Once the researcher has clearly defined the problem and developed an appropriate research design and data collection instruments, the next step in the research process is to select those elements from which the information will be collected. If it were possible a researcher would collect data from every member of the population of interest. Such a compete study of a population is called a census. However since conducting a census is a very expensive and time consuming exercise, a more efficient way would be to collect information from a potion of the population.
Such a potions of the population are known as a samples. A researcher may therefore study a sample and on the basis of the information collected from the sample, make inferences about the population. The ability to make this Inference from a sample to a population depends on the method by which the sample Is selected. Unlike sampling, If the researcher were to collect data from each and every element of the population then the procedure would be called a census or 100% Investigation. The study of population behavior or characteristics through samples has various I.
Accuracy of results- Due to its smaller size and nature, sampling results have more accurate results as compared to census I’. Least cost – Studies involving the use of samples take comparatively less costs in terms of resources used. Iii. Greater speed for data collection – Collecting data from samples is faster because the researcher is dealing with smaller subjects. ‘v. Appropriate for a quality tests- Where the life span of say electric bulbs manufactured is to be tested by subjecting them to destructive high voltage, it would be appropriate to destroy only a proposition rather than the entire population elements.
Sampling frame A sampling frame is the actual list of sampling units’ from which the sample is selected. It is a list of elements from which the sample is actually drawn. In a simple sampling design, the sampling frame is a list of the study population. Sampling procedure The sampling procedure involves the use of eight steps:- Determination of relevant population and parameters The first step is the selection of the population which we are interested in studying. A properly defined population includes the explicit definition of all elements of concerns.
This definition usually includes four components. These are elements, Sampling units, place and time. The second step involves selecting an appropriate sampling frame. This is intended to represent the elements of the population, and the ideal sampling frame is a complete listing of all elements of the population. However, such a listing is rarely available and the sampling frame actually used is likely to differ somewhat from the theoretical target population. Choose between random and non random sampling Random and non -random sampling are also known as probability and non – arability sampling.
Random (probability) sampling gives each element in the target population an equal and non-zero probability of being selected. Non – random ( non probability) sampling means that not all elements within a target population have an equal chance of being selected. Probability sampling offers the researcher the advantage of being able to calculate the sampling error of measure, where the non- probability sample does not offer this possibility. Selection of the sampling method. This is the stage where we decide how we are going to choose the actual elements of the study sample.
If we choose random or probability sampling methods, the options we have include simple random sampling, systematic random sampling, stratified random sampling, cluster random sampling or multistage cluster sampling methods. It we opt for non – random or non – probability samples the choices we have include quota sampling, convenience sampling, purposive sampling or snow ball sampling. Determination of the necessary sample size. Samples should be large enough to be representative of the population or interest for analysis of sub-groups and for statistical analysis.
Target sample size also have to low for non- response and in longitudinal design allow for sample attrition due to deaths or dropouts over time. Samples which are very large, have a risk of rejecting a true null hypothesis that there is no differences between groups which are being compared ( type 1 error) and accepting the hypothesis that there are difference which is actually false. Samples which are too small have a risk of failing to a type II error. A type II error is the failure to reject a null hypothesis when it is actually false ii the acceptance of no differences when they actually exist.
Select sample and collect data. This is the stage when you select the actual individual elements under investigation following the procedures laid out. You then collect data using an appropriate data gathering techniques such as interviews, questionnaires or observations. Sample validation Validation involves determining if the sample we have selected is representative of the target population that we wish to generalize our results. In which case, we may which to compare the characteristics of the sample with those already known to exist within the population from which the sample was drawn.
Sampling methods Once the population of interest is determined, the researcher has to decide whether data will be collected from all study units or from some of the units in the population. The nature of sampling Most people intuitively understand the idea of sampling on the taste from a drink which tells us whether it is sweet or sour. If we select a few employment record out of a complete set, we usually assume our selection reflects the characteristics of the full set.
If some of our staff favors a flexible work schedule, we infer that others will The basic idea of sampling is that by selecting some of the elements in a population, e may draw conclusions about the entire population. A population element is the subject on which the measurement is being taken. It is the unit of study. For example, each office worker questioned about a flexible work schedule is a population element, and each business account analyzed is an element of an account population. A population is the total collection of elements about which we wish to make some inferences.
All office workers in the firm compose a population of interest; all 4,000 files define a population of interest. A census is a count of all the elements in a population. If 4,000 files define the populations, a census would obtain information form every one of them. Why Sample? The economic advantages of taking a sample rather than a census are massive: 1. Why should we spend thousands of shillings interviewing all 4,000 employees in our company if we can find out what we need to know by asking only a few hundreds? 2. Deeming argues that the quality of a study is often better with sampling than with a census.
