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![# 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](https://image.slidesharecdn.com/reportonsamplingv2-130323083651-phpapp02/75/Sampling-An-Introduction-16-2048.jpg)
![# 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](https://image.slidesharecdn.com/reportonsamplingv2-130323083651-phpapp02/75/Sampling-An-Introduction-17-2048.jpg)
Uploaded byKarla Maolen Visbal
Sampling: An Introduction
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.
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Overview of sampling presented by Karla Maolen B. Visbal.
Sampling is a technique studying a part of the universe to draw conclusions for the whole (Uppreti & Sirgh, 2006).
Defines statistical population which is the set concerning which inferences are drawn based on random samples.
Illustrates the relationship between theoretical population, empirical population, sample size loss, and final data sample.
Discusses the importance of sample size in research and notes factors affecting its determination.
Differentiates between probability sampling (random methods) and non-probability sampling (researcher-chosen).
Outlines methods under probability sampling: Simple Random, Systematic Random, Stratified Sampling, and Cluster Sampling.
Explains SRS where individuals are randomly selected, with notes on 'with' and 'without' replacement sampling methods.
Describes sampling every nth member from the population as a systematic approach to sampling.
Lists hospitals in Manila used as subjects for sampling in research, illustrating systematic sampling.
Defines stratified sampling where population is divided into strata sampled to ensure representation.
Outlines definitions and strategies for cluster sampling, including one-stage and multi-stage approaches.
Introduces non-probability sampling methods including Theoretical, Snowball, Quota, and Convenience Sampling.
Describes linear and exponential types of snowball sampling, focusing on biases and selection methods.
Discusses advantages like accessing hidden populations and disadvantages such as community bias.
Explains segmentation of populations into exclusive subgroups and example of proportional sampling.
Defines convenience sampling method while cautioning against its bias due to self-selection.
Discusses the impact of sampling errors using an example involving biased data collection.
Open floor for questions regarding the presentation content on sampling.
Thanks the audience for their participation and attention during the presentation.
Lists various sources that were referenced throughout the presentation regarding statistical methods.
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Sampling: An Introduction
- 1. Sampling Prepared by: Karla MaolenB. Visbal
- 2. Definition Sampling isa 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 Aset 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 Anappropriate 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 SimpleRandom 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 SRSwith replacement: Each observation in the data set has an equal chance to be selected. Can be selected over and over again
- 10. Simple Random SRSwith replacement:
- 11. Simple Random SRSwith replacement:
- 12. Simple Random SRSwithout 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.
- 13. Simple Random SRSwithout replacement:
- 14. Simple Random SRSwithout replacement:
- 15. Systematic Random Everynth member of the population is sampled (Calkins, 1995-2005).
- 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 Thepopulation 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 Apopulation 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-StageCluster 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 Refersto 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 SnowballSampling Linear Researcher starts with one subject. Through referral, the researcher only gets only one subject.
- 26. Types of SnowballSampling Exponential Non-Discriminative The first subject refers to multiple subjects. All multiple subjects are sampled.
- 27. Types of SnowballSampling 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 Apopulation 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 OrSampling 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 includedrich, white people with a telephone in their homes. Sampling Harry S. Truman Errors 33rd President of the United States
- 33.
- 34.
- 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
- #6 Although a large sample is no guarantee of avoiding bias, too small a sample is a recipe for disaster.
- #11 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.
- #12 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.)
- #14 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.
- #15 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.)
- #20 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.
- #22 a multi-stage cluster sampling
- #23 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.
- #24 Non-probability sampling does not involve random selection of
- #25 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.
- #29 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.
- #30 Similar to stratified sampling only in larger proportions.
- #31 Similar to stratified sampling only in larger proportions.
- #33 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.