requires four steps:
* Determine the strata that the population will be divided into. The strata are the characteristics that the population is divided into, perhaps gender, age,...

...Concept and basics of probability sampling 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 probability sampling 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 probability sampling 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-probability sampling methods.
Types of probability sampling methods
Simple Random Sampling
What is it?
Simple random sampling is selecting randomly some units from a known and well defined population. In this method the sampling frame should be known and all units should have same chance for...

...Types of Sampling
In applications:
Probability Sampling: Simple Random Sampling, Stratified Random Sampling, 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-probability sampling 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 Random Sampling: 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.
Stratified Random Sampling: Divide the population into "strata". There can be any number of these. Then choose a simple random sample from each stratum. Combine those into the overall sample. That is a...

...Samples and Sampling
The term "sampling," as used in research, refers to the process of selecting the individuals who will participate (e.g., be observed or questioned) in a research study.
A sample is any part of a population of individuals on whom information is obtained. It may, for a variety of reasons, be different from the sample originally selected.
Samples and Populations
The term "population," as used in research, refers to all the members of a particular group. It is the group of interest to the researcher, the group to whom the researcher would like to generalize the results of a study.
A target population is the actual population to whom the researcher would like to generalize; the accessible population is the population to whom the researcher is entitled to generalize.
A representative sample is a sample that is similar to the population on all characteristics.
Two-stage random sampling A process in which clusters are first randomly selected and then individuals are selected from each cluster.
Stratified random sampling The process of selecting a sample in such a way that identified subgroups in the population are represented in the sample in the same proportion as they exist in the population.
Random sampling Methods designed to select a representative sample by using chance selection so that biases will not systematically alter the sample.
Cluster random...