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Samplels & Sampling Techniques


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Samplels & Sampling Techniques

  1. 1. Population sample Sampling
  2. 2. The Research Locals This portion of the presentation contains a description of the place where the respondents are to be obtained. It includes a brief description of the location whether urban or rural; city or country; provincial; commercial or agricultural; and the regional or local characteristics.
  3. 3. This study shall be conducted in the Cordillera Administrative Region (CAR) which covers the provinces of Abra, Apayao, Benguet, Ifugao, Kalinga and Mountain Province and the City of Baguio. The CAR provinces belong to three (3) income classifications. Benguet province is second class, Abra, Apayao, Kalinga and Ifugao are considered as third class while Mt. Province is classified as fourth class province. In this study, one province from each income classification and the City of Baguio shall be selected as the source of data and information. Benguet province shall represent the second class province, Mt. Province shall be picked up to represent the fourth class province while Abra shall represent the third class provinces due to accessibility purposes and to the close association of its upland areas to the provinces of Apayao and Kalinga. Example
  4. 4. Bradfield et al (1980) define “sampling” as the process of measuring a small portion of something followed by a general statement about the whole thing. A sample is a collection of individuals selected from a larger population
  5. 5. For a study to produce a valid and reliable result, sufficient representatives of samples and appropriate sampling techniques should be adopted. Sampling is the process of getting a representative part of the population being studied. A representative of the samples is determined in a manner that the characteristics, properties and variations are reflected. Population Sample
  6. 6. A sample is any sub-aggregate drawn from the population (Fergusson, 1976). Ary et al defines sampling as the process involving the taking a part of the population. It is pointed out that a good sampling is an effective means of reducing the number of persons contacted to get a relatively accurate picture of the sample population’s attitude and opinion (Orth, 1976).
  7. 7. Sampling The process of selecting a portion of the target population in such a way that the individuals chosen represent, as nearly as possible, the characteristics of the target population
  8. 8. The Population and Sample Size •A population refers to a group to which an investigator would like the result to be generalized. Hence, it is a larger group about which a generalization is made (Gay, 1976). It also refers to “all members of any well- defined class of people, events or objects” (Kerlingen, 1973). •A sample is a small group taken from a larger population composed of members being studied. A good example size should be a representative of the population and should be adequate in size to be reliable. If all members of the population have an equal chance of being selected, then the sample is a representative of the population.
  9. 9. Slovin (1960) presents a formula to determine the size of a sample as follows: Where: n=______N______ 1 + (N)(e) ² n= Sample Size N=Population Size
  10. 10. Example: In your study, the size of your population is 10, 000. What is the size of your sample if you allow 2% margin of error? Using above captioned formula the sample size could be computed as follows: n=______10,000_______ 1 + 10,000(.02) ² = ______10,000_______ 1 + 10, 000(.0004) =_____10,000______ 1 + 4 = ____10,000____ 5 = 2,000 Therefore, your representative sample size is 2,000 out of 10,000 Therefore, your representative sample size is 2,000 out of 10,000
  11. 11. This formula is not applicable to a small population. Pagoso et al (1980), presents a Table below showing the sample size for specified margins of error, indicating the margins of error that are not applicable to population sizes as indicated by “NH” as follows Population Margin of Error 1% 2% 3% 4% 5% 10% 500 NA NA NA NA 222 83 1,500 NA NA 638 441 316 94 2,500 NA 1,250 769 500 345 96 3,000 NA 1,364 811 517 353 97 4,000 NA 1,538 870 541 364 98 5,000 NA 1,667 909 556 370 98 6,000 NA 1,765 938 566 375 98 7,000 NA 1,842 959 574 378 99 8,000 NA 1,908 976 580 381 99 9,000 NA 1,908 989 584 383 99 10,000 5,000 2,000 1,020 588 388 99 50,000 8,333 2,333 1,087 617 387 100
  12. 12. Depending upon the type of research use, Gay (1978) as cited by Sevilla et al (1978) offers some minimum acceptable sizes as follows: 1. Descriptive Research, 20% for a smaller population as small as 500 and below and 10% of the population for a larger population as large as 1,000. 2. Experimental Research, 30 per group as minimum although 15 subjects are acceptable (Sevilla et al, 1988).
