Aries Man Secrets Pdf Free Download
Research is aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts or practical application of such new or revised theories or laws. A sample provides needed information about the population quickly. However, there is no guarantee that any sample will be precisely representative of the population from which it comes. It is cheaper to observe a part rather than the whole. This chapter is a discussion on sampling in research and it is mainly designed to equip researchers with knowledge of the general issues to consider when sampling. The purpose of sampling in research, dangers of sampling and how to minimize them, types of sampling and guides for deciding the sample size are discussed. For a clear flow of ideas, a few definitions of the terms used are given. They highlight the types and methods of sampling, sampling errors and discusses techniques of sample size determination. Different types of sampling technique, how to carry them out, and their advantages and disadvantages are also introduced.
Figures - uploaded by Never Mujere
Author content
All figure content in this area was uploaded by Never Mujere
Content may be subject to copyright.
Discover the world's research
- 20+ million members
- 135+ million publications
- 700k+ research projects
Join for free
How to Submit Proof Corrections Using Adobe Reader
Using Adobe Reader is the easiest way to submit your proposed amendments for your IGI Global proof. If you
don't have Adobe Reader, you can download it for free at http://get.adobe.com/reader/. The comment
functionality makes it simple for you, the contributor, to mark up the PDF. It also makes it simple for the IGI Global
staff to understand exactly what you are requesting to ensure the most flawless end result possible.
Please note, however, that at this point in the process the only things you should be checking for are:
Spelling of Names and Affiliations, Accuracy of Chapter Titles and Subtitles, Figure/Table Accuracy,
Minor Spelling Errors/Typos, Equation Display
As chapters should have been professionally copy edited and submitted in their final form, please remember that
no major changes to the text can be made at this stage.
Here is a quick step -by- step guide on using the comment functionality in Adobe Reader to submit your changes.
1. Select the Comment bar at the top of page to View or Add Comments. This will open the Annotations
toolbar.
2. To note text that needs to be alte red, like a subtitle or your affiliation, you may use the Highlight Text
tool. Once the text is highlighted, right-click on the highlighted text and add your comment. Please be
specific, and include what the text currently says and what you would like it to be changed to.
3. If you would like text inserted, like a missing coma or punctuation mark, please use the Insert Text at
Cursor tool. Please make sure to include exactly what you want inserted in the comment box.
4. If you would like text removed, such as an erroneous duplicate word or punctuation mark, please use the
Add Note to Replace Text tool and state specifically what you would like removed .
This proof is copyrighted by IGI Global. It is being provided as a courtesy to the author to review prior
to publication. Posting on any online site (for example, ResearchGate, Academia.edu, ArXiv) or
distributing this proof without written permission of IGI Global is prohibited and a violation of copyright
law.
Mixed Methods Research
for Improved Scientific
Study
Mette Baran
Cardinal Strich University, USA
Janice Jones
Cardinal Strich University, USA
A volume in the Advances in Knowledge
Acquisition, Transfer, and Management (AKATM)
Book Series
Detailed Table of Contents
Foreword ............................................................................................................................................. xvi
Preface ...............................................................................................................................................xviii
Acknowledgment .............................................................................................................................. xxiv
Section 1
Research Paradigms
Research paradigms address the philosophical dimensions of social sciences. A research paradigm is
a set of fundamental assumptions and beliefs as to how the world is perceived which then serves as a
thinking framework that guides the behavior of the researcher. This chapter explains the different points
of view on paradigms. The historical background of mixed methods research, including the debate
among the three methodology communities, is described. In addition, the nature of mixed methods and
its rational is discussed.
Chapter 1
The Nature of Research Methodologies: Terms and Usage within Quantitative, Qualitative, and
Mixed Methods ....................................................................................................................................... 1
Ben Tran, Alliant International University, USA
Mixed methods research is, generally speaking, an approach to knowledge (theory to practice) that
attempts to consider multiple viewpoints, perspectives, positions, and standpoints. As such, before the
advent of mixed methods, many studies used multiple methods to achieve the benefits of triangulation
without restricting themselves to any paradigmatic membership or methodological category. Today,
the primary philosophy of mixed research is that of pragmatism. Mixed methods research is, generally
speaking, an approach to knowledge (theory to practice) that attempts to consider multiple viewpoints,
perspectives, positions, and standpoints. This chapter will cover the history and the foundation of research
methodologies and explain the purpose of research within various methodologies. This chapter will
also explain the various terminologies used within research and research design as well as the meaning
of these terminologies. This chapter will not cover statistics, however, mixed methods, methodology,
research, and paradigm, statistical research methodology will be touched upon.
Chapter 2
Moving from Tension to Texture: The Paradigmatic Roots of Mixed Methods Research ...................28
Preston B. Cosgrove, Cardinal Stritch University, USA
Peter M. Jonas, Cardinal Stritch University, USA
Much like a jigsaw puzzle box top guides one in how to connect the pieces, an individual's research
paradigm operates as a conscious or subconscious influence in conducting a research project. This
chapter starts by making the argument for the critical role of research paradigms before moving into
a thorough investigation of the paradigmatic origins of the qualitative-quantitative "debate." While
mixed-methods research is often seen as the mediator in the dispute, the authors then articulate four
broad ways in which mixed-methods research addresses the paradigm divide at the heart of qualitative
and quantitative research. The result is paradigmatically complex, but offers researchers flexibility as
they seek to address their research question.
Chapter 3
Mixed Method Research: A Concept .................................................................................................... 40
Aroop Mukherjee, Universiti Putra Malaysia, Malaysia
Nitty Hirawaty Kamarulzaman, Universiti Putra Malaysia, Malaysia
Mixed methods have emerged as the third research community in the social and behavioural sciences
during the past decades, joining quantitative and qualitative methods of scholarly inquiry. Mixed methods
research, research paradigm, methodology, and action research have encouraged the combined use of
quantitative and qualitative research to answer complex questions in the recent years. Mixed methods
research integrates both methods, the quantitative and the qualitative, to present research findings within
a single system process. The chapter aims to provide an insight between the mixed method research and
action research, which includes the basic foundation of mixed method research and research paradigm.
The chapter will discuss the concept of action research and how the mixed method is applied to action
research and its processes. A brief idea about the future plan of action required for mixed method research
to attain better research designs and processes has also been discussed in the chapter.
Section 2
Designing a Mixed Methods Research Study
Researchers need to make numerous methodical decisions including the research design which is the
blueprint for the study. This chapter outlines the design decisions that need to be made. The design is
chosen based on which strategy is best suited to answer the research question(s). The three basic mixed
methods designs are discussed: Parallel Convergent, Sequential (Explanatory or Exploratory), and
Embedded Design. Additional decisions need to be made about the implementation of the study including
the sequencing of the data collection, weight of the two methods (quantitative and qualitative), and when
and how to converge the data.
Chapter 4
Research Design .................................................................................................................................... 67
Mette L. Baran, Cardinal Stritch University, USA
This chapter introduces the various design choices researchers need to decide on prior to conducting
the study. In the first section of this chapter a detailed description of what research design is offered
followed by an explanation that the type of information that is collected is based on whether the research
question is descriptive, explanatory, or exploratory. The major strategic implementation methods for
quantitative, qualitative, and mixed methods are then discussed. The three strategies for mixed methods
research are Parallel Convergent, Sequential, and Embedded Design are presented in detail along with
the rationale for their use. Finally, in the last section, the strands or sequencing of the data collection
phase of the study is explained.
Chapter 5
Designs of Mixed Method Research ..................................................................................................... 80
Amir Manzoor, Bahria University, Pakistan
Mixed methods research is becoming an increasingly popular approach in the discipline fields of sociology,
psychology, education and health sciences. Calls for the integration of quantitative and qualitative
research methods have been advanced in these fields. A key feature of mixed methods research is its
methodological pluralism, which frequently results in research which provides broader perspectives
than those offered by mono-method designs. The central premise of mixed methods is that the use of
quantitative and qualitative approaches in combination provides a better understanding of research
problems and complex phenomena than either approach alone. The purpose of this chapter is to review
designs of mixed methods research. The study surveys the common designs of mixed methods research
and examine the main characteristics of each in terms of purposes, strengths, and issues, and posits
suggestions on the application of these designs.
Section 3
Sampling in Research
This chapter explains the role of sampling in research. Sampling plays an important role in any research
study and careful consideration needs to be placed on who to include as participants as part of the
design process. Researchers need to determine sample sizes for the quantitative and qualitative data
and whether or not to include the same participants for both strands of the study. In addition, decisions
around random sampling or purposeful sampling must be considered.
Chapter 6
Sampling in Research ..........................................................................................................................108
Never Mujere, University of Zimbabwe, Zimbabwe
Research is aimed at the discovery and interpretation of facts, revision of accepted theories or laws in
the light of new facts or practical application of such new or revised theories or laws. A sample provides
needed information about the population quickly. However, there is no guarantee that any sample will
be precisely representative of the population from which it comes. It is cheaper to observe a part rather
than the whole. This chapter is a discussion on sampling in research and it is mainly designed to equip
researchers with knowledge on the general issues on sampling. The purpose of sampling in research,
dangers of sampling and how to minimize them, types of sampling and guides for deciding the sample
size. For a clear flow of ideas, a few definitions of the terms used are given. It highlights the types and
methods of sampling, sampling errors and discusses techniques of sample size determination. Different
types of sampling technique, how to carry them out, and their advantages and disadvantages are also
introduced.
Section 4
Data Collection Using Innovative Tools
Collecting data is critical to the design of the study and the researcher needs to determine whether the
data should be collected concurrently--namely quantitative and qualitative data at the same time, or
sequentially--collecting and analyzing one type of data at a time. While the traditional forms of data
collection have been stable over the years--collecting archival data, surveys/questionnaires, interviews,
and observation--alternative forms of data collection tools are starting to emerge. This allows the
researcher to explore an issue at a more holistic level.
Chapter 7
Analyzing Qualitative Data: Visualizing Lived Experiences through Poems and Photography ........ 124
Carolyn N. Stevenson, Kaplan University, USA
Use of photography and poetry offer a way for participants to express lived experiences through a visual
and written means of self-expression. These forms of data collection can provide a rich, thick description
of those often overlying on the peripherals of society. Traditional means of qualitative research such
as interview and observation can at times create a barrier between the researcher and the participants
because of the face-to-face interaction. Participants may be uncomfortable expressing authentic feeling
during a formal interview process. By offering participants the opportunity to personally select descriptive
photographs and articulate expression in their own voice through poetry, an authentic expression occurs.
Section 5
Analyzing Data
Researchers need to give great consideration to the data analysis process, mastering both the deductive
and the inductive analytical stages. There are two sources of interpretation and the researcher needs
to move swiftly between a pragmatic positivist approach to a more interpretary stance relying on
observations and words to better understand the context surrounding the study in order to build theory.
