January 2016
\nThe Prospectus
\nCompleting the Prospectus
\nThe DBA Doctoral Study Prospectus consists of several detailed small sections. A sample prospectus is in the appendix. The goal for the prospectus is to create a plan for developing your doctoral study proposal. Therefore, you need to have some detailed information for the prospectus, but you do not need to know all the specific details of the study that you will ultimately conduct. For example, you may identify employee satisfaction as a variable of the study, but at this point, you do not yet need to identify the instrument that you plan to use to measure the variable.<\/p>\n
Each research project is different, and because this outline is general, reviewers often ask to include additional information in your prospectus. For example, feasibility is one criterion for evaluating your prospectus, and if you are considering a unique sample group, your committee may ask you to explore that aspect in more detail before moving forward.<\/p>\n
The DBA Doctoral Study Prospectus will follow APA 6th edition guidelines and formatted as .doc or. docx file. As you work on the document, also review the tools available on the CRQ website, the Doctoral Capstone Research Guide, DDBA Doctoral Study Template, and Doctoral Study Rubric. Appendices A, B and C contain an annotated outline, sample \u201cquantitative\u201d prospectus, and Prospectus Rubric, respectively.
\nAppendix D is a graphical depiction of a three-step formula for \u201cqualitative\u201d business problem alignment.
\nSubmitting the Prospectus
\nStudents will work with their chair in DDBA 8100, Doctoral Study Mentoring, to complete the prospectus. You will use the example Prospectus (Appendix A) as a guide and template; there is no other official Prospectus template. Students should aim to have an approved Prospectus by the end of their 3rd DDBA 8100 course. As is the case for the proposal and doctoral study, for which you will receive feedback on working drafts, prospectus development is an iterative process. Committee members will use the Prospectus Rubric (Appendix C) to evaluate the Prospectus. Follow the submission guidelines identified in the course submission instructions.<\/p>\n
Appendix A – Annotated Outline
\nTitle Page<\/p>\n
The recommended title of the business study should not exceed 12 words to include the topic, the variables and relationship between them (quantitative studies), and the most critical keywords. Double-space the title if over one line of type and center it under the word Prospectus.<\/p>\n
Include your name, your program of study (and specialization if applicable) and Banner ID Number, double-spaced and centered under the title.<\/p>\n
Title<\/p>\n
Include the title as it appears on the title page. Double-space if over one line of type and centered at the top of the page. The title follows the word Prospectus and a colon.<\/p>\n
Problem Statement<\/p>\n
Provide a one-paragraph statement (150 words max) that is the result of a review of research findings, appropriate peer-reviewed\/government sources, and current practice and that contains the following information:<\/p>\n
1.\tHook: (a WOW statement supported with a peer reviewed citation no older than five years from anticipated date CAO will sign.)<\/p>\n
2.\tAnchor (includes a number supported with a peer reviewed\/government citation no older than 5 years from your anticipated CAO signature)<\/p>\n
3.\tThe general business problem is XXXX<\/p>\n
4.\tThe specific business problem is some (identify who has the specific business problem) has limited information on XXX<\/p>\n
Review the Problem Statement Video tutorial to aid you in completing the Problem Statement. The video tutorial is located at: http:\/\/youtu.be\/IYWzCYyrgpo<\/p>\n
Purpose Statement<\/p>\n
Provide a one-paragraph Purpose Statement (200 words max) and that contains the following information:<\/p>\n
Quantitative Study: (a) methodology, (b) design, (c) research variables (independent and dependent), (d) specific population, (e) geographical location, and (f) social change statement.<\/p>\n
Note: A correlation study must examine the relationship between “more than” two variables. In other words, a simple bivariate correlation analysis is not substantive for a doctoral study. As a minimum, a multiple linear regression, using at least two predictor (independent) variables, is required.<\/p>\n
Qualitative Study: (a) methodology, (b) design, (c) specific population, (d) geographical location, and (e) social change statement.<\/p>\n
Please review the Purpose Statement Video tutorial to aid you in completing the Purpose Statement. Located the video tutorial at: http:\/\/youtu.be\/pLP4r0mfT9A.<\/p>\n
Nature of the Study<\/p>\n
The Nature of the Study component serves two purposes. The first purpose is describing and justifying the methodology (i.e. quantitative, qualitative, or mixed-method). The second purpose is describing and justifying the design (i.e. case study, phenomenological, correlation). Therefore, a well-crafted Nature of the Study can be presented in two paragraphs but not exceed one page.<\/p>\n
The first paragraph is to describe and justify the methodology. State why you selected a specific method and why other methods were not appropriate. The second paragraph is to describe and justify the design. State why you selected a specific design and why other designs were not appropriate. Map to the rubric and only include the required content!<\/p>\n
Research Question(s)\/Hypotheses<\/p>\n
List the research question that will lead to the development of the requirements in the study and steps for accomplishing the requirements. A research question informs the research design by providing a foundation for:<\/p>\n
\u2022 Generation of hypotheses in quantitative studies,
\n\u2022 Questions necessary to build the design structure for qualitative studies (i.e. interview questions),<\/p>\n
\u2022 Process by which different methods will work together in mixed studies.<\/p>\n
Interview Questions (Qualitative)<\/p>\n
The interview questions are to be informed by the conceptual framework. Please see the Theoretical\/Conceptual Framework video tutorial at: http:\/\/youtu.be\/P-01xVTIVC8.<\/p>\n
Theoretical\/Conceptual Framework<\/p>\n
In one paragraph, describe the theoretical base or conceptual framework from the scholarly literature that will ground the study (providing citations). Base this description on the problem, purpose, and background of your study. Specifically, identify and describe:<\/p>\n
(a) theory: theoretical base or conceptual framework,
\n(b) author of the theoretical base or conceptual framework (if applicable),
\n(c) date of the theoretical base or conceptual framework (if applicable),
\n(d) key tenets, propositions, constructs, variables, hypotheses, etc., and
\n(e) how the theoretical base or conceptual framework is applicable and fits to the study.<\/p>\n
Review the Theoretical\/Conceptual Framework video tutorial at: http:\/\/youtu.be\/P-01xVTIVC8 to aid in completing Theoretical\/Conceptual Framework section.<\/p>\n
Significance of the Study<\/p>\n
Provide one to two paragraphs, informed by the topic in the problem statement, which describe(s):<\/p>\n
1.\tThe value to the business\/social impact.<\/p>\n
2.\tContribution to effective practice of business<\/p>\n
3.\tPotential contribution to positive social change and improvement of business practice.<\/p>\n
References<\/p>\n
Include references formatted in the correct style (APA 6th edition, modeled at the end of this guide) for all citations within the Doctoral Study Prospectus.<\/p>\n
Student and Committee Information
\nDate of Review:
\nStudent\u2019s Name (Last, First):
\nStudent ID (for office use only):
\nChairperson:
\nSecond Committee Member:
\nUniversity Research Reviewer:
\nPerson Conducting this Review:
\nNote: Type in the applicable information.
\nAppendix B
\nProspectus<\/p>\n
Relationship Between Transformational Leadership and Employee Turnover Intentions
\nby
\nAlpha B. Gamma<\/p>\n
Doctor of Business Administration Prospectus \u2013 Name of DBA Specialization
\nSubmitted in Partial Fulfillment of the Requirements for the Degree
\nof
\nDoctor of Business Administration
\nWalden University
\nStudent ID: A00000000
\nMonth Year<\/p>\n
Prospectus: Relationship Between Transformational Leadership and Employee Turnover Intentions
\nProblem Statement
\nLosing highly skilled technical employees disrupts organizational functioning, service delivery, and administration (Bothma & Roodt, 2012). From a financial perspective, employee turnover can cost employers between 90 and 200 % of annual pay (Hom, Mitchell, Lee, & Griffeth, 2102). The general business problem is that employee intent to leave is a major antecedent of actual employee turnover (Siddiqi, 2013). The specific business problem is that some information technology (IT) small business owners do not know the relationship between IT employee perceptions of their leaders\u2019 transformation leadership characteristics and employee turnover intention.
\nPurpose Statement
\nThe purpose of this quantitative correlation study is to examine the relationship between IT employee perceptions of their leaders\u2019 transformation leadership characteristics and employee turnover intention. The targeted population consists of IT business leaders located in Orlando, Florida. The independent variables are employee perceptions of their leaders\u2019 (a) idealized attributes, (b) idealized behaviors, (c) intellectual stimulation, (d) inspirational motivation, and (e) individualized consideration. The dependent variable is employee turnover intention . The implications for social change include the potential to (include social change implications).<\/p>\n
Nature of the Study
\nQuantitative methodology is the foundation of the postpositivist worldview. The researcher uses descriptive and inferential statistics, by-products of the quantitative methodology, to describe the population and infer the sample results to the broader population (Orcher, 2014). The justification of the quantitative method results from the need to test the efficacy of transformational leaderships constructs in predicting employee turnover intentions. Conversely, researchers employing qualitative methodology seek to explore (seeking how or why answers), rather than explain a phenomenon or outcome (Yin, 2014). Therefore, the qualitative method is not appropriate for this study.
\nResearchers employing correlation designs do not seek cause and effect (Pallant, 2013). A key focus of correlation designs is tracing the distribution of the dependent variable or some characteristic of the distribution (such as its mean) as a function of one or more predictor variable (Pallant, 2013). Researchers employing experimental and quasi-experimental designs seek cause and effect relationships (Orcher, 2014). However, the purpose of this study is not to seek cause and effect; thus, the experimental and quasi-experimental designs are not appropriate for this study.
\nQuantitative Research Question
\nWhat is the relationship between employee perception of their leaders\u2019 (a) idealized attributes, (b) idealized behaviors, (c) intellectual stimulation, (d) inspirational motivation, (e) individualized consideration, and employee turnover intention?
\nHypotheses
\nNull Hypothesis (H0): There is no statistically significant relationship between employee perception of their leaders\u2019 (a) idealized attributes, (b) idealized behaviors, (c) intellectual stimulation, (d) inspirational motivation, (e) individualized consideration, and employee turnover intention?
\nAlternative Hypothesis (H1): There is a statistically significant relationship between employee perception of their leaders\u2019 (a) idealized attributes, (b) idealized behaviors, (c) intellectual stimulation, (d) inspirational motivation, (e) individualized consideration, and employee turnover intention?
\nTheoretical\/Conceptual Framework
\nBurns (1978) developed the transformational leadership. Burns used the theory to offer an explanation for leadership based upon the premise that leaders are able to inspire followers to change expectations, perceptions, and motivations to work toward common goals. Burns identified the following key constructs underlying the theory (a) idealized attributes, (b) idealized behaviors, (c) intellectual stimulation, (d) inspirational motivation, and (e) individualized consideration. As applied to this study, the transformational leadership theory holds that I would expect the independent variables (transformational leadership constructs), measured by the Multifaceted Leadership Questionnaire, to predict employee turnover intention because (provide a rationale based upon the logic of the theory and extant literature). Figure 1 is a graphical depiction of the transformational leadership theory as it applies to examining turnover intentions.
\nFigure 1. Graphical model of transformational leadership theory as it applies to examining turnover intentions.
\nSignificance of the Study
\nOrganizational leaders are faced with maximizing profitability. Therefore organizational leaders seek to minimize employee turnover to maximize profitability and maintain critical knowledge capital within their organizations. This study is significant to business practice in that it may provide a practical model for understanding better the relationship between transformational leadership characteristics and employee turnover intentions. A significant predictive model can aid and support leaders in predicting turnover intentions, and more important, employing interventions to mitigate employee turnover intentions. The implications for positive social change include to potential provide significant knowledge to organizational leaders conducive to minimizing turnover and maximizing profitability.
\nReferences
\nBothma, C. F., & Roodt, G. (2012). Work-based identity and work engagement as potential antecedents of task performance and turnover intention: Unravelling a complex relationship. SA Journal of Industrial Psychology, 38, 27-44. doi:10.4102\/sajip.v38i1.893
\nBurns, J. M. (1978). Leadership. New York, NY: Harper
\nHom, P. W., Mitchell, T. R., & Lee, T. W., & Griffeth, (2012). Reviewing employee turnover: Focusing on proximal withdrawal states and an expanded criterion. Psychological Bulletin, 138, 831-858. doi:10.1037\/a0027983.
\nOrcher, L. T. (2014). Conducting research: social and behavioral methods (2nd ed.). Glendale, CA: Pyrczak Publishing.
\nPallant, J. (2013). SPSS survivor manual: A step-by-step guide to data analysis using SPSS for Windows (5th ed.). Berkshire, England: Open University Press.
