Differences Between Sampling Frame and Population Discussion Questions

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After reading "Sampling Frame" from our textbook in chapter 14 and looking at peer reveiw journals, discuss the following questions:

What is sampling frame?

What are the differences between sampling frame and population?

Use business examples to support your statements.
Challenge your classmates or extend their posts.

thankssee for classmate feedbacks1. A sample frame is a list of elements in the population from which the sample is actually drawn. According to Cooper & Schindler (2014) sampling frames are closely related to the population. It is best if the sampling frame only includes the population members only but it should be complete and accurate. Sampling frames can also be known as a survey frame. For example if a particular sample is made up of all random units then each of the included unites will have an equal chance of being drawn upon and an equal chance of occurring (Improving Health in Slums Collaborative, 2019). An example of how sampling frames can be uses in business is for market research. If a business has a collection of customers their research department can pull customer names from that database of those who they believe would take part in collecting additional data. The word population is very general. Within the research realm there are many different types of populations. According to Cooper & Schindler (2014) the general definition of population includes the elements in which an individual wishes to make a particular inference. There is also the population element which includes the individual participants or object on which a measurement is taken. The population element can be expressed as a unit in the sample. The population parameter is a descriptor that summarizes particular variables that are of interest to the researcher (Qader et al, 2020). The sample frame is essentially the box that holds the population (whatever is being studied or sampled from) in one place for easy choosing.ReferencesCooper, D. R. & Schindler P. S. (2014). Business Research Methods, 12th ed. McGraw-Hill/ Irwin. New York, NYImproving Health in Slums Collaborative (2019). A protocol for a multi-site, spatially-referenced household survey in slum settings: methods for access, sampling frame construction, sampling, and field data collection. BMC Medical Research Methodology, 19. https://0634jatn0-mp03-y-https-www-proquest-com.prx-keiser.lirn.net/central/docview/2242943210/3D805EEA1BCF4724PQ/1?accountid=35796Qader, S. H., Lefebvre, V., Tatem, A. J., Pape, U. & Warren, J. et al. (2020). Using gridded population and quadtree sampling units to support survey sample design in low-income settings. International Journal of Health Geographics, 19. https://0634jatn0-mp03-y-https-www-proquest-com.pr...2.A sampling frame is a list of all the items in your population. It's a complete list of everyone or everything you want to study. Sampling frame is the actual set of units from which a sample has been drawn: in the case of a simple random sample, all units from the sampling frame have an equal chance to be drawn and to occur in the sample. In the ideal case, the sampling frame should coincide with the population of interest. When developing a research study, one of the first things that you need to do is clarify all of the units that you are interested in studying. Units could be people, organizations, or existing documents. In research, these units make up the population of interest. When defining the population, it's really important to be as specific as possible.A sampling frame is a list of all the items in your population. It's a complete list of everyone or everything you want to study. Sampling frame (synonyms: "sample frame", "survey frame") is the actual set of units from which a sample has been drawn: in the case of a simple random sample, all units from the sampling frame have an equal chance to be drawn and to occur in the sample. In the ideal case, the sampling frame should coincide with the population of interest. When developing a research study, one of the first things that you need to do is clarify all of the units (also referred to as cases) that you are interested in studying. Units could be people, organizations, or existing documents. In research, these units make up the population of interest. When defining the population, it's really important to be as specific as possible.The problem is it's not always possible or feasible to study every unit in a population. For example, you might be interested in American college students' attitudes about owning houses. It would obviously be too time-consuming and costly to collect information from every college student in the United States. In cases like these, you can study a portion or subset of the population called a sample. The process of selecting a sample needs to be deliberate, and there are various sampling techniques that you can use depending upon the purpose of the research.Prior to selecting a sample, you need to define a sampling frame, which is a list of all the units of the population of interest. You can only apply your research findings to the population defined by the sampling frame. An ideal sampling frame will have the following qualities:- All units have a logical, numerical identifier.- All units can be found -their contact information, map location, other relevant information is present.- The frame is organized in a logical, systematic fashion.- The frame has additional information about the units that allow the use of more advanced sampling frames.- Every element of the population of interest is present in the frame.- Every element of the population is present only once in the frame.- No elements from outside the population of interest are present in the frame- The data is 'up-to-date'.The difference between a population and a sampling frame is that the population is general and the frame is specific. The population is what you are trying to measure and the sampling frame is the group of individuals who you are trying to sample from. You may not be able to capture all purchasers through people who have email because there are plenty of people who don't have an email, but you can get close.ReferencesCooper, D. R., & Schindler, P. S. (2014). Business research methods. Boston: Irwin/McGraw-Hill.