  13. 13. 3.Ex post facto or causal comparative group – 15 subjects per research 3.Experimental research – 15 subjects per group. Some believe that 30 per group should be considered minimum
  14. 14. Lynch et al 1972, and cited by Ardoles, 1992, suggested the formula below to determine the sample size: n= NZ² x p (1-p) _ Where: n= Sample Size Nd² + Z² p (1-p) N= Population Z= the value of the normal variables (1.96) for a reliability level of 0.95 p= the largest possible proportion (0.50)
  15. 15. For instance, if the population size in your research is 10,000 and the desired reliability is 0.95, with 0.05 as allowable sampling error and the proportion of a target population with a certain characteristics important to the study is 0.50. What is the size of the population? The sample size could be computed as follows: n= 10,000 (1.96) ² x 0.50 (1-0.50) = 10,000 (3.84) x 0.50(0.50) 10,000 (.0025) + 3.84 x 0.50(0.50) = 38,400 x 0.25 25 + 3.84 x 0.25 = 9,600 25 + 0.96 = 9,600 25.96 = 369.79 or 370
  16. 16. Pagoso et al, proposed the formula for computing the size of the sample as: n=____N____ 1 + (N)(e) ² Where: n= the size of the sample N= the size of the Population e= the margin of error (should not be higher than 5%) which is ideally 3% Example: If the population is 10,000 and the margin of error to be adopted is 3%, the size of the population is computed as follows: n=____10,000_____ 1 + 10,000 (.03) ² =_____10,000_____ 1 + 10,000 (.0009) =_____10,000_____ 1+ 9 =_____10,000_____ 10 N = 1,000
  17. 17. Department Total Number 10% Sample Size Elementary 6,000 0.1 600 Secondary 3,000 0.1 300 College 1,000 0.1 100 10,000 1,000 Then: The sample proportion should be computed using this formula: n = 1,000 =0.1 N 10,000 Hence: Every sub-group is represented accordingly in the sample.
  18. 18. Sampling Error The fluctuation of a statistic from one sample to another drawn from the same population. (Can be estimated with probability sampling. Note: the larger the sample, the less sampling error.
  19. 19. There are basically two types of sampling, namely: 1.Probability Sampling A probability sampling is a type of sampling wherein the selection of samples is done with the members of the population having equal chance to be selected as part of the representative sample. It is further classified into the following sub-types: simple random, stratified random, cluster, systematic sampling, selective sampling Sampling procedures use some form of randomization to select samples from the population 2.Non Probability Sampling Using other than random Procedures such as: convenience sampling, purposive sampling, quota sampling
  20. 20. It is further classified into the following sub-types: a)Simple Random Sampling In this type of sampling every member of the population has an equal chance of being chosen to be included in the sample. It is the simplest probability sampling which is usually done by using lottery or raffle method of getting the representative samples of the population. This method is easily done by listing numerically all the names of the members of the population from the first to the last member. Write their individual numbers in small pieces of paper, then place these in a box and draw them after shaking the box very well until the total desired samples are obtained.
  21. 21. b) Stratified Sampling This is the selection of samples from the different classes or strata of the population involved in the research. Each class is treated as a different population. A simple random sampling is then used in each class with proportionate and equal percentage of representation from each stratum. An example is when a study is conducted with Groups of respondents were the residents grouped as Commercial, Industrial and Residential of Urdaneta City for the year 2012 and 2013 Application for Business, Building, and Occupancy Permit
  22. 22. The first groups of respondents were the eighteen (18) BFP personnel of Urdaneta City who are the implementers of the Fire Code of the Philippines. The other groups of respondents were the residents grouped as Commercial, Industrial and Residential of Urdaneta City who applied for Business, Building, and Occupancy Permit for the year 2006 and 2007. The numbers of applicants were: Commercial - N=251, Industrial – N=190, and Residential – N=56. The representative sample by group was determined using the Slovin formula (Parel, 1984). The computed samples by group were: Commercial (n=113), Industrial (n=86) and Residential (n=25) yielding a total of 224. The formula is shown below. n = ______N_______ 2 1 + N e Where: n = sample; N = population; e = standard error.
  23. 23.  Stratified sampling is a strategy for selecting samples in such a way that specific subgroups or strats will have a sufficient number of representatives within the sample to provide ample number of sub analysis of the members of these subgroups C. Stratified sampling
  24. 24.  Cluster sampling refers to the selection of members of a sample rather than individual. It is sampling in which groups, not individual, are randomly selected.  use in multi stage sampling process  Used when target population is very large  Can results in more sampling error  Statistical analysis more complicated D. Cluster sampling
  25. 25. Cluster sampling may be used when it is either impossible or impractical to compile an exhaustive list of the elements that make up the target population. Usually, however, the population elements are already grouped into subpopulations and lists of those subpopulations already exist or can be created. To conduct a cluster sample, the researcher first selects groups or clusters and then from each cluster, selects the individual subjects either by simple random sampling or systematic ransom sampling or, if the cluster is small enough, the researcher may choose to include the entire cluster in the final sample rather than a subset from it. For example, let’s say the target population in a study was church members in Region 1. The researcher could, however, create a list of churches in the region 1, choose a sample of churches, and then obtain lists of members from those churches.