\nSiddiqi, M. A. (2013). Examining work engagement as a precursor to turnover intentions of service employees. International Journal of Information, Business and Management, 5(4), 118-132. Retrieved from http:\/\/ijibm.elitehall.com
\nYin, R. K. (2014). Case study research: Design and methods (5th ed.). London: SAGE Publications.<\/p>\n
Appendix C – DBA Prospectus Rubric
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators\tType Met, Not Met, or N\/A in Each Cell
\n(1.3) Problem Statement<\/p>\n
a.\tProvides a \u201ccurrent\u201d hook supported by peer reviewed or government citation less than 5-years old from anticipated graduation date.<\/p>\n
b.\tProvides a \u201ccurrent\u201d data driven anchor supported by peer reviewed or government citation less than 5-years old from anticipated completion date<\/p>\n
c.\tStates the general business problem Note: This element should start as follows: The general business problem is\u2026
\nd.\tStates the specific business problem. Be sure to state who has the specific problem (i.e. small business leaders, project managers, supply chain managers, etc.) Note: This element should start as follows: The specific business problem is that some (identify who has the problem)\u2026
\ne.\tEnsures the specific business problem aligns with the research question and purpose statement.<\/p>\n
f.\tProblem Statement does not exceed 150 words.
\n\u2022\tCheck with Ulrich\u2019s Periodical Directory http:\/\/library.waldenu.edu\/728.htm to ensure citations are peer reviewed.
\n\u2022\tSee Problem Statement Video Tutorial at: http:\/\/youtu.be\/IYWzCYyrgpo.<\/p>\n
(1.4) Purpose Statement: Describes the intent of the research . The Purpose Statement is a mini story and must not exceed 200 words. The Purpose Statement must address the following six elements:
\na.\tIdentifies the research method as qualitative , quantitative, or mixed-methods.
\nb.\tIdentifies research design (i.e. case study, phenomenological, quasi-experimental, correlational, etc.).
\nc.\tIf quantitative or mixed method: Identifies a minimum of two independent (experimental\/quasi-experimental designs) or predictor (correlational designs) and dependent variable(s). Note: The quantitative study must include at least two independent\/predictor variables.
\nd.\tIdentifies specific population group for proposed study.
\ne.\tIdentifies geographic location of the study.
\nf.\tIdentifies contribution to social change.
\ng.\tEnsures the first sentence links\/aligns directly with the specific business problem.
\n\u2022\tSee Purpose Statement Video Tutorial at: http:\/\/youtu.be\/pLP4r0mfT9A.<\/p>\n
(1.5) Nature of the Study: Provides a brief discussion on the research method (i.e. quantitative or qualitative) and design (i.e. correlation for quantitative study; phenomenological, case study, etc., for a qualitative design); cite a minimum of one source (The method and design will be discussed in greater detail in Section 2).<\/p>\n
\u2022\tNote: A single paragraph can be used for each component: one for the method and one for the design.
\na.\tIdentifies the selection of one method (qualitative, quantitative, or mixed methods) and why other methods would not work (cite a minimum of one source).
\nb.\tIdentifies the selection of the design (within the method) and why it was selected over other designs (cite a minimum of one source).
\n(1.6) Research Questions (Quantitative Only)<\/p>\n
a.\tLists research question(s) in about 10-15 words (20 words max).
\nb.\tEnsures research question(s) aligns with the specific business problem and first line of the Purpose Statement.
\nc.\tIncludes the independent\/predictor, covariates (control variables), mediator\/moderator, etc., and dependent variables as identified in the Purpose Statement.<\/p>\n
d.\tLists research sub-questions that align with each hypotheses set.
\n(1.7) Hypotheses (Quantitative\/Mixed-Method Only): States, in accurate format, the null and alternative hypotheses for each research question .
\n(1.8) Research Question – Qualitative Only<\/p>\n
a.\tLists overarching research question in about 10-15 words (20 words max).
\nb.\tEnsures research question aligns with the specific Business Problem and Purpose Statement.<\/p>\n
Section 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators\tType Met, Not Met, or N\/A in Each Cell
\n(1.9) Interview Questions – Qualitative Only<\/p>\n
a.\tLists each interview or focus group question. Questions must contribute knowledge to the research question and be informed by the theoretical\/conceptual framework.
\nb.\tEnsures interview\/focus group questions align with the research question and theoretical\/conceptual framework.
\n(1.10) Theoretical\/Conceptual Framework : Clearly and concisely identify the theory\/conceptual framework. In quantitative studies, the theoretical framework is the appropriate term and in qualitative studies the conceptual framework is the appropriate term. The student will articulate the theoretical\/conceptual framework with concepts from the literature to ground and complement the applied business study.<\/p>\n
\u2022\tThis component should not exceed one page. It will be expanded upon in the literature review. See Theoretical\/Conceptual Framework Video Tutorial at: http:\/\/youtu.be\/P-01xVTIVC8<\/p>\n
a.\tIdentifies and describes the theory or conceptual model for theoretical\/conceptual framework.
\nb.\tIdentifies theorist(s) of the theory or conceptual model for theoretical\/conceptual framework (if applicable).
\nc.\tIdentifies date of the theory or conceptual model for theoretical\/conceptual framework (if applicable).<\/p>\n
d.\tIdentifies key concepts\/propositions\/tenets of the theory or conceptual model for theoretical\/conceptual framework.<\/p>\n
e.\tIdentifies how\/why the theory or conceptual model for theoretical\/conceptual framework is applicable and fits\/applies to the study.
\n(1.13) Significance of the Study<\/p>\n
a.\tStates why the study is of value to business.
\nb.\tStates how this study may contribute to effective practice of business (improvement of business practice).
\nc.\tIdentifies how the results might contribute to positive social change.<\/p>\n
General Comments
\nComments on the following indicators of quality apply to the manuscript as a whole.
\nType Met, Not met, or N\/A in Each Cell
\nWriting Style and Composition<\/p>\n
The DBA Doctoral Prospectus is written in scholarly language (accurate, balanced, objective, tentative). The writing is clear, precise, and avoids redundancy\/errors. Statements are specific and topical sentences are established for paragraphs. The flow of words is smooth and comprehensible. Bridges are established between ideas. Few direct quotes exist.<\/p>\n
Organization and Form<\/p>\n
The Prospectus addresses the following organization and form requirements:<\/p>\n
a. Is logically and comprehensively organized, using subheadings where appropriate,
\nb. Has a professional, scholarly appearance,
\nc. Is written with correct grammar, punctuation, and spelling; does not contain anthropomorphisms
\nd. Includes citations for the following: quotations, paraphrasing, facts, and references to research studies
\ne. In-text citations are in the reference list; references have corresponding in-text citations, and
\nf. Tables and Figures are appropriately in APA, 6th edition format.<\/p>\n
Appendix D
\n(Qualitative Example)<\/p>\n
Doctor of Business
\nAdministration
\nDoctoral Study Rubric and
\nResearch Handbook
\nMarch 2016
\n2016
\nii
\nFOREWORD
\nWalden University
\nDBA Doctoral Study Rubric and Research Handbook1
\nMarch 2016
\nThis document consists of two components: the Doctoral Study Rubric2
\nand the Research
\nHandbook. Thus, the purpose of this document is two-fold. First, the purpose of the rubric is to
\nguide DBA students and DBA Doctoral Study supervisory committees as they work together to
\ndevelop high-quality proposals and Doctoral Study research. The committee will use the rubric
\nto provide on-going and flexible evaluation and reevaluation of the proposal and DBA Doctoral
\nStudy drafts. The University Research Reviewer (URR), who reviews the proposal\/DBA
\nDoctoral Study on behalf of the University, will also use this rubric to communicate feedback
\nand any required revisions.
\nSecond, the Research Handbook is an accompanying guide to the rubric that provides detailed
\ninstructions and knowledge pertaining to corresponding rubric components. The doctoral student
\nis still responsible for utilizing self-identified resources to aid in the understanding and
\npresentation of the rubric requirements. Elements in the Doctoral Study rubric correspond to
\nelements in the Research Handbook. For example, one will find more detailed information on the
\nProblem Statement (Heading # 1.3 in the DBA Rubric) in Heading # 1.3 (Problem Statement) of
\nthe Research Handbook. Using the Doctoral Study Rubric in conjunction with the Research
\nHandbook when writing the proposal\/Doctoral Study is highly recommended.
\nIn the writing process, use the DBA Template and Rubric as a suggested outline for the DBA
\nProposal and Doctoral Study and as a basis for feedback on early drafts.
\nBefore the Proposal Oral Conference or DBA Doctoral Study Oral Conference, the
\ncommittee and URR will complete the rubric in MyDR and upload the proposal per the process
\nchecklist. Find the MyDR Process Checklist at
\nhttp:\/\/academicguides.waldenu.edu\/researchcenter\/osra\/dba.The guidance on orals is located at
\nhttp:\/\/academicguides.waldenu.edu\/researchcenter\/osra\/oraldefense.
\nAfter the Proposal Oral Conference or DBA Doctoral Study Oral Conference, and once the
\nstudent completes any committee or methodologist revision requests for the proposal\/Doctoral
\nStudy, the committee will review the proposal\/Doctoral Study and make any needed
\nmodifications. When the committee members agree that the student met all of the rubric
\nrequirements for the proposal and passed the oral defense, the chair then notes in MyDR that the
\nstudent passed the oral defense.<\/p>\n
1 The DBA Rubric and Research Handbook video tutorial can be viewed at: http:\/\/youtu.be\/KiiDGmLbRN0.
\n2 The guidance in the rubric supersedes any guidance you might see depicted elsewhere. For example, the Problem
\nStatement video tutorial on YouTube depicts a maximum word count of 250 for the Problem Statement. The
\nProblem Statement is recommended not to be too lengthy (recommended not to exceed 150 words). It is
\nrecommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as
\nappropriate).
\n2016
\niii
\nAbout consensus: For the final copy of the proposal or DBA Doctoral Study, there must be
\nunanimous agreement by the DBA Doctoral Study supervisory committee before the student
\nproceeds to the next step in the process checklist.
\nNote: Students must use a minimum of 85% peer-reviewed sources from the total number of
\nsources. Students should use a minimum of 85% of sources from the total sources that were
\npublished within 5-years from the date of the anticipated completion date (date the CAO
\napproves the final study). Other than data collected from the study site, students cannot use
\nmagazines, trade publications, summary textbooks, websites, and blogs as references.
