# Population Sample And Sampling Techniques Pdf

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## Sampling Methods

The entire group of people or objects to which the researcher wishes to generalize the study findings Meet set of criteria of interest to researcher Examples. All institutionalized elderly with Alzheimer ' s in St. Samples Terminology used to describe samples and sampling methods. 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 Examples.

A list of all institutionalized elderly with Alzheimer ' s in St. Louis area who are members of the St. Probability Sampling Methods Also called random sampling. Using a table of random numbers in book. Subgroup sample sizes equal the proportions of the subgroup in the population Example: A high school population has. Subgroup sample sizes are not equal to the proportion of the subgroup in the population Example. Cluster random sampling. A random sampling process that involves stages of sampling The population is first listed by clusters or categories Procedure.

Randomly select 1 or more clusters and take all of their elements single stage cluster sampling ; e. 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.

A random sampling process in which every kth e. Probably will have to return to the beginning of the list to complete the selection of the sample. Non-probability sampling methods Characteristics. 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 E.

Also known as network sampling Subjects refer the researcher to others who might be recruited as subjects. Sample Size General rule - as large as possible to increase the representativeness of the sample Increased size decreases sampling error Relatively small samples in qualitative, exploratory, case studies, experimental and quasi-experimental studies Descriptive studies need large samples; e.

Background Information for Understanding Power Analysis:. 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. 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 Power, or 1- b is the probability of rejecting the null hypothesis and obtaining a statistically significant result. In the real world, the actual situations is that the null hypothesis is :. Correct decision: the actual true null is accepted. Type I error: the actual true null hypothesis is rejected. Population Effect Size - Gamma g Gamma g measures how wrong the null hypothesis is; it measures how strong the effect of the IV is on the DV; and it is used in performing a power analysis Gamma g is calculated based on population data from prior research studies, or determined several different ways depending on the nature of the data and the statistical tests to be performed The textbook discusses 4 ways to estimate gamma population effect size based upon:.

Also called systematic bias or systematic variance The difference between sample data and population data that can be attributed to faulty sampling of the population Consequence of selecting subjects whose characteristics scores are different in some way from the population they are suppose to represent This usually occurs when randomization is not used.

The assignment of subjects to treatment conditions in a random manner. It has no bearing on how the subjects participating in an experiment are initially selected. Definition - a complete set of elements persons or objects that possess some common characteristic defined by the sampling criteria established by the researcher. The entire group of people or objects to which the researcher wishes to generalize the study findings.

Meet set of criteria of interest to researcher. All institutionalized elderly with Alzheimer ' s. May be limited to region, state, city, county, or institution. Louis county nursing homes. Louis area. All low birth weight infants admitted to the neonatal ICUs in St. 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. Probability Sampling Methods. 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: age, gender, ethnic origin, SES, diagnosis, geographic region, institution, or type of care.

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.

Sample Size. 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.

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. Power, or 1- b is the probability of rejecting the null hypothesis and obtaining a statistically significant result. In the real world, the actual situations is that the null hypothesis is : True. In the real world, the actual situations is that the null hypothesis is : False.

## Methods of sampling from a population

By Dr. Saul McLeod , updated In psychological research we are interested in learning about large groups of people who all have something in common. We call the group that we are interested in studying our 'target population'. In some types of research the target population might be as broad as all humans, but in other types of research the target population might be a smaller group such as teenagers, pre-school children or people who misuse drugs. It is more or less impossible to study every single person in a target population so psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in. This is important because we want to generalize from the sample to target population.

## An introduction to sampling methods

Published on September 19, by Shona McCombes. Revised on February 25, Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

*In this paper, the basic elements related to the selection of participants for a health research are discussed. Sample representativeness, sample frame, types of sampling, as well as the impact that non-respondents may have on results of a study are described. The whole discussion is supported by practical examples to facilitate the reader's understanding.*

#### Table of contents

Sign in. Sampling helps a lot in research. If anything goes wrong with your sample then it will be directly reflected in the final result. There are lot of techniques which help us to gather sample depending upon the need and situation. This blog post tries to explain some of those techniques.

Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. In actual practice, the task is so difficult that some sampling bias occurs in almost all studies to a lesser or greater degree. In order to assess the degree of this bias, the informed reader of medical literature should have some understanding of the population from which the sample was drawn. The ultimate decision on whether the results of a particular study can be generalized to a larger population depends on this understanding. The subsequent deliberations dwell on sampling strategies for different types of research and also a brief description of different sampling methods.

Sign in. Sampling helps a lot in research. If anything goes wrong with your sample then it will be directly reflected in the final result. There are lot of techniques which help us to gather sample depending upon the need and situation. This blog post tries to explain some of those techniques.

Когда улица сделала поворот, Беккер вдруг увидел прямо перед собой собор и вздымающуюся ввысь Гиральду. Звон колоколов оглушал, эхо многократно отражалось от высоких стен, окружающих площадь.

Target Population. Select Sampling. Frame. Choose Sampling. Technique. Determine. Sample Size. Collect Data. Assess. Response Rate.

Let us extend in this chapter what we have already presented in the beginning of Descriptive. Statistics, including now the definition of some sampling.