Mixed methods researchers operate between statistical enumeration and analysis seeking to confirm
hypotheses while coding qualitative data to detect patterns; hence they are required to demonstrate a
repertoire for methodologies.
Chapter 8
Analyzing Quantitative Data ............................................................................................................... 150
Sema A. Kalaian, Eastern Michigan University, USA
Rafa Kasim, Indiana Tech University, USA
The main purpose of this chapter is to present a conceptual and practical overview of some of the basic
and advanced statistical tools for analyzing quantitative data. Analyzing quantitative data involves two
broad analytical methods that serve two main purposes, which are descriptive and inferential statistical
methods. The chapter covers both descriptive and inferential quantitative methods. It covers some of the
descriptive statistical methods such as mean, median, mode, variance, standard deviation, and graphical
methods (e.g., histograms). It also covers inferential statistical methods such as correlation, simple
regression, multiple regression, t-test for two independent samples, t-test for two dependent samples,
and analysis of variance (ANOVA).
Chapter 9
Analyzing Quantitative Data in Mixed Methods Research for Improved Scientific Study ................ 166
Christopher Boachie, Central University College, Ghana
The purpose of the chapter is to review the role of quantitative methods in corporate research and the
methods for analysing quantitative data. The study used a secondary data on quantitative research
methods and a survey of published articles on schools, businesses and non- profit organizations. The key
findings show that exploratory data can be analyzed using graphs and charts and hypothesis testing can
be employed to test statements made. Impacts of one variable on another and the relationships between
variables can be explained using correlation and regression analysis. The implications are that the value
of a quantitative analysis arises when it is possible to identify features that occur frequently across the
many participatory discussions aimed at studying a particular research theme.
Chapter 10
How Marketers Conduct Mixed Methods Research: Incorporating the Exploratory Sequential
Design with the Hierarchy of Effects Model ......................................................................................188
Roger Baran, DePaul University, USA
The complimentary nature of qualitative and quantitative research methods are examined with respect
to a study assessing the market's view of a training and development institute in the Middle East. The
qualitative portion consisted of focus groups conducted with seven distinct market segments served by
the institute. The results proved insightful with respect to uncovering and understanding differences of
opinion among the seven groups; however, taken alone, the qualitative research would have been very
misleading with respect to the institute's standing in the Middle East.
Chapter 11
Making Sense of All the Words: Analyzing Qualitative Data ............................................................ 198
A. J. Metz, University of Utah, USA
This chapter provides an introduction to the process of qualitative analysis and to use step by step examples
to provide an idea of how the process of qualitative analysis actually works. Crabtree and Miller, 1992,
note that there are many different strategies for analysis, in fact, they suggest there are as many strategies
as there are qualitative researchers. This chapter is intended to give the researcher a place to begin and to
inspire a deeper dive into this rewarding form of data analysis. Stake, (1995, p. 71) writes that qualitative
data analysis is "a matter of giving meaning to first impressions as well as to final compilations. Analysis
essentially means taking something apart. We take our impressions, our observations, apart… we need
to take the new impression apart, giving meaning to the parts". While qualitative data analysis can be
time consuming the rewards that come from immersion in the data far outweigh the time spent doing so.
Section 6
Data Analysis: Examples of Research Studies using Mixed Methods
In this section three studies are used as innovative examples of how researcher use mixed methods to
provide holistic interpretations of phenomena. Mixed methods researchers need to be experienced in
three types of data analysis strategies. This chapter provides examples of innovative mixed methods
research studies combining the use of inductive and deductive logics. The strength of a mixed methods
approach is that it provides ample opportunities for researchers to be creative and eclectic in their
approach, examining an issue from various angles using a myriad of data collection tools and data
analysis techniques.
Chapter 12
Examining Online Communication: A Method for the Quantitative Analysis of Qualitative Data ... 214
Michael G. Hughes, HumRRO, USA
Jennifer A. Griffith, Alfred University, USA
Cristina Byrne, University of Oklahoma, USA
Darin S. Nei, Hogan Assessment Systems, USA
Lauren Harkrider Beechly, IBM, USA
Thomas A. Zeni, East Central University, USA
Amanda Shipman, Kenexa, USA
Shane Connelly, University of Oklahoma, USA
Michael D. Mumford, University of Oklahoma, USA
Methods of individual communication continue to expand through online media. Given the dynamic
nature of online communications, traditional methods for studying communications may not suffice. A
hybridized content analytic approach that combines qualitative and quantitative methods offers a unique
methodological tool to researchers who seek to better understand computer-mediated communications
and the psychological characteristics of those who communicate online by evaluating qualitative
information using quantitative methods. This means of measurement allows researchers to statistically
evaluate whether investigated phenomena are occurring in combination with the richness that qualitative
assessment provides. As with any approach to computer-mediated communication, various ethical
considerations must be borne in mind, and, thus, are discussed in concert with this hybridized approach
to content analysis.
Chapter 13
Mentoring and Support for the edTPA: A Mixed Methods Program Review of edTPA Support
Practices .............................................................................................................................................. 237
Randa Suleiman, Cardinal Stritch University, USA
Clavon Byrd, Cardinal Stritch University, USA
The research question was: How effective is the current edTPA mentoring and support program for
teacher candidates? This research utilized mixed method interactive program evaluation. An online survey
collected teacher candidates' perceptions of mentoring and support for edTPA. The survey questions
were organized around four constructs: Preparedness, support from instructor, support from university
supervisors, and support from cooperating teacher. With N = 46, a comprehensive data analysis was
conducted that identified areas of strength and need of the program. As a result, the researchers developed
an edTPA mentoring and support program model to be implemented and evaluated in Fall 2014.
Chapter 14
Morphological Ontology Design Engineering: A Methodology to Model Ill-Structured Problems .. 263
Joey Jansen van Vuuren, Council for Scientific and Industrial Research, South Africa
Louise Leenen, Council for Scientific and Industrial Research, South Africa
Marthie M. Grobler, Council for Scientific and Industrial Research, South Africa
Ka Fai Peter Chan, Council for Scientific and Industrial Research, South Africa
Zubeida C. Khan, Council for Scientific and Industrial Research, South Africa
In the Social-technical domain scientists are often confronted with a class of problems that are termed
messy, ill-structured or wicked. These problems address complex issues that not well-defined, contain
unresolvable uncertainties, and are characterized by a lack of common agreement on problem definition. This
chapter proposes a new mixed methods research technique, Morphological Ontology Design Engineering
(MODE), which can be applied to develop models for ill-structured problems. MODE combines three
different research methodologies into a single, methodology. MODE draws from research paradigms
that include exploratory and descriptive research approaches to develop models. General morphological
analysis offers a systematic method to extract meaningful information from domain experts, while ontology
based representation is used to logically represent domain knowledge. The design science methodology
guides the entire process. MODE is applied to a case study where an ontological model is developed to
support the implementation of a South African national cybersecurity policy.
Section 7
Conducting Research from Start to Finish
The numerous steps to be considered when conducting a research study can be daunting for an inexperienced
researcher. Similarly, teaching these skills to students requires considerable thought as to how to best
provide an integrated approach to teaching both strands and then combining this orientation. This
section provides a step by step linear approach to the research process. The mixed methods researcher
needs to manage numerous competencies starting with the philosophical assumptions underlying the
use of mixed methods. The researcher must gather considerable data from each component and make
meaningful inferences. In addition, a mixed methods study needs to integrate, link and connect the two
strands of research in order to provide a comprehensive understanding of the issue under investigation.
Chapter 15
Creating and Implementing a Research Study .................................................................................... 294
Mette L. Baran, Cardinal Stritch University, USA
Janice E. Jones, Cardinal Stritch University, USA
This chapter serves as a guideline for outlining the core characteristics of mixed methods research
(MMR) and the various steps researchers undertake in order to conduct a research study. The purpose
is to create a worksheet assisting the researcher step by step from beginning to end following the seven
steps to conducting research. While the focus is on MMR the steps are similar for any type of research
methodology, it is important to note that MMR is not a limiting form of research. Researchers need a
mixed method research question and a mixed methods purpose statement for the research project. This
chapter will also help explain why mixed method research is one of the best approaches in answering a
research question. Finally the chapter include a suggestion to the importance of adding a visual diagram
of the mixed methods research project into the research project and into the final report.
About the Contributors .................................................................................................................... 304
About the Contributors
Mette L. Baran completed her Ed.D. in Administrative Leadership and Supervision from DePaul
University. She obtained an M.B.A. in International Business and a baccalaureate degree in Marketing
from DePaul University. Mette is a tenured assistant professor in the School of Leadership-Doctoral
Studies Department within the College of Education and Leadership at Cardinal Stritch University and
teaches leadership, learning, higher Education, and research courses. Her background includes being a
faculty member and senior executive at Robert Morris University in Chicago including the positions as
campus director, director of education, and director of development. She is an international consultant
preparing U.S. professional for their overseas assignments. Dr. Baran's research interests and expertise
include looping, student attitudes and achievement, charter schools, middle school education, higher
education administration and access, international family policy, and peace study. She has authored the
book, The Impact of Looping in Middle School. She is a member of the Board of Trustees to Robert
Morris University. In addition, she is a Board member of several not-for-profit organizations.
Janice E. Jones received her Ph.D. in Counseling Psychology from the University of Wisconsin-
Milwaukee, her Master's in Educational Psychology from the University of Wisconsin-Milwaukee and
her bachelor's in Business Administration from Mt. Senario College. Janice is a tenured associate pro-
fessor in the School of Leadership-Doctoral Studies Department within the College of Education and
Leadership at Cardinal Stritch University. Janice teaches in the Doctoral Leadership Studies Department
in both leadership and higher education courses in addition to the Master's In Educational leadership
programs at Cardinal Stritch University. Janice has worked as a consultant for the Department of Public
Instruction for the state of Wisconsin and has provided workshops, trainings and professional staff de-
velopment for a wide variety of K-12 schools throughout the state. Janice has also worked on a number
of grants for both the private and public sector. Janice's areas of expertise and research interests overlap
and include issues around early childcare, students with disabilities, vocational development across the
lifespan and work/family conflict. Janice has presented locally, regionally, nationally and internation-
ally at research conferences. Having a strong commitment to improve the lives of children, Janice is
a member of the Executive Board of Kids Matter, Inc. which is working to improve the lives of foster
children in Milwaukee County.