\n2016
\niv
\nTABLE OF CONTENTS
\nFOREWORD………………………………………………………………………………………………………………..II
\nDBA DOCTORAL STUDY RUBRIC…………………………………………………………………………….. 1
\nDBA RESEARCH HANDBOOK…………………………………………………………………………………. 26
\nSECTION 1: FOUNDATION OF THE STUDY ………………………………………………………. 27
\n1.1 – Abstract……………………………………………………………………………………………………… 28
\n1.2 – Background of the Problem………………………………………………………………………….. 28
\nApplied DBA Versus a Speculative\/Theoretical PhD …………………………………… 28
\nPreparing the Background of the Problem…………………………………………………… 29
\n1.3 – Problem Statement………………………………………………………………………………………. 30
\nAvoiding Rubric Creep …………………………………………………………………………….. 31
\nStrategy for Mapping to the Rubric ……………………………………………………………. 31
\nSpecific Business Problem………………………………………………………………………… 31
\nAligning the Specific Business Problem With the Purpose Statement and RQ … 33
\n1.4 – Purpose Statement ………………………………………………………………………………………. 35
\nSix Elements of the Purpose Statement ………………………………………………………. 35
\n1.5 – Nature of the Study……………………………………………………………………………………… 37
\nHypothetical Quantitative Example……………………………………………………………. 38
\nHypothetical Qualitative Example……………………………………………………………… 38
\n1.6 – Research Question (Quantitative Only)………………………………………………………….. 39
\n1.7 – Hypotheses (Quantitative\/Mixed-Method Only)……………………………………………… 40
\nHypotheses……………………………………………………………………………………………… 40
\nCorrelation ……………………………………………………………………………………………… 40
\nQuasi-experimental………………………………………………………………………………….. 40
\n1.8 – Research Question (Qualitative Only)……………………………………………………………. 40
\n1.9 – Interview Questions (Qualitative Only) …………………………………………………………. 42
\nExample Research Question ……………………………………………………………………… 43
\nExample Applied DBA Interview Questions……………………………………………….. 43
\n1.10 – Theoretical\/Conceptual Framework …………………………………………………………….. 43
\n1.11 – Operational Definitions ……………………………………………………………………………… 46
\n1.12 – Assumptions, Limitations, and Delimitations ……………………………………………….. 46
\n2016
\nv
\n1.13 – Significance of the Study……………………………………………………………………………. 47
\n1.14 – Review of the Professional and Academic Literature …………………………………….. 47
\n1.15 \u2013 Transition ………………………………………………………………………………………………… 49
\nSECTION 2: THE PROJECT…………………………………………………………………………………. 50
\n2.1 – Purpose Statement ………………………………………………………………………………………. 51
\n2.2 – Role of the Researcher…………………………………………………………………………………. 51
\n2.3 – Participants ………………………………………………………………………………………………… 52
\n2.4 – Research Method ………………………………………………………………………………………… 53
\n2.5 – Research Design …………………………………………………………………………………………. 53
\nData Saturation in Qualitative Study Designs ……………………………………………… 53
\nHow to Use Multiple Sources to Support Claims and Decisions ……………………. 54
\n2.6 – Population and Sampling (Quantitative Only) ………………………………………………… 54
\nPopulation ………………………………………………………………………………………………. 54
\nSampling ………………………………………………………………………………………………… 55
\n2.7 – Population and Sampling (Qualitative Only) ………………………………………………….. 55
\nDefining the Population ……………………………………………………………………………. 55
\nSampling ………………………………………………………………………………………………… 55
\nData Saturation and Sampling …………………………………………………………………… 56
\n2.8 – Ethical Research …………………………………………………………………………………………. 56
\n2.9 – Data Collection\u2014Instruments (Quantitative)………………………………………………….. 57
\n2.10 – Data Collection \u2013 Instruments (Qualitative)………………………………………………….. 57
\n2.11 – Data Collection Technique …………………………………………………………………………. 60
\nQuantitative Studies…………………………………………………………………………………. 60
\nQualitative Studies…………………………………………………………………………………… 60
\n2.12 – Data Organization Technique (Qualitative Only) ………………………………………….. 60
\n2.13 – Data Analysis (Quantitative Only) ………………………………………………………………. 60
\n2.14 – Data Analysis (Qualitative Only) ………………………………………………………………… 61
\n2.15 – Study Validity (Quantitative Only) ……………………………………………………………… 63
\nInternal Validity………………………………………………………………………………………. 63
\nExternal Validity……………………………………………………………………………………… 65
\n2.16 – Reliability and Validity (Qualitative Only)…………………………………………………… 65
\n2016
\nvi
\nReliability……………………………………………………………………………………………….. 65
\nValidity ………………………………………………………………………………………………….. 65
\n2.17 – Transition and Summary ……………………………………………………………………………. 66
\nSECTION 3: APPLICATION TO PROFESSIONAL PRACTICE AND
\nIMPLICATIONS FOR CHANGE………………………………………………………………… 67
\n3.1 – Introduction ……………………………………………………………………………………………….. 68
\nQuantitative Example……………………………………………………………………………….. 68
\nQualitative Example…………………………………………………………………………………. 68
\n3.2 – Presentation of Findings (Quantitative)………………………………………………………….. 68
\nQuantitative Example……………………………………………………………………………….. 68
\nTests of Assumptions……………………………………………………………………………….. 69
\nDescriptive Statistics………………………………………………………………………………… 71
\nInferential Results……………………………………………………………………………………. 71
\n3.3 – Presentation of Findings (Qualitative)……………………………………………………………. 74
\n3.4 – Application to Professional Practice………………………………………………………………. 74
\n3.5 – Implications for Social Change …………………………………………………………………….. 74
\n3.6 – Recommendations for Action……………………………………………………………………….. 75
\n3.7 – Recommendations for Further Research ………………………………………………………… 75
\n3.8 – Reflections…………………………………………………………………………………………………. 75
\n3.9 – Conclusion…………………………………………………………………………………………………. 75
\n3.10 – Appendices\/Table of Contents…………………………………………………………………….. 75
\nAPPENDIX A: WALDEN UNIVERSITY DOCTOR OF BUSINESS ADMINISTRATION
\nPROGRAM VIDEO TITLES AND URL ADDRESSES …………………………………….. 76
\nAPPENDIX B: QUANTITATIVE RESEARCH PRIMER: PROBLEM STATEMENT,
\nPURPOSE STATEMENT, RESEARCH QUESTION(S), AND HYPOTHESES…. 77
\nAPPENDIX C: MAJOR QUANTITATIVE DESIGNS…………………………………………………. 83
\nAPPENDIX D: SAMPLING TYPOLOGIES………………………………………………………………… 84
\nAPPENDIX E: SAMPLE POWER ANALYSIS……………………………………………………………. 85
\nAPPENDIX F: SAMPLE QUANTITATIVE LITERATURE REVIEW OUTLINE……….. 86
\nAPPENDIX G: SAMPLE APA TABLES……………………………………………………………………… 89
\nAPPENDIX H: SAMPLE INTERVIEW PROTOCOL…………………………………………………. 95
\n2016
\nvii
\nBIBLIOGRAPHY: SUGGESTED READINGS LISTS…………………………………………………. 97
\nAssumptions, Limitations, and Delimitations…………………………………………………………. 98
\nAssumptions……………………………………………………………………………………………. 98
\nLimitations……………………………………………………………………………………………. 100
\nDelimitations…………………………………………………………………………………………. 103
\nCase Study Sources…………………………………………………………………………………………… 105
\nCase Study Seminal Books………………………………………………………………………………… 110
\nData Saturation and Data Collection Sources……………………………………………………….. 111
\nEthical Considerations\/IRB ……………………………………………………………………………….. 117
\nEthnography Sources………………………………………………………………………………………… 129
\nFocus Groups…………………………………………………………………………………………………… 137
\nInterview Protocol Sources………………………………………………………………………………… 142
\nInterviews Sources……………………………………………………………………………………………. 144
\nJournaling Sources……………………………………………………………………………………………. 155
\nMember Checking Sources………………………………………………………………………………… 158
\nMixed Methods Research ………………………………………………………………………………….. 160
\nNotetaking and Fieldwork………………………………………………………………………………….. 166
\nPhenomenological Sources………………………………………………………………………………… 168
\nPilot Studies…………………………………………………………………………………………………….. 174
\nQualitative Research Foundation………………………………………………………………………… 175
\nQualitative and Quantitative Sources…………………………………………………………………… 180
\nReliability, Validity, Transferability, and Generalizability Sources…………………………. 189
\nSampling and Incentives……………………………………………………………………………………. 196
\nSensemaking ……………………………………………………………………………………………………. 202
\nQualitative Software Analysis Sources………………………………………………………………… 205
\nTriangulation Sources……………………………………………………………………………………….. 210
\nForeword
\n2016
\n1
\nThe Rubric \u2013 Student, Committee, and Evaluation
\nDBA DOCTORAL STUDY RUBRIC
\n2016
\n2
\nStudent and Committee Information3
\nStudent\u2019s Name (Last, First):
\nStudent ID (For office use only):
\nChairperson:
\nSecond Committee Member:
\nUniversity Research Reviewer:
\nStudent to provide total number of references:
\n(As you consider your references, it is recommended that in business 85%
\nshould be within the past 5 years).
\nNote: Provide the required information in the yellow highlighted column.<\/p>\n
3 Chair will complete the yellow highlighted fields in this section before submitting the rubric. Be sure to include the
\nnames of all members of the committee.
\nMarch 2016
\n3
\nEvaluation4
\n5Date\/Stage of the Rubric:
\nDate of Review
\nBefore Proposal Oral Defense
\nBefore Proposal Oral (Revised)6
\nBefore Doctoral Study Oral Defense
\nBefore Doctoral Study Oral (Revised)7
\nNote: Place an \u201cX\u201d in column (yellow highlight) associated with the appropriate stage.
\nEvaluation of State of the DBA Doctoral Study or Proposal:
\nNo changes required, advance to next step; rubric requirements met
\nChanges required for resubmission; rubric requirements not met
\nNote: Place an \u201cX\u201d in the column (yellow highlight) associated with the appropriate evaluation
\ndecision.
\nMember Information:
\nName of member providing this review
\nRole of the member providing this review
\nNote: Enter the information in the yellow highlighted column.<\/p>\n
4 Each member of the committee completes the evaluation.
\n5 Be sure to follow the Process Checklist (located at http:\/\/academicguides.waldenu.edu\/researchcenter\/osra) naming
\nconvention when sending the document through the review process. Following the naming convention is vital for
\ntracking student progress throughout the doctoral study process.
\n6 Check when second and subsequent rubrics are needed if previous proposal defense was not passed.
\n7 Check when second and subsequent rubrics are needed if previous Doctoral Study defense was not passed.
\nMarch 2016
\n4
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(1.1) Abstract (To be completed only after completion of Section 3)
\na. Includes a WOW statement illuminating the problem under study.
\nb. Identifies the design (i.e., case study, phenomenological, quasi-experimental,
\ncorrelation, etc.) NOTE: Do not mention the method (qualitative\/quantitative)
\nin the abstract.
\nc. Identifies the study\u2019s population and geographical location.
\nd. Identifies theoretical (quantitative) or conceptual framework (qualitative) that
\ngrounded the study; theory\/conceptual framework names are lower case.
\ne. Describes the data collection process (e.g., interviews, surveys, questionnaires,
\netc.).
\nf. Describes the data analysis process (e.g., modified van Kaam method) to
\nidentify themes; in qualitative studies (e.g., t test, ANOVA, or multiple
\nregression), to report statistical data in a quantitative study.) Omit SW Titles.
\ng.
\nh. mentioned are used.
\ni. Identifies two or three themes that morphed from the study (qualitative).
\nj. Presents the statistical results for each research question (quantitative studies).
\nk. Describes how these data may contribute to social change (use the word social
\nchange and be specific on who specifically may benefit).
\n8
\nl. Ensures the first line in the abstract is not indented.
\nm. Ensures Abstract does not exceed one page.
\nn. Use plural verbs with data (e.g., the data were – the word data is the plural of
\ndatum).<\/p>\n
8 Begin this section as follows: The implications for positive social change include the potential to\u2026\u201d.
\nMarch 2016
\n5
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\no. Ensures all numbers are expressed in digits (i.e., 1, 2, 10, 20, etc.) and not
\nspelled out unless beginning a sentence; Ensures Abstract does not include
\nseriation (i.e., (a), (b), (c), etc.).
\n(1.2) Background of the Problem9
\nProvides a brief and concise overview of the context or background of the problem.
\nDBA Doctoral Studies are focused on applied business research. This sets the stage
\nfor the study. This heading should comprise no more than one page in length.
\n(1.3) Problem Statement
\nPlease review the video tutorial located @: http:\/\/youtu.be\/IYWzCYyrgpo to aid you in preparing the
\nProblem Statement.
\na. Provides a hook10 supported by peer- reviewed or government citation 5 or
\nless years old from anticipated completion date (CAO approval).
\nb. Provides an anchor11 supported by peer- reviewed or government citation 5 or
\nless years old from anticipated completion date (CAO approval).
\nc. States the general business problem Note: This element should start as follows:
\nThe general business problem is\u2026
\nd. States the specific business problem. Be sure to state who has the specific
\nproblem (i.e., small business leaders, project managers, supply chain
\nmanagers, etc.) Note: This element should start as follows: The specific
\nbusiness problem is that some (identify who has the problem)\u2026<\/p>\n
9
\nInclude an introductory paragraph before the Background of the Problem component. However, do not label this
\nintroductory paragraph with a L1 APA heading. The purpose of the background is to introduce the topic and
\nproblem you will address. Briefly indicate why the problem deserves new research. More important, the Doctoral
\nStudy must address applied research, so you will want to identify the need to solve an applied business problem. The
\ngoal of this section is to encourage readers to continue reading, to generate interest in the study, and provide an
\ninitial frame of reference for understanding the entire research framework
\n10 The hook should be a succinct WOW statement to catch the reader\u2019s attention.
\n11 An anchor comprises a number, percentage, dollar value, ratio, index, etc.
\nMarch 2016
\n6
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\ne. Ensures the specific business problem aligns with the research question and
\npurpose statement.
\nf. Problem Statement should be clear and succinct (It is recommended not to be
\napproximately 150 words).
\n\uf0b7 Check with Ulrich\u2019s Periodical Directory http:\/\/library.waldenu.edu\/728.htm to
\nensure citations are peer reviewed.12
\n\uf0b7 See Problem Statement Video Tutorial at: http:\/\/youtu.be\/IYWzCYyrgpo.
