The document discusses different types of sampling techniques used in research. It defines sampling as studying only a part of the total population to make conclusions about the entire population. The main types discussed are probability sampling methods like simple random sampling, systematic random sampling, stratified sampling, and cluster sampling. It also discusses non-probability sampling methods such as snowball sampling, quota sampling, theoretical sampling, and convenience sampling. For each method, it provides details on how the sampling is conducted and examples to illustrate the technique. It concludes by listing some references used in compiling the information presented.
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
simplest way of explanation from a smart study.Sample techniques used in sampling. there are two types of techniques used in the process of sampling such as probability sampling and non probability sampling and here i have explained only Non- probability sampling.
Non- Probability Sampling & Its MethodsArpit Surana
A detailed explanation of non-probability sampling and its methods have been covered. There are 4 types of non- probability sampling methods:
1. convenience sampling
2. purposive sampling
3. quota sampling (both controlled and uncontrolled)
4. snowball sampling (all 3 ways of performing)
Meaning with adequate examples, pros and cons have been covered
For and query or further information, Kindly contact:
Arpit Surana
https://www.linkedin.com/in/arpitsurana116/
arpitsurana116116@gmail.com
A presentation about the added value of combining qualitative and quantitative methods. It begins with a brief discussion of qualitative research and how it is distinct from yet shares basic principles with quantitative research, followed by a discussion of four important ways mixed methods -- integrating qualitative and quantitative -- adds value to our research efforts, and then a discussion of mixed methods research -- what it is, typologies, alternatives to typologies, and the use of diagrams.
simplest way of explanation from a smart study.Sample techniques used in sampling. there are two types of techniques used in the process of sampling such as probability sampling and non probability sampling and here i have explained only Non- probability sampling.
Non- Probability Sampling & Its MethodsArpit Surana
A detailed explanation of non-probability sampling and its methods have been covered. There are 4 types of non- probability sampling methods:
1. convenience sampling
2. purposive sampling
3. quota sampling (both controlled and uncontrolled)
4. snowball sampling (all 3 ways of performing)
Meaning with adequate examples, pros and cons have been covered
For and query or further information, Kindly contact:
Arpit Surana
https://www.linkedin.com/in/arpitsurana116/
arpitsurana116116@gmail.com
A presentation about the added value of combining qualitative and quantitative methods. It begins with a brief discussion of qualitative research and how it is distinct from yet shares basic principles with quantitative research, followed by a discussion of four important ways mixed methods -- integrating qualitative and quantitative -- adds value to our research efforts, and then a discussion of mixed methods research -- what it is, typologies, alternatives to typologies, and the use of diagrams.
sampling in research, a written report which consists of the following: definitions and terminologies, the sampling types and methods, the sampling process, the sampling storage, and sampling errors.
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The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
2. Definition
Sampling is a technique wherein only a part
of the universe is studied and conclusions
are drawn on that basis for the entire
universe (Uppreti & Sirgh, 2006).
3. Statistical Population
A set of entities
concerning which
statistical inferences are
to be drawn, based on a
random sample taken
from a population.
A subpopulation is a
subset of a population.
4. Universe Population Original Sample
(theoretical sample (empirical sample
population) population)
Loss (non-response)
Final Sample (data)
5. Sample Size
An appropriate number size is crucial to any well-
planned research investigation.
The question has no definite answer value due to
many factors.
6. Types of Sampling
Probability Sampling
Likelihood of any member of the population from
being included in the sample.
Involves random sampling methods
Non-probability Sampling
Purposive Sampling: The researcher chooses the
sample based on who they think would be
appropriate for the study.
Does not involve random selection
7. Methods of Sampling
Probability
Sampling
Simple Systemic Cluster
Stratified
Random Random Sampling
8. Simple Random
Simple Random Sample or SRS is a subset of
individuals (sample) chosen from a larger set (a
population) (Yates, 2008).