  26. 26. Method Best when Simple random sampling Whole population is available. Stratified sampling (random within target groups) There are specific sub-groups to investigate (eg. demographic groupings). Systematic sampling (every nth person) When a stream of representative people are available (eg. in the street). Cluster sampling (all in limited groups) When population groups are separated and access to all is difficult, eg. in many distant cities. Probability methods This is the best overall group of methods to use as you can subsequently use the most powerful statistical analyses on the results.
  27. 27. Non Probability Sampling
  28. 28. a) Quota Sampling This method involves the taking of the desired number of respondents with the required characteristics proportionate to the population under study. Sample This is selecting interviewees in proportion to the consumer profile within your target market. A good example of this appears below, where the percentage of buyers for each gender of chocolate reflects the respondent quote
  29. 29. Researcher uses some knowledge of the population to build some representativeneness into sampling plan. Divides population into different strata and samples from each of them Usually better than convenience
  30. 30. b) Convenience or Accidental Sampling This sampling technique involves the conduct of a study wherein respondents are selected based on the convenience of the researcher. Involves the use of the most convenient and readily available subjects for the sample. Example: man on on the street interviews; teachers uses students.
  31. 31. Convenience sampling is the most widely used yet weakest form of sampling. There is no way to evaluate all of the biases that may be operating
  32. 32. Two basic principles in random sample: •Equi probability - equally likely to occur; having equal probability. •independence  The table of random samples is considered the most systematic technique in getting sample units at random  The lottery or fishball technique is another type of random sampling  The lottery or fishball technique use either sampling without replacement or with replacement
  33. 33. Snowball sampling Variations of above, used when subjects are hard to find. One subject recommends another. Even more prone to bias
  34. 34. Purposive Sampling “Judgmental Sampling” •Respondents are selected deliberately depending on the intentions of the researcher as well as objectives of the study •Proceeds on the belief that researcher knows enough about the population and its element to handpick the sample.
  35. 35. Quota methods For a particular analysis and valid results, you can determine the number of people you need to sample. In particular when you are studying a number of groups and when sub- groups are small, then you will need equivalent numbers to enable equivalent analysis and conclusions. Method Best when Quota sampling (get only as many as you need) You have access to a wide population, including sub-groups Proportionate quota sampling (in proportion to population sub- groups) You know the population distribution across groups, and when normal sampling may not give enough in minority groups Non-proportionate quota sampling (minimum number from each sub- group) There is likely to a wide variation in the studied characteristic within minority groups
  36. 36. Selective methods Sometimes your study leads you to target particular groups. Method Best when Purposive sampling (based on intent) You are studying particular groups Expert sampling (seeking 'experts') You want expert opinion Snowball sampling (ask for recommendations) You seek similar subjects (eg. young drinkers) Modal instance sampling (focus on 'typical' people) When sought 'typical' opinion may get lost in a wider study, and when you are able to identify the 'typical' group Diversity sampling (deliberately seeking variation) You are specifically seeking differences, eg. to identify sub- groups or potential conflicts
  37. 37. Convenience methods Good sampling is time-consuming and expensive. Not all experimenters have the time or funds to use more accurate methods. There is a price, of course, in the potential limited validity of results. Method Best when Snowball sampling (ask for recommendations) You are ethically and socially able to ask and seek similar subjects. Convenience sampling (use who's available) You cannot proactively seek out subjects. Judgment sampling (guess a good-enough sample) You are expert and there is no other choice.
  38. 38. Ethnographic methods When doing field-based observations, it is often impossible to intrude into the lives of people you are studying. Samples must thus be surreptitious and may be based more on who is available and willing to participate in any interviews or studies. Method Best when Selective sampling (gut feel) Focus is needed in particular group, location, subject, etc. Theoretical sampling (testing a theory) Theories are emerging and focused sampling may help clarify these. Convenience sampling (use who's available) You cannot proactively seek out subjects. Judgment sampling (guess a good-enough sample) You are expert and there is no other choice.
  39. 39. Rivera, Jr M and Rivera Roela Victoria Practical Guide to Thesis and Dissertation Katha Publishing Inc. Quezon City, Philippines (2007) Jennifer Villanueva Types of Descriptive Research a/types-of-descriptive-research (2013)