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Chapter 15 DATA PREPARATION AND DESCRIPTION McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. Learning Objectives Understand . . .  The importance of editing the collected raw data to detect errors and omissions.  How coding is used to assign number and other symbols to answers and to categorize responses.  The use of content analysis to interpret and summarize open questions. 15-2 Learning Objectives Understand . . .  Problems with and solutions for “don’t know” responses and handling missing data.  The options for data entry and manipulation. 15-3 Pull Quote “Pattern thinking, where you look at what’s working for someone else and apply it to your own situation, is one of the best ways to make big things happen for you and your team.” David Novak, chairman and CEO, Yum! Brands, Inc. 15-4 Data Preparation in the Research Process 15-5 Monitoring Online Survey Data Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data . 15-6 Editing Accurate Arranged for simplification Consistent Criteria Uniformly entered Complete 15-7 Field Editing Field editing review Entry gaps identified Callbacks made Results validated 15-8 Central Editing Be familiar with instructions given to interviewers and coders Do not destroy the original entry Make all editing entries identifiable and in standardized form Initial all answers changed or supplied Place initials and date of editing on each instrument completed 15-9 Sample Codebook 15-10 Precoding 15-11 Coding Open-Ended Questions 6. What prompted you to purchase your most recent life insurance policy? _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ 15-12 Coding Rules Appropriate to the research problem Exhaustive Categories should be Mutually exclusive Derived from one classification principle 15-13 Content Analysis 15-14 Types of Content Analysis Syntactical Referential Propositional Thematic 15-15 Open-Question Coding Locus of Responsibility Mentioned A. Company _____________ B. Customer _____________ C. Joint CompanyCustomer _____________ F. Other _____________ Not Mentioned _______________ Locus of Responsibility A. Management _______________ 1. Sales manager 2. Sales process _______________ 3. Other 4. No action area identified _______________ B. Management 1. Training C. Customer 1. Buying processes 2. Other 3. No action area identified D. Environmental conditions E. Technology F. Other Frequency (n = 100) 10 20 7 3 15 12 8 5 20 15-16 Proximity Plot 15-17 Handling “Don’t Know” Responses Question: Years of Purchasing Do you have a productive relationship with your present salesperson? No Don’t Know 10% 40% 38% 1 – 3 years 30 30 32 4 years or more 60 30 30 100% n = 650 100% n = 150 100% n = 200 Less than 1 year Total Yes 15-18 Data Entry Keyboarding Digital/ Barcodes Database Programs Optical Recognition Voice recognition 15-19 Missing Data Solutions Listwise Deletion Pairwise Deletion Replacement 15-20 Key Terms • Bar code  Don’t know response • Codebook  Editing • Coding  Missing data • Content analysis  Optical character • Data entry • Data field  • Data file  • Data preparation  • Data record  recognition Optical mark recognition Precoding Spreadsheet Voice recognition • Database 15-21 Chapter 15 ADDITIONAL DISCUSSION OPPORTUNITIES McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. CloseUp: Dirty Data Invalid: entry errors Incomplete: missing, siloed, turf wars Inconsistent: across databases Incorrect: lost, falsified, outdated Solutions: Data Steward, Data Protocols, Error Detection Software 15-23 Snapshot: CBS labs 39 Million Visitors Show Screenings Dial Testing Surveys Focus Groups 15-24 PicProfile: Content Analysis QSR’s XSight software for content analysis. 15-25 Snapshot: Netnography Data Posted on Internet & intranets Product & company reviews Employee experiences Message board posts Discussion forum posts 15-26 Research Thought Leader “The goal is to transform data into information, and information into insight. Carly Fiorina former president and chairwoman, Hewlett-Packard Co 15-27 PulsePoint: Research Revelation 55 The percent of white-collar workers who answer work-related calls or email after work hours. 15-28 Chapter 15 DATA PREPARATION AND DESCRIPTION McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. Photo Attributions Slide Source 6 Courtesy of CfMC Research Software 8 Courtesy of Western Watts 14 Courtesy of xSight 15 Eric Audras/Getty Images 19 Purestock/SuperStock 20 ©Pamela S. Schindler 24 ©fStop/SuperStock 25 Courtesy of QSR (xSight) 26 Scott Dunlap/Getty Images 15-30 Chapter 16 EXPLORING, DISPLAYING, AND EXAMINING DATA McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. Learning Objectives Understand . . .  That exploratory data analysis techniques provide insights and data diagnostics by emphasizing visual representations of the data.  