Eulogio “Amang” Rodriguez Institute of Science and Technology Nagtahan, Sampaloc, Manila
College of Education

Doctor of Education
Major: EDUCATIONAL MANAGEMENT

Subject:Seminar in Project Development, Industrial Planning Design, Implementation and Evaluation Professor:Dr. Elidio T. Acibar
Reporter:Evelyn L. Embate
Topic:Sampling

SAMPLING
Measuring a small portion of something and then making a general statement about the whole thing. Advantages of sampling
Sampling 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 within a reasonable period of time so that the data are still true, valid, and reliable. Sampling saves the sources of data from being all consumed

The act of gathering data may consume all the sources of information without sampling.

Sampling Concepts and Terminology
* Element
The unit about which information is collected and which provides the basis of analysis. They are the members of the...

...MULTIPHASE SAMPLING
Multiphase sampling is one of the probability sampling techniques that usually consist of two or more of both probability and non-probability techniques in choosing the target sample
The researchers will going to use purposive sampling in the first step
On the other hand, the researchers will use cluster sampling technique, a probability sampling technique to randomize the population.
Simple randomization sampling can be done using fish bowl method to get the names of the participants that will be included into two groups; the experimental group and the control group.
Between-group design of experimental research. In this design of experiments, a between-group design is an experiment that has two or more groups of subjects each being tested by a different testing factor simultaneously.
The between-group design to measure the effect of colors on the participants’ memory using a control and experimental group.
Within-Subjects Designs
A within-subjects design is an experiment in which the same group of subjects serves in more than one treatment. Note that I’m using the word "treatment" to refer to levels of the independent variable, rather than "group". It’s probably always better to use the word "treatment", as opposed to group. The term "group" can be very misleading when you are using a within-subjects design because the same "group" of...

...Population and Sampling
MTH/231
August 29, 2012
Importance of Population and Sampling
History from Political Arithmetic to Statistics
The history timeline show evidence of statistical data as early as Ancient Greece time but records show statistics in late 16th century, when it was introduced by, John Graunt, William Petty, and Pascal and later in 17th century by Gottfried Achenwall. It was an exciting time when success and discoveries raised the confidence of scientists, physicist and astronomers to think that laws of nature are not of divine intervention. As the time evolved and new discoveries were attained from political arithmetic like, mortality demographics, census data, economy, and International Statistical Congresses, they all led to changing its name to ‘statistics’.
Population
Every 10 years the country conducts a census of population to provide data that can be of use for research, business marketing, planning, surveys, and different sampling. The first U.S. census took place in 1790. What is ‘population’? The common term “population” describes people that live in a town which is located in a certain region within a certain county or state and their respective characteristic such age, sex, ethnicity, marital status, or other. The statistic term
“population” consist of all members, elements of the defined group. It includes all subjects to be studied or collecting information on for data driven...

...Simple Random Sampling
3.1 INTRODUCTION
Everyone mentions simple random sampling, but few use this method for population-based surveys. Rapid surveys are no exception, since they too use a more complex sampling scheme. So why should we be concerned with simple random sampling? The main reason is to learn the theory of sampling. Simple random sampling is the basic selection process ofsampling and is easiest to understand. If everyone in a population could be included in a survey, the analysis featured in this book would be very simple. The average value for equal interval and binomial variables, respectively, could easily be derived using Formulas 2.1 and 2.3 in Chapter 2. Instead of estimating the two forms of average values in the population, they would be measuring directly. Of course, when measuring everyone in a population, the true value is known; thus there is no need for confidence intervals. After all the purpose of the confidence interval is to tell how certain the author is that a presented interval brackets the true value in the population. With everyone measured, the true value would be known, unless of course there were measurement or calculation errors. When the true value in a population is estimated with a sample of persons, things get more complicated. Rather then just the mean or proportion, we need to derive the standard error for the variable of...

...Application of sampling distribution
Joe Greene, a new manager at Pilgrim Bank wants to better understand profitability data for bank’s customers. Joe is able to obtain a random sample of 31,634 customers on the following variables – Profitability (in $, for the most recent completed year, i.e. 2006), whether or not the customer uses the online banking channel, customer tenure, age and income where available, as well as the customer’s residential area. Descriptive statistics for Profits indicates that the average profit per customer is $111.50 with a standard deviation of $272.84.
a. Is Joe justified in assuming that this is a “large” sample? (see slide 7-14)
YES, BECAUSE 31,634 IS A LOT MORE THAN 30 OBVERSATIONS. GENERALLY, THE LARGER THE SAMPLE, THE MORE RELIABLE ARE THE ESTIMATES. THE KEY IS TO HAVE A RANDOMLY SELECTED SAMPLE TO REDUCE THE RISK OF BIASED ESTIMATES
b. Joe has been informed that the bank serves approximately 5 million customers nationwide – should he worry about using finite population correction factor (slide 7-9).
FINITE POPULATION CORRECTION (FPC) IS NOT NECESSARY, SINCE THE POPULATION OF APPROXIMATELY 5 MILLION IS QUITE LARGE. FPC IS ONLY RECOMMENDED FOR SMALL POPULATIONS.
c. Joe wants to estimate average profit for the entire population based on his sample. He knows that the “point estimate” for average profit would be $111.50, but, he will need to calculate the margin of error. The first step for this is...

