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.

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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

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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.

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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

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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-

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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

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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.

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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

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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.

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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-

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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.

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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,

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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).

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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

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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

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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.

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MacNealy, M. S. (1999). Strategies for empirical research in writing. New York: Longman.

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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 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 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 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 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 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 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 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.