FCE 3900 EDUCATIONAL RESEARCH LECTURE 8 P O P U L A T I O N A N D S A M P L I N G T E C H N I Q U E

Transcription

1 FCE 3900 EDUCATIONAL RESEARCH LECTURE 8 P O P U L A T I O N A N D S A M P L I N G T E C H N I Q U E

2 OBJECTIVE COURSE Understand the concept of population and sampling in the research. Identify the type of sampling Can make a random sampling of the population

3 POPULATION Population is a set of individuals where the problem exists or group of individuals or objects that are observed. The population must have the same characteristics or properties from one another although different in other ways. Population is normally distributed with a mean of the absolute value of the population. Population information is known as a parameter.

4 CONT POPULATION Population sampling refer to the proses thought which a group of representative individuals is selected from a population for the purpose of statistical analysis. Performing population sampling correctly is extremely important, as error can be lead to invalid or misleading data. There are a number of technique used in population sampling to ensure that the individuals can be used to generate data which can in turn in used it make generalization about a large population.

5 SAMPLING TECHNIQUE Sampling is a method or procedure for specifying how a sample will be taken from a population for investigating whole population Data is gathered on a small part of the whole parent population or sampling frame, and used to inform what the whole picture is like The difference between the sample estimate and the true population is the sampling error.

6 PURPOSE OF SAMPLING Make inferences on the population from a sample using inferential statistics. Reduce costs, energy, and research time. Cheaper to collect information from a person rather than the whole population. However, researchers must be careful that the sample is truly representative population. Allows the study done in the area or space research is the greater. Allow researchers to obtain information that is really required when measuring the overall population can not be done

7 WHY SAMPLE? The factor time. Financial factor. Equipment / labour. The population is very large. The partly accessible populations Therefore an appropriate sampling strategy is adopted to obtain a representative, and statistically valid sample of the whole.

8 Sampling considerations Larger sample sizes are more accurate representations of the whole The sample size chosen is a balance between obtaining a statistically valid representation, and the time, energy, money, labour, equipment and access available A sampling strategy made with the minimum of bias is the most statistically valid Most approaches assume that the parent population has a normal distribution where most items or individuals clustered close to the mean, with few extremes A 95% probability or confidence level is usually assumed, for example 95% of items or individuals will be within plus or minus two standard deviations from the mean This also means that up to five per cent may lie outside of this - sampling, no matter how good can only ever be claimed to be a very close estimate

9 TYPES OF SAMPLING DESIGN Probability Sampling Techniques Random sampling Stratified Sampling Systematic sampling Cluster sampling Cluster Random Sampling Two stage random sampling Non-probability Sampling Techniques Convenience Sampling Purposive or judgment Sampling Quota Sampling Snowball Sampling

10 PROBABILITY SAMPLING Probability sampling is a sampling technique where the samples are gathered in a process that gives all the individuals in the population equal chances of being selected. In probability sampling, every individual in the population have equal chance of being selected as a subject for the research. This method guarantees that the selection process is completely randomized and without bias.

11 CONT. PROBABILITY SAMPLING The most basic example of probability sampling is listing all the names of the individuals in the population in separate pieces of paper, and then drawing a number of papers one by one from the complete collection of names. The advantage of using probability sampling is the accuracy of the statistical methods after the experiment. It can also be used to estimate the population parameters since it is representative of the entire population. It is also a reliable method to eliminate sampling bias.

12 NON PROBABILITY SAMPLING Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected. In this type of population sampling, members of the population do not have equal chance of being selected. Due to this, it is not safe to assume that the sample fully represents the target population. It is also possible that the researcher deliberately chose the individuals that will participate in the study.

13 CONT.. NON-PROBABILITY SAMPLING Non-probability population sampling method is useful for pilot studies, case studies, qualitative research, and for hypothesis development. This sampling method is usually employed in studies that are not interested in the parameters of the entire population. Some researchers prefer this sampling technique because it is cheap, quick and easy.

14 CONVENIENCE SAMPLING Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This non-probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

15 PURPOSIVE OR JUDGMENT SAMPLING A purposive, or judgmental, sample is one that is selected based on the knowledge of a population and the purpose of the study. The researcher selects the sample based on judgment. This is usually and extension of convenience sampling. For example, if a researcher is studying the nature of school spirit as exhibited at a school pep rally, he or she might interview people who did not appear to be caught up in the emotions of the crowd or students who did not attend the rally at all. In this case, the researcher is using a purposive sample because those being interviewed fit a specific purpose or description.

16 QUOTA SAMPLING Quota sampling is the non-probability equivalent of stratified sampling. Its like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. A quota sample is one in which units are selected into a sample on the basis of pre-specified characteristics so that the total sample has the same distribution of characteristics assumed to exist in the population being studied. For example, if you a researcher conducting a national quota sample, you might need to know what proportion of the population is male and what proportion is female as well as what proportions of each gender fall into different age categories, race or ethnic categories, educational categories, etc. The researcher would then collect a sample with the same proportions as the national population.

17 SNOWBALL SAMPLING Snowball sampling is a special nonprobability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population Snowball Sample. A snowball sample is appropriate to use in research when the members of a population are difficult to locate, such as homeless individuals, migrant workers, or undocumented immigrants. A snowball sample is one in which the researcher collects data on the few members of the target population he or she can locate, then asks those individuals to provide information needed to locate other members of that population whom they know. For example, if a researcher wishes to interview undocumented immigrants from Mexico, he or she might interview a few undocumented individuals that he or she knows or can locate and would then rely on those subjects to help locate more undocumented individuals. This process continues until the researcher has all the interviews he or she needs or until all contacts have been exhausted.