\n(1.4) Purpose Statement
\nDescribes the intent of the research13
\n. The Purpose Statement is a ministory and it should be
\napproximately 200 words. The Purpose Statement must address the following six elements:
\na. Identifies the research method as qualitative14, quantitative15, or mixedmethod.
\nb. Identifies research design16 (i.e., case study, phenomenological, quasiexperimental,
\ncorrelational, etc.).
\nc. If quantitative or mixed method: Identifies a minimum of two17 independent
\n(experimental\/quasi-experimental designs) or predictor (correlational designs)
\nand at least one dependent variable18. Note: The quantitative study must
\ninclude at least two independent\/predictor variables.
\n19 Ensures the independent<\/p>\n
12 Ulrich\u2019s is not 100% correct; the student must verify peer review status via the journal home page.
\n13 The first sentence of the purpose statement must align with the research question and specific business problem in
\nthe problem statement.
\n14 Visit the Center for Research Quality qualitative methodology tutorial at:
\nhttp:\/\/academicguides.waldenu.edu\/researchcenter\/resources\/Design
\n15 See the quantitative Research Primer located at Appendix B; Visit the Center for Research Quality quantitative
\nmethodology tutorial at: http:\/\/academicguides.waldenu.edu\/researchcenter\/resources\/Design
\n16 See Appendix C for a depiction of basic quantitative designs and their characteristics.
\n17 Covariates, mediator, and moderator variables are types of independent\/predictor variables; be sure to clearly
\nidentify these types of variables as applicable.
\n18 The terms \u201cindependent\u201d and \u201cpredictor variables are often used interchangeably in correlation studies. Please be
\nconsistent with the chosen terminology.
\n19 See Heading 1.6, Research Questions (Quantitative Only), in the Research Handbook.
\nMarch 2016
\n7
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\nvariables appropriately align with the variables\/constructs identified in
\ncomponent 1.10, Theoretical\/Conceptual Framework.
\nd. Identifies specific population group for proposed study.
\ne. Identifies geographic location of the study.
\nf. Identifies contribution to social change.
\ng. Ensures the first sentence links\/aligns directly with the specific business
\nproblem.
\n\uf0b7 See Purpose Statement Video Tutorial at: http:\/\/youtu.be\/pLP4r0mfT9A.
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(1.5) Nature of the Study20
\nProvides a brief discussion on the research method (i.e., quantitative or qualitative) and design (i.e.,
\ncorrelation for quantitative study; phenomenological, case study, etc., for a qualitative design); cite a
\nminimum of one source (The method and design will be discussed in detail in Section 2).
\n\uf0b7 Note: A single paragraph is sufficient for each component: one for the method and one for the
\ndesign.
\na. Identifies the selection of one method (qualitative, quantitative, or mixed
\nmethod) and why other methods would not work (cite a minimum of one
\nsource).
\nb. Identifies the selection of the design21 (within the method) and why it was
\nselected over other designs (cite a minimum of one source).
\n(1.6) Research Questions (Quantitative Only)
\na. Lists research question(s) in about 10-15 words.<\/p>\n
20 A single paragraph can be used for each component: one for the method and one for the design.
\n21 See Appendix C for a brief depiction of the major research designs.
\nMarch 2016
\n8
\nb. Ensures research question(s)22 align(s) with the specific business problem and
\nfirst line of the Purpose Statement.
\nc. Includes the independent\/predictor and dependent\/criterion variables as
\nidentified in the Purpose Statement; ensures the independent\/predictor
\nvariables appropriately align with the constructs\/variables identified in
\ncomponent 1.10, Theoretical\/Conceptual Framework.
\n(1.7) Hypotheses (Quantitative\/Mixed-Method Only)
\nStates, in accurate format, the null and alternative hypotheses for each research
\nquestion23
\n.
\n(1.8) Research Question – Qualitative Only
\na. Lists overarching research question in approximately 10-15 words.
\nb. Ensures research question aligns with the specific Business Problem and
\nPurpose Statement.
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(1.9) Interview Questions – Qualitative Only
\na. Lists each interview or focus group question. Questions must contribute
\nknowledge to the research question. Questions must be open-ended, and
\ncannot be answered with a Yes or No.
\nb. Ensures interview\/focus group questions align with the research question.<\/p>\n
22 The research question(s) must contain the independent\/predictor and dependent\/criterion variables identified in the
\nPurpose Statement.
\n23 Hypotheses must include the variables identified in the research question.
\nMarch 2016
\n9
\n(1.10) Theoretical\/Conceptual Framework24
\nClearly and concisely identify the theoretical\/conceptual framework. In quantitative studies, the
\ntheoretical framework is the appropriate term and in qualitative studies, the conceptual framework is
\nthe appropriate term. The student will articulate the theoretical\/conceptual framework with concepts
\nfrom the literature to ground and complement the applied business study.
\n\uf0b7 This component should not exceed one page. It will be expanded upon in the literature review.
\nSee Theoretical\/Conceptual Framework Video Tutorial at: http:\/\/youtu.be\/P-01xVTIVC8
\na. Identifies and describes the theory or conceptual model for
\ntheoretical\/conceptual framework.
\nb. Identifies theorist(s) of the theory or conceptual model for
\ntheoretical\/conceptual framework.
\nc. Identifies date of the theory or conceptual model for theoretical\/conceptual
\nframework (if applicable).
\n25
\nd. Identifies key concepts\/propositions\/tenets of the theory or conceptual model
\nfor theoretical\/conceptual framework26
\n.
\ne. Quantitative only – Ensures the theoretical constructs\/variables underlying the
\ntheory are clearly identified and align with the constructs\/variables
\n(independent variables) identified in the Purpose Statement and Research
\nQuestion(s).
\nNote: The independent variables\/constructs represent the underlying concepts of
\nthe theoretical framework in quantitative research.
\n\uf0b7 Identifies how\/why the theory or conceptual model for theoretical\/conceptual
\nframework is applicable and fits\/applies to the study.
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(1.11) Operational Definitions<\/p>\n
24 The theoretical\/conceptual framework informs the research (quantitative) and interview (qualitative) questions. Be
\nsure to review the Theoretical\/Conceptual Framework Video Tutorial at: http:\/\/youtu.be\/P-01xVTIVC8
\n25 Some literature identifies the specific date the theorist introduced the theory; provide this date if this is the case. If
\ndate is missing, then requirement (c) is not applicable.
\n26 Ensures the independent variables appropriately align with the theoretical framework(s) identified in component
\n1.10, Theoretical\/Conceptual Framework.
\nMarch 2016
\n10
\na. Presents technical terms, jargon, or special word used in the study.
\nb. Lists in alphabetical order. Formats in italics followed by an italicized colon.
\nThe definition follows on the same line. (This is similar to an APA Level 5
\nheading with a colon replacing the period.)
\nc. Provides citations (for each definition) from credible sources (peer-reviewed,
\nseminal work\/text, government sites, etc).
\nd. Does not include terms found in a basic academic dictionary (i.e., Webster\u2019s).
\ne. Does not exceed 10 key operational definitions.
\n(1.12) Assumptions, Limitations, and Delimitations
\na. Defines the term Assumptions and provides citation; lists facts that the student
\nassumes to be true but cannot actually be verified.
\nb. Defines the term Limitations and provides citation; lists potential weaknesses
\nof the study that are not within the control of the researcher.
\nc. Defines the term Delimitations and provides citation; identifies the bounds of
\nthe study.
\n(1.13) Significance of the Study27
\na. States why the study findings may be of value to businesses.
\nb. States how this study may contribute to effective practice of business
\n(improvement of business practice).
\nc. Identifies how the results might contribute to positive social change.<\/p>\n
27 This area is important in determining Doc Study of the Year Award-justify well.
\nMarch 2016
\n11
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(1.14) Review of the Professional and Academic Literature28
\nA. Literature Review Opening Narrative
\ni. Contains a brief discussion of the content of the literature that includes a
\ncritical analysis and synthesis of various sources\/content of the literature
\n(journals, reports, and scholarly seminal books, etc.) to convince readers of
\ndepth of inquiry.
\nii. Explains the organization of the review.
\niii.Explains the strategy for searching the literature.
\niv. The majority of references should be from peer-reviewed sources. (Consider
\n85% of the total sources should be peer-reviewed.)
\nv. The majority of references should be current. (As you consider your
\nreferences, it is recommended that in business around 85% should be within
\nthe past 5 years).
\nB. Application to the Applied Business Problem
\ni. Introduces the purpose of the study.
\nii. Identifies hypotheses if a quantitative\/mixed method study.
\niii.Contains a critical analysis and synthesis of literature pertaining to the
\ntheoretical\/conceptual framework the student identified in item #1.10,
\nTheoretical\/Conceptual Framework, above29
\n. The student includes a critical
\nanalysis with supporting and contrasting theories\/conceptual models for the
\ntheory in the theoretical\/conceptual framework.
\nSection 1
\nFoundation of the Study
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell<\/p>\n
28 The average length of substantive literature review is between 30 to 40 pages (25 pages minimum). However, the
\nneed for depth and breadth is required. See quantitative example at Appendix F and visit the Writing Center at:
\nhttp:\/\/writingcenter.waldenu.edu\/50.htm for more information on writing the literature review.
\n29 A key portion of the Review of the Literature must focus on the specific theoretical\/conceptual framework you
\nare using in your study. This is a \u201c key requirement for you to be able to adequately address items 3.2g, Presentation
\nof Findings (quantitative studies) and 3.3c, Presentation of Findings (qualitative studies).
\nMarch 2016
\n12
\niv.Contains a critical analysis and synthesis of literature pertaining to the
\nindependent variables (quantitative\/mixed-method studies) the student
\nidentified in item # 4c (Purpose Statement).
\nv. Contains a critical analysis and synthesis of literature pertaining to the
\ndependent variable(s) (quantitative\/mixed-method studies) the student
\nidentified in item # 4c (Purpose Statement).
\nvi. Discusses measurement of variables (quantitative\/mixed-method studies) the
\nstudent identified in item # 4c (Purpose Statement).
\nvii. Contains a critical analysis and synthesis of literature pertaining to potential
\nthemes and phenomena (qualitative studies) the student identified in the
\nPurpose Statement.
\nviii. Compares and contrasts different points of view, and the relationship of the
\nstudy to previous research and findings (sample size\/geographical location
\nvariance, etc.).
\nix. Provides a comprehensive critical analysis and synthesis of the literature.
\nC. Relevancy of the Literature
\nThe literature review is well organized. Introduce the purpose of the study.
\nInclude hypotheses if a quantitative\/mixed method study) in the opening
\nnarrative.
\nD. Literature Review Organization
\ni. Presented in a well-organized manner.
\nii. Adheres to APA formatting standards.
\n(1.15) Transition
\na. Ends with a Transition Heading that contains a concise summary30 of key
\npoints of Section 1.
\nb. Provides an overview introducing Sections 2 and 3.<\/p>\n
30 A concise summary recaps the major elements of the review of the literature and does not introduce new
\ninformation.
\nMarch 2016
\n13
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(2.1) Purpose Statement
\nBegins Section 2 with a restatement of the Purpose Statement presented in
\nSection 1.
\n\uf0b7 Note: Copy-and paste the purpose statement from Section 1
\n(2.2) Role of the Researcher
\nDescribes the role of the researcher in the data collection process and provides a peer-reviewed or
\nseminal source. Describes any relationship the researcher may have had with the topic, participants,
\nor research area.
\na. Describes the role of the researcher in the data collection process and provides
\na peer-reviewed or seminal source.
\nb. Describes any relationship the researcher may have had with the topic,
\nparticipants, or research area.
\nc. Provides a brief description of the researcher\u2019s role related to ethics and the
\nBelmont Report31 protocol.
\nd. Qualitative studies: Describes how the student will mitigate bias and avoid
\nviewing data through a personal lens\/or perspective.
\ne. Qualitative studies with interviews: Briefly describes the rationale for an
\ninterview protocol.
\nf. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.3) Participants32
\na. Describes the eligibility criteria for study participants.
\nb. Discusses strategies for gaining access to participants.
\nc. Identifies strategies for establishing a working relationship with participants.
\nd. The participants\u2019 characteristics must align with the overarching research
\nquestion.<\/p>\n
31 See Belmont Report at: https:\/\/www.tikessays.com\/write-my-paper\/hhs.gov\/ohrp\/humansubjects\/guidance\/belmont.html.
\n32 Select \u201cN\/A\u201d and explain why if participants are not used in the study.
