# Sample Methods.pptx

5 May 2023
1 sur 27

### Sample Methods.pptx

• 1. COLLAGE OF HEALTH SCIENCE FACULTY : BSN STUDENT : MOHAMED AMIN HAJI ALI COURSE : EPIDEMIOLOGY TOPIC: SAMPLING TECHNIQUE OR (SAMPLE METHODS) WORK : PRESENTATION SEMSTER : SIX GROUP : ( G )
• 2. Sampling technique and (Sample methods) Table Content At the end of this Unit, we will be able to …  Explain of sampling technique or {sample methods}.  Define sampling technique and (sample methods)  Characteristics of the sampling technique.  Classify the purposive sampling technique.  What are the Types of sampling technique?  List methods of probability sampling  Types methods of none probability sampling  Difference between probability and non-probability  Mention the features of probability sampling.
• 3. INTRODUCTION  When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample.  The sample is the group of individuals who will actually participate in the research.  To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method .
• 4. Characteristics of sampling technique  To identify characteristics of a sample in your survey , there are many factors to consider of your samples . The first four characteristics you need to focus on are  gender  Age  Income level  And Education level .  All four of these characteristics must be proportional to that of the population .
• 5. Types of sampling methods There are two primary types of sampling methods that you can use in your research:  Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.  Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.  You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.
• 6. Population vs. sample First, you need to understand the difference between a population and a sample, and identify the target population of your research.  The population is the entire group that you want to draw conclusions about.  The sample is the specific group of individuals that you will collect data from.  The population can be defined in terms of geographical location, age, income, or many other characteristics.  It can be very broad or quite narrow: maybe you want to make inferences about the whole adult population of your country; maybe your research focuses on customers of a certain company, patients with a specific health condition, or students in a single school.
• 8. COUNT.. SAMPLING FRAME  The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population) .  Example: Sampling frame : You are doing research on working conditions at a social media marketing company. Your population is all 1000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee. SAMPLE SIZE  The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are
• 9. COUNT.. Different sample size calculators and formulas depending on what you want to achieve with statistical analysis. The sampling frame : is the information that locates and the dimensions of the universe. A good sample should satisfy the below conditions: 1. Representativeness : the sample should be the best representative of the population under study. 2. Accuracy’s defined as : the degree to which bias is absent from the simple. 3. Size : A good simple most be adequate in the size and reliability.
• 10. What is probability sampling Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. THERE ARE FOUR MAIN TYPES OF PROBABILITY SAMPLE
• 11. TYPES OF PROBABILITY SAMPLE EXAMPLES OF PROBABILITY SAMPLING INCLUDE : 1. Simple Random Sampling (SRS)  This is the most basic scheme of random sampling. To select a simple random sample you need to:  Make a numbered list of all the units in the population from which you want to draw a sample. Each unit on the list should be numbered in sequence from 1 to N (Where N is the Size of the population).  Decide on the size of the sample  Select the required number of sampling units, using a “lottery” method or a table of random numbers.
• 12. TYPES OF PROBABILITY SAMPLE 2. Cluster Sampling  When a list of groupings of study units is available (e.g. villages, etc.) or can be easily compiled, a number of these groupings can be randomly selected. The selection of groups of study units (clusters) instead of the selection of study units individually is called cluster sampling. Clusters are often geographic units (e.g. districts, villages) or organizational units (e.g. clinics) . 3. Systematic Sampling  Individuals are chosen at regular intervals (for example, every 5th, 10th, etc.) from the sampling frame. Ideally we randomly select a number to tell us where to start selecting individuals from the list.
• 13. COUNT.. For example, a systematic sample is to be selected from 1000 students of a school. The sample size is decided to be 100. The sampling fraction is: 100/1000 = 1/10. The number of the first student to be included in the sample is chosen randomly by picking one out of the first ten pieces of paper, numbered 1 to 10. If number 5 is picked, every tenth student will be included in the sample, starting with student number 5, until 100 students are selected. Students with the following numbers will be included in the sample : 5,15, 25, 35,45, . . . , 985, 995.
• 14. COUNT.. Systematic Sampling is usually less time consuming and easier to perform than SRS.  It provides a good approximation to SRS. Should not be used if there is any sort of cyclic pattern in the ordering of the subjects on the list.  Unlike SRS, systematic sampling can be conducted without a sampling frame (useful in some situations where a sampling frame is not readily available).
• 15. COUNT.. 4. Stratified Sampling :  If it is important that the sample includes representative groups of study units with specific characteristics (for example, residents from urban and rural areas), then the sampling frame must be divided into groups, or strata, according to these characteristics. Random or systematic samples of a predetermined size will then have to be obtained from each group (stratum). This is called stratified sampling.
• 16. Probability sampling methods 1. Simple random sampling In a simple random sample, every member of the population has an equal chance of being selected. 2. Systematic sampling : is similar to simple random sampling, but it is usually slightly easier to conduct. 3. Stratified sampling Stratified : sampling involves dividing the population into subpopulations that may differ in important ways. 4. Cluster sampling