* * *
Roger J. Baran received his Ph.D. and MBA degrees from the University of Chicago Graduate School
of Business and his BBA (cum laude) from the University of Notre Dame. His dissertation was awarded
first prize by the American Marketing Association in the North America competition. He is a fellow of
304
About the Contributors
the National Opinion Research Center, has served on the U.S. Department of Commerce Census Advisory
Committee of the American Marketing Association, and was Chair of the Bank Marketing Association
National Research and Planning Council. Dr. Baran serves as a consultant in the area of marketing strat-
egy, marketing research and customer relationship management for many well-known companies in the
U.S., Europe, Asia, and Middle-East. He is currently Executive Vice-President of the Asian Forum on
Business Education based in Bangkok, Thailand. Dr. Baran joined DePaul University after serving as
Director of Marketing Research at Continental Bank of Chicago. His teaching and publishing specialties
are marketing research, global marketing management, marketing of services, marketing management
and customer relationship management. He has served as visiting associate professor of marketing at
the University of Chicago Graduate School of Business, Helsinki School of Economics and Business
Administration; University of Hamburg; University of the Thai Chamber of Commerce, Siam University,
and Mahidol University in Bangkok, Thailand; KIMEP University in Kazakhstan and Prague School of
Economics. For seven years he served as DePaul's Director of Asian and Middle East Graduate Programs,
managing its MBA programs in Hong Kong and Bahrain and establishing partner relationships with
schools in Thailand and China Dr. Baran wrote the first textbook on Customer Relationship Manage-
ment with colleagues R. Galka and D. Strunk for Thomson Southwestern which was published in 2008.
Along with R. Galka he published CRM: The Foundation of Contemporary Marketing Strategy in 2013
and they are currently writing a second edition (Routledge: Taylor & Francis Group). Cengage Publish-
ing commissioned his book Principles of Maketing: MBA Primer for their MBA Business Series and
published it in 2011. His book, Practical Bank Marketing Research was the first book published by the
Bank Marketing Association dealing exclusively with the topic. His book International Joint Ventures
in East Asia with Yigang Pan and Erdener Kaynak was published in 1995. His monograph, The CEO's
Guide to Maximizing Managers' Daily Use of MBA Concepts, was awarded 1st prize by the American
Association of Collegiate Schools of Business Mid-Continent East Association. He has published in
numerous marketing, banking and international journals.
Lauren Harkrider Beechly is a Consultant for IBM's Smarter Workforce team where she develops
and validates assessments, helping clients to identify the right candidates for their jobs. Dr. Beechly
leads many projects including test development, selection process consulting, job analysis, competency
modeling, predictive studies, and return on investment studies. Previously, Dr. Beechly worked as an
internal human capital management consultant where she led company-wide job evaluations, developed
career progressions, and analyzed human capital data to recommend strategic HR initiatives and inform
talent decisions. She earned Bachelors' degrees in Human Resource Management and Psychology, a
Master's and Ph.D. in Industrial-Organizational Psychology, and a minor in Quantitative Psychology
from the University of Oklahoma.
Christopher Boachie is a lecturer of Central Business School, Central University College in Accra
Ghana, an academic and a practising Chartered Accountant (ACCA-UK) with specialization in corporate
finance, international economics and trade and financial risk management. He was educated in Kwame
Nkrumah University of Science and Technology in Kumasi Ghana, Technical University of Freiberg
in Germany and the London School of Accountancy (UK). He is currently reading his PhD at Open
University of Malaysia. He has considerable, teaching, consulting and practise experience in the appli-
cation of accountancy and finance theory and financing of international trade and risk management .He
is a Chartered Accountant with the Association of Certified Chartered Accountants of UK. He was an
305
About the Contributors
Investment Manager and in charge of oil and gas unit of International Energy Insurance. He has worked
and consulted for Stephens and Co in London, LD and sons both in Ghana and Italy. He is a founding
Director of Premia consulting firm in Ghana. His professional focus is on the corporate financial analysis,
financial accounting and reporting and has a strong passion for financial risk management.
Clavon Byrd is an Assistant Professor and Department Chair of Teacher Education at Cardinal Stritch
University in Milwaukee, WI. Clavon worked in K – 12 education for 17 years, including 10 years as a
principal. Clavon earned a doctorate in educational leadership. His areas of interest are teacher prepara-
tion, edTPA support, and experiences of minority teachers and students.
Cristina Byrne graduated from the University of Oklahoma with a PhD in Industrial/Organizational
Psychology where she studied leadership, creativity, innovation, communications, and ideological groups.
Ka Fai Peter Chan is a researcher at the Council of Scientific and Industrial Research (CSIR) in the
department of Defence Peace Safety and Security with the focus on cyber defence. His research interest
lies in formal methods, cybersecurity awareness and network security.
Preston Cosgrove is an Assistant Professor at Cardinal Stritch University where he teaches research
in the Doctoral Leadership program. He also serves as Chair of the First Year Experience and Chair of
the Integrated Leadership Program.
Jennifer A. Griffith, PhD, is an Assistant Professor of Management at Alfred University. Her research
interests include leadership, emotions, gender, and communication, specifically computer-mediated
communication.
Marthie Grobler has been working as a Cyber Security Researcher at the Council for Scientific
and Industrial Research (CSIR) since January 2008. She has a PhD Computer Science (Live Digital
Forensics), and a MSc Computer Science (Information Security Governance), both from the University
of Johannesburg. Her research focus is on cyber security awareness, strategic data management and
incident management and response. She is co-editor of the now published ISO/IEC 27037, Guidelines
for identification, collection and acquisition and preservation of digital evidence; and ISO/IEC 27035,
Incident management. Marthie is an ISACA Certified Information Security Manager and is appointed
as a visiting Professor at the University of Johannesburg, Academy for Computer Science and Software
Engineering. She is currently supervising a number of post graduate students, and Managing Editor of
the Journal of Contemporary Management.
Michael Hughes is an I/O psychologist whose primary areas of expertise include test development
and validation, and analyses of high-stakes testing data. His research interests also include complex skill
acquisition, training, and ideological groups.
Peter M. Jonas is a tenured Professor in the Doctoral Leadership Department at Cardinal Stritch
University in Milwaukee WI. Dr. Jonas has a doctorate in History from Marquette University but has been
working in administration and as a faculty member in higher education for more than 35 years. He has
been at Cardinal Stritch University for 30 years teaching, researching, and serving in various leadership
306
About the Contributors
capacities (e.g., Director of Institutional Research, Dean in the College of Business, Director of Strategic
Planning and Assessment). For the past 18 years Dr. Jonas has been teaching research and statistics in
the doctoral program at Stritch and has served as the department chairperson for 16 years. Over the years
Dr. Jonas has written three books in support of my research Outcomes Assessment in Higher Education
Linked with Strategic Planning and Budgeting [2nd ed.] (2013), Laughing and Learning: An Alterna-
tive to Shut-up and Listen (2009), Secrets of Connecting Leadership and Learning to Humor (2004).
He has made more than 100 presentations across the country talking about humor, research, leadership,
and assessment. In addition, he has authored more than 40 books, manuals, and articles in professional
periodicals, in addition to serving as a consultant (typically in the area of professional development and
program evaluation) for more than 25 different organizations and projects.
Sema Kalaian is a Professor of Statistics and Research Methods in the College of Technology at
Eastern Michigan University. Professor Kalaian was a recipient of the (1) "Best Paper" award from the
American Educational Research Association (AERA), and (2) "Distinguished Paper Award" from the
Society for the Advancement of Information Systems (SAIS). Over the years, Dr. Kalaian taught introduc-
tory and advanced statistical courses such as Statistical and Research Methods, Multivariate Statistics,
Survey Research, Multilevel Modeling, Structural Equation Modeling, Meta-Analysis, and Program
Evaluation. Professor Kalaian's research interests focus on the development of new statistical methods
and its applications. Much of her methodological developments and applications have focused on the (a)
development of the multivariate meta-analytic techniques for combining evidence from multiple primary
studies; (b) applications of the meta-analysis methods to multi-site studies; (c) developments of statisti-
cal methods for analyzing Delphi survey data; and (d) applications of multilevel modeling methods for
meta-analysis to Science, Technology, Engineering, and Mathematics (STEM) teaching and learning
research. Recently, Professor Kalaian completed a major grant project funded by the National Science
Foundation (NSF) to investigate the effectiveness of various forms of active small-group learning methods
in STEM disciplines. For more information about the project visit https://arc.uchicago.edu/reese/users/
skalaian and http://people.emich.edu/skalaian/stem/index.htm.
Nitty Hirawaty Kamarulzaman is a senior lecturer in the Department of Agribusiness and Information
Systems, Universiti Putra Malaysia. Her research interest include supply chain management, sustainable
logistics, reverse logistics, agribusiness marketing, and consumer purchasing behavior.
Rafa Kasim is professor of statistics and research methods at Indiana Tech University. Prior to that,
Dr. Kasim served as a professor of statistics and research design in the College of Education at Kent State
University. He was also a senior statistician at the Evaluation, Management & Training Associates Inc.
(EMT). His research focused on the application of multilevel analysis to study the effects of educational
and social contexts on educational outcomes and human development in large-scale longitudinal data
sets. Some of Dr. Kasim work has also addressed the issues of selection and attrition bias in multi-site
large studies. He has collaborated on numerous studies in fields such as small groups versus lecture-based
traditional learning in STEM, adult literacy, education, and substance abuse treatments. Some of his
work appears in book chapters in Application of Multilevel Models, Multilevel Meta-analysis: Effective-
ness of Small-group Learning Methods Compared to Lecture-based Instruction in Science, Technol-
307
About the Contributors
ogy, Engineering, and Mathematics College classrooms, Small-group versus Competitive Learning in
Computer Science Classrooms: A Meta-Analytic Review, Predictive Analytics, Journal of Educational
and Behavioral Statistics, Harvard Educational Review and Advances in Health Sciences Education.
Zubeida C. Khan is a PhD student and researcher.
Louise Leenen is a Principal Scientist in the Cyber Defence Research Group at the CSIR in South
Africa. She holds a PhD Computer Science from the University of Wollongong in Australia. Her research
focus is on AI applications in cyber defence.
Dale MacKrell has a long track record of professional contributions as a researcher and educator in
the Information Systems discipline. Her scholarly interests include business intelligence and gender rela-
tions, and a cooperative learning project for a not-for-profit organisation operating in the homelessness
sector. Dale has published in Information Systems Journal, Decision Support Systems journal and the
Australasian Journal of Information Systems as well as numerous Australian and international confer-
ences. Dale is an Assistant Professor in Information Systems at the University of Canberra in Australia.
Amir Manzoor holds a bachelor's degree in engineering from NED University, Karachi, an MBA
from Lahore University of Management Sciences (LUMS), and an MBA from Bangor University,
United Kingdom. He has many years of diverse professional and teaching experience working at many
renowned national and internal organizations and higher education institutions. His research interests
include electronic commerce and technology applications in business. He is a member of Chartered
Banker Institute of UK and Project Management Institute, USA.
