SAMPLING METHODS &TYPES, & TECHNIQUES & EXAMPLES.pptx

SAMPLING METHODS &TYPES, & TECHNIQUES & EXAMPLES

1. SAMPLING METHODS TYPES, TECHNIQUES & EXAMPLES

2. Population vs. sample Population is the entire group that want to draw conclusions about Sample is the specific group of individuals that will collect data from.

3. Sample Sample is the specific group of individuals that will collect data from. A sample refers to a group or section of a population from which information is to be obtained. Sampling Sampling is a statistical technique where the researchers take a predetermined number of observations from a larger population. Sampling is the selection of group with a view to obtain information about the whole is group of persons that represents particular community

4. Survey sampling Survey sampling describes the process of selecting a sample of elements from a target population to conduct a survey. The term " survey " may refer to many different types or techniques of observation. In survey sampling it most often involves a questionnaire used to measure the characteristics and/or attitudes of people. Different ways of contacting members of a sample once they have been selected is the subject of survey data collection . The purpose of sampling is to reduce the cost and/or the amount of work that it would take to survey the entire target population.

5. A survey that measures the entire target population is called a census . Survey samples can be broadly divided into two types: probability samples and super samples. Probability-based samples implement a sampling plan with specified probabilities (perhaps adapted probabilities specified by an adaptive procedure). Probability-based sampling allows design-based inference about the target population. The inferences are based on a known objective probability distribution that was specified in the study protocol. Inferences from probability-based surveys may still suffer from many types of bias. Surveys that are not based on probability sampling have greater difficulty measuring their bias or sampling error . Surveys based on non-probability samples often fail to represent the people in the target population Random sampling and design-based inference are supplemented by other statistical methods, such as model-assisted sampling and model-based sampling For example, many surveys have substantial amounts of nonresponse. Even though the units are initially chosen with known probabilities, the nonresponse mechanisms are unknown. For surveys with substantial nonresponse, statisticians have proposed statistical models with which the data sets are analyzed.

6. 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).

7. Example about S ample & S ampleing

8. Sample Method : The research was made by the survey in accordance to the convenience of the employees. So the sample type is convenient sampling. Sampling Area: The research was conducted at Welcome Hotel Grand Bay, Beach Road, Visakhapatnam Sample Population : There are total 500 employees working in the organization. Sample Size : All items in any field of in query constitute a ‘universe’ or ‘population’. A finite subject of the population gives a sample. The statistical units in the sample are called sample units. The number of units in the sample is called the size of the sample. If the size of the sample is less than or equal to 30 then it is called small samples. Otherwise, if the size of the sample is greater than 30, it is called as large samples. Out of the total strength the sample taken amongst workers. i.e: 60 respondents.

9. Sampling methods are divided into two types: probability sampling & non – probability sampling .

10. Types of Probability Sampling 1.Simple Random Sampling known as Method of chance Selection 2.Stratified Sampling   known as proportional random sampling or quota random sampling . 3.Cluster Sampling   known as multi-stage sampling 4.Systematic Sampling   known as quasi random sampling Types of Non-Probability Sampling 1.Quota Sampling known as non-probability sampling method 2.Opinion Sampling known as representative sampling 3.Snowball Sampling known as chain sampling or network sampling 4.Discretionary Sampling known as purposive sampling / Judgmental

11. Probability sampling Probability sampling is a sampling method that uses random selection methods. The essential characteristic of probability sampling is that everyone in a population has an equal chance of selection. The probability sampling method allows you to create a representative population sample. . Probability sampling uses statistical theory. It randomly selects a small group of people from a large existing population and then predicts that all the answers together will match the population. Probability sampling involves random selection, allowing to make strong statistical inferences about the whole group. For example, in a population of 100, each person would have a 1 in 100 chance of being selected Non-probability sampling Non-probability sampling is a technique where a sampler selects samples based on subjective judgment rather than random selection. Unlike in probability sampling, where everyone in a population has a chance of getting selected, in non-probability sampling, not all population members can participate. Non-probability sampling is advantageous in exploratory studies such as the pilot survey (a survey implemented on a smaller sample compared to the default sample size). Non-probability is used where it is impossible to draw a random probability sample due to time or cost considerations. Non-probability sampling is a less stringent method. This sampling method is highly dependent on the experience of the researchers. Non-probability sampling is commonly carried out using observational methods and is widely used in qualitative research. Non-probability involves non-random selection based on convenience or other criteria, allowing to easily collect data

12. Simple Random Sampling is a random and automated method to select a sample. Simple Random Sampling method assigns numbers to the individuals and then randomly chooses numbers. The selected members are then included in the sample. The samples are chosen in two ways: Through a lottery system and random number generation software. Simple Random Sampling technique generally works in large populations and has both advantages and disadvantages. Stratified Sampling   is a method in which a large population is divided into two smaller groups, which usually do not overlap but represent the entire population. These groups can be organized during sampling, and each group can be sampled separately after sampling. Stratified Sampling method , the samples are classified and analysed by gender, age, ethnicity, etc. Stratified Sampling divides subjects into mutually exclusive groups and uses simple random sampling to select group members. Quota Sampling Also called “accidental.” It is generally established based on a good knowledge of the strata of the population and the most “representative” or “adequate” individuals. Therefore, it is similar to stratified random sampling but does not have the random nature of the former. In this type of sampling, “quotas” consist of several individuals who meet certain conditions, for example, 20 individuals between the ages of 25 and 40, females, and residents in New Delhi. Once the quota is determined, the first ones found to meet these characteristics are chosen. This method is widely used in opinion polls.   Opinion Sampling type of sampling is characterized by a deliberate effort to obtain “representative” samples by including supposedly specific groups. Its use is widespread in pre-election polls of areas that have marked voting trends in the previous voting. That is, the result of the elections in that area was the same as the overall result.  

13. Cluster Sampling   is a method that randomly selects participants when they are geographically dispersed. For example, we have 1000 participants from the entire population of Bangalore. Let’s assume obtaining a complete list of all these is impossible. But instead, what the researcher does is select areas at random (i.e., localities, societies, etc.) and select randomly within those boundaries. Cluster sampling usually analyzes a population in which the sample consists of several elements, for example, city, family, university, etc.   Systematic Sampling   is a comprehensive implementation of the same probability technique in which each group member is selected at regular periods to form a sample. When this sampling method is used, there is an equal chance that each member of a population will be selected. Snowball Sampling is s ome elements in the universe lead to others, which then lead to others until a sufficient sample is obtained, completing the census of the universe. Although it may seem useless, it is frequently used when we know the population, for example, with populations such as students, criminals, and certain types of diseases, among others. Discretionary Sampling is more commonly known as purposive sampling. Discretionary Sampling type of sampling, the subjects are chosen to be part of the sample with a specific objective. With discretionary sampling, the researcher believes that some subjects are more suitable for research than others. For this reason, those are deliberately chosen as subjects.