Individuals (n) are randomly chosen, in such a way
that every set of n individuals has an equal chance
of being the sample actually selected (Calkins,
1995-2005).
9. Simple Random
SRS with replacement:
Each observation in the data set has an equal chance to
be selected.
Can be selected over and over again
12. Simple Random
SRS without replacement:
In a simple random sample without replacement each
observation in the data set has an equal chance of being
selected.
Once selected it can not be chosen again.
16. # Name of Hospital in Manila
1 Amisola Maternity Hospital - Hermosa Street, Manuguit, Tondo
2 Canossa Health and Social Center Foundation, Inc. - E. Jacinto Street, Magsaysay Village, Tondo
3 Chinese General Hospital and Medical Center - Blumentritt Street, Santa Cruz
4 Clinica Arellano General Hospital - Doroteo Jose Street, Santa Cruz
5 De Ocampo Memorial Medical Center - Nagtahan Street, Santa Mesa
6 Dr. Jose Fabella Memorial Hospital - Lope de Vega Street, Santa Cruz
7 Dr. Mirando Unciano, Sr. Medical Center - V. Mapa Street, Santa Mesa
8 Esperanza Health Center - Santa Mesa
9 F. Lanuza Health Center and Lying-in Clinic - Alvarez Street, Santa Cruz
10 GAT Andres Bonifacio Memorial Medical Center - Delpan Street, Tondo
11 Hospital of the Infant Jesus - Laong Laan Street, Sampaloc
12 Jose R. Reyes Memorial Medical Center - San Lazaro Compound, Rizal Avenue, Santa Cruz
13 Manila Doctors' Hospital - United Nations Avenue, Ermita
14 Maria Clara Health Center and Lying-in Clinic - Maria Clara corner Prudencio Streets, Sampaloc
15 Mary Chiles General Hospital - Dalupan Street, Sampaloc
16 Mary Johnston Hospital - Juan Nolasco Street, Tondo
17 Medical Center Manila[1] - General Luna Street, Ermita
18 Metropolitan Medical Center - Masangkay Street, Tondo
19 Nephrology Center of Manila - San Andres Street corner Leon Guinto Street, Malate
20 Ospital ng Maynila Medical Center - Quirino Avenue corner Roxas Boulevard, Malate
17. # Name of Hospital in Manila
1 Amisola Maternity Hospital - Hermosa Street, Manuguit, Tondo
2 Canossa Health and Social Center Foundation, Inc. - E. Jacinto Street, Magsaysay Village, Tondo
3 Chinese General Hospital and Medical Center - Blumentritt Street, Santa Cruz
4 Clinica Arellano General Hospital - Doroteo Jose Street, Santa Cruz
5 De Ocampo Memorial Medical Center - Nagtahan Street, Santa Mesa
6 Dr. Jose Fabella Memorial Hospital - Lope de Vega Street, Santa Cruz
7 Dr. Mirando Unciano, Sr. Medical Center - V. Mapa Street, Santa Mesa
8 Esperanza Health Center - Santa Mesa
9 F. Lanuza Health Center and Lying-in Clinic - Alvarez Street, Santa Cruz
10 GAT Andres Bonifacio Memorial Medical Center - Delpan Street, Tondo
11 Hospital of the Infant Jesus - Laong Laan Street, Sampaloc
12 Jose R. Reyes Memorial Medical Center - San Lazaro Compound, Rizal Avenue, Santa Cruz
13 Manila Doctors' Hospital - United Nations Avenue, Ermita
14 Maria Clara Health Center and Lying-in Clinic - Maria Clara corner Prudencio Streets, Sampaloc
15 Mary Chiles General Hospital - Dalupan Street, Sampaloc
16 Mary Johnston Hospital - Juan Nolasco Street, Tondo
17 Medical Center Manila[1] - General Luna Street, Ermita
18 Metropolitan Medical Center - Masangkay Street, Tondo
19 Nephrology Center of Manila - San Andres Street corner Leon Guinto Street, Malate
20 Ospital ng Maynila Medical Center - Quirino Avenue corner Roxas Boulevard, Malate
18. Stratified Sampling
The population is divided into two or more strata and
each subpopulation is sampled.