• 17. Features of probability sampling: 1: To provide equal changes to all individuals in the population getting selected, this feasible only if the use randomization. 2: Probability sampling technique the changes of sampling bais are relatively less because subject are randomly selected
• 18. What is non-probability sampling? Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research. Types of non-probability sampling  Here are the types of non-probability sampling methods :
• 20. COUNT.. Convenience sampling is a non-probability sampling technique where samples are selected from the population only because they are conveniently available to the researcher. Researchers choose these samples just because they are easy to recruit, and the researcher did not consider selecting a sample that represents the entire population. Ideally, in research, it is good to test a sample that represents the population. But, in some research, the population is too large to examine and consider the entire population. It is one of the reasons why researchers rely on convenience sampling, which is the most common non-probability sampling method, because of its speed, cost-effectiveness, and ease of availability of the sample.
• 21. COUNT.. Consecutive Sampling This non-probability sampling method is very similar to convenience sampling, with a slight variation. Here, the researcher picks a single person or a group of a sample, conducts research over a period, analyzes the results, and then moves on to another subject or group if needed. Consecutive sampling technique gives the researcher a chance to work with many topics and fine-tune his/her research by collecting results that have vital insights. Quota Sampling Hypothetically consider, a researcher wants to study the career goals of male and female employees in an organization. There are 500 employees in the organization, also known as the population. To understand better about a population, the researcher will need only a sample, not the entire population. Further, the researcher is interested in particular strata within the population. Here is where quota sampling helps in dividing the population into strata or groups.
• 22. Judgmental or Purposive sampling  In the judgmental sampling method, researchers select the samples based purely on the researcher’s knowledge and credibility. In other words, researchers choose only those people who they deem fit to participate in the research study. Judgmental or purposive sampling is not a scientific method of sampling, and the downside to this sampling technique is that the preconceived notions of a researcher can influence the results. Thus, this research technique involves a high amount of ambiguity. Snowball sampling  Snowball sampling helps researchers find a sample when they are difficult to locate. Researchers use this technique when the sample size is small and not easily available. This sampling system works like the referral program. Once the researchers find suitable subjects, he asks them for assistance to seek similar subjects to form a considerably good size sample.
• 23. Non-probability sampling examples Here are three simple examples of non-probability sampling to understand the subject better. 1. An example of convenience sampling would be using student volunteers known to the researcher. Researchers can send the survey to students belonging to a particular school, college, or university, and act as a sample. 2. In an organization, for studying the career goals of 500 employees, technically, the sample selected should have proportionate numbers of males and females. Which means there should be 250 males and 250 females. Since this is unlikely, the researcher selects the groups or strata using quota sampling.  Researchers also use this type of sampling to conduct research involving a particular illness in patients or a rare disease. Researchers can seek help from subjects to refer to other subjects suffering from the same ailment to form a subjective sample to carry out the study.
• 24. COUNT.. When to use non-probability sampling?  Use this type of sampling to indicate if a particular trait or characteristic exists in a population.  Researchers widely use the non-probability sampling method when they aim at conducting qualitative research, pilot studies, or exploratory research.  Researchers use it when they have limited time to conduct research or have budget constraints.  When the researcher needs to observe whether a particular issue needs in-depth analysis, he applies this method.  Use it when you do not intend to generate results that will generalize the entire population.
• 25. Advantages of non-probability sampling  Here are the advantages of using the non-probability technique  Non-probability sampling techniques are a more conducive and practical method for researchers deploying surveys in the real world. Although statisticians prefer probability sampling because it yields data in the form of numbers, however, if done correctly, it can produce similar if not the same quality of results and avoid sampling errors.  Getting responses using non-probability sampling is faster and more cost-effective than probability sampling because the sample is known to the researcher. The respondents respond quickly as compared to people randomly selected as they have a high motivation level to participate.
• 26. Difference between probability and non- probability Probability sampling Non Probability sampling The sample is selected at random. Sample selection based on the subjective judgment of the researcher. Everyone in the population has an equal chance of getting selected Not everyone has an equal chance to participate. Used when sampling bias has to be reduced . The researcher does not consider sampling bias. Useful when the population is diverse Useful when the population has similar traits . Used to create an accurate sample The sample does not accurately represent the population. Finding the right respondents is not easy Finding respondents is easy.