A. J. Metz is an Assistant Professor in the Department of Educational Psychology at the University
of Utah. She earned a M.Ed. in Vocational Rehabilitation Counseling and a Ph.D. in Urban Education
(specialization in Counseling Psychology) from the University of Wisconsin-Milwaukee. Her research
examining factors related to academic and career success in underrepresented and underserved student
populations has led to numerous journal articles, book chapters, conference presentations, workshops,
and most recently a student success textbook. Dr. Metz has extensive teaching, counseling, and career
advising experience in high schools, community colleges, and four-year public and private institutions
of higher education. She is passionate about mentoring students and received an Early Career Teaching
Award in 2015. She has served on the board of directors of the Utah Psychological Association for six
years most notably as president.
Never Mujere is a lecturer and Doctor of Philosophy (DPhil) candidate specializing in Water Re-
sources in the Department of Geography and Environmental Science at the University of Zimbabwe
(UZ). Since joining the University in 2006 as a staff member, he has been involved the teaching and
research. He teaches undergraduate and post-graduate courses in water resources, waste management,
research methods, disaster management and climate change and environmental issues. Never Mujere
has published 10 journals articles, 3 book chapters, 3 books and presented more than 10 papers at lo-
cal and international conferences. Never is a member of the International Association of Hydrological
Sciences (IAHS). He is an early career scientist and researcher who is a team player, highly versatile,
creative and results-oriented.
308
About the Contributors
Aroop Mukherjee is a PhD Scholar in the Department of Agribusiness and Information Systems,
Universiti Putra Malaysia. His research interest include agility, agribusiness, supply chain management,
supply chain strategies, sustainability, innovation, knowledge management, and agri-informatics.
Carolyn Stevenson is a veteran educator currently working as a faculty member for OC@KU (Open
College at Kaplan University). Carolyn has over 17 years teaching and administrative experience in higher
education. She holds a Master of Arts degree in Communication, Master of Business Administration,
and Doctor of Education with an emphasis in Higher Education. Prior to pursuing a career in higher
education, she worked in the publishing field and served as a technical writing consultant. She currently
serves as Associate Editor for the International Journal of Technologies and Educational Marketing
(IJTEM), published by IGI-Global; Editorial Board Member and Reviewer for the Journal of Education
and Learning published by the Canadian Center of Science and Education; and Membership Committee
Member for the Qualitative Research Special Interest Group (AERA). Recent publications include a
chapter entitled: "Leading across Generations: Issues for Higher Education Administrators" published in
the Handbook of Research on Transnational Higher Education Management, by IGI Global; Technical
Writing: A Comprehensive Resource for Technical Writers at all Levels, (Martinez, Hannigan, Wells,
Peterson and Stevenson) Revised and Updated Edition, Kaplan Publishing; and Building Online Commu-
nities in Higher Education Institutions: Creating Collaborative Experience (with co-editor Joanna Bauer).
Randa Suleiman currently works as an assistant professor at Cardinal Stritch University Teacher
Preparation Program. She worked in K-12 education for fifteen years in private, public school districts in
USA and Internationally. Graduated from Cardinal Stritch University in May 2010 with a PhD degree in
Leadership for the Advancement of Learning and Service in Higher Education. Randa earned National
Board certification in early adolescent science in 2008. Currently working on developing a teacher as-
sessment mentoring and support program. Areas of interest are teacher preparation, assessment, educator
effectiveness, edTPA, and science education.
Ben Tran received his Doctor of Psychology (Psy.D) in Organizational Consulting/Organizational
Psychology from California School of Professional Psychology at Alliant International University in
San Francisco, California, United States of America. Dr. Tran's research interests include domestic and
expatriate recruitment, selection, retention, evaluation, & training, CSR, business and organizational
ethics, organizational/international organizational behavior, knowledge management, and minorities in
multinational corporations. Dr. Tran has presented articles on topics of business and management ethics,
expatriate, and gender and minorities in multinational corporations at the Academy of Management,
Society for the Advancement of Management, and International Standing Conference on Organizational
Symbolism. Dr. Tran has also published articles and book chapters with the Social Responsibility Jour-
nal, Journal of International Trade Law and Policy, Journal of Economics, Finance and Administrative
Science, Financial Management Institute of Canada, and IGI Global.
Joey Jansen van Vuuren is the Research Group Leader for Cyber Defence for Scientific Research
at the CSIR South Africa. She gives the strategic research direction for the research group that is mainly
involved in research on network forensics, social media, and national security for the SANDF and Gov-
309
About the Contributors
ernment sectors on Cyber Defence. As Cyber threats became extremely important for South Africa with
the recent broadband changes, she focused her research around cyber security and government policies
required to ensure national security. In particular her group is involved in contract research for all the
main players responsible for the implementation of the National Cybersecurity Policy Framework for
South Africa. She already presented this research on several national and international conferences and
published journal articles on cyber security in South Africa. She was also a keynote speaker on inter-
national conferences and was invited for several radio interviews on this topic and published an article
scientific magazine on the implementation of Cyber policies in South Africa.
Thomas A. Zeni is an Assistant Professor of Management, and the Chickasaw Nation Professor
of Business Administration at East Central University in Ada, OK. He holds a Ph.D. in Industrial &
Organizational Psychology, as well as an MS and MBA degree from the University of Oklahoma. His
undergraduate degree in Psychology is from Mercy College in Dobbs Ferry, NY. In addition, he main-
tains a Senior Professional in Human Resources (SPHR) certification and is a SHRM Senior Certified
Professional. Dr. Zeni teaches undergraduate and graduate coursework in Business Communication, Hu-
man Resource Management, Personnel Selection & Assessment, Compensation & Benefits, Training &
Development, Principles of Management, Strategic Management, Organizational Behavior, Leadership,
and Applied Statistics. His research interests include leadership, business ethics, emotions in organiza-
tions, and quantitative design and methodology.
310
108
Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 6
DOI: 10.4018/978-1-5225-0007-0.ch006
ABSTRACT
Research is aimed at the discovery and interpretation of facts, revision of accepted theories or laws in
the light of new facts or practical application of such new or revised theories or laws. A sample provides
needed information about the population quickly. However, there is no guarantee that any sample will
be precisely representative of the population from which it comes. It is cheaper to observe a part rather
than the whole. This chapter is a discussion on sampling in research and it is mainly designed to equip
researchers with knowledge on the general issues on sampling. The purpose of sampling in research,
dangers of sampling and how to minimize them, types of sampling and guides for deciding the sample
size. For a clear flow of ideas, a few definitions of the terms used are given. It highlights the types and
methods of sampling, sampling errors and discusses techniques of sample size determination. Different
types of sampling technique, how to carry them out, and their advantages and disadvantages are also
introduced.
INTRODUCTION
Scientific research is important to search or investigate exhaustively (Berinstein, 2003). However, popu-
lations about which inferences must be made are maybe quite large, costly or inaccessible to reach. This
makes it physically impossible to conduct a census. In such cases, selecting a representative sample may
be the only way to get the information required about the population. Researchers may choose from
a variety of sampling methods (Salant & Dillman, (1994). Choice of the sampling method to be used
depends on research goals and whether or not the researcher wants to generalize the findings from the
sample. It is vital to be aware of possible errors due to the sample method chosen so that the study is
regarded as valid. This is a literature review of what sampling is, how to create a sample, and highlights
the advantages and disadvantages or limitations of the sampling techniques.
Sampling in Research
Never Mujere
University of Zimbabwe, Zimbabwe
109
Sampling in Research
BACKGROUND
To understand population characteristics, it is important to select a sample. Sampling is an important
component of any piece of research because of the significant impact that it can have on the quality of
research findings. The main reasons for sampling to obtain a sample rather than a complete enumeration
(a census) of the population are; economy, timeliness, the large size of many populations, inaccessibility
of some of the population and destructiveness of the observation accuracy. To draw conclusions about
populations from samples, we use inferential statistics which enables us to determine a population`s
characteristics by directly observing only a portion or sample of the population. Taking a sample requires
fewer resources than a census.
When the researcher is interested in the entire population (i.e signifies the units that we are interested
in studying), it is vital to take a census. It is often impractical and sometimes undesirable to try and study
the entire population due to resources constraints such as time (Salant & Dillman, 1994; Frey et al.,
2000). Therefore, we choose to study just a sample of the population. A sample consists of only those
units (e.g., students) from the population of interest.
SAMPLING
A sample is group of people, objects or items that are taken from a large population for a measurement.
The sample should be representative of the population to ensure that we can generalize the findings from
the research sample to the population as a whole. (Jopnes, 1955; Salant & Dillman, 1994). Sampling is
the act, process, or technique of selecting a suitable sample, or a representative part of a population for
the purpose of determining parameters or characteristics of the whole population. In sampling, popula-
tion units such as people (e.g. students enrolled at a university or studying a particular course), cases
(i.e., recruitment agencies, organizations, institutions, countries, etc.) and pieces of data (e.g. customer
transactions at a particular supermarket, university applications in a country) are selected. To draw con-
clusions about populations from samples, one must use inferential statistics, to enable us to determine a
population's characteristics by directly observing only a portion (or sample) of the population.
Purpose of Sampling
A sample is needed as it may not be practical and almost never economical to conduct a census of whole
population because (Leyman, 1983; Lohr, 1999):
1. The large size of many populations.
2. The time factor- a sample may provide needed information quickly.
3. Inaccessibility (associated with cost or time or just access) of some of the population e.g. prisoners,
people with severe mental illness, disaster survivors etc.
4. Destructiveness of the observation e.g. to determine the quality of a fuse and whether it is defec-
tive, it must be destroyed. Therefore if you tested all the fuses, all would be destroyed.
5. Accuracy and sampling i.e. a sample may be more accurate than a sloppily conducted census.
110
Sampling in Research
SAMPLING ERROR
A sample is expected to mirror the population from which it comes. However, there is no guarantee that
any sample will be precisely representative of the population from which it comes. What makes a sample
unrepresentative of its population is the sampling error). Sampling error is the degree to which a sample
might differ from the population. Sampling error comprises the differences between the sample and the
population that are due solely to the particular participants that have been selected. When inferring to
the population, results are reported plus or minus the sampling error. There are two basic causes for
sampling error (Berinstein, 2003; Salant & Dillman, 1994).
1. Chance: The error that occurs just because of bad luck, resulting in untypical choices and unusual
units in a population. Chance may dictate that a disproportionate number of untypical observa-
tions will be made. Unusual units in a population do exist and there is always a possibility that an
abnormally large number of them will be chosen. The main protection against this kind of error is
to use a large enough sample.
