...Sampling techniques: Advantages and disadvantages Technique | Descriptions | Advantages | Disadvantages | Simple random | Random sample from whole population | Highly representative if all subjects participate; the ideal | Not possible without complete list of population members; potentially uneconomical to achieve; can be disruptive to isolate members from a group; time-scale may be too long, data/sample could change | Stratified random | Random sample from identifiable groups (strata), subgroups, etc. | Can ensure that specific groups are represented, even proportionally, in the sample(s) (e.g., by gender), by selecting individuals from strata list | More complex, requires greater effort than simple random; strata must be carefully defined | Cluster | Random samples of successive clusters of subjects (e.g., by institution) until small groups are chosen as units | Possible to select randomly when no single list of population members exists, but local lists do; data collected on groups may avoid introduction of confounding by isolating members | Clusters in a level must be equivalent and some natural ones are not for essential characteristics (e.g., geographic: numbers equal, but unemployment rates differ) | Stage | Combination of cluster (randomly selecting clusters) and random or stratified random sampling of individuals | Can make up probability sample by random at stages and within groups; possible to select random sample when population lists are very localized...
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...Explain each sampling technique discussed in the “Visual Learner: Statistics” in your own words, and give examples of when each technique would be appropriate. Statistical Hypotheses: There are two types of statistical hypotheses to include null and alternative hypothesis. Null Hypothesis - Samples observations resulting purely by chance. Alternative Hypothesis - Sample observations influenced by non-random causes. In example, when determining the end result in a coin flip. A null hypothesis may result in half Heads and half in Tails. However, the alternative hypothesis may reflect differently with the number of Heads and the number of Tails received. In conclusion, if the number of flips totaled 100 times, resulting in 60 Heads and 40 Tails, we would determine the null hypothesis would be rejected and not of use. The coin would be deemed as not fair nor balanced. Blocking - Is the act of grouping experimental units into block segments with comparable characteristics that may influence the end result of the experiment. The design with both control and randomization, are taken into consideration (Easton, Valerie J 2014). In example, a study being carried out has a total of 100 subjects. The plan is to divide the subjects into random, yet equally split groups of four. Census - Is data collected from every individual within a population and calculated to assist with achieving statistical information. An example of a census is the United States Census. It...
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...Sampling techniques: Advantages and disadvantages |Technique |Descriptions |Advantages |Disadvantages | |Simple random |Random sample from whole population|Highly representative if all subjects |Not possible without complete list of | | | |participate; the ideal |population members; potentially uneconomical| | | | |to achieve; can be disruptive to isolate | | | | |members from a group; time-scale may be too | | | | |long, data/sample could change | |Stratified random |Random sample from identifiable |Can ensure that specific groups are |More complex, requires greater effort than | | |groups (strata), subgroups, etc. |represented, even proportionally, in the|simple random; strata must be carefully | | | |sample(s) (e.g., by gender), by |defined | | | |selecting individuals from strata list | ...
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...RESEARCH WORK 1. Research Design 2. Types of Research Design 3. Data 4. Sources of Data 5. Descriptive Research Design 6. Types of Descriptive Research Design 7. Sampling 8. Population 9. Stratum 10. Sample 11. Sample Size 12. Randomization 13. Sampling Unit 14. Target Population 15. Respondent Population 16. Sampling Design 17. Margin of Error 18. Probability / Level of Confidence 19. Probability Sampling Designs 20. Simple Random Sampling 21. Systematic Sampling 22. Stratified Sampling 23. Cluster Sampling 24. Multi-stage Sampling 25. Non-Probability Sampling Designs 26. Purposive Sampling 27. Convenience Sampling 28. Quota Sampling 29. Snowball Sampling 1. Research Design a. The research design is a comprehensive master plan of the research study to be undertaken, giving a general statement of the methods to be used. The function of a research design is to ensure that requisite data in accordance with the problem at hand is collected accurately and economically. Simply stated, it is the framework, a blueprint for the research study which guides the collection and analysis of data. The research design, depending upon the needs of the researcher may be a very detailed statement or only furnish the minimum information required for planning the research project. b. The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring...