Gender and age groups would be commonly used
strata.
Each stratum must share the same characteristic.
Random sampling may be used to select a certain
number of data points from each stratum.
19. Stratified Sampling Strategies
1. Using sampling fraction in each strata that is
proportional to that of the total population.
a. Ex: 60% male and 40% female in a population; 3 males
and 2 females/strata
2. Optimum Allocation/Disproportionate Allocation. In
sampling units with differing sizes, larger units are
more likely to be sampled than the smaller ones.
20. Cluster Sampling
A population is divided into clusters and a few of
these (often randomly selected) clusters are
exhaustively sampled.
Clusters are natural or predefined groups (e.g.
families, classrooms, schools, etc.)
21. Cluster Sampling
Example:
How many bicycles are owned in a community of
10,000 households?
o From the 500 blocks in the whole community, take
20 blocks, with 20 households each.
o Sample every household.
22. Cluster Sampling
One-Stage Cluster Sampling
When a researcher includes all of the subjects from the
chosen clusters into the final sample
Multi-Stage Cluster Sampling
Instead of using all the elements contained in the
selected clusters, the researcher randomly selects
elements from each cluster.
Stage 1: Constructing Clusters
Stage 2: Defining elements
23. Methods of Sampling
Non-
Probability
Sampling
Theoretical Snowball Quota Convenience
24. Theoretical Sampling
Refers to the process of choosing new research sites
or cases to compare with one that have already
been studied.
Its purpose is to gain a deeper understanding of
analysed cases and facilitate the development of
analytic frame and concepts used in their research.
25. Types of Snowball Sampling
Linear
Researcher starts with one subject. Through
referral, the researcher only gets only one subject.
26. Types of Snowball Sampling
Exponential Non-Discriminative
The first subject refers to multiple subjects. All
multiple subjects are sampled.
27. Types of Snowball Sampling
Exponential Discriminative
Among the multiple referrals by the primary
subjects at each level, only one is chosen as the
subject of research.
28. Snowball Sampling
Advantages Disadvantages
Locate Hidden Population Community Bias
People located are Not Random
population specific Vague Population Size
Wrong Anchoring
29. Quota Sampling
A population is first segmented into mutually
exclusive subgroups.
Judgment is used to select the target participants.
The researcher aims to represent the major
characteristics of the population by sampling a
proportional amount of each.
30. Quota Sampling
Example:
Proportional Quota of 100 people is 40% women
and 60% men
Sample 40 women and 60 men
31. Convenience Sampling
Or Sampling of Convenience is done as
convenient, often allowing the element to choose
whether or not it is sampled.
Be wary of convenience sampling because the data
may be seriously biased.
32. Samples only included rich, white
people with a telephone in their
homes.
Sampling
Harry S. Truman Errors
33rd President of the United States
35. References:
Wilks, S. (1962). Mathematical Statistics). John Wiley.
Uppretti, D. & Sirgh, J. (2006). Encyclopedia of Statistics Volume 1. 1st Edition. Dominant
Publishers and Distributors.
Trochim, W. (2006). Non-probability Sampling.
http://www.socialresearchmethods.net/kb/sampnon.php. Retrieved on December 27, 2012.
Calkins, K. (1998-2005). Probability and Sampling/Distributions.
http://www.andrews.edu/~calkins/math/edrm611/edrm07.htm#ERROR. Retrieved on
December 27, 2012.
Yates, Daniel S.; David S. Moore, Daren S. Starnes (2008). The Practice of Statistics, 3rd Ed.
Bruin, J. 2006. newtest: command to compute new test. UCLA: Statistical Consulting Group.