...Backround Info
Weeds are nature’s most widely dispersed group of plants, and their job is to insure that the soil always has the protection of a green blanket. Without weeds, the fertile soil which took Nature centuries to build, would erode away without the protection of a plant cover. The objective of this experiment is to determine whether an undisturbed habitat will yield a higher species diversity and density than a disturbed habitat. The experiment will investigate the sample area’s using the method of Quadrant sampling. Quadrants are placed in a grid pattern on the sample area. The occurrence of organisms in these squares is noted. It is used to estimate population parameters when the organisms present are too numerous to count in total. In this case, Quadrant sampling will be used to estimate and compare population species diversity and density of a abandoned lot and a city pathway using the Simpson’s index and Jaccard coefficient. The procedure is to count all the individuals in 3 quadrants per habitat and to use this information to work out the abundance or percentage cover value for the whole area. The quadrant is square and covers 0.25 m2 (0.5m x 0.5m). The grid will be 2 m by 2 m. Which results in 4M2.
Estimated Total number of individuals counted
average density = Number of quadrants X area of each quadrant
Purpose: Investigate...

...12
SAMPLING MECHANICS
Sampling is an activity that involves the selection of individual people, data or things, from a target population/universe.
A population, or universe, is the entire set people data or things that is the subject of exploration.
A census involves obtaining information, not from a sample, but rather from the entire population or universe.
A sample (as opposed sampling) is a subset of the population/universe.
For Marketing Research purposes, sampling usually involves people, not data or things.
Sampling Plans are strategies and mechanics for selecting members of the sample from the population:
1. Define the population. It is usually limited based on some set of characteristics, e.g., males, aged 21-39, who have consumed alcoholic beverages within the past 3 months for a beer study.
2. Choose data collection methodology. What kind of information do you require from the sample, how will they be identified, where are they available, etc.
3. Set sampling frame. This is as exhaustive a list as operationally and economically possible that represents the population and is also accessible utilizing the selected methodology.
4. Choose sampling method.
• Probability samples are those that allow all members of the sampling frame an equal opportunity of selection. Probability samples include Simple Random,...

...
Life Sciences Sampling and Populations Paper
MTH/231
Life Sciences Sampling and Populations Paper
The core of biostatistics consists of the definition of a population and sampling, as they are the indicators of the fundamental concepts that are essential to understanding the statistics of the life and health sciences. The idea that a sample is illustrative of a given population, since a sample is derived from a specific, yet larger pool of information seems factually representative. Random sampling aides research in that it applies experimental design to the selection process and is the fairest means of sample collection, providing equal chance to the members of a given population being signified.
Populations
Populations, as defined by Triola and Triola (2006) are “a complete collection of all elements (scores, people, measurements and so on) to be studied. The collection is complete in the sense that it includes all the subjects to be studied. To serve as an example, all Kansan 9th graders will functionally be considered for a population. The data collected from the population are referred to as parameters and therefore are descriptive. The subjects or observations within the population are labeled with an N, or in the case of the example, figuratively, would be N=6000. The first function in this type of data collection is to have identified the target population, which has been done already....

...1 Sampling Methods
When surveying, for any purpose, it is important to recognise that the results are only as representative as the survey subjects (the sample), and as such much academic research has been performed in to techniques for selection, broadly placing them in one of two categories probability sampling and non-probability sampling.
In short, with probability sampling the participants are selected by chance. There are dozens of methods of selecting members, using a variety of mathematical techniques, but the key is that each subject has a random, calculable chance of being selected. There is no human intervention involved in the selection.
Method Characteristics
Simple (random) Sampling The sample is selected entirely at random
Stratified The population is first divided in to exclusive subgroups based on some predetermined criteria (e.g. location), then samples are selected at random
Proportionate Stratified As above, but a smaller group that would otherwise not provide statistically valid results may be oversampled then the results weighted to correct for this. For example, if a particular group is too small to provide a statistically significant sample, more members of that group would be sampled
Clustering The starting point for the sample is randomised, then assumes that the sample at that point is representative of the region. For example, selecting a street corner, interviewing...