In Iowa a random sample might miss Muslims because there are not many in that state. However, the sample will no longer be representative of the actual proportions in the population. This may limit generalizing to the state population.
But the quota will guarantee that the views of Muslims are represented in the survey. A subset of a purposive sample is a snowball sample -- so named because one picks up the sample along the way, analogous to a snowball accumulating snow. A snowball sample is achieved by asking a participant to suggest someone else who might be willing or appropriate for the study. Snowball samples are particularly useful in hard-to-track populations, such as truants, drug users, etc.
Non-probability samples are limited with regard to generalization. Because they do not truly represent a population, we cannot make valid inferences about the larger group from which they are drawn.
Validity can be increased by approximating random selection as much as possible, and making every attempt to avoid introducing bias into sample selection. Examples of nonprobability samples.
Using the random numbers table. Two of each species. Random sample The term random has a very precise meaning. The defining characteristic of a quota sample is that the researcher deliberately sets the proportions of levels or strata within the sample.
This is generally done to insure the inclusion of a particular segment of the population. The proportions may or may not differ dramatically from the actual proportion in the population. The researcher sets a quota , independent of population characteristics.
A purposive sample is a non-representative subset of some larger population, and is constructed to serve a very specific need or purpose. A researcher may have a specific group in mind, such as high level business executives. It may not be possible to specify the population -- they would not all be known, and access will be difficult.
The researcher will attempt to zero in on the target group, interviewing whomever is available. A convenience sample is a matter of taking what you can get. It is an accidental sample. All school-age children with asthma treated in pediatric asthma clinics in university-affiliated medical centers in the Midwest. Could be extremely large if population is national or international in nature. Frame is needed so that everyone in the population is identified so they will have an equal opportunity for selection as a subject element.
Louis county nursing homes affiliated with BJC. A list of all low birth weight infants admitted to the neonatal ICUs in St. A list of all school-age children with asthma treated in pediatric asthma clinics in university-affiliated medical centers in the Midwest. A list of all pregnant teens in the Henderson school district.
Sample reflects the characteristics of the population, so those sample findings can be generalized to the population. Most effective way to achieve representativeness is through randomization; random selection or random assignment. Every element member of the population has a probability greater than of being selected for the sample.
Everyone in the population has equal opportunity for selection as a subject. Increases sample's representativeness of the population. Decreases sampling error and sampling bias. Types of probability sampling - see table in course materials for details. Elements selected at random.
Assign each element a number. Select elements for study by: A table displaying hundreds of digits from 0 to 9 set up in such a way that each number is equally likely to follow any other. Computer generated random numbers table. Population is divided into subgroups, called strata, according to some variable or variables in importance to the study.
Variables often used include: Subgroup sample sizes equal the proportions of the subgroup in the population. With proportional sample the sample has the same proportions as the population. Subgroup sample sizes are not equal to the proportion of the subgroup in the population. With disproportional sample the sample does not have the same proportions as the population. A random sampling process that involves stages of sampling.
The population is first listed by clusters or categories. Midwest region of the US. Or, in a second stage randomly select clusters from the first stage of clusters; eg 3 states within the Midwest region. In a third stage, randomly select elements from the second stage of clusters; e.
Use a table of random numbers to determine the starting point for selecting every 40th subject. With list of the subjects in the sampling frame, go to the starting point, and select every 40th name on the list until the sample size is reached.
Not every element of the population has the opportunity for selection in the sample. Historically, used in most nursing studies. Selection of the most readily available people or objects for a study. Selection of sample to reflect certain characteristics of the population. Similar to stratified but does not involve random selection. Quotas for subgroups proportions are established. Purposive - aka judgmental or expert ' s choice sampling. Researcher uses personal judgement to select subjects that are considered to be representative of the population.
Typical subjects experiencing problem being studied. Subjects refer the researcher to others who might be recruited as subjects. Time Frame for Studying the Sample. General rule - as large as possible to increase the representativeness of the sample. Relatively small samples in qualitative, exploratory, case studies, experimental and quasi-experimental studies. Descriptive studies need large samples; e. As the number of variables studied increases, the sample size also needs to increase in order to detect significant relationships or differences.
A minimum of 30 subjects is needed for use of the central limit theorem statistics based on the mean. Statistical tests used require minimum sample or subgroup size. Background Information for Understanding Power Analysis: Type I and Type II errors. Based on the statistical analysis of data, the researcher wrongly rejects a true null hypothesis; and therefore, accepts a false alternative hypothesis.
Probability of committing a type I error is controlled by the researcher with the level of significance, alpha. Alpha a is the probability that a Type I error will occur. Based on the statistical analysis of data, the researcher wrongly accepts a false null hypothesis; and therefore, rejects a true alternate hypothesis. Probability of committing a Type II error is reduced by a power analysis.
Probability of a Type II error is called beta b.
Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample. Importance As you can see, choosing a sample is a complicated process.
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.
SAMPLING IN RESEARCH Sampling In Research Mugo Fridah W. INTRODUCTION This tutorial is a discussion on sampling in research it is mainly designed to eqiup beginners with knowledge on the general issues on sampling that is the purpose of sampling in research, dangers of. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection. RESEARCH METHOD - SAMPLING 1.
Sampling is a process used in statistical analysis in which a group of observations are extracted from a larger set. Before sampling, the population is divided into characteristics of importance for the research. For example, by gender, social class, education level, religion, etc. Then the population is randomly sampled within each category or stratum.