It also means the laws of probability and statistics apply, allowing us to make certain inferences. FIVE MAIN TECHNIQUES THAT CAN BE USED TO SELECT A PROBABILITY SAMPLE 1. SIMPLE RANDOM SAMPLING Simple random sampling (sometimes called just random sampling) involves you selecting the sample at random from the sampling frame. In this approach, all elements are given equal chance of being included in the sample. No one from the population is excluded from the pool. Random Sampling could be implemented through: * The lottery method.
In the lottery method of sampling, the names of the entire population are written on pieces of paper and a given number of names (or sample elements) are drawn at random. * Consulting the table of random numbers. If you already have a list of names of the population from which you wish to draw your samples, you can: 1. Number them. 2. Get a table of random numbers. 3. Close your eyes move your pencil over the table, and then point. See where your pencil stops. 4. The number where your pencil stopped will be your first sample. 5.
Select the second sample. 6. Proceed until you are able to get the total number of sample you need. A basic requirement for this method is a sampling frame for the total population. If there are 4,000 persons in the list, all names should be included in the sampling frame. It is also necessary to consider the sample size. Sample size can be computed using the Lynch et al. (1974) Formula. The formula gives a 95% reliability in estimating the sample size. It gives you the opportunity to adjust sample size according to your resource capability.
The Dissertation on Sampling Probability
... books and generated random numbers to draw the sample. Cluster Sampling: The problem with random sampling methods when we have to sample a population that’s disbursed across a ... draw the sample. That is it uses large sample size. The size of the sample requires ensuring the statistical reliability is usually under random sampling rather than ...
You may estimate how much you can feasibly accept by adjusting the sampling error (d) that you are willing to commit. The sampling error (. 025, . 05, and . 10) simply means the level of error you are willing to consider. The Lynch et al. formula is: n=NZ? p(1-p)Nd? +Z? p(1-p) Where: Z = 1. 96 (the value of the normal variable for a reliability level of 0. 95. This means having a 95% reliability in obtaining the sample size. ) p = . 50 (the proportion of getting a good sample) 1-p = . 50 (the proportion of getting a poor sample) = . 025 or . 05 or . 10 (your choice of sampling error) N = population size n = sample size Given the above formula: If: N = 3,000 d = . 05 Then: n= 30001. 962x . 50(1-. 50)3000. 052+1. 96? x . 50(1-. 50) = 30003. 8416x . 253000. 0025+3. 8416 x . 25 = 11,524. 8 x . 257. 5+ . 9604 = 2882. 28. 46 = 340. 56 or 341 The advantage of applying this formula is that the bigger the population, the smaller the sample size. 2. systematic sampling This is sampling after every regular interval.
This can be undertaken if the features of the population normally characterize what would be applied to simple random sampling. The key here is to determine the population and prepare the sampling frame. Number each one. 1. Determine the sample size. 2. Determine the interval. Interval (I) = N (population size)n (sample size) Randomly select the first number. When you have exhausted the numbers in the list, continue counting from the beginning of the list. Another way is to pick another number as the next random start and then select the elements in the interval until all samples are determined. . stratified sampling Stratified sampling is a modification of random sampling in which you divide the population into two or more relevant and significant strata based on one or a number of attributes. In effect, your sampling frame is divided into a number of subsets. A random sample (simple or systematic) is then drawn from each of the strata. To be able to implement this, you have to: * Obtain the sampling frame per stratum. * Determine the sample size per stratum. * Apply either the simple random or systematic sampling strategy per stratum.
The Essay on Types of Sampling
* How do we decide which to use? * How do we analyze the results differently depending on the type of sampling? Non-probability Sampling: Why don’t we use non-probability sampling schemes? Two reasons: * We can’t use the mathematics of probability to analyze the results. * In general, we can’t count on a non-probability sampling scheme to produce representative samples. In ...
In the case of the legislators, this means grouping legislators into senators and congressmen and getting the sample size per group. SenatorsN = 24| CongressmenN = 250| n = 24 n = 69 (with d of . 10) Two features of the sample make stratified sampling applicable: 1. If the population is heterogeneous and its characteristics need to be differentiated for varying responses to the factors or variables to be studied; and 2. The elements are geographically concentrated in a given area. 4. CLUSTER SAMPLING
This method entails random selection of groups in a population who could serve as the respondents of the study or from whom random samples could be drawn. This is best applied if we are dealing with populations with homogeneous characteristics but who are geographically dispersed in different parts of the country and getting all of them is not necessary. If a program targets small farmers from 20 depressed barangays in lahar-driven areas in Pampanga, one way to simplify the process of drawing the samples is by randomly selecting clusters (barangays) from the list.
Random or systematic sampling could be applied to select from the total population of clusters. The clusters that may be obtained will depend on the resources available. Cluster sampling differs from stratified sampling because all elements in the population are considered in the latter but samples are drawn per group. In cluster sampling, only the randomly selected groups are considered and are the bases for drawing the respondents. The other groups are excluded. These two could be differentiated in the following manner: Small farmers| Medium-level farmers| 1st cluster sample st cluster sample | | | | | | | | 2nd cluster sample 2nd cluster sample | | | | | | | Small farmers Small farmers 2nd stratum 2nd stratum 1st stratum 1st stratum 5. MULTI-STAGE SAMPLING Multi-stage sampling is a development of cluster sampling. It is normally used to overcome problems associated with a geographically dispersed population when face-to-face contact is needed or where it is expensive and time consuming to construct a sampling frame for a large geographical area. The technique involves taking a series of cluster samples, each involving some form of random sampling.
The Term Paper on A Comparison Of The Status Of Women Within Two Ethnic Groups
A Comparison of the Status of Women within Two Ethnic Groups It is not a secret that throughout the history women suffered an underprivileged social status. This particularly applies to Muslim society, where even up to this day women are often thought of as having no soul. With the change of American immigration policy in sixties, the people of predominantly non-White origins started to pour into ...
It can be divided into four phases. Phase 1 * Choose sampling frame of relevant discrete groups. * Number each group with a unique number. The first is numbered 1, the second 2, and so on. * Select a small sample of relevant discrete groups using some form of random sampling * Choose sampling frame of relevant discrete groups. * Number each group with a unique number. The first is numbered 1, the second 2, and so on. * Select a small sample of relevant discrete groups using some form of random sampling From these relevant discrete groups, select a sampling frame of relevant discrete sub-groups. * Number each sub-group with a unique number as describe in Phase 1. * Select a small sample of relevant discrete sub-groups using some form of random sampling * From these relevant discrete groups, select a sampling frame of relevant discrete sub-groups. * Number each sub-group with a unique number as describe in Phase 1. * Select a small sample of relevant discrete sub-groups using some form of random sampling Phase 2 Repeat Phase 2 if necessary. * Repeat Phase 2 if necessary. Phase 3 * From these relevant discrete groups, select a sampling frame of relevant discrete sub-sub-groups. * Number each sub- sub-group with a unique number as describe in Phase 1. * Select your sample using some form of random sampling. * From these relevant discrete groups, select a sampling frame of relevant discrete sub-sub-groups. * Number each sub- sub-group with a unique number as describe in Phase 1. * Select your sample using some form of random sampling. Phase 4