2. Sampling Bias: A tendency to favor the selection of participants that have particular character-
istics. Sampling bias occurs when the units that are selected from the population for inclusion in
the sample do not reflect the population. Sampling bias is usually the result of a poor sampling
plan. The most notable is the bias of non-response when for some reason some participants have
no chance of appearing in the sample e.g. no internet access for completion of an online question-
naire. There can be two causes of this type of bias (i) selecting a wrong study population and (ii)
poor research design e.g. only one group within the study population agreed to participate in the
study. This can lead to the sample being unrepresentative of the population of interest.
Sampling or margin of error, e is estimated as (Smith, 2013):
e z n = √
αδ
//
2 (1)
where: n is the desired sample size, z is the z-score, δ is the standard deviation and e is the margin of error.
SAMPLE SIZE
Sample size depends on the nature of the analysis to be performed, the desired precision of the estimates
one wishes to achieve, the kind and number of comparisons that will be made, the number of variables
that have to be examined simultaneously and how heterogenous the sampled population is. These fac-
tors also influence the sample design and data collection procedures. For example, if the key analysis
of a randomized experiment consists of computing averages for experiments and controls in a project
and comparing differences, then a sample under 100 might be adequate, assuming that other statistical
assumptions hold (Salant & Dillman (1994).
There are several approaches to determining sample size and the most popular of these is the one
that studies the power of a test of hypothesis. This will ensure that sample size is large enough to ensure
that results are statistically significant but not so big that one could have achieved the same results with
111
Sampling in Research
a much smaller size (Lohr, 1999). For a pure random sample drawn from an infinite population, the fol-
lowing formula can be taken as the basis for computing the sample size, n (Smith, 2013):
n Z = −
( ) /
αδ δ
2
2 2
1e (2)
where: n is the desired sample size, z is the z-score, δ is the standard deviation and e is the margin of error.
Suppose one wishes to determine the number of respondents in a survey from a 95% confidence
level, 0.5 standard deviation, and a margin of error (confidence interval) of +/- 5%. The sample size n
is estimated as:
n= ((1.96)2 * 0.5(0.5)) / (0.05)2= (3.8416 * 0.25) / 0.0025=.9604 / 0.0025=384.16
Therefore 385 respondents are needed
Sampling Frame
The sampling frame is very similar to the population being studied, and may be exactly the same. When
selecting units from the population to be included in your sample, it is sometimes desirable to get hold
of a list of the population from which you select units. This is the case when using certain types of
sampling technique (i.e., probability sampling techniques). This list can be referred to as the sampling
frame (Babbie, 1990).
SAMPLING METHODS
When one is interested in a population, it is typically vital to study a sample of that population rather
than attempt to study the whole population. The purpose of sampling techniques is to help researchers
select units to be included in the sample. They differ in the manner in which the elementary units are
chosen. Broadly speaking, there are two groups of sampling technique: probability sampling techniques
and non-probability sampling techniques. Sampling methods are classified as either probability or non-
probability (Salant & Dillman, 1994). In probability samples, each member of the population has a
known non-zero probability of being selected.
Probability Sampling Techniques
Probability sampling techniques use random selection (i.e., probabilistic methods) of units from your
sampling frame (i.e., similar or exactly that same as your population) to be included in your sample.
These procedures (i.e., probabilistic methods) are very clearly defined, making it easy to follow them.
Since the characteristics of the sample researchers are interested in vary, different types of probability
sampling technique exist to help you select the appropriate units to be included in your sample. The
advantage of probability sampling is that sampling error can be calculated. Probability sampling tech-
112
Sampling in Research
niques include simple random, systematic random, stratified random, cluster, multistage and multiphase
sampling (Cochran, 1953).
Simple Random Sampling
With the simple random sample, there is an equal chance (probability) of selecting each unit from the
population being studied when creating the sample. The aim of the simple random sample is to reduce
the potential for human bias in the selection of cases to be included in the sample. As a result, the simple
random sample provides us with a sample that is highly representative of the population being studied,
assuming that there is limited missing data. A simple random sample can only be carried out if the list
of the population is available and complete (Cochran, 1953). To create a simple random sample, there
are six steps: (a) defining the population; (b) choosing sample size; (c) listing the population; (d) assign-
ing numbers to the units; (e) finding random numbers; and (f) selecting sample from random number
table. Cite?
Advantages of Simple Random Sampling
1. Allows researchers to make generalizations (i.e., statistical inferences) from the sample to the
population
2. Generalizations are more likely to have external validity.
Disadvantages of Simple Random Sampling
1. It may be challenging to gain access to that list because the list may be protected by privacy poli-
cies or require a lengthy process to attain permissions.
2. There may be no single list detailing the population one is interested in hence, it may be difficult
and time consuming to bring together numerous sub-lists to create a final list from which you want
to select your sample.
3. Many lists will not be in the public domain and their purchase may be expensive.
4. Some of these populations will be expensive and time consuming to contact, even where a list is
available (e.g., postal, telephone, email).
Systematic Random Sampling
In systematic random sampling there is an equal chance ( probability) of selecting each unit from within
the population when creating the sample. The aim of the systemic random sample is to reduce the po-
tential for human bias in the selection of cases to be included in the sample. As a result, the systemic
random sample provides us with a sample that is highly representative of the population being studied,
assuming that there is limited missing data (Fink, 1995). To create a systemic random sample, there are
seven steps: (a) defining the population; (b) choosing your sample size; (c) listing the population; (d)
assigning numbers to cases; (e) calculating the sampling fraction by dividing sample size (s) with total
population size (N); (f) selecting the first unit from the random number table; and (g) selecting sample
113
Sampling in Research
Advantages of Systematic Random Sampling
1. Sample easy to select.
2. Suitable sampling frame can be identified easily.
3. Sample is evenly spread over entire reference population.
4. Allows us to make statistical conclusions from the data collected that will be considered to be valid.
Relative to simple random sample, selection of units using a systematic procedure is superior because
it improves the potential for the units to be more evenly spread over the population.
Disadvantages of Systematic Random Sampling
A systematic random sample can only be carried out if a complete list of the population is available.
Attaining a complete list of the population can be difficult for a number of reasons:
1. Even if a list is readily available, it may be challenging to gain access to that list. The list may be
protected by privacy policies or require a length process to attain permissions.
2. It may be difficult, expensive and time consuming to bring together numerous sub-lists to create a
final list from which you want to select your sample.
3. Many lists will not be in the public domain and their purchase may be expensive.
4. Some populations may be expensive and time consuming to contact by postal, telephone or email.
Stratified Random Sampling
Stratified random sampling is a type of probability sampling technique when one is interested in par-
ticular strata (meaning groups) within the population (e.g., males vs. females; houses vs. apartments.).
Stratified random sample involves dividing the population into two or more strata (groups). There would
an equal chance (probability) that each stratum to be selected from the sample. The stratified random
sample also improves the representation of particular strata (groups) within the population, as well as
ensuring that these strata are not over-represented. Together, this helps the researcher to compare strata,
as well as make more valid inferences from the sample to the population.
To create a stratified random sample: (a) define the population, (b) choose the relevant stratification,
(c) list the population, (d) list the population according to the chosen stratification, (e) calculate a propor-
tionate stratification and (f) use a simple random or systematic sample to select sample (Smith, 2013).
Advantages of Stratified Random Sampling
1. Reduce the potential for human bias in the selection of cases to be included in the sample.
2. Provides with a sample that is highly representative of the population being studied, assuming that
there is limited missing data.
3. Allows researchers to make statistical conclusions from the data collected that will be considered
to be valid.
4. The selection of units using a stratified procedure can be viewed as superior because it improves
the potential for the units to be more evenly spread over the population.
114
Sampling in Research
5. Where the samples are the same size, a stratified random sample can provide greater precision than
a simple random sample.
Because of the greater precision of a stratified random sample compared with a simple random sample,
it may be possible to use a smaller sample, which saves time and money.
Disadvantages of Stratified Random Sampling
1. Sampling frame of entire population has to be prepared separately for each stratum.
2. Many lists will not be in the public domain and their purchase may be expensive.
3. It may be challenging to gain access to the data list due to protection by privacy policies or require
a length process to attain permissions.
4. There may be no single list detailing the population of interest.
5. It may be difficult and time consuming to bring together numerous sub-lists to create a final list
from which to select the sample.
6. When examining multiple criteria, stratifying variables may be related to some, but not to others,
further complicating the design, and potentially reducing the utility of the strata.
7. In some cases (such as designs with a large number of strata, or those with a specified minimum
sample size per group), stratified sampling can potentially require a larger sample than would other
methods.
8. In terms of human populations, some of these populations may be expensive and time consuming
to contact by postal, telephone or email even where a list is available.
Cluster Sampling
Cluster sampling, on the surface, is very similar to stratified sampling in that survey population mem-
bers are divided into unique, non-overlapping groups prior to sampling. These groups are referred to as
clusters instead of strata because they are naturally occurring groupings such as schools, households,
or geographic units Whereas a stratified sample involves selecting a few members from each group or
stratum, cluster sampling involves the selection of a few groups and data are collected from all group
members. This sampling method is used when no master list of the population exists but cluster lists are
obtainable (Babbie, 1990; Berinstein, 2003). To create a cluster sample, the following steps are taken:
(a) sample of areas are chosen, (b) sample of respondents within those areas is selected. (c) population
divided into clusters of homogeneous units, usually based on geographical contiguity, (d) Sampling
units are groups rather than individuals, (e) sample of clusters is then selected and (f) all units from the
selected clusters are studied.
Advantages
1. Cuts down on the cost of preparing a sampling frame.
2. This can reduce travel and other administrative costs.
Disadvantages
115
Sampling in Research
1. . Sampling error is higher for a simple random sample of same size.
Non-Probability Sampling Techniques
A core characteristic of non-probability sampling techniques is that samples are selected based on non-
random manner or the subjective judgment of the researcher. Non-probability sampling represents a group
of sampling techniques that help researchers to select units from a population that they are interested in
studying. Hence, the degree to which the sample differs from the population remains unknown. Whilst
some researchers may view non-probability sampling techniques as inferior to probability sampling
techniques, there are strong theoretical and practical reasons for their use. Theoretically, for researchers
following a quantitative research design, non-probability sampling techniques can often be viewed as an
inferior alternative to probability sampling techniques. In applied social science research it is permis-
sible to consider varieties of non-probability sampling alternatives where it is not practical or theoreti-
cally sensible to do random sampling (Trochim, 2006). Practically, non-probability sampling is often
used because the procedures used to select units for inclusion in a sample are much easier, quicker and
cheaper when compared with probability sampling. Non-probability sampling techniques include: quota
sampling, judgment sampling, convenience sampling, purposive sampling, self-selection sampling and
snowball sampling. Convenient sampling includes samples selected by a researcher on the basis of ease
of accessibility of the sample objects to the researcher.
