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the whole definition, process, types, fundamentals and more just squeezed into one presentation

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- 2. Sampling is the process of selecting participants from the population. Sampling refers to the process used to select any number of persons to represent the population according to some rules or plan on basis of some selected measures. In general statistics and survey methodology, sampling is basically concerned with the selection of a subset of individuals from within a statistical population to estimate the characteristics of the whole population.
- 3. THE CONCEPT OF SAMPLING POPULATION ELEMENTS SAMPLE SUBJECTS
- 4. A sample is the group of people who take part in the investigation. The people who take part are referred to as “participants”. The target population is the total group of individuals from which the sample might be drawn. Generalisability refers to the extent to which we can apply the findings of our research to the target population we are interested in. Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference
- 5. FUNDAMENTALS OF SAMPLING POPULATION A population can be defined as including all people or items with the characteristic one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.
- 6. • Note also that the population from which the sample is drawn may not be the same as the population about which we actually want information. Often there is large but not complete overlap between these two groups due to frame issues etc . • Sometimes they may be entirely separate - for instance, we might study rats in order to get a better understanding of human health, or we might study records from people born in 2008 in order to make predictions about people born in 2009.
- 7. SAMPLING FRAME In the most straightforward case, such as the sentencing of a batch of material from production (acceptance sampling by lots), it is possible to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible. There is no way to identify all rats in the set of all rats. Where voting is not compulsory, there is no way to identify which people will actually vote at a forthcoming election (in advance of the election) As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample . The sampling frame must be representative of the population
- 8. STATISTIC AND PARAMETER A statistic is a numerical value based upon sample, whereas parameter is the numerical value based upon population. When we calculate mean from a sample this is called a statistic because it describes the characteristics of a sample. When the same mean is calculated from a population it is called parameter because it describes characteristics of a population.
- 9. CONFIDENCE LEVEL AND SIGNIFICANCE LEVEL The confidence level is the expected percentage of times stipulating that the actual value will fall within the stated precision limits. Confidence level indicates the likelihood that the answer will fall within the given precision range. Significance level is that level that indicates the likelihood that the answer will fall outside the range. CONFIDENCE >>>>><<<<<SIGNIFICANCE
- 10. SAMPLING DESIGN Sampling design refers to the procedure the researcher would follow or adopt in selecting same sampling units from which inferences about population is drawn. A sample design is made up of two elements. Sampling method. Sampling method refers to the rules and procedures by which some elements of the population are included in the sample. Some common sampling methods aresimple random sampling , stratified sampling , and cluster sampling . Estimator. The estimation process for calculating sample statistics is called the estimator. Different sampling methods may use different estimators. For example, the formula for computing a mean score with a simple random sample is different from the formula for computing a mean score with a stratified sample. Similarly, the formula for the standard errormay vary from one sampling method to the next.
- 11. The sampling error is a number that describes the precision of an estimate from any one of those samples. It is usually expressed as a margin of error associated with a statistical level of confidence. For example, a presidential preference poll may report that the incumbent is favored by 51% of the voters, with a margin of error of plus- or-minus 3 points at a confidence level of 95%. This means that if the same survey were conducted with 100 different samples of voters, 95 of them would be expected to show the incumbent favored by between 48% and 54% of the voters (51% ± 3%).
- 12. The sampling process comprises several stages: Defining the population of concern Specifying a sampling frame, a set of items or events possible to measure Specifying a sampling method for selecting items or events from the frame Determining the sample size Implementing the sampling plan Sampling and data collecting Reviewing the sampling process
- 13. PROBABILITY SAMPLING NON-PROBABILITY SAMPLING Simple random sampling Systematic random sampling Stratified random sampling Multistage sampling Multiphase sampling Cluster sampling Convenience sampling Purposive sampling Quota sampling Accidental sampling Systematic sampling Snowball sampling Saturation sampling Dense sampling
- 14. The best sampling is probability sampling, because it increases the likelihood of obtaining samples that are representative of the population. Probability samples are selected in such a way as to be representative of the population. They provide the most valid or credible results because they reflect the characteristics of the population from which they are selected (e.g., residents of a particular community, students at an elementary school, etc.). There are two types of probability samples: random and stratified. Sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample & THAT EVERY ELEMENT HAS SOME KNOWN CHANCE OF BEING SELECTED are termed probability sampling methods.
- 15. SOME PROCESSES ARE USED IN ORDER TO MAKE PROBABILITY SAMPLING A SUCCESS………. Randomization: a technique for insuring that any member of a population has an equal chance of appearing in a sample. With randomization, sample statistics will on average have the same values as the population parameters. When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.
- 17. Non-probability sampling methods cannot specify the probability that a given sample will be selected. Example: snowball sampling methods (Edin and Lein) Why use such methods? They are often inexpensive They can provide information about groups that are difficult to sample or require great trust or will get lengthy unstructured interviews. Some social variables and their relationships are universal, which makes sampling method irrelevant! This is assumed for many psychology studies and medical studies.
- 18. Critical evaluation of Sampling Methods
- 19. Every subset of a specified size n from the population has an equal chance of being selected. STEPS INVOLVED; Get a list or “sampling frame” a. This is the hard part! It must not systematically exclude anyone. Generate random numbers Select one person per random numbers
- 20. • Applicable when population is small, homogeneous & readily available • All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection. • It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame. • A table of random number or lottery system is used to determine which units are to be selected. Advantages Estimates are easy to calculate. Simple random sampling is always an EPS design, but not all EPS designs are simple random sampling. Disadvantages If sampling frame large, this method impracticable. Minority subgroups of interest in population may not be present in sample in sufficient numbers for study
- 21. Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list. A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10').