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...Amanda Vanterpool MTH 270 September 15, 2013 Data Project #1 I am interested in the population of people who go to Lakewood pacific theaters on a Friday night. I want to find out how many people will choose to watch Insidious Chapter Two over The Family. To get a random sample I would ask the movie theater manager if I could see the record book of how many people went to see insidious and how many people went to see the family between six pm and eleven pm. I would use my calculator to generate three random numbers between six and eleven and compare the data. To get a systematic sample, I would stand outside the ticket booth and ask every fifth person who purchased a ticket what they chose to see. I would do this until I reached 100 people to use as my sample. To get a stratified sample, I would separate the population into teens and adults. I would interview every fifth teen and every fifth adult till I reached 100 for each sub group. To get a cluster sample, separate the population into teens and adults and interview every teen and adult that purchases a ticket. To get a convenience sample, I would go to watch one of the movies and interview a few people before I went in to watch the movie. I would choose the stratified sample to collect my data because I like the idea of dividing the population into different sections. The issue I encountered was that there were other movies playing so not every fifth person saw the movies I was centering my data...
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...Sampling Designs • 1. Simple random sampling (SRS) Steps: – (1) Assign a single number to each element in the sampling frame. – (2) Use random numbers to select elements into the sample until the desired number of cases is obtained. • The method is not very different from winning a lottery. 2. Systematic Sampling • Steps: – (1) Calculate the sampling interval as the ratio between population size and sample size, I = N/n. – (2) Arrange all elements in the population in an order. – (3) Select a case in the first interval randomly. – (4) Select every ith case from this point. 2. Systematic Sampling (continued) I I 1st element, randomly chosen I I I I – Systematic Sampling is easier and simpler than SRS – The text warns of a danger to this method. What is it? 3. Stratified Sampling • Stratified sampling is more complicated than SRS. The advantage is the guaranteed representativeness in some important characteristics. • For example, say that we want to select a sample of 100 individuals. Sex ratio in the sample is up to chance if we do SRS. We can guarantee the 50-50 split if we do stratified sampling: Stratified Sampling, Graphic Representation Population SRS SRS Sample • Stratified sampling is often used to reduce the variability of a sample. Oversampling, Graphic Representation Population SRS SRS Sample • Increasing the representation of a group in a sample. This is often done when groups are very different in size...
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...Sampling and data collection in research paper BSHS/435 January 18 2016 Sampling and data collection in research paper Introduction In this essay, I will discuss two ways for him to service professional to conduct research through sample lien and data collection. Through the use of research and he was there's a vessel can be better assist their clients ensuring their clients have the skills needed to reach their goals and objectives. What is the purpose of sampling Sampling allows the human service professional to research one narrow topic at a time instead of a whole group "A major reason for studying samples rather than the whole group is that the whole group sometimes is so large that studying it is not feasible" ( Monette, D. R., Sullivan T. J., & DeJong, C. R. (2011). A human service professional will receive better information through sampling; a sample is one or more elements of a population. The characteristics of probability and nonprobability "Probability sampling techniques is too sure that each element has an equal chance of inclusion"(Monette, D. R., Sullivan T. J., & DeJong, C. R. (2011). There are four aspects of probability sampling, simple random sampling, systematic sampling, stratified sampling, and area sampling. When probability sampling is not always the appropriate technique to use "Nonprobability sample's in which the investigator does not know the probability of beads populations elements inclusion in the sample"(Monette...