Convenience Sampling
A convenience sample is one where the units that are selected for inclusion in the sample are the easiest
to access. For example, a researcher may simply stand at one of the main campus entrances and invite
the first ten students that pass by to take part in the interview. This type of sampling is useful in getting
general ideas about the phenomenon of interest. It saves time, money and effort. It is the poorest way of
getting samples, has the lowest credibility and yields information-poor cases (Kish, 1965).
Advantages of Convenience Sampling
1. It is easy to carry out with few rules governing how the sample should be collected.
2. The relative cost and time required to carry out a convenience sample are small in comparison to
probability sampling techniques.
3. Enables researcher to achieve the sample size in a relatively fast and inexpensive way.
4. Help in gathering useful data and information that would not have been possible using probability
sampling techniques, which require more formal access to lists of populations.
Disadvantages of Convenience Sampling
1. Often suffers from biases because a convenience sample can lead to the under-representation or
over-representation of particular groups within the sample.
116
Sampling in Research
2. Since the sampling frame is not known, the sample is unlikely to be representative of the popula-
tion being studied thus, undermining the ability to make generalizations from the sample to the
population studied.
Quota Sampling
Quota sampling is a type of non-probability sampling technique. With proportional quota sampling, the
aim is to end up with a sample where the strata (groups) being studied (e.g., males vs. females students)
are proportional to the population being studied. To create a quota sample, there are three steps: (a)
choosing the relevant stratification and dividing the population accordingly; (b) calculating a quota for
each stratum; and (c) continuing to invite cases until the quota for each stratum is met.
Advantages of Quota Sampling
1. Quota sampling is particularly useful when one is unable to obtain a probability sample, but still
trying to create a sample that is as representative as possible of the population being studied. In
this respect, it is the non-probability based equivalent of the stratified random sample.
2. Unlike probability sampling techniques, quota sampling is much quicker and easier to carry out
because it does not require a sampling frame and use of random sampling techniques. This makes
it popular in undergraduate and master's level dissertations where there is a need to divide the
population being studied into strata (groups).
3. The quota sample improves the representation of particular strata (groups) within the population,
as well as ensuring that these strata are not over-represented.
4. The use of a quota sample, which leads to the stratification of a sample (e.g., male and female
students), allows us to more easily compare these groups (strata).
Disadvantages of Quota Sampling
1. Make it impossible to determine the possible sampling error.
2. Ease of access and cost considerations, resulting in sampling bias.
3. It is not possible to make statistical inferences from the sample to the population. This can lead to
problems of generalization.
4. Can increase costs and time to carry out the research.
Self-Selection Sampling
Self-selection or volunteer sampling is appropriate when one wants to allow units or cases, whether
individuals or organizations, to choose to take part in research on their own accord. The key component
is that research subjects volunteer to take part in the research. This allows units to choose to take part
in research on their own accord. (http://dissertation.laerd.com/purposive-sampling.php). As a sampling
strategy, self-selection sampling can be used with a wide range of research designs and research methods.
For example, survey researchers may put a questionnaire online and subsequently invite anyone within
a particular organization to take part. It is an effective sampling strategy in experimental research set-
117
Sampling in Research
tings. Scientists that conduct experiments using human subjects may advertise the need for volunteers
to take part in drug trials or research on physical activity (Kish, 1965). The key component is that re-
search subjects (or organizations) volunteer to take part in the research on their own accord. There may
be a wide range of reasons why people (and organizations) volunteer for such studies, including having
particularly strong feelings or opinions about the research, a specific interest in the study or its find-
ings, or simply wanting to help out a researcher (Henry, 1990).Creating a self-selection sample involves
two simple steps: (a) publicizing your need for units; and (b) checking the relevance of units and either
inviting or rejecting them.
Advantages of Self-Selection Sampling
1. This can reduce the amount of time necessary to search for appropriate units; that is, those indi-
viduals or organizations that meet the selection criteria needed for your sample.
2. The potential units or cases (individuals or organizations) are likely to be committed to take part
in the study, which can help in improving attendance (where necessary), and greater willingness to
provide more insight into the phenomenon being studied (e.g., a respondent many be more willing
to spend the time filling in qualitative, open-ended questions in an online survey, where others may
leave them blank).
Disadvantages of Self-Selection Sampling
1. There is likely to be a degree of self-selection bias. For example, the decision to participate in
the study may reflect some inherent bias in the characteristics/traits of the participants (e.g., an
employee with a 'chip of his shoulder' wanting to give an opinion).
2. This can either lead to the sample not being representative of the population being studied, or
exaggerating some particular finding from the study.
Snowball Sampling
Some populations can be hard-to-reach and/or hidden because they exhibit some kind of social stigma,
illicit or illegal behaviors, or other trait that makes them atypical and/or socially marginalized. Such
populations include; drug addicts, homeless people, suffers of AIDS/HIV and prostitutes. Individuals
that are drug users or prostitutes, for example, are likely to be less willing to identify themselves and
take part in a piece of research than many other social groups. Snowball sampling can be used to gain
access to such populations (Biernacki & Waldorf, 1981). To create a snowball sample, there are two
steps: (a) trying to identify one or more units in the desired population; and (b) using these units to find
further units and so on until the sample size is met.
Advantages of Snowball Sampling
1. The unknown and/or secretive nature of some social groups may also make it difficult to identify
strata that warrant investigation.
118
Sampling in Research
2. In the case of drug users, it may be obvious to identify strata such as gender (i.e. male or female),
types of drug user (e.g. causal, addict), and so forth, but others may be unknown to the researcher.
3. May also be helpful in exploring potentially unknown characteristics that are of interest before
settling on your sampling criteria.
4. There may be no other way of accessing the sample, making snowball sampling the only viable
choice of sampling strategy (Biernacki & Waldorf 1981; Henry, 1990).
Disadvantages of Snowball Sampling
1. It is impossible to determine the possible sampling error and make statistical inferences from the
sample to the population.
2. Not representative of the population being studied.
3. It can be difficult to identify units to include in the sample because there is no obvious list of the
population interested in e.g. drug users or prostitutes (Biernacki & Waldorf 1981).
Purposive Sampling
Purposive sampling is also known as judgmental, selective or subjective sampling. It reflects a group
of sampling techniques that rely on the judgement of the researcher when it comes to selecting the units
(e.g., people, cases/organizations, events, pieces of data) that are to be studied (Kish, 1965; Fowler, 1993).
The main goal of purposive sampling is to focus on particular characteristics of a population that are of
interest, which will best answer the research questions. The sample being studied is not representative
of the population, but for researchers pursuing qualitative or mixed methods research designs, this is not
considered to be a weakness. Rather, it is a choice, the purpose of which varies depending on the type of
purposing sampling technique that is used. For example, in homogeneous sampling, units are selected
based on their having similar characteristics because such characteristics are of particular interested to
the researcher. By contrast, critical case sampling is frequently used in exploratory, qualitative research
in order to assess whether the phenomenon of interest even exists (amongst other reasons).
There are a number of different types of purposive sampling techniques which include maximum
variation sampling, homogeneous sampling, typical case sampling, extreme (or deviant) case sampling,
total population sampling and expert sampling (Kish, (1965). Patton, 1990). Each of these purposive
sampling techniques has a specific goal, focusing on certain types of units, all for different reasons. The
different purposive sampling techniques can either be used on their own or in combination with other
purposive sampling techniques. During the course of a qualitative or mixed methods research design,
more than one type of purposive sampling technique may be used.
Maximum Variation Sampling
Maximum variation sampling, also known as heterogeneous sampling, is a purposive sampling technique
used to capture a wide range of perspectives relating to the thing that you are interested in studying; that
is, maximum variation sampling is a search for variation in perspectives, ranging from those conditions
that are view to be typical through to those that are more extreme in nature. By conditions, we mean
the units (i.e., people, cases/organizations, events, pieces of data) that are of interest to the researcher.
These units may exhibit a wide range of attributes, behaviors, experiences, incidents, qualities, situations,
119
Sampling in Research
and so forth. The basic principle behind maximum variation sampling is to gain greater insights into
a phenomenon by looking at it from all angles. This can often help the researcher to identify common
themes that are evident across the sample.
Homogeneous Sampling
Homogeneous sampling aims to achieve a homogeneous sample; that is, a sample whose units (e.g.,
people, cases) share the same characteristics or traits (e.g., a group of people that are similar in terms of
age, gender, background, occupation). In this respect, homogeneous sampling is the opposite of maxi-
mum variation sampling. A homogeneous sample is often chosen when the research question that is
being address is specific to the characteristics of the particular group of interest, which is subsequently
examined in detail.
Typical Case Sampling
Typical case sampling is a purposive sampling technique used when one is interested in the normality/
typicality of the units (e.g., people, cases, events, settings/contexts, places/sites) you are interested, be-
cause they are normal/typical. The word typical does not mean that the sample is representative in the
sense of probability sampling (i.e., that the sample shares the same/similar characteristics of the popu-
lation being studied). Rather, the word typical means that the researcher has the ability to compare the
findings from a study using typical case sampling with other similar samples (i.e., comparing samples,
not generalizing a sample to a population). Therefore, with typical case sampling, you cannot use the
sample to make generalizations to a population, but the sample could be illustrative of other similar
samples. Whilst typical case sampling can be used exclusively, it may also follow another type of purpo-
sive sampling technique, such as maximum variation sampling, which can help to act as an exploratory
sampling strategy to identify the typical cases that are subsequently selected.
Extreme (or Deviant) Case Sampling
Extreme (or deviant) case sampling is a type of purposive sampling that is used to focus on cases that
are special or unusual, typically in the sense that the cases highlight notable outcomes, failures or suc-
cesses. These extreme (or deviant) cases are useful because they often provide significant insight into
a particular phenomenon, which can act as lessons (or cases of best practice) that guide future research
and practice. In some cases, extreme (or deviant) case sampling is thought to reflect the purest form of
insight into the phenomenon being studied.
Critical Case Sampling
This is a type of purposive sampling technique that is particularly useful in exploratory qualitative
research, research with limited resources, as well as research where a single case or small number of
cases can be decisive in explaining the phenomenon of interest. Critical case sampling may be used to
investigate whether a phenomenon is worth investigating further, before adopting a maximum variation
sampling technique is used to develop a wider picture of the phenomenon. It is this decisive aspect of
critical case sampling that is arguably the most important (Patton, 1990).
120
Sampling in Research
Total Population Sampling
This is a type of purposive sampling technique where one chooses to examine the entire population that
have a particular set of characteristics (e.g., specific experience, knowledge, skills or exposure to an
event). In such cases, the entire population is often chosen because the size of the population that has
the particular set of characteristics of interest is very small.