18 RANDOM SAMPLING The simple random sample is the basic sampling method assumed in statistical methods and computations Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased. For example, to collect a simple random sample, each unit of the target population is assigned a number. A set of random numbers is then generated and the units having those numbers are included in the sample. For example, let s say you have a population of 1,000 people and you wish to choose a simple random sample of 50 people. First, each person is numbered 1 through 1,000. Then, you generate a list of 50 random numbers (typically with a computer program) and those individuals assigned those numbers are the ones you include in the sample.

19 CONT RANDOM SAMPLING Advantages: Can be used with large sample populations Avoids bias Disadvantages: Can lead to poor representation of the overall parent population or area if large areas are not hit by the random numbers generated. This is made worse if the study area is very large There may be practical constraints in terms of time available and access to certain parts of the study area

20 SYSTEMATIC SAMPLING Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file. For example, if the population of study contained 2,000 students at a high school and the researcher wanted a sample of 100 students, the students would be put into list form and then every 20th student would be selected for inclusion in the sample. To ensure against any possible human bias in this method, the researcher should select the first individual at random.

21 CONT SYSTEMATIC SAMPLING Advantages: It is more straight-forward than random sampling A grid doesn't necessarily have to be used, sampling just has to be at uniform intervals A good coverage of the study area can be more easily achieved than using random sampling Disadvantages: It is more biased, as not all members or points have an equal chance of being selected It may therefore lead to over or under representation of a particular pattern

22 STRATIFIED SAMPLING Stratified sampling is used when representatives from each subgroup within the population need to be represented in the sample. The first step in stratified sampling is to divide the population into subgroups (strata) based on mutually exclusive criteria. Random or systematic samples are then taken from each subgroup. The sampling fraction for each subgroup may be taken in the same proportion as the subgroup has in the population. Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers.

23 CONT.. STRATIFIED SAMPLING For example, if 40 samples are to be selected, and 10% of the customers are managers, 60% are users, 25% are operators and 5% are database administrators,then 4 managers, 24 users, 10 operators and 2 administrators would be randomly selected. Stratified sampling can also sample an equal number of items from each subgroup. For example, a development lead randomly selected three modules out of each programming language used to examine against the coding standard.

24 TYPES OF STRATIFIED SAMPLING A. Stratified systematic sampling The population can be divided into known groups, and each group sampled using a systematic approach. The number sampled in each group should be in proportion to its known size in the parent population. For example: the make-up of different social groups in the population of a town can be obtained, and then the number of questionnaires carried out in different parts of the town can be stratified in line with this information. A systematic approach can still be used by asking every fifth person. B. Stratified random sampling A wide range of data and fieldwork situations can lend themselves to this approach - wherever there are two study areas being compared, for example two woodlands, river catchments, rock types or a population with sub-sets of known size, for example woodland with distinctly different habitats. Random point, line or area techniques can be used as long as the number of measurements taken is in proportion to the size of the whole. For example: if an area of woodland was the study site, there would likely be different types of habitat (sub-sets) within it. Random sampling may altogether miss' one or more of these.

25 CONT STRATIFIED SAMPLING Advantages: It can be used with random or systematic sampling, and with point, line or area techniques If the proportions of the sub-sets are known, it can generate results which are more representative of the whole population It is very flexible and applicable to many geographical enquiries Correlations and comparisons can be made between sub-sets Disadvantages: The proportions of the sub-sets must be known and accurate if it is to work properly It can be hard to stratify questionnaire data collection, accurate up to date population data may not be available and it may be hard to identify people's age or social background effectively

26 CLUSTER SAMPLING A cluster sample is a simple random sample of groups or clusters of elements (vs. a simple random sample of individual objects). This method is useful when it is difficult or costly to develop a complete list of the population members or when the population elements are widely dispersed geographically.

27 CONT..CLUSTER SAMPLING 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. For example, let s say the target population in a study was church members in the United States. There is no list of all church members in the country. The researcher could, however, create a list of churches in the United States, choose a sample of churches, and then obtain lists of members from those churches. Cluster sampling may increase sampling error due to similarities among cluster members.

28 SAMPLING SIZE Numerical techniques for determining sample sizes will be described later, but suffice it to say that the larger the sample size is, the more accurate we can expect the sample estimates to be.

29 TYPES OF ERRORS: Selection error Non Answer Error

30 SELECTION ERROR.. Selection error is if any of the elements of the population has a higher probability of being selected than the rest. Let us imagine that we want to measure how satisfied the clients of a gymnasium are, and for that, we are going to interview some of them from 10 to 12 in the morning. This means that the people who go to the gymnasium in the afternoon will not be represented, and then the sample will not be representative of all the clients. A way to avoid this kind of errors is choosing the sample so that all the clients have the same probability of being selected.

31 NON-ANSWER ERROR Non-answer error is it is also possible that some of the elements of the population do not want or cannot answer certain questions. Or it can also happen, when we have a questionnaire including personal questions, that some of the members of the population do not answer honestly. This errors are generally very complicated to avoid, but in case that we want to check honesty in answers, we can include some questions (filter questions) to detect if the answers are honest.

32 SAMPLING ERROR Sampling error refers to differences between the sample and the population that exist only because of the observations that happened to be selected for the sample. Increasing the sample size will reduce this type of error.