\nMarch 2016
\n14
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\ne. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\nMarch 2016
\n15
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(2.4) Research Method
\nExpands on the discussion in Heading 1.5 (Nature of the Study).
\na. Identifies the use of a specific research method by indicating whether the
\nproposed study is quantitative, qualitative, or mixed methods.
\nb. Justifies the use of the research method over the other research methods.
\nc. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.5) Research Design
\nExpands on the discussion in Heading 1.5 (Nature of the Study).
\na. Identifies the use of a specific research design.
\nb. Justifies the use of the research design over other key designs for the study.
\nc. For qualitative studies, identifies how the student will ensure data saturation.
\nd. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.6) Population and Sampling (Quantitative Only)
\na. Describes the population from which the sample will come.
\nb. Demonstrates that population aligns with the overarching research question.
\nc. Describes and justifies the sampling method (i.e., probabilistic or
\nnonprobabilistic) and specific subcategory (i.e., simple random or
\nconvenience). Addresses the strength and weaknesses associated with the
\nchosen sampling method and subcategory ( Appendix C.)
\nd. Justifies sample size via power analysis (see example in Appendix E). Provides
\njustification for the proposed effect size, alpha, and power levels.
\ne. Cites the source for calculating or the tool used to calculate the sample size.
\nf. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\nMarch 2016
\n16
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(2.7) Population and Sampling (Qualitative Only)
\na. Justifies the number of participants33
\n\uf0b7 Describes and justifies the sampling method (e.g., purposeful, snowball,
\netc.).
\n\uf0b7 Describes and justifies the number of participants.
\n\uf0b7 Identifies how the student will ensure data saturation.
\nb. Demonstrates criteria for selecting participants and interview setting are
\nappropriate to the study. (Rich descriptions are encouraged.)
\nc. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.8) Ethical Research
\na. Discusses the informed consent process. Includes informed consent form in an
\nappendix and lists in the Table of Contents.
\nb. Discusses participant procedures for withdrawing from the study.
\nc. Describes any incentives for participating.
\nd. Clarifies measures that the student will use to assure that the ethical protection
\nof participants is adequate.
\ne. Refers to agreement documents in the (a) appendices, and (b) Table of
\nContents.
\nf. Includes statement that the student will store the data securely for 5 years to
\nprotect confidentiality of participants.
\ng. Final Doctoral Study includes the Walden IRB approval number.
\nh. Identifies how the student will protect names of individuals or organizations to
\nkeep the participants and organizations confidential.
\ni. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.9) Instrumentation (Quantitative Only)<\/p>\n
33 The DBA policy for phenomenological studies is a minimum of 20 participants.
\nMarch 2016
\n17
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\na. States the name of the instrument(s).
\nb. Identifies name of publisher\/developer(s) and year of development (if
\napplicable).
\nc. Discusses concept(s) measured by the instrument(s).
\nd. Includes a detailed description of data that comprise each construct\/variable
\nmeasured by the instrument(s).
\ne. Identifies scale of measurement (i.e., nominal, ordinal, interval, ratio) for each
\nconstruct\/variable measured by the instrument. Please see Scales of
\nMeasurement video tutorial at: http:\/\/youtu.be\/PDsMUlexaMY.
\nf. Discusses appropriateness to the current study (i.e., why is this the best
\ninstrument to use for measuring the variables\/constructs?)
\ng. Discusses instrument administration (e.g., how long, any special
\nrequirements\/tools, special instructions, pencil and paper, online, etc.).
\nh. Describes how scores are calculated and what the scores mean; identifies items
\nto be reverse- coded (if applicable).
\ni. Identifies where and\/or with what populations the instrument was normed;
\nidentifies where and with what populations other researchers have used the
\ninstrument(s) for collecting data.
\nj. Identifies published reliability (e.g., test-retest reliability, internal consistency,
\nsplit-half, etc.) and validity properties (e.g., construct validity, concurrent
\nvalidity, convergent validity, and discriminant validity) of the instrument(s)34
\n.
\nk. Identifies strategies used to assess validity (e.g., construct validity, concurrent
\nvalidity, convergent validity, discriminant validity) and reliability (e.g., testretest
\nreliability, internal consistency, split-half, etc.).
\nl. Discusses and justifies any adjustments or revisions to the use of standardized
\nresearch instruments.
\nm. Identifies where in appendices the instrument(s) (or copy of permission to use
\ninstrument or purchase is (are) located). Ensures Table of Contents lists
\nappendices. [Copies of the instrument may not be reproduced in an Appendix
\nwithout written permission.]<\/p>\n
34 Published reliability and validity properties might be found in the test review and in other studies where
\nthe instrument was used to collect data.
\nMarch 2016
\n18
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\nn. Describes where raw data will be available (appendices, tables, or by request
\nfrom the researcher).
\no. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.10) Data Collection Instruments (Qualitative Studies Only)
\na. In addition to identifying the student as the primary data collection instrument,
\nidentifies the data collection instrument\/process (e.g., informal interview,
\nsemistructured interviews, phenomenological in-depth interviews, focus
\ngroups, company\/archival documents, etc.).
\nb. Clarifies how the student will use the data collection instrument\/technique (the
\nprocess\/protocol).
\nc. Identifies how the student will enhance the reliability and validity of the data
\ncollection instrument\/process (e.g., member checking, transcript review, pilot
\ntest, etc.).
\nd. Identifies where in appendices the instrument (e.g., interview protocol, focus
\ngroup protocol, interview questions, etc.) is (are) located. Ensures Table of
\nContents lists appendices.
\ne. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.11) Data Collection Technique
\na. Describes the technique used to collect data such as an online\/paper survey,
\ninterview, observation, site visit, video recording (think recipe card\u2014step-bystep-process
\nand describe richly. Provides abridged interview protocol (see
\nAppendix H), focus group protocol, observation protocol, etc. and identifies
\nlocation in an appendix.
\nb. Describes advantages and disadvantages of data collection technique.
\nc. As applicable, describes the process for conducting a pilot study after IRB
\napproval.
\nd. For qualitative studies, identifies how the student will use member checking of
\nthe data interpretation or transcript review (if applicable).
\ne. Supports every decision with a minimum of three scholarly peer-reviewed or
\nseminal sources.
\nMarch 2016
\n19
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(2.12) Data Organization Techniques (Qualitative Only).
\na. Describes the systems for keeping track of data, emerging understandings such
\nas research logs, reflective journals, and cataloging\/labeling systems.
\nb. Reminds readers all raw data will be stored securely for 5 years.
\nc. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.13) Data Analysis (Quantitative Only)
\na. Restates the research questions and hypotheses from Section 1.
\nb. Describes and defends, in detail, the statistical analyses that the student will
\nconduct (e.g., multiple regression, two-way ANOVA, etc.).
\nc. Describes and defends, in detail, why other statistical analyses are not
\nappropriate.
\nd. Provides explanation of data cleaning and screening procedures as appropriate
\nto the study.
\ne. Provides explanation for addressing missing data.
\nf. Identifies and explains the assumptions pertaining to the statistical analyses.
\ng. Identifies the process for testing\/assessing the assumptions.
\nh. Identifies appropriate actions to be taken take if the assumptions are violated35
\n.
\ni. Describes how the student will interpret inferential results (i.e. key parameter
\nestimates, effect sizes, confidence intervals, probability values, odds ratios,
\netc.).
\nj. Identifies statistical software and version that the student will use in the data
\nanalysis process (e.g., SPSS, Excel, R, etc.).
\nk. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).<\/p>\n
35 Bootstrapping can be used as an effective method for addressing violations of assumptions.
\nMarch 2016
\n20
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(2.14) Data Analysis (Qualitative Studies Only)
\na. Identifies the appropriate data analysis process for the research design (e.g., one
\nof the four types of triangulation for case study; modified van Kaam, van
\nMaanen, etc. for phenomenology).
\nb. Provides a logical and sequential process for the data analysis.
\nc. Details the student\u2019s conceptual plan or software (e.g., NVivo, Atlasti,
\nEthnograph, Excel, etc.) for coding, mind-mapping, and identifying themes.
\nd. Identifies how the student will focus on the key themes, correlate the key themes
\nwith the literature (including new studies published since writing the proposal)
\nand the conceptual framework.
\ne. It is recommended to support claims and decisions with multiple scholarly peerreviewed
\nor seminal sources (as appropriate).
\n(2.15) Study Validity (Quantitative Only)36
\na. Experimental\/quasi-experimental designs only: Describes threats to external
\nvalidity (e.g., testing reactivity, interaction effects of selection and experimental
\nvariables, specificity of variables, reactive effects of experimental arrangements,
\nand multiple-treatment interference, as appropriate to the study) and how the
\nstudent will address the threats to external validity.
\nb. Experimental\/quasi-experimental designs only: Describes threats to internal
\nvalidity (e.g., history, maturation, testing, instrumentation, statistical regression,
\nexperimental mortality, and selection-maturation interaction, as appropriate to
\nthe study) and how the student will address the threats to internal validity.
\nc. Discusses threats to statistical conclusion validity37 (e.g., factors that affect the
\nalpha\/Type I error rate) and how the student will address the threats to statistical
\nconclusion validity.
\nd. Describes the extent to which, and rationale for justifying if, and if so why,
\nresearch findings can be generalized to larger populations (external validity) and
\napplied to different settings.<\/p>\n
36 Items \u201ca\u201d and \u201cb\u201d pertain to experimental and quasi-experimental designs only. Item \u201cc\u201d pertains to all quantitative
\ndesigns. Discuss validity as it pertains to the study outcomes. This component is not to address the reliability and
\nvalidity of the study instruments. The reliability and validity of the study instruments is addressed in item 2.9
\n(quantitative) and 2.10 (qualitative). Item \u201cd\u201d, external validity, pertains to all quantitative designs.
\n37 The three factors to be discussed are (a) reliability of the instrument, (b) data assumptions, and (c) sample size.
\nMarch 2016
\n21
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\ne. It is recommended to support claims and decisions with multiple scholarly
\npeer-reviewed or seminal sources (as appropriate).
\n(2.16) Reliability and Validity (Qualitative Only): A key difference from quantitative research is
\nthe reliability and validity headings. The analogous criteria for qualitative studies are credibility,
\ntransferability, dependability, and confirmability. These criteria are not measurable and need to be
\nestablished using qualitative methods such as member checking–Marshall and Rossman (2016) have
\na good definition, and triangulation (data triangulation, investigator triangulation, theoretical
\ntriangulation, and methodological triangulation). See Norman Denzin\u2019s (1978, 2009) works on
\ntriangulation). Please review more detailed information on qualitative validity at:
\nhttps:\/\/www.tikessays.com\/write-my-paper\/socialresearchmethods.net\/kb\/qualval.php
\nReliability
\na. Identifies how the student will address dependability. (i.e., member checking of
\ndata interpretation, transcript review, pilot test, etc.).
\nb. It is recommended to support claims and decisions with multiple scholarly peerreviewed
\nor seminal sources (as appropriate)..
\nValidity
\nc. Identifies how the student will ensure credibility (i.e., member checking of the
\ndata interpretation, participant transcript review, triangulation, etc.).
\nd. Identifies how the student will address transferability in relation to the reader
\nand future research.
\ne. Identifies how the student will address confirmability.
\nf. Identifies how the student will ensure data saturation.
\ng. It is recommended to support claims and decisions with multiple scholarly peerreviewed
\nor seminal sources (as appropriate).
\n(2.17) Transition and Summary
\na. Ends with a Transition Statement that contains a summary of key points.
\nb. Includes an overview of what the student will cover in Section 3.
\nProposal Stage. Before IRB approval, the paper is written in future tense and after
\nIRB approval, the paper is changed to past tense.
\nWriting Style. The paper is written in predominantly active voice without slang,
\neuphemisms, or anthropomorphisms.
\nMarch 2016
\n22
\nSection 2
\nThe Project
\n(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\nFollows APA 6th edition in the text and in the reference list
\nReferences: Of the total sources cited, a minimum of 85% must be peer reviewed (it is
\nrecommended that in business 85% should be within the past 5 years of anticipated completion date);
\nensures there is a match between citations and reference list.
\nCongratulations! This ends the Proposal section. See the Process Checklist located
\nat the Center for Research Quality website (see URL below).
\nhttp:\/\/researchcenter.waldenu.edu\/Documents\/DBA_Process_Checklist.pdf
\nMarch 2016
\n23
\nSection 3
\nApplication for Professional Practice and Implications for Social Change (FOR
\nDBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\n(3.1) Introduction
\na. Begins with the purpose of the study. Do not repeat the entire purpose
\nstatement. Typically, the first sentence of the purpose statement will suffice.