- 22. As described above, systematic sampling is an EPS method, because all elements have the same probability of selection (in the example given, one in ten). It is not 'simple random sampling' because different subsets of the same size have different selection probabilities - e.g. the set {4,14,24,...,994} has a one-in-ten probability of selection, but the set {4,13,24,34,...} has zero probability of selection.
- 23. ADVANTAGES: Sample easy to select Suitable sampling frame can be identified easily Sample evenly spread over entire reference population DISADVANTAGES: Sample may be biased if hidden periodicity in population coincides with that of selection. Difficult to assess precision of estimate from one survey.
- 24. The population is divided into two or more groups called strata, according to some criterion, such as geographic location, grade level, age, or income, and subsamples are randomly selected from each strata. Steps invoved: 1. Separate your population into groups or “strata” 2. Do either a simple random sample or systematic random sample from there a. Note you must know easily what the “strata” are before attempting this b. If your sampling frame is sorted by, say, school district, then you’re able to use this method
- 25. Where population embraces a number of distinct categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. Every unit in a stratum has same chance of being selected. Using same sampling fraction for all strata ensures proportionate representation in the sample. Adequate representation of minority subgroups of interest can be ensured by stratification & varying sampling fraction between strata as required.
- 26. Drawbacks to using stratified sampling. First, sampling frame of entire population has to be prepared separately for each stratum Second, when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata. Finally, in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods
- 27. Stratification is sometimes introduced after the sampling phase in a process called "poststratification“. This approach is typically implemented due to a lack of prior knowledge of an appropriate stratifying variable or when the experimenter lacks the necessary information to create a stratifying variable during the sampling phase. Although the method is susceptible to the pitfalls of post hoc approaches, it can provide several benefits in the right situation. Implementation usually follows a simple random sample. In addition to allowing for stratification on an ancillary variable, poststratification can be used to implement weighting, which can improve the precision of a sample's estimates.
- 28. Cluster sampling is an example of 'two-stage sampling' . First stage a sample of areas is chosen; Second stage a sample of respondents within those areas is selected. Population divided into clusters of homogeneous units, usually based on geographical contiguity. Sampling units are groups rather than individuals. A sample of such clusters is then selected. All units from the selected clusters are studied
- 29. • Identification of clusters – List all cities, towns, villages & wards of cities with their population falling in target area under study. – Calculate cumulative population & divide by 30, this gives sampling interval. – Select a random no. less than or equal to sampling interval having same no. of digits. This forms 1st cluster. – Random no.+ sampling interval = population of 2nd cluster. – Second cluster + sampling interval = 4th cluster. – Last or 30th cluster = 29th cluster + sampling interval Two types of cluster sampling methods. One-stage sampling. All of the elements within selected clusters are included in the sample. Two-stage sampling. A subset of elements within selected clusters are randomly selected for inclusion in the sample.
- 30. Cons Larger sampling error [variation in score form sample to sample] Typically “requires” larger sample Pros Saves time Saves money Should allow for closer supervision in the field Requires enumerating only part of the population
- 31. Although strata and clusters are both non- overlapping subsets of the population, they differ in several ways. All strata are represented in the sample; but only a subset of clusters are in the sample. With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. However, with cluster sampling, the best results occur when elements within clusters are internally heterogeneous
- 32. Complex form of cluster sampling in which two or more levels of units are embedded one in the other. This technique, is essentially the process of taking random samples of preceding random samples. Not as effective as true random sampling, but probably solves more of the problems inherent to random sampling. An effective strategy because it banks on multiple randomizations. As such, extremely useful. Multistage sampling used frequently when a complete list of all members of the population not exists and is inappropriate. Moreover, by avoiding the use of all sample units in all selected clusters, multistage sampling avoids the large, and perhaps unnecessary, costs associated with traditional cluster sampling.
- 33. A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing.
- 34. Advantages Selection of whichever individuals are easiest to reach. It is done at the “convenience” of the researcher. Use results that are easy to get. Disadvantages Results may be contaminated.
- 35. Quota sampling is intended to overcome the most obvious flaw of availability sampling—that the sample will just consist of whoever or whatever is available, without any concern for its similarity to the population of interest. The distinguishing feature of a quota sample is that quotas are set to ensure that the sample represents certain characteristics in proportion to their prevalence in the population. Apparently, many reputable national polls use quota sampling and have been doing quite well. Cons Take more effort than many Need trustworthy poll takers Pros Although don’t know “odds…” experience shows it’s typically “pretty good”
- 36. In purposive sampling, each sample element is selected for a purpose, usually because of the unique position of the sample elements. Purposive sampling may involve studying the entire population of some limited group (directors of shelters for homeless adults) or a sub- set of a population (mid-level managers with a reputation for efficiency). Or a purposive sample may be a “key informant survey,” which targets individuals who are particularly knowledgeable about the issues under investigation. The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched
- 37. It may be defined as drawing or selecting every Nth person from a predetermined list of elements or indivisuals. This sampling plan possesses certain characteristics of randomness (first item selected is a random one), at the same time possesses some non probability traits as well(such as excluding all persons between every Nth element chosen.
- 38. Snowball sampling is useful for hard-to-reach or hard-to-identify populations for which there is no sampling frame, but the members of which are somewhat interconnected (at least some members of the population know each other). It can be used to sample members of such groups as drug dealers, prostitutes, practicing criminals, participants in Alcoholics Anonymous groups, gang leaders, informal organizational leaders, and homeless persons. Pros May be able to find difficult to locate groups Cons May run into a “vein” or network which isn’t at all representative of such kinds of people
- 39. That’s all folks THANK YOU