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...Sampling is one of the most important factor in educational research, sampling is a way of sele cting a fraction of people in a population of research in order to find the needed information which can generalised and extend their results further than the respondents, The researcher may not manage to collect the information for every individual of interest as it is costly, time consuming and stress nous, as(Mcmillin,1996) stated that when conducting an educational research, it is usually impractical and unnecessary to measure all the elements in the population of interest. The are two main types of sampling procedures suitable for conducting educational research, which is probability also known as random sampling and non-probability or non-random sampling. This paper will further discuss the two categories of sampling procedures that are commonly used in educational research and highlight the strength and weakness of using the discussed sampling procedures. Probability sampling is the type of research in which the researcher chooses the respondents randomly. In this type of research each respondent has an equivalent possibility of taking part in the research. probability sampling is a way of choosing the respondents randomly as (Mcmillin,1996) highlighted that “this way of random selection implies at least every member of the population or the subgroups has an equal chance of being selected” Each of the population has equal chance of being picked up into the sample, moreover...
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...Cluster Sampling- Cluster sampling takes the population and splits them into random groups which are called clusters. Depending on how many people are needed the researcher will choose a couple of random clusters to use in the research. Cluster sampling is used when it is close to impossible to get info of all the population that is trying to be sampled. For example it would be appropriate to use it if you were trying to get a survey on America's grocery stores. You would find all the stores and randomly select grocery stores in order to conduct your research. Convenience Sample- The sample or group used in this kind of sample are only used because they are easily accessible and don't involve pre-planning. They may not represent the whole population but they are easy to find and are very cheap for researchers. Another pro of this sampling is that it pretty much randomizes the sample on its own. An example on when this is used is if you want the opinion of students on if they liked a class you might choose the first 5 to walk out or the first 5 on the attendance sheet. Basically which on ever is more convenient for you. Random Sample- The subset of individuals are randomly selected from a population. Because researchers usually cannot obtain data from every single person in a group, a smaller portion is randomly selected to represent the entire group as a whole. The goal of a random sample is to represent the population. You want to use this type of sampling when you cannot get...
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...Topic Name Customers Attitude towards AIRTEL BANGLADESH Section: I Group: C Submitted To: Dr.Md.Mamun Habib Name ID 1. DAS, AMIT 10-17652-3 2. ISLAM, NABILA 10-17731-3 3. Chapter 1: Introduction 1.1 Introduction of the study: As we know that there are 6 operators in Bangladesh who provide their service in telecommunicating service sector. All the operators are trying to provide the best service. So there is a huge competition is going on between them. From our research we will try to find out the current position of Airtel and the customers satisfaction level. 2. Research Objective: We know that Airtel is one of the leading cellular service providers in Bangladesh. People from all ages are using Airtel. So, our main target is to communicate with all ages people who are using Airtel sim and find out their comment and on the basis of that make an overall comment. 1.3 Scope of Research: ...
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...Total number of elements in the sample SAMPLING * the process of selecting a small number of elements from a larger defined target group of elements such that the information gathered from the small group will allow judgments to be made about the larger groups SAMPLING TECHNIQUES * PROBABILITY SAMPLING a method of selecting a sample wherein each element in the population has a known, nonzero chance of being included in the sample. Simple Random Sampling a probability sampling method wherein all members of the population have equal chances of being included in the study. When to use: * If the members of the population are not so spread geographically. * If the members are homogeneous with respect to the characteristic under study. Systematic Sampling probability sampling method wherein the selection of the first member (random start - r) is at random (using simple random sampling) and the selection of the other members in sample is systematic by successively taking every kth member from the random start, where k is the sampling interval. When to use: * If the arrangement of the members is according to magnitude. * If there is no available list of members of the population. Stratified Sampling * a probability sampling method where we divide the population into nonoverlapping subpopulations or strata and then select sample (using simple random sampling or systematic sampling) from each stratum based on a stratification...