\nb. Provides a brief summary of the findings (do not exceed one page).
\n(3.2) Presentation of Findings (Quantitative Only)
\na. Describes the statistical test(s), the variables, and the purpose of the test(s) and
\nhow they relate to the hypotheses.
\nb. Presents relevant descriptive statistics38 (i.e. mean, standard deviation for scale
\nvariables; frequencies and percentages for nominal variables).
\nc. Provides evaluation of statistical assumptions from Heading 2.13e.
\nd. Reports inferential statistical analyses results, organized by research question,
\nin proper APA statistical notation\/format. Includes the alpha level chosen for
\nthe test, test value, p (significance level) values, effect size, degrees of
\nfreedom, confidence intervals (when appropriate), etc.
\ne. Includes appropriate tables39 and figures to illustrate results, as per the current
\nedition of the Publication Manual of the American Psychological Association.
\nf. Summarizes answers to research questions.<\/p>\n
38 See the following link for further information on descriptive statistics:
\nhttps:\/\/www.tikessays.com\/write-my-paper\/socialresearchmethods.net\/kb\/statdesc.php
\n39 See Appendix E for basic formatted descriptive and inferential statistic tables.
\nMarch 2016
\n24
\nSection 3
\nApplication for Professional Practice and Implications for Social Change (FOR
\nDBA DOCTORAL STUDY DOCUMENTS)
\nQuality Indicators
\nType Met,
\nNot Met, or
\nN\/A in Each
\nCell
\ng. 40Describes in what ways findings confirm, disconfirm, or extend knowledge
\nof the theoretical framework and relationship(s) among variables by
\ncomparing the findings with other peer- reviewed studies from the literature
\nreview that includes studies addressed during the proposal stage and new
\nstudies since writing the proposal. Ties findings or disputes findings to the
\nexisting literature on effective business practice.
\nh. Analyzes and interprets the findings in the context of the theoretical
\nframework, as appropriate.
\ni. Ensures interpretations do not exceed the data, findings, and scope.
\n(3.3) Presentation of Findings (Qualitative Only)
\na. Lists the overarching research question.
\nb. Identifies each theme. Analyzes and discusses findings in relation to the
\nthemes.
\nc.
\n41Describes in what ways findings confirm, disconfirm, or extend knowledge
\nin the discipline by comparing the findings with other peer-reviewed studies
\nfrom the literature review that includes new studies since writing the proposal.
\nd. Ties findings to the conceptual framework
\ne. Ties findings or disputes findings to the existing literature on effective
\nbusiness practice.
\n(3.4) Application to Professional Practice42
\nProvides a detailed discussion on the applicability of the findings with respect to the
\nprofessional practice of business. This major subsection provides a rich academic
\nargument for why and how the findings are relevant to improved business practice.<\/p>\n
40 It is important to ensure the review of the literature is a critical analysis and synthesis of the theory and variables
\nidentified in the study.
\n41 It is important the student includes a critical analysis and synthesis of the new literature (studies) published since
\nthe proposal and correlates the literature with the findings in the study.
\n42 This is an important area for Doctoral Study of the Year Award.
\nMarch 2016
\n25
\n(3.5) Implications for Social Change43
\nExpresses implications in terms of tangible improvements to individuals,
\ncommunities, organizations, institutions, cultures, or societies as the findings could
\nbeneficially affect social change\/behaviors.
\n(3.6) Recommendations for Action
\na. Ensures recommendations flow logically from the conclusions and contain
\nsteps to useful action.
\nb. States who needs to pay attention to the results.
\nc. Indicates how the results might be disseminated via literature, conferences,
\ntraining, etc.
\n(3.7) Recommendations for Further Research44
\nLists recommendations for further study related to improved practice in business.
\nIdentifies how limitations identified in Section 1.12b, Limitations, can be addressed in
\nfuture research.
\n(3.8) Reflections
\nIncludes a reflection on the researcher’s experience within the DBA Doctoral Study
\nprocess, in which the researcher discusses possible personal biases or preconceived
\nideas and values, the possible effects of the researcher on the participants or the
\nsituation, and any changes to the researcher\u2019s thinking after completing the study.
\n(3.9) Conclusion
\nCloses with a strong concluding statement making the take-home message clear to
\nthe reader.
\n(3.10) Appendices\/Table of Content
\na. Consent form(s) attached. (Redact\/blackout all personal or identifying data.)
\ninformation.)
\nb. Organizational permission (Blackout name).
\nc. Sample of Instrument (i.e., survey, interview protocol with interview
\nquestions, observation protocol, etc.; copyrighted surveys cannot be included
\nw\/o written permissions.)<\/p>\n
43 This is an important area for Doctoral Study of the Year Award.
\n44 Limitations identified in section 1.12b, as a minimum, are ideal sources for future studies.
\nMarch 2016
\n26
\nDBA RESEARCH HANDBOOK
\nDoctor of Business
\nAdministration
\nResearch Handbook
\nMarch 2016
\n27
\nSECTION 1: FOUNDATION OF THE STUDY
\nSection 1 \u2013 Foundation of
\nthe Study
\nMarch 2016
\n28
\nNote: This handbook is not in the DBA Doctoral Study Template. Make certain that the proposal
\nand study conform to DBA Doctoral Study Template heading sequencing, and formatting with
\nthe correct margins and line spacing.
\n1.1 – Abstract
\nThe abstract must not exceed one page. The abstract text must be double-spaced with no
\nparagraph breaks. The first line must not be indented. Describe the overall research problem
\nbeing addressed in the first couple of sentences and indicate why it is important (e.g., who would
\ncare if the problem were solved). You can include a general introduction of the issue in the first
\nsentence, but you need to move to a clear statement of the research problem. Identify the purpose
\nand theoretical foundations, summarize the key research question(s), and briefly describe the
\noverall research design and data analytic procedures. Identify the key results, themes, one or two
\nconclusions, and recommendations that capture the heart of the research. Conclude with a
\nstatement on the implications for positive social change. Here are some form and style tips: (a)
\nlimit the abstract to one page; (b) maintain the scholarly language used throughout the doctoral
\nstudy; (c) keep the abstract concise, accurate, and readable; (d) use correct English; one may use
\npassive voice in the abstract; (e) ensure each sentence adds value to the reader\u2019s understanding of
\nthe research; (f) use the full name of any term and if the acronym is used more than once in the
\nabstract include the acronym in parentheses. Do not include references or citations in the
\nabstract. Per APA style, unless at the beginning of a sentence, use numerals in the abstract, and
\ndon\u2019t identify the titles of any software. Do not include seriation (i.e., (a), (b), (c), etc.)
\n1.2 – Background of the Problem
\nThe purpose of the background is to introduce the topic and problem you will address.
\nBriefly, you want to indicate why the problem deserves new research. More important, the
\nDoctoral Study must address applied research, so you will want to identify the need to study how
\nsome business leaders are solving or have solved an applied business problem. The goal of this
\nheading is to encourage readers to continue reading, to generate interest in the study, and provide
\nan initial frame of reference for understanding the entire research framework.
\nApplied DBA Versus a Speculative\/Theoretical PhD
\nA DBA study is an applied business study linking theory to professional practice.
\nStudents can use the following criteria to ensure that they have a clear DBA business study or a
\nDBA business study rather than a PhD business study. In contrast to a DBA study, a PhD study
\nis a hypothetical\/theoretical study that leads to expanding or creating theory rather than solving a
\nbusiness problem.
\nQualitative studies. A qualitative study about people\u2019s perceptions on how to address a
\nbusiness problem is hypothetical and is a PhD study. In contrast, a qualitative study is about a
\nstrategy that a business leader or manager has implemented \/is implementing to solve a business
\nproblem or a strategy that a business leader or manager has implemented to solve a business
\nproblem is an applied DBA study.
\nMarch 2016
\n29
\nQuantitative studies. A quantitative study that includes one or more variables in which
\nthe leader or manager cannot change to solve a business problem is a hypothetical\/theoretical
\nPhD study. Whereas, a quantitative study that includes only variables which business leaders or
\nbusiness managers can manipulate or change to solve a business problem is an applied DBA
\nstudy.
\nPreparing the Background of the Problem
\nThe Background of the Problem can be effectively accomplished in no more than one
\npage; brevity and clarity are essential. The Review of the Literature will provide a more detailed
\ndiscussion on the literature pertaining to the topic\/problem. Immersing yourself in the literature
\non your topic\/problem is crucial to uncovering a viable business problem. Do not underestimate
\nthe importance of the literature in helping identifying a viable business problem.
\nThe research topic is broad in nature; do not narrow the focus too quickly. You want to
\nprovide the reader, especially those not familiar with the topic, time to become familiar with the
\ntopic. Transition the reader to a more a concise presentation of the specific business
\ntopic\/problem under study. This component focuses on identifying why the study is important,
\nhow the study relates to previous research on the topic\/problem, and gives the reader a firm sense
\nof what your study is going to address and why. The Background of the Problem contains
\ninformation supporting the business problem. Do not describe, explain, justify, etc., the need for
\nthe study in the Problem Statement. Provide these critical elements (description, explanation,
\njustification, etc.) in the Background of the Problem component. As such, the Problem Statement
\ncan be written effectively in as little as four sentences: (a) hook, (b) anchor (c) general business
\nproblem, and (d) specific business problem. Transfer the supporting references in the
\nBackground of the Problem to the Problem Statement, but submit in a concise manner. For
\nexample, the hook and anchor reference provided in the Background of the Problem should be
\nused in the Problem Statement.
\nMarch 2016
\n30
\nInclude a transition statement that leads to problem statement that will provide more
\nspecificity regarding the problem identified in the Background or the Problem component. A
\nwell-written transition signals a change in content. It tells your reader that they have finished one
\nmain unit and are moving to the next, or it tells them that they are moving from a general
\nexplanation to a specific example or application. A transition form the background to the
\nProblem Statement is often as brief as one sentence, as follows: The background to the problem
\nhas been provided; the focus will now shift to the Problem Statement. Tip: Many potential
\nbusiness topics\/problems can be found in the Area for Future Research heading of most peerreviewed
\njournal articles.
\n1.3 – Problem Statement
\nAs shown in the following graphic, the Problem Statement must include four specific
\ncomponents the (a) hook, (b) anchor, (c) general business problem, and (d) specific business
\nproblem. The Problem Statement is not to exceed 150 words. One should utilize the Tool\/Word
\nCount feature in Microsoft Word to ensure the word count does not exceed the 150 maximum
\nword requirement. More important, ensure the problem statement reflects an applied business
\nproblem; avoid Rubric Creep45
\n. You must ensure you map to the rubric requirements. This is the
\nmost critical component of the doctoral study and will be highly scrutinized in the review
\nprocess. Again, the Problem Statement is not to identify causes for the problem, solutions to the
\nproblem, or any other superfluous information. A well-written problem statement can be
\npresented in four to five sentences. Please review the training video (see link below) developed
\nby the DBA methodology team to aid in writing your problem statement. The video will help
\nadd clarity and save you time. The Problem Statement Video Tutorial can be found at:
\nhttp:\/\/youtu.be\/IYWzCYyrgpo.<\/p>\n
45 Rubric creep occurs when the problem statement does not reflect an applied business problem.
\nMarch 2016
\n31
\nDBA students are seeking a degree in business and must ensure the problem statement is
\nbusiness focused. The problem statement must not represent a problem that has a social,
\npsychological, educational, or other discipline specific emphasis. A business problem is
\nsomething that is a problem for a business from the perspective of the business managers or the
\nindustry\u2019s leaders. Therefore, it is important to adopt a management perspective, and not that of
\nsocial advocates. The perspective must be from the position of the managers and leaders of
\nbusiness who can address the problem.
\nAvoiding Rubric Creep
\nTo ascertain if a problem addresses a business issue or has Rubric creep\/Rubric drift,
\nplease consider the following:
\n\uf0b7 An important indicator that a business related problem is a specific business problem is
\nthat the problem statement relates to a key business process that organizational leaders
\nneed to address and effectively meet the organization\u2019s mission.
\n\uf0b7 A business problem relates to one or more critical success factors (CSFs). Business
\nleaders use business processes to function effectively to complete one or more CSF\u2019s
\nneeded to carry out their business mission.
\n\uf0b7 A business problem is one that a business manager\/leader can solve.
\nConduct a final check of the problem statement by putting the hook, anchor, general business
\nproblem, and specific business problem in bullet form and check for alignment among the four
\nbullets. When you can ensure that the problem statement aligns throughout, write in scholarly
\nnarrative form (no bullets).