Lorh, S. 1999. Sampling: Design and Analysis. Duxbury Press.
Charles C. Ragin, 'Constructing Social Research: The Unity and Diversity of Method', Pine Forge
Press, 1994
36. References:
Barney G. Glaser & Anselm L. Strauss, 'The Discovery of Grounded Theory:
Strategies for Qualitative Research', Chicago, Aldine Publishing
Company, 1967
_______. Snowball Sampling. http://www.transtutors.com/homework-
help/management/marketing/market-research/snowball-sampling/
Retrieved on January 7, 2013
Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP.
Editor's Notes
Although a large sample is no guarantee of avoiding bias, too small a sample is a recipe for disaster.
Consider a population of potato sacks.Each population MAY have 12, 13, 14, 15, 16, 17, or 18 potatoes… and all of them are equally likely to have those numbers. In this population, there is exactly one sack with each number.
So the whole population has seven sacks. If I sample two with replacement, then I first pick one (say 14). I had a 1/7 probability of choosing that one. Then I replace it. Then I pick another. Every one of them still has 1/7 probability of being chosen. And there are exactly 49 different possibilities here (assuming we distinguish between the first and second.)
Consider a population of potato sacks.Each population MAY have 12, 13, 14, 15, 16, 17, or 18 potatoes… and all of them are equally likely to have those numbers. In this population, there is exactly one sack with each number.
So the whole population has seven sacks. If I sample two with replacement, then I first pick one (say 14). I had a 1/7 probability of choosing that one. Then I replace it. Then I pick another. At this point, there are only six possibilities: 12, 13, 15, 16, 17, and 18. So there are only 42 different possibilities here (again assuming that we distinguish between the first and the second.)
OPTIMAL ALLOCATION: Samples are put into units, with highest to lowest values or strata based on an element. Ex. Radio stations who pay higher copyright fees are more likely to be sampled.
a multi-stage cluster sampling
One-Stage Cluster Sampling-Can be expensive and inappropriateMulti-Stage -The technique is used frequently when a complete list of all members of the population does not exist and is inappropriate.
Non-probability sampling does not involve random selection of
The key issue in this sampling technique is whether the group (or category or approach) utilized in directing the sampling process has theoretical relevanceWhy This Matters:The importance of this approach is that it can be beneficial in advancing our comparisonsIt can thus assist us in verifying or demanding alteration in our working hypotheses hence, it assists the shaping of our emergent theory.
Advantages1. Locate hidden populations: It is possible for the surveyors to include people in the survey that they would not have known.2. Locating people of a specific population: There is no lists or other obvious sources for locating members of the population of specific interest.Disadvantages1. Community Bias: The first participants will have strong impact on the sample. Snowball sampling is inexact, and can produce varied and inaccurate results. The method is heavily reliant on the skill of the individual conducting the actual sampling, and that individual’s ability to vertically network and find an appropriate sample. To be successful requires previous contacts within the target areas, and the ability to keep the information flow going throughout the target group.2. Not Random: Snowball sampling contradicts many of the assumptions supporting conventional notions of random selection and representativeness[11] However, Social systems are beyond researcher’s ability to recruit randomly. Snowball sampling is inevitable in social systems.3. Vague Overall Sampling Size: There is no way to know the total size of the overall population.[12]4. Wrong Anchoring: Another disadvantage of snowball sampling is the lack of definite knowledge as to whether or not the sample is an accurate reading of the target population. By targeting only a few select people, it is not always indicative of the actual trends within the result group. Identifying the appropriate person to conduct the sampling, as well as locating the correct targets is a time consuming process which renders the benefits only slightly outweighing the costs.
Similar to stratified sampling only in larger proportions.
Similar to stratified sampling only in larger proportions.
Former President Truman holding a copy of the Chicago Times.Truman vs. Dewey:Samples only included rich, white people with a telephone in their homes.