How cross-tabulation is used to examine relationships involving categorical variables, serves as a framework for later statistical testing, and makes an efficient tool for data visualization and later decision-making. 16-2 Pull Quote “On a day-to-day basis, look for inspiration and ideas outside the research industry to influence your thinking. For example, data visualization could be inspired by an infographic you see in a favorite magazine, or even a piece of art you see in a museum.” Amanda Durkee, partner Zanthus 16-3 Researcher Skill Improves Data Discovery DDW is a global player in research services. As this ad proclaims, you can “push data into a template and get the job done,” but you are unlikely to make discoveries using a template process. 16-4 Exploratory Data Analysis Exploratory Confirmatory 16-5 Data Exploration, Examination, and Analysis in the Research Process 16-6 Research Values the Unexpected “It is precisely because the unexpected jolts us out of our preconceived notions, our assumptions, our certainties, that it is such a fertile source of innovation.” Peter Drucker, author Innovation and Entrepreneurship 16-7 Frequency: Appropriate Social Networking Age 16-8 Bar Chart 16-9 Pie Chart 16-10 Frequency Table 16-11 Histogram 16-12 Stem-and-Leaf Display 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 455666788889 12466799 02235678 02268 24 018 3 1 06 3 36 3 6 8 16-13 Pareto Diagram 16-14 Boxplot Components 16-15 Diagnostics with Boxplots 16-16 Boxplot Comparison 16-17 Mapping 16-18 SPSS Cross-Tabulation 16-19 Percentages in Cross-Tabulation 16-20 Guidelines for Using Percentages Don’t average percentages Don’t use too large a percentage Don’t use too small a base Changes should never exceed 100% Higher number is the denominator 16-21 Cross-Tabulation with Control and Nested Variables 16-22 Automatic Interaction Detection (AID) 16-23 Exploratory Data Analysis This Booth Research Services ad suggests that the researcher’s role is to make sense of data displays. Great data exploration and analysis delivers insight from data. 16-24 Key Terms  Automatic interaction        detection (AID) Boxplot Cell Confirmatory data analysis Contingency table Control variable Cross-tabulation Exploratory data analysis (EDA)  Five-number summary  Frequency table  Histogram  Interquartile range       (IQR) Marginals Nonresistant statistics Outliers Pareto diagram Resistant statistics Stem-and-leaf display 16-25 Chapter 16 ADDITIONAL DISCUSSION OPPORTUNITIES McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. Snapshot: Novation No standarded vocabulary across companies Serve variety of users Ad hoc analysis with sophisticated visualizations Big data with sophisticated analytical tool. 16-27 Snapshot: Digital Natives vs. Digital Immigrants 30 subjects = 15 natives, 15 immigrants Monitored media behaviors 300 hours of real-time data Biometric Monitoring: emotional engagement 16-28 Snapshot: Empowering Excel 16-29 Snapshot: Internet-age Researchers “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades . . . .” 16-30 Research Thought Leader “As data availability continues to increase, the importance of identifying/filtering and analyzing relevant data can be a powerful way to gain an information advantage over our competition.” Tom H.C. Anderson founder & managing partner Anderson Analytics, LLC 16-31 PulsePoint: Research Revelation 65 The percent boost in company revenue created by best practices in data quality. 16-32 Geograph: Digital Camera Ownership 16-33 CloseUp: Working with Data Tables 16-34 CloseUp: Original Data Table 16-35 CloseUp: Arranged by Spending Most to Least 16-36 CloseUp: Arranged by Average Annual Purchases, Most to Least 16-37 CloseUp: Arranged by Average Transaction, Most to Least 16-38 CloseUp: Arranged by Estimated Average Transaction, Least to Most 16-39 Chapter 16 EXPLORING, DISPLAYING, AND EXAMINING DATA McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. Photo Attributions Slide Source 4 Courtesy of Radius Global Market Research 18 Courtesy of RealtyTrac 21 Vstock/Alamy 24 Courtesy of Booth Research Services 27 Courtesy of Novation 28 Realistic Reflections 29 Courtesy of DecisionPro; Digital Vision/Getty Images 30 Vstock LLC/Getty Images 16-41
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What is the sampling frame?
A sampling frame is the list of items, elements, or people from which the sample is
obtained. In other words, a sampling frame is the correct and complete list of the study
population members only (Cooper & Schindler, 2013). Simply, a sampling frame is the complete
list of everything or everyone under a study. For example, if an organization is researching the
“causes of poor customer services,” the sampling frame will be the list of all customers who have
reported poor services. Similarly, if the organization wants to conduct a study on “causes of high
absenteeism,” the sampling frame will be the list of employees who have intentionally or
unintentionally failed to turn up to the workplace. Further...