\nStrategy for Mapping to the Rubric
\n\uf0b7 Read the rubric requirements for a heading.
\n\uf0b7 Read what you wrote in the heading.
\n\uf0b7 Read the rubric requirements for a heading again.
\n\uf0b7 Read what you wrote in the section and highlight (in the proposal and the rubric) the
\nrubric elements that you addressed in the heading.
\n\uf0b7 Revise the heading as needed to include the rubric elements that you missed and
\neliminate superfluous narrative.
\n\uf0b7 Start the process at the top again until you have mastered the rubric elements in the
\nheading.
\nSpecific Business Problem
\nThe specific business problem is the genesis of one\u2019s study. It is vital that one has a clear
\nand precise specific business problem. One will align the contents of the Research Question and
\nPurpose Statement with the specific business problem.
\nThe qualitative specific business problem. The qualitative specific business problem
\nmust be well defined and not contain multiple issues (variables in quantitative studies). The
\nMarch 2016
\n32
\nfollowing graphic depicts how to include the elements needed in a qualitative specific business
\nproblem.
\nThe quantitative specific business problem. The quantitative specific business problem
\nmust be well defined and contain the key variables. The following graphic depicts how to include
\nthe elements needed in a qualitative specific business problem.
\nMarch 2016
\n33
\nAligning the Specific Business Problem With the Purpose Statement and RQ
\nMake certain that the specific business problem, Purpose Statement, and Research
\nQuestion (RQ) align. A good technique to use to enhance the alignment is to put the specific
\nbusiness problem, RQ, and first sentence of the Purpose Statement together on a blank document
\nto ensure that you are using the same words. Notice the suggested order differs from the order
\nthe headings appear in the study.
\nQualitative alignment example. The graphic below provides an example of alignment
\namong the Specific Business Problem, Research Question, and first sentence of the Purpose
\nStatement using the same key words. Pay attention to the words one uses in identifying the issue
\nthat the leader lacks or has in limited supply. The word determines how one can collect data.
\n\uf0b7 Some business leaders lack understanding\u2026 To ascertain what one understands will
\nrequire a quantitative design.
\n\uf0b7 Some business leaders lack knowledge\u2026 To ascertain a business leader\u2019s knowledge will
\nrequire a quantitative design.
\n\uf0b7 Some business leaders lack strategies (or have limited plans, processes, procedures)\u2026 To
\nascertain a business leader\u2019s strategies may involve interviews, focus groups, company
\narchival records and documents, company policies and procedures, company
\nintranet\/Internet site, and direct\/participant observation (in some cases) to collect data.
\nUsually interviews or focus groups are the primary data collection method.
\n\uf0b7 Some business leaders lack skills\u2026 To ascertain a business leader\u2019s skills will involve
\ndirect\/participant observation as the primary data collection method.
\nMarch 2016
\n34
\nQuantitative alignment example. Notice how the Specific Business Problem, Research
\nQuestion, and first sentence of the Purpose Statement use the same key words with the exception
\nthat the research question and subsequent first sentence in the purpose statement do not address
\nthe business leader\u2014this is a difference between qualitative and quantitative studies. The
\nfollowing is an example of alignment for a quantitative correlational study.
\nMarch 2016
\n35
\n1.4 – Purpose Statement
\nThere is a difference in the rubric requirements for a quantitative versus a qualitative
\nstudy. The Purpose Statement must include the following components: (a) methodology, (b)
\ndesign, (c) independent and dependent variables (for quantitative studies only), (d) specific
\npopulation and justification for using the chosen population, (e) geographical location, and (f)
\nthe study\u2019s potential for effecting social change. The Purpose Statement is not to exceed 200
\nwords. One should utilize the Tool\/Word Count feature in Microsoft Word to ensure the word
\ncount does not exceed 200 words. The Purpose Statement is to be a concise statement and must
\nnot include detailed design information (sample size, data collection, etc.). Please be sure to map
\nto the rubric. Please review the purpose statement video at: http:\/\/youtu.be\/pLP4r0mfT9A. This
\nvideo tutorial will be helpful to you in preparing your Purpose Statement.
\nSix Elements of the Purpose Statement
\nAs mentioned above, the Purpose Statement consists of six elements. These six elements,
\nand their contents, are:
\nMethodology. The first element to be presented in the Purpose Statement is the research
\nmethodology. The methodology is the overall philosophical assumption the researcher uses for
\ndesigning and developing the study. In other words, the methodology is a worldview of how
\nknowledge is acquired. The qualitative method is a means for exploring and understanding the
\nmeaning individuals or groups ascribe to a business problem. The qualitative method involves
\nresearchers using open-ended questions to learn what a business leader is doing or has done to
\nsolve a business problem. The quantitative method involves researchers using closed-ended
\nquestions to test hypotheses. Mixed-method studies contain a qualitative study methodology and
\na quantitative study methodology and must meet the requirements of both methodologies.
\nMixed-method studies are rarely conducted in the DBA program. You simply need to identify
\nthe methodology for or your study in a single sentence. There is no other information required
\nother than this single statement.
\nDesign. The second element to be presented in the Purpose Statement is the research
\ndesign. While there are numerous designs, the most common qualitative designs seen in DBA
\ndoctoral studies are the case study design, miniethnography, focus group, and the
\nphenomenological design. The correlational design is the most common design for quantitative
\nstudies. You simply need to identify the design of your study. There is no other information
\nrequired other than this single statement.
\nVariables (quantitative study only)46
\n. A variable is any entity that can take on different
\nvalues. Another definition of a variable is that it is a characteristic or condition that changes or
\nhas different values for different individuals or units of analyses (i.e. sample units). More so,
\nvariables are the corner stone of quantitative research, where the researcher seeks to explain the
\nrelationships among variables or to compare group differences regarding a variable or variables<\/p>\n
46 See section 1.6 \u201cResearch Questions\u201d for more information on variable requirements.
\nMarch 2016
\n36
\nof interest. Another important distinction for term variable is the distinction between an
\nindependent and dependent variable.
\nAn independent variable is the variable you have control over (experimental designs),
\nwhat you can choose and manipulate. A dependent variable is also known as a response variable
\nor explained variable. The independent variable is usually what you think will affect the
\ndependent variable. In some cases, you may not be able to manipulate the independent variable.
\nIt may be something that is already there and is fixed (i.e. company size), something you would
\nlike to evaluate with respect to how it predicts, influences, impacts, or causes a change in the
\ndependent variable (i.e. employee satisfaction).
\nAs it applies to your research, the dependent variable is normally the problematic
\nvariable in DBA studies where the researcher it trying to explain what influences, affects, causes
\nor can predict the problem. For example, if the specific business problem is low employee
\nsatisfaction then employee satisfaction is the dependent variable. The researcher then selects
\nindependent variables that are thought to predict, influence, impact, or cause the dependent
\nvariable, in this case, employee satisfaction.
\nThus, it is extremely important to identify clearly the independent and dependent
\nvariables in the Purpose Statement component of the proposal. Identification of the variables
\ninforms other research components such as sample size and type of statistical analysis that is to
\nbe conducted. See more on variables at: https:\/\/www.tikessays.com\/write-my-paper\/socialresearchmethods.net\/kb\/variable.php
\nTargeted population. A population is the larger group that you are studying. The
\npopulation is not to be misconstrued as the sample, or your study\u2019s participants. You will select
\nyour sample, or study participants from the larger population. For example, your population
\nmight be all small business leaders in New York. You will however, select a subset of small
\nbusiness leaders in New York to serve as your sample or participants. Remember, you are to
\naddress the broader population in this component of the Purpose Statement.
\nIn a qualitative ethnographic or case study, you will need to define the population with
\nthe scope of the study. For example, if you are conducting a single case study, the population
\nwill be people that meet the participant criteria within that organization\/company. Likewise, in a
\nmultiple case study the population will be the people that meet the participant criteria within the
\norganizations\/companies in the study.
\nExamples for a case study with the following research question: What strategies do
\ndepartment store managers use to motivate their sales associates?
\nSingle case study example. The population will be department store managers in one
\nNew England department store who have a strategy to motivate their sales associates.
\nMultiple case study example. The population will be department store managers in four
\nNew England department stores who have a strategy to motivate their sales associates.
\nGeographical location. The geographical location simply identifies the geographical
\nlocation of your study\u2019s participants. The participants might be in a particular country, region,
\nMarch 2016
\n37
\nstate, or city. Of course, this may vary based upon the purpose of your study. In the decision to
\nidentify the geographic location, one must ensure that the confidentiality of the company(ies) and
\nparticipants. If one is conducting a study in an automotive manufacturing facility and there are
\nonly one or two companies in the city or state (i.e. Alabama), one should define the geographic
\nlocation to avoid the specific sample units being easily identifiable (i.e., southern United States).
\nSocial change. The final element of your Purpose Statement requires you to provide a
\npositive social change statement. Positive social change involves improvement of human or
\nsocial conditions by promoting the worth, dignity, and development of individuals, communities,
\norganizations, institutions, cultures, or societies. Focus on explaining \u201cWHO\u201d may benefit, and
\n\u201cHOW\u201d the \u201cWHO\u201d may benefit from your study\u2019s findings and recommendations.
\nQuantitative hypothetical example. The purpose of this quantitative correlation study is
\nto examine the relationship between leadership styles, size of business, and business revenue.
\nThe independent variables are leadership style and size of business size. The dependent variable
\nis business revenue. The targeted population will consist of business leaders of microelectronic
\ncompanies in the southeast United States. The implications for positive social change include the
\npotential to (provide social change statement).
\nNote: DBA doctoral studies require the highest level or rigor and scholarship. One
\nfocus of rigor and scholarship is that of the number of predictor or independent variables
\nexamined in quantitative doctoral studies. Nonexperimental research (i.e. correlation, quasiexperimental,
\netc.) requires the use of at least two independent or predictor variables.
\nQualitative hypothetical example (case study). The purpose of this qualitative multiple
\ncase study is to explore the strategies that department store managers use to motivate their sales
\nassociates. The targeted population will comprise of department store managers form one of the
\nthree department stores in the southeast region of the United States who have implemented
\nstrategies to motivate their sales associates. The implication for positive social change includes
\nthe potential to (provide social change statement).
\nNote: In a case study, and often in ethnographic studies, the population is limited to those
\npeople meeting the participant criteria in the company or companies being studies. In a
\nphenomenological or narrative study, the population includes all people who meet the participant
\ncriteria.
\n1.5 – Nature of the Study
\nThe Nature of the Study component serves two purposes (a) describing and justifying the
\nmethodology (i.e. quantitative, qualitative, mixed-method) and (b) describing and justifying the
\ndesign (i.e. case study, phenomenological, correlation, sequential explanatory, etc.). Therefore, a
\nwell-crafted Nature of the Study can be presented in two paragraphs and not exceed one page.
\nThe first paragraph describes and justifies the methodology and the second paragraph describes
\nand justifies the design. These two components should not be intermingled. A common error in
\nthis heading is to restate the purpose, identify variables, analyses, etc. and include other
\nsuperfluous information. Again, map to the rubric and only include the required content!
\nMarch 2016
\n38
\nRemember that the Nature of the Study succinctly represents your defense of your choice
\nof method and design; therefore, it must have depth. You must demonstrate to the reviewers
\nthat you have done the reading and research needed to support your research method and design.
\nThat evidence also includes discussing why you did not choose other methods and designs.
\nKeep this heading deep yet brief. You will have time to expand upon the Nature of the Study
\nlater in the Research Method and Design heading.
\nHypothetical Quantitative Example47
\nI chose a quantitative methodology for this study. Using a quantitative study enables one
\nto identify results that can be used to describe or note numerical changes in numerical
\ncharacteristics of a population of interest; generalize to other, similar situations; provide
\nexplanations of predictions, and explain casual relationships (cite). Thus, the quantitative method
\nis appropriate for this study because the purpose of the study is to analyze numerical data and
\ninfer the results to a larger population. A mixed methods study contains the attributes of both
\nquantitative and qualitative methods (cite). The qualitative method is appropriate when the
\nresearch intent is to explore business processes, how people make sense and meaning, and what
\ntheir experiences are like (cite). Therefore, the qualitative and qualitative portions of a mixedmethod
\napproach are not appropriate for this study.