Expert Sampling
Expert sampling is a cornerstone of a research design known as expert elicitation. This is used when
research needs to glean knowledge from individuals that have particular expertise. This expertise may
be required during the exploratory phase of qualitative research, highlighting potential new areas of
interest or opening doors to other participants. Alternately, the particular expertise that is being inves-
tigated may form the basis of research, requiring a focus only on individuals with specific expertise.
Expert sampling is particularly useful where there is a lack of empirical evidence in an area and high
levels of uncertainty, as well as situations where it may take a long period of time before the findings
from research can be uncovered.
Advantages of Purposive Sampling
1. Provides wide range of sampling techniques that can be used such as homogeneous sampling, critical
case sampling and expert sampling. For example, critical case sampling may be used to investigate
whether a phenomenon is worth investigating further, before adopting an expert sampling approach
to examine specific issues further.
2. Provide researchers with the justification to make theoretical, analytic and/or logical generalizations
from the sample that is being studied
Disadvantages of Purposive Sampling
1. Is prone to researcher bias because of its judgmental and subjective component of purpose sampling
2. The subjectivity and non-probability based nature of unit selection (i.e., selecting people, cases/
organizations etc.) means that it can be difficult to defend the representativeness of the sample, it
can be difficult to convince the reader that the judgment used to select units to study was appropriate
and it can also be difficult to convince the reader that research using purposive sampling achieved
theoretical/analytic/logical generalization.
CONCLUSION
Research is an organized and systematic way of finding answers to questions. The purpose of this chap-
ter was to describe the use of sampling in research. It explains the sample size determination, different
types of sampling techniques, and advantages and limitations of sampling techniques. It has been shown
that a sample can be viewed as part or segment of a population which is selected for the study. Using
a sample in research saves money and time, if a suitable sampling strategy is used, appropriate sample
size selected and necessary precautions taken to reduce on sampling and measurement errors. Then, a
121
Sampling in Research
sample should yield valid and reliable information. Combination or mixed purposeful sampling com-
bines various sampling strategies to achieve the desired sample. This helps in triangulation, allows for
flexibility, and meets multiple interests and needs. When selecting a sampling strategy it is necessary
that it fits the purpose of the study, the resources available, the question being asked and the constraints
being faced. This holds true for sampling strategy as well as sample size.
REFERENCES
Babbie, E. (1990). Survey research methods. Belmont, CA: Academic Press.
Berinstein, P. (2003). Business statistics on the web: Find them fast – at little or no cost. Cyber Age Books.
Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral
sampling. Sociological Methods & Research, 10(2), 141–163.
Cochran, W. G. (1953). Sampling techniques. New York: John Wiley & Sons.
Faugier, J., & Sargeant, M. (1997). Sampling hard to reach populations. Journal of Advanced Nursing,
26(4), 790–797. doi:10.1046/j.1365-2648.1997.00371.x PMID:9354993
Fink, A. (1995). How to sample in surveys (Vol. 6). London: Sage Publications.
Fowler, F. J. (1993). Survey research methods (2nd ed.; Vol. 1). London: Sage Publications.
Frey, L. R., Botan, C. H., & Kreps, G. L. (2000). Investigating communication: An Introduction to re-
search methods (2nd ed.). Boston: Allyn and Bacon.
Henry, G. T. (1990). Practical sampling (Vol. 21). London: Sage Publications. doi:10.4135/9781412985451
Jones, H. L. (1955). The Application of Sampling Procedures to Business Operations. Journal of the
American Statistical Association, 50(271), 763–774.
Kish, L. (1965). Survey Sampling. New York: John Wiley & Sons.
Lapin, L. L. (1987). Statistics for modern business decisions. Harcourt, Jovanovich, Inc.
Lohr, S. L. (1999). Sampling: Design and Analysis. Albany, NY: Duxbury Press.
Lyman, H. R. (1983). An introduction to statistical methods and data analysis (4th ed.). Dioxburg Press.
MacNealy, M. S. (1999). Strategies for empirical research in writing. New York: Longman.
Patton, M. Q. (1990). Qualitative evaluation and research methods. London: New SAGE, Publications.
Salant, P., & Dillman, D. A. (1994). How to conduct your own survey. London: John Wiley & Sons, Inc.
Smith, S. (2013). Determining sample size: How to ensure you get the correct sample size. LLC, Qualtrics.
122
Sampling in Research
KEY TERMS AND DEFINITIONS
Population: A population is a group of individual persons, objects, or items from which samples are
taken for measurement for example a population of presidents or professors, books or students.
Research: Scientific research is systematic, controlled, empirical, and critical investigation of natural
phenomena guided by theory and hypotheses about the presumed relations among such phenomena.
Sample: A sample is a finite part of a statistical population whose properties are studied to gain infor-
mation about the whole population. When dealing with people, it can be defined as a set of respondents
(people) selected from a larger population for the purpose of a survey.
Sample Frame: Collection of units such as people, objects and events that have a possibility of
being selected. It approximates the population as closely as possible and the sample is drawn from the
sample frame.
Sample Size: The number of units in the sample.
Sampling: Sampling is the act, process, or technique of selecting a suitable sample, or a representative
part of a population for the purpose of determining parameters or characteristics of the whole population.
Strata: Sub-groups within a population.
... A mixed-methods research design was adopted. According to Johnson and Onwuegbuzie [37], this model generally uses separate quantitative and qualitative where the strength of one adds to the strength of the other. Figure 2 below shows the procedure followed using the mixed methods approach. ...
... The purposive and convenience sampling methods were used to select the participants. As purported by Modal [39] and Mujere [37] purposive sampling is sometimes known as judgmental, selective or subjective sampling. Occasionally known as grab or opportunity sampling is a non-probability sampling method that involves the sample being drawn from that part of the population that is close to the researcher. ...
... Expert sampling was done where the researcher looked for individuals who have particular expertise in water resources management. Mujere [37] puts it that expert sampling is a keystone of a research design and is known as expert elicitation. This implies that the participants were tied to the objectives. ...
- Chenjerai Ziti
The water sector is at the center of climate change impacts matrix. The objective of this study was to determine the sustainability of the response strategies in the water sector in the Mutirikwi sub-catchment in Masvingo. The study obtained its data form surveys recorded meteorological data from the Zimbabwe Meteorological Services Department's four weather stations in the catchment, namely, Makoholi, Masvingo Airport, Buffalo Range and Zaka. Time series analysis of temperature data from 1952 to 2002 and rainfall pattern from 1972 to 2010 was done using the XL STAT software. The results show that the seasonal average rainfall decrease from 1972 to 2010 was not statistically significant (p=0.635, α=0.05), but the decline bears a significant environmental impact. The seasonal maximum temperature increase was statistically significant (p=0.000, α=0.05), and the seasonal minimum temperature increase was not significant (p=0.226, α=0.05). Vulnerability varies spatially within urban areas and between urban and rural. Vulnerability is determined by social status and aggravated by management issues. Borehole use has increased and water harvesting, water reuse and water transfers are the main adaptive responses used in the catchment. The study concludes that current response strategies are not sustainable. There is a need to design area-specific response mechanisms that are all inclusive to ensure that communities are able to adapt to climate change with regard to water resources.
... The target population of this study was 330 humanitarian aid organizations carrying out their operations in Kenya as derived from the NGO Coordination Board of Kenya. This study was a census examining the entire population [49], supply chain managers in this case, which have a particular set of characteristics such as specific experience, knowledge, skills or exposure to an event. Questionnaires were used to obtain primary data for the study. ...
- Erastus Kiswili Nyile
- Ismail Noor Shale
- Anthony Osoro
In today's volatile and uncertain humanitarian environment, adopting a purely lean or a purely agile supply chain is not effective. Humanitarian organizations are struggling to obtain the highest possible performance from their supply chains by utilizing and adopting various supply chain designs. This is upon realization that despite the huge chunks of money pumped into humanitarian sector, stringent oversight by donors and expectations from vulnerable populations, humanitarian supply chains still respond in a sluggish, inefficient and poorly coordinated manner to emergencies. The purpose of this study was to explore the influence of supply chain responsiveness and waste management on performance of humanitarian aid organizations in Kenya. The underpinning theories and model in this study included; Decoupling Point theory; Theory of Constraints and SCOR model. Survey research design was employed for this study. The study entailed a census survey of all the 330 humanitarian aid organizations in Kenya with supply chain managers as the unit of observation. Questionnaires were used to collect primary data. Descriptive statistics and inferential statistics was used aided by SPSS version 24 to facilitate data analysis. The data was presented using a combination of statistical and graphical techniques. Trend analysis was used to spot a pattern on the sub-constructs of performance of humanitarian aid organizations for five years. The study findings revealed that supply chain responsiveness and waste management are positively associated with performance of humanitarian aid organizations. From the findings, most humanitarian aid organizations had knowingly or unknowingly partially implemented leagility design in their supply chains. The findings further showed that despite the rise in disaster resource allocation, the culture of preparedness was lacking in the country. Based on these findings and conclusions, the study recommended that to achieve and sustain an efficient and responsive supply chain, humanitarian aid organizations should design, implement and fully adopt leagility design in their humanitarian supply chains. Humanitarian aid organizations are recommended to embrace advanced technologies to improve their supply chain leagility. Donors on the other hand were encouraged to strengthen local capacity of affected communities and increase their funding on humanitarian aid operations. In addition, supply chain professionals should come up with new ways of predicting demand in a volatile, uncertain, complex and ambiguous environment learning from data from previous disasters. The study further recommends for a creation of a disaster preparedness plan that gives the way forward in times of tragedies or disasters.
... The target population of this study was 330 humanitarian aid organizations carrying out their operations in Kenya as derived from the NGO Coordination Board of Kenya. This study was a census examining the entire population [49], supply chain managers in this case, which have a particular set of characteristics such as specific experience, knowledge, skills or exposure to an event. Questionnaires were used to obtain primary data for the study. ...