He suggests, sampling possesses the possibility of better interviewing (testing), more thorough investigation of missing, wrong or suspicious information. Research findings substantiate this opinion. 3. Sampling also provides much quicker results than does a census. The speed of execution reduces the time between the recognition of a need for information and the availability of that information. 4. Some situations require sampling. When we test the breaking strength of materials, we must destroy them; a census would mean complete destruction of all materials. 6. In few cases, it would be impossible or dangerous to use whole population. . E testing of vaccine for AIDS- could result in death. The advantages of sampling over census studies are less compelling when the population is small and the variability is high. Two conditions are appropriate for a census study: a census is 1. Feasible when the population is small and 2. Necessary when the elements are quite different form each other. When the population is small and variable, any sample we draw may not be representative of the population from which it is drawn. The resulting values we calculate from the sample are incorrect as estimates of the population values.
When the sample is drawn properly, however, some simple elements underestimate the parameters and others over estimate them. Variations in these values counteract each other, this counteraction results in a sample value that is generally close to the population value. For these offsetting effects to occur, however, there must be enough members in the sample, and they must be drawn in a way to favor neither overestimation nor underestimation. Key steps in the sampling procedures Figure below outlines the step-by -step procedures that researcher can follow when drawing a sample from a population. The sampling procedure study.
But, the population should be defined very carefully, and in such a manner the another researcher would be able to identify it sufficiently well to reproduce it. The researcher, for example, must specify whether the population consists of individuals such as housewives, college students or lawyers etc. Secondly, researcher must determine the sampling frame. A sampling frame is the list of study objects from which the sample will be drawn. An ideal sample frame research agencies, government departments or organization The researcher must next determine the sampling procedure ii. Either probability or non – probability techniques.
The researcher must then determine the appropriate sample size. A rule of thumb is that the larger the samples, the more accurate the conclusions draw are likely to be. Finally, the researcher must them determine the appropriate sample size. Types of sampling designs The members of a sample are selected either on a probability basis or by another means. Probability sampling is based on the concept of random selection, a controlled procedure that assures that each population element is given a known nonzero chance of selection. It contrast, non probability sampling is non random and subjective.
Each member does not have a known, nonzero chance of being included. Allowing interviewers to choose sample member ‘at random’ (meaning ‘ as they wish ‘ or Wherever they find them’) is not random sampling. Only probability samples provides estimates of precision. I I Element selection probability I Convenience I Purposive Judgment I Quota I Snowball I Probability I Unrestricted I I Restricted I Presentation Basis I Non Simple random I Complex random I Systematic I Cluster I Stratified I Multi-stage cluster The unrestricted, simple random sample is the simplest form of probability sampling.
Since all probability samples must provide a known nonzero chance of selection for ACH population element, the simple random sample is considered a special case in which each population element has a known and equal chance of selection. In this section, we use the simple random sample to build a foundation for understanding sampling procedures and choosing probability samples. 2. Simple Random Sampling In simple random sampling, all study objects have an equal chance of being included in the sample. Researchers begin with a complete list of all members of a population and then choose sample items at random.
It should be noted that in simple random impaling, each study object is selected completely independently of other objects. The sampling process involves assigning a unique identification number to each study object in the sampling frame. After this, the researcher must design a method of selecting study objects in a manner that allows all equal chance of being selected. One way of doing this is writing these identification numbers on accomplices of paper, mixing them thoroughly in a box, and then picking the papers without looking.
The numbers on the pieces of paper picked identify the study objects to be included in the sample. In some cases, however, this procedure (lottery method) may be impractical or tedious. Another procedure used in selecting study objects in simple random sampling involves the use of tables of random numbers. The researcher begins picking randomly objects from any presented place in the table of random numbers. Then s/he systematically chooses numbers by either moving vertically or horizontally. The sample will therefore consist of the study objects whose numbers are chosen.
Complex probability Sampling Simple random sampling is often impractical. It requires a population list that is often not available. The design may also be wasteful because it fails to use all the information about a population. In addition, the carrying out of a simple random design may be expensive in time and money. These problems have led to the development of alternative designs that are superior to the simple random design in A more efficient sample in a statistical sense is one that provides a given precision (standard error of the mean) with a smaller sample size.
A sample that is economically more efficient is one that provides a desired precision at a lower dollar cost. We achieve this with designs that enable us to lower the costs of data collecting, usually through reduced travel expense and interviewer time. In the discussion that follows, four alternative probability sampling approaches are considered: systematic, stratified, cluster and multi-stage. 4. Systematic Sampling This method is frequently used in production and quality control sampling.
In this approach, every nth element in the population is sampled, beginning with a random start of an element in the range of 1 to n. After a randomly selected start point a sample item would be selected every nth item. Assume that in an assembly line it was decided to sample every 10th item and a start point of 67 was chosen randomly, the sample would be the following items: 67th, 77th, 87th attend so on.
The gap between selections is known as the sampling interval and is itself often randomly selected. A concern with this technique is the possible periodicity in the population that may coincide with the sampling interval and cause bias. 5. Stratified Sampling Most populations can be segregated into several mutually exclusive sub-populations, or strata. Thus, the process by which the sample is constrained to include elements room each of the segments is called stratified random sampling.