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...and not always be completely related to the needs of a specific project. Qualitative research: Subjective research where the researcher will ask a customer for their opinion or thoughts. Quantitative research: Research that uses a large sample of people to gather statistical information. Sampling: Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. In business and medical research, sampling is widely used for gathering information about a population. Random sampling: In this technique, each member of the population has an equal chance of being selected as subject. The entire process of sampling is done in a single step with each subject selected independently of the other members of the population. Quota sampling: In quota sampling, a population is first segmented into mutually exclusive sub-groups. Then judgment is used to select the subjects or units from each segment based on a specified proportion. Quota sampling is useful when time is limited, a sampling frame is not available, the research budget is very tight or when detailed accuracy is not important. Stratified sampling: Stratification is the process of dividing members of the...
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...What is “Statistics”? Data collection Chapter 1 of text “a way to get information from data” A framework for dealing with variability A way to make decisions under uncertainty Statistical inference: the problem of determining the behaviour of a large population by studying a small sample from that population Why is statistics important in business? Financial management (capital budgeting) Marketing management (pricing) Marketing research (consumer behaviour) Operations management (inventory) Accounting (forecasting sales) Human resources management (performance appraisal) Information systems Economics (summarising, predicting) See http://www.youtube.com/watch?v=D4FQsYTbLoI What is a population? What is a sample? Population: a collection of the whole of something – e.g. all female students of ANU; all people who live in Tuggeranong; all people who play the flute. Sample: a set of individuals drawn from a population e.g. the female students in STAT1008 are a sample of all female students at ANU. If we have a population…. We can get parameters – true values for things like the centre and spread of the population We know the answers – what proportion are this tall? We look at the population and get the answer. If we have a sample… We can get statistics – these are values that estimate the parameters e.g. sample centre and sample spread used to estimate population centre and population spread We have to use inference to do this estimation – what proportion are this...
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...Simple Random Sampling is done when every individual subject in the population has an equal chance of being selected for the sample, without any bias (Explorable). For example, if a researcher wants to represent the population as a whole, they can pick random numbers or names out a hat or use a program to randomly choose names so the information is not biased. Stratified Sampling is performed by, dividing the population into at least two (or more) groups or sections, which share certain characteristics, called “strata” (Explorable). For example, a researcher who wants to compare the average economic status of different racial groups may use this technique in order to divide the population into groups based on race and ethnicities and then compare the whole average from each ethnic group. Cluster Sampling is done by dividing the population into separate sections or “clusters” and then picks a cluster randomly and chooses all the members from those clusters for the sample (Explorable). For example, using a geographical cluster, in order to look at the academic performance of students. The researcher can divide Nassau County in Long Island into clusters based on the towns. Then, randomly select a certain number of these clusters or towns and include all the students from those clusters to be part of the sample. Systematic Sampling is performed by, using and selecting a point at which to begin and then selecting every x number after that point (Explorable). For example,...
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...Chapter 1: The Role of BR I. The Nature of Research A. BR defined B. Applied & basic BR C. The scientific method II. Managerial Value of BR A. Identifying problems or opportunities B. Diagnosing & assessing problems or opportunities C. Selecting & implementing a course of action D. Evaluating the course of action III. When is BR Needed? A. Time constraints B. Availability of data C. Nature of the decision D. Benefits vs costs IV. BR In The 21st Century A. Communication technologies B. Global BR Chapter 3: Theory Building I. Introduction A. What is a theory? B. What are the goals of theory? II. Research Concepts, Constructs, Proposition, Variables & Hypotheses A. Research concepts & constructs B. Research proposition & hypotheses III. Understanding Theory A. Verifying theory B. Theory building Chapter 5: The Human Side of BR: Organizational & Ethical Issue I. Introduction II. Ethical issue in BR A. Ethical qs are philosophical qs B. General rights & obligation of concerned parties C. Rights & obligation of the research participant * The obligation to be truthful * Participants’ right to privacy * Active & Passive research * Deception in research designs & the right to be informed * Experiment designs * Descriptive research * Protection from harm D. Rights & obligation of the...
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