- Nyile Erastus Kiswili
- Ismail Noor Shale
- Anthony Osoro
In today's volatile and uncertain humanitarian environment, adopting a purely lean or a purely agile supply chain is not effective. Humanitarian organizations are struggling to obtain the highest possible performance from their supply chains by utilizing and adopting various supply chain designs. This is upon realization that despite the huge chunks of money pumped into humanitarian sector, stringent oversight by donors and expectations from vulnerable populations, humanitarian supply chains still respond in a sluggish, inefficient and poorly coordinated manner to emergencies. The purpose of this study was to explore the influence of supply chain responsiveness and waste management on performance of humanitarian aid organizations in Kenya. The underpinning theories and model in this study included; Decoupling Point theory; Theory of Constraints and SCOR model. Survey research design was employed for this study. The study entailed a census survey of all the 330 humanitarian aid organizations in Kenya with supply chain managers as the unit of observation. Questionnaires were used to collect primary data. Descriptive statistics and inferential statistics was used aided by SPSS version 24 to facilitate data analysis. The data was presented using a combination of statistical and graphical techniques. Trend analysis was used to spot a pattern on the sub-constructs of performance of humanitarian aid organizations for five years. The study findings revealed that supply chain responsiveness and waste management are positively associated with performance of humanitarian aid organizations. From the findings, most humanitarian aid organizations had knowingly or unknowingly partially implemented leagility design in their supply chains. The findings further showed that despite the rise in disaster resource allocation, the culture of preparedness was lacking in the country. Based on these findings and conclusions, the study recommended that to achieve and sustain an efficient and responsive supply chain, humanitarian aid organizations should design, implement and fully adopt leagility design in their humanitarian supply chains. Humanitarian aid organizations are recommended to embrace advanced technologies to improve their supply chain leagility. Donors on the other hand were encouraged to strengthen local capacity of affected communities and increase their funding on humanitarian aid operations. In addition, supply chain professionals should come up with new ways of predicting demand in a volatile, uncertain, complex and ambiguous environment learning from data from previous disasters. The study further recommends for a creation of a disaster preparedness plan that gives the way forward in times of tragedies or disasters.
... The target population of this study was 330 humanitarian aid organizations carrying out their operations in Kenya as derived from the NGO Coordination Board of Kenya. This study was a census examining the entire population[62], supply chain managers in this case, which have a particular set of characteristics such as specific experience, knowledge, skills or exposure to an event. Questionnaires were used to obtain primary data for the study. ...
- Erastus Kiswili Nyile
- Ismail Noor Shale
- Anthony Osoro
Humanitarian organizations are struggling to obtain the highest possible performance from their supply chains by utilizing and adopting various supply chain designs. This is upon realization that despite the huge chunks of money pumped into humanitarian sector, stringent oversight by donors and expectations from vulnerable populations, humanitarian supply chains still respond in a sluggish, inefficient and poorly coordinated manner to emergencies. The purpose of this study was to establish the influence of supply chain integration on performance of humanitarian aid organizations in Kenya. The study was anchored on the Relational View Theory and the Theory of Performance. Survey research design was employed for this study as it enabled the combination of both qualitative and quantitative research approaches. The study entailed a census survey of all the 330 humanitarian aid organizations carrying out their operations in Kenya with supply chain managers as the unit of observation. Objectively developed questionnaires were used to collect primary data. Descriptive statistics and inferential statistics was used aided by SPSS version 24 to facilitate data analysis. The data was presented using a combination of statistical and graphical techniques. The study findings revealed that supply chain integration was positively associated with performance of humanitarian aid organizations. The study recommends that humanitarian aid organizations should improve the use of information technology and computerized structures to integrate supply chain processes and ensure distinguishability of internal activities and procedures.-49 Additionally, humanitarian aid organizations are recommended to boost their supply chain integration by exploring and embracing advanced and emerging technologies such as big data analytics, internet of things, cloud computing, machine learning, artificial intelligence and block chain. A multi-stakeholder approach that involves representatives from public sector, private sector, humanitarian sector, academia, military, beneficiaries and the media should be involved in drafting disaster management legislations and push for disaster preparedness to be enshrined in it. Further, humanitarian aid organizations have and will always play a crucial part in complex emergencies. This study proposed that all humanitarian supply chain actors come together and formulate a common response to complex emergencies, making use of the different competences of different players.
... Because most high schoolers are under age 18, all parents of the school were emailed a consent form requesting parental permission for their child to participate in the study. 170 students who received parental permission were asked to complete an anonymous Google survey via email because anonymous surveys produce better data quality than non-anonymous ones (Murdoch, et al., 2014), emailed surveys promote large samples (Wright, 2017), and self-selecting participants think more deeply than selected participants (Mujere, 2016). ...
- Aleksa Jarasunas
Recent studies have shown that creative engagement is linked to increases in positive affect and decreases in negative affect. To further investigate this relationship, the present study investigated the extent to which a change in affect corresponds to time spent in individual creative pursuits compared to time spent in group creative pursuits. A sample of 41 students at a high-achieving school participated in an emailed survey. The participants reported the amount of time they engage creatively, their experience of positive and negative emotions, and their activation of emotional stability. Multiple linear regressions and partial correlations revealed that an increase in time spent in group creative pursuits was linked to an increase in positive affect, while an increase in time spent in individual creative pursuits was associated with a decrease in positive affect. Spending time in either creative setting, however, was unrelated to negative affect.
- Mohammad Bin Amin
- Saju Shaha
- Samiya Bint Halim
Green management is the integral approach for minimizing the adverse environmental impact of the organization's supply chain for increasing overall performance. In order to improve the process of maximum utilization of resources and to achieve sustainable aspects in a business structure, performance-oriented components are indispensable. The current study analyzed organizational performance measures based on the green management activities in context of consumer products of Bangladesh. The performance measures have been defined using literary resources and reviewed further with an empirical exploration. Hypothesized relationships were also developed to investigate the interconnection within green management and performances of consumer item businesses. A total 266 respondents with meaningful responses were assembled for the study in concern. Moreover, the study results showed the greater impact on the performance outcomes of selected dimensions of green management. The current study would substantiate help to stakeholders interlinked with consumer product industry not only to examine the extent of association between environmentally sustainable focused factors with overall performance but also to formulate strategies to burgeon productivity significantly. Additionally, with various empirical attributes and formulated hypotheses this research can be even further evaluated by statistical instruments across diverse businesses and markets.
- Jing (Bill) Xu
- Pamela Sau-ying Ho
Previous studies have shown that experiential learning can effectively advance students' education goals, but they have not attached importance to the perspective of destination marketing. This study fills a gap in the research on this topic. The study examined college students' study tours as a typical form of educational tourism by surveying 208 Chinese college students and graduates who had experienced such programmes. The results showed that study tour experiences resulted in destination associations among the students, which commonly led to revisit intentions. The findings demonstrated that destination associations combine destination memory, destination image and affective attachment. These components played mediating roles in the relationship between study tour experiences and destination revisit intention. This study informs destination marketers concerning the need to strategically develop their marketing strategies by capitalising on the educational tourism niche market.
In this study, we aimed to analyze homeowners' level of awareness and perceived risk about buffelgrass invasion in the Tucson, Arizona Wildland-Urban Interface (WUI), as well as the factors influencing their participation in buffelgrass control and fire risk mitigation efforts. Data for the study were generated through the administration of an online survey among 117 members of Home Owner Associations (HOAs) in the Tucson WUI. The results showed that the overwhelming majority of respondents were aware of buffelgrass, but their knowledge about buffelgrass control mechanisms appeared to be limited. Respondents also more frequently expressed concern about the risks posed by buffelgrass invasion to general targets, such as the Sonoran Desert ecosystem, native plants and wildlife than risks to their private property and neighborhoods. The results also showed that the level of involvement in HOAs, and leadership in HOAs had significant positive effects on homeowners' participation in buffelgrass control efforts. Homeowners' duration of residence also had a significant negative effect on participation in buffelgrass control efforts, suggesting that newcomers may be more involved than long-term residents. Similarly, the number of months respondents spent in Tucson per year had a negative effect on the number of hours spent on buffelgrass control efforts. Respondents' perceived risk about buffelgrass invasion also had a positive effect on the hours spent on buffelgrass control as well as their level of involvement in fire risk mitigation efforts. These results highlight the importance of local institutions and community heterogeneity in social responses to threats in WUI communities. Policies aimed at building the resilience of WUI communities need to account for their complexity as coupled social-ecological systems.
- Nokonwaba Mnguni
The purpose of this study was to gain an in-depth understanding of the female drug mule phenomenon and explain the reasons why female drug mules are involved in drug smuggling. The objectives of the study were to develop a profile for female drug mules incarcerated in South African correctional centres, specifically Kgoši Mampuru II and Johannesburg Female Correctional Centre; to establish risk factors for being recruited and used as drug mules; to explain the mules' motivations for being involved in drug smuggling; to determine the physical and emotional impact drug smuggling has on the drug mules; and to determine the methods used by drug mules to smuggle drugs. A qualitative research approach, with the use of one-on-one semi-structured interviews, was used to obtain information from participants.
- Aleksa Jarasunas
This presentation delineates the process of my AP Research project, higlighting background research, methodology, results, limitations, and implications.
- Jean Faugier RMN MSc PhD
- Mary Sargeant BA MA(Econ
- Jean Faugier
- Mary Sargeant
Studies on 'hidden populations', such as homeless people, prostitutes and drug addicts, raise a number of specific methodological questions usually absent from research involving known populations and less sensitive subjects. This paper examines the advantages and limitations of nonrandom methods of data collection such as snowball sampling. It reviews the currently available literature on sampling hard to reach populations and highlights the dearth of material currently available on this subject. The paper also assesses the potential for using these methods in nursing research. The sampling methodology used by Faugier (1996) in her study of prostitutes, HIV and drugs is used as a current example within this context.
- Patrick Biernacki
- Dan Waldorf
In spite of the fact that chain referral sampling has been widely used in qualitative sociological research, especially in the study of deviant behavior, the problems and techniques involved in its use have not been adequately explained. The procedures of chain referral sampling are not self-evident or obvious. This article attempts to rectify this methodological neglect. The article provides a description and analysis of some of the problems that were encountered and resolved in the course of using the method in a relatively large exploratory study of ex-opiate addicts.
Practical Surveys. Cornerstones of a Quality Survey. Deciding What Information You Need. Choosing a Survey Method. When and How to Select a Sample. Writing Good Questions. Questionnaire Design. Setting Your Survey in Motion and Getting It Done. From Questionnaires to Survey Results. Reporting Survey Results. Advice, Resources, and Maintaining Perspective. References. Index.
- Howard L. Jones
* Revision of paper presented at a luncheon meeting of the Chicago Chapter, American Statistical Association, October 16, 1952.The purpose of this short article is to give business executives an insight into sampling theory and procedures without confusing them with mathematical symbols or unexplained technical terms. Sampling is defined, and the principal applications in the telephone business are briefly described. The procedures used are grouped into three broad categories—judgment sampling, systematic sampling, and random sampling—the relative advantages of which are pointed out. Various ways of minimizing the cost of random sampling are discussed. A word of caution is added regarding the dangers of improperly selected samples.
Source: https://www.researchgate.net/publication/313471921_Sampling_in_Research
Posted by: jenaejenaeasfoure0272363.blogspot.com
Post a Comment for "Aries Man Secrets Pdf Free Download"