\nSpecifically, the correlation design is chosen for this study. A correlation researcher
\nexamines the relationship between or among two or more variables (cite). The correlation design
\nis appropriate for this study because a key objective for this study is to predict the relationship
\nbetween a set of predictor variables (leadership style and size of business) and a dependent
\nvariable (company revenue). Other designs, such as experimental and quasi-experimental designs
\nare appropriate when the researcher seeks to assess a degree of cause and effect (cite). This
\nprincipal objective for this study is to identify a predictive model; thus the experimental and
\nquasi-experimental designs are not appropriate.
\nHypothetical Qualitative Example
\nThe three research methods include qualitative, quantitative, and mixed methods (cite). I
\nselected the qualitative method to use open-ended questions. Qualitative researchers use openended
\nquestions to discover what is occurring or has occurred (cite). In contrast, quantitative
\nresearchers use closed ended questions to test hypotheses (cite). Mixed methods research
\nincludes both a qualitative element and quantitative element (cite). To explore (your topic), I
\nwill not be testing hypotheses which is part of a quantitative study or the quantitative portion of a
\nmixed methods study.<\/p>\n
47 Note: As you can see, the example clearly starts with topic sentences (red text) that foreshadow what is to be
\naddressed in the paragraph. Notice the quantitative method paragraph does not address the design, as the topic
\nsentence does not suggest the design is the focus of the paragraph. The design is not foreshadowed in the topic
\nsentence. Remember, a topic sentence alerts the reader to the main topic of the paragraph.
\nMarch 2016
\n39
\nI considered four research designs that one could use for a qualitative study on (2-3
\nwords identifying your topic): (a) miniethnography, (b) focus group, (c) narrative, and (d) case
\nstudy. (Note: Select the designs that you considered and are applicable to an applied qualitative
\nstudy.) Miniethnography involves\u2026 (Briefly discuss miniethnography, 1-sentence defining with
\na citation, 1-sentence if needed why it is or is not the optimal choice). Business researchers use
\nfocus groups to\u2026 (Briefly discuss focus groups, 1-sentence defining with a citation, 1-sentence
\nif needed why it is or is not the optimal choice). A narrative design entails\u2026 (Briefly discuss
\nnarrative designs, 1-sentence defining with a citation, 1-sentence if needed for why it is or is not
\nthe optimal choice). Case study researchers\u2026 (Briefly discuss case study, 1-sentence defining
\nwith a citation, 1-sentence is needed why it is or is not the optimal choice).
\n1.6 – Research Question (Quantitative Only)
\nDBA doctoral studies require the highest level or rigor and scholarship. One focus
\nof rigor and scholarship is that of the number of predictor or independent variables
\nexamined in quantitative doc studies. Non-experimental research (i.e. correlation, quasiexperimental,
\netc.) requires the use of at least two independent or predictor variables. This
\nis due to the \u201cthird variable\u201d problem. A third variable also known as a confounding or
\nmediator variable can confound the relationship between the independent and dependent
\nvariable. This confounding can lead the researcher to incorrectly interpret the results,
\nleading to an incorrect rejection of the null hypothesis.
\nAs such, all DBA quantitative studies require the examination of at least two predictor, or
\nindependent variables. This affects the statistical analysis, as simple bivariate correlations
\n(correlation designs) or one-way ANOVAs cannot be used as inferential statistical tests. Other
\nstatistical procedures, such as partial correlation, semipartial correlation, mediation and
\nmoderation, and multiple regression analyses, as a minimum must be used for correlation studies.
\nQuasi-experimental, causal comparative, etc., designs must employ statistical analyses (i.e.
\nfactorial ANOVAs), as a minimum, which examines more than one independent variable.
\nBelow are appropriate and inappropriate examples of correlation and quasi-experimental
\nresearch questions. These examples depict predictor\/independent variables, which are (a)
\nemployee job satisfaction and (b) leadership experience. The dependent variable is company
\ngross revenue.
\n\uf0b7 Appropriate Correlation Example (two predictor variables): Does a linear
\ncombination of employee job satisfaction and leadership experience significantly
\npredict employee productivity?
\n\uf0b7 Inappropriate Correlation Example (only one predictor variable): Does
\nemployee job satisfaction significantly predict employee productivity?
\n\uf0b7 Appropriate Quasi-experimental Example (two independent variables): Do
\nemployee job satisfaction and leadership experience significantly influence employee
\nproductivity?
\nMarch 2016
\n40
\n\uf0b7 Inappropriate Quasi-experimental Example (only one independent variable):
\nDoes employee job satisfaction significantly influence employee productivity?
\n1.7 – Hypotheses (Quantitative\/Mixed-Method Only)
\nHypotheses
\nTwo major elements in the research design are the hypotheses and the variables used to
\ntest them. A hypothesis is a provisional idea whose merit deserves further evaluation. Two
\nhypotheses, the null (H0) and alternative (H1)
\n, are to be stated for each research question. Below
\nare appropriate examples of correlation and quasi-experimental\/casual comparative null and
\nalternative hypotheses; note how they mirror the research questions identified above in the
\nQuantitative Research Questions heading. These examples depict predictor\/independent
\nvariables, which are (a) employee job satisfaction and (b) leadership experience. The dependent
\nvariable is company gross revenue. The H0 and H1 reflect the appropriate statistical notation and
\nare to be included. See more on hypotheses at:
\nhttps:\/\/www.tikessays.com\/write-my-paper\/socialresearchmethods.net\/kb\/hypothes.php
\nCorrelation
\n\uf0b7 Null Hypothesis (H0): The linear combination of employee job satisfaction and
\nleadership experience will not significantly predict employee productivity.
\n\uf0b7 Alternative Hypothesis (H1): The linear combination of employee job satisfaction and
\nleadership experience will significantly predict employee productivity.
\nQuasi-experimental
\n\uf0b7 Null Hypothesis (H0): Employee job satisfaction and leadership experience do not
\nsignificantly influence employee productivity.
\n\uf0b7 Alternative Hypothesis (H1): Employee job satisfaction and leadership experience
\nsignificantly influence employee productivity.
\n1.8 – Research Question (Qualitative Only)
\nIn a qualitative study, the Research Question uses the same words as in the Specific
\nBusiness Problem to identify the specific business leader and identify what the leader has limited
\nsupply of or is lacking. The following examples demonstrate how to align the research question
\nwith the specific business problem.
\nMarch 2016
\n41
\nMarch 2016
\n42
\n1.9 – Interview Questions (Qualitative Only)
\nIn qualitative studies, the researcher must first identify the population for the study
\n(business leaders that have solved or are solving the specific business problem) and align the
\ninterview questions with the population and the research question. Interview questions must (a)
\nprovide answers to the research question, (b) not go beyond the research question (i.e., no
\ndemographics if not part of the research question), (c) be in the language (word choice) that the
\nparticipant will understand, (d) be open-ended questions (no Yes or No answerable questions),
\nand (e) be applied DBA rather than speculative PhD questions (see the example below).
\nInterview questions should be straightforward and ask what or how the business leader
\nhas addressed the research problem. Typically, case study and ethnographic interviews will be
\nsemistructured, semiformal, unstructured, or informal. Phenomenological studies use the
\nphenomenological long interview with only one to three questions to have a longer discussion
\ngetting in depth data and reaching a state of epoch\u00e9. Students should critically read about the
\ndifferent interviewing techniques and select the best technique for the study design.
\nSemistructured and semiformal interviews frequently include six to ten interview
\nquestions to allow time for probing questions. The final interview question in a semistructured or
\ninformal interview frequently asks the participant to share any additional information for
\naddressing the research question(s): What additional information would you like to share about
\nXYZ? One typically uses an unstructured or informal interview technique when having a more
\ncasual discussion often spreading the interview questions out over time during field visits (i.e.,
\nduring a direct observation or participant observation phase in data collection).
\nIn contrast, the phenomenological long interview typically has one or two interview
\nquestions. Although phenomenological interview questions are written as a question, the
\ninterview protocol involves creating an in depth discussion (typically 1-2 hours) and reaching a
\nstate of epoch\u00e9. The phenomenological long interview requires more study and preparation as
\ncompared to more traditional interviewing techniques used in ethnography and case study
\ndesigns.
\nMarch 2016
\n43
\nBe cautious not to confuse the interviewing process with the interviewing questions. The
\nconcept of semistructured questions or semistructured interview questions does not exist.
\nSemistructured interviews (semiformal, unstructured, or informal interviews) are a specific
\ninterviewing technique\/process. All qualitative interview questions are open-ended. However,
\nthe interview questions are not semistructured.
\nExample Research Question
\nWhat strategies do department store managers use to motivate their sales associates?
\nExample Applied DBA Interview Questions
\n1. What strategies are you using to motivate your sales associates?
\n2. What method did you find worked best to motivate your sales associates?
\n3. How did your sales associates respond to your different motivation techniques?
\nExample Speculative\/Theoretical PhD Questions (do not use)
\n1. What strategies should managers use to motivate sales associates?
\n2. What method do you think will work best to motivate sales associates?
\n3. How do you feel your sales associates respond to other motivation techniques?
\n1.10 – Theoretical\/Conceptual Framework
\nA theoretical (for quantitative studies) or conceptual framework (for qualitative studies)
\noffers a systematic view of a phenomenon. In other words, the framework provides a lens
\nthrough which to view a phenomenon.
\nIdentifying the Best Theory or Conceptual Model
\nMake certain that the theory aligns with the research question. Consider the following when
\nsearching for a theory or conceptual model for the conceptual framework.
\n\uf0b7 Critically read peer- reviewed studies related to your topic and identify the theories that
\nthe sources found aligned with their studies. After one has read and synthesized
\nnumerous peer-reviewed studies related to the topic for the annotated bibliography, one
\nwill notice a few theories (or conceptual models) that aligned with several studies.
\n\uf0b7 Critically read the seminal work on the theories (or conceptual models) that you found in
\npeer-reviewed studies related to your topic.
\n\uf0b7 Related studies may be about the concept and not the specific industry.
\n\uf0b7 For example, if one is studying how the family owned wrecking yard leaders succession
\nplan, one could look at studies on leadership training and development in other types of
\norganizations.
\nMarch 2016
\n44
\n\uf0b7 Quantitative. Select the theory or conceptual model that best aligns with the research
\nquestion and provides an interrelated set of constructs, variables, hypotheses, or
\npropositions that offer an explanation for phenomenon.
\n\uf0b7 Qualitative. Select the theory or conceptual model that best aligns with the research
\nquestion.
\nAs you can see, it is important to immerse yourself in the literature pertaining to your
\nconceptual framework to gain a good understanding of the framework. More important, your
\nliterature review must include an exhaustive review of the literature pertaining to the conceptual
\nframework you are proposing for your study. This is extremely important, as you will be
\nrequired to discuss your findings as they confirm, disconfirm, extend, etc., the extant literature
\non your conceptual framework. You must critically analyze and synthesize the studies where
\nyour conceptual framework has been the lens through which the phenomenon has been viewed.
\nAs outlined in the DBA Rubric, you are required to present a brief overview of your
\ntheory or conceptual framework in Section one of the proposal. Please note this is not to be a
\ndetailed review of your theory or framework. The detailed review is required in the Review of the
\nLiterature heading. Here, a model for presenting the theory or framework heading is offered.
\nYou will want to state the name of the theory or identify the conceptual framework, identify the
\ntheorist if applicable, list key concepts of the theory or framework, identify any propositions or
\nhypotheses, and identify how the theory or framework applies to your study. Please note there
\nare obvious variations to this model depending upon your particular study and topic. However,
\nthe intent is to briefly present the key aspects of your theory and or framework and show how it
\nfits into your study.
\nQuantitative Example
\nBurns (1978) developed the transformational leadership theory. Burns used the theory to
\noffer an explanation for leadership based upon the premise that leaders are able to inspire
\nfollowers to change expectations, perceptions, and motivations to work toward common goals.
\nBurns identified the following key constructs underlying the theory (a) idealized attributes, (b)
\nidealized behaviors, (c) intellectual stimulation, (d) inspirational motivation, and (e)
\nindividualized consideration. As applied to this study, the transformational leadership theory
\nholds that I would expect the independent variables (transformational leadership constructs),
\nmeasured by the Multifaceted Leadership Questionnaire, to predict employee turnover intention
\nbecause (provide a rationale based upon the logic of the theory and extant literature). The
\nfollowing figure48 is a graphical depiction of the transformational leadership theory as it applies
\nto examining turnover intentions.<\/p>\n
48 Graphical models are useful for depicting the theoretical framework in quantitative studies.
\nMarch 2016
\n45
\nLet\u2019s examine the theoretical framework from the perspective of possible lenses through
\nwhich to view phenomena. Assume the business problem or phenomenon is the failure rate of
\nsmall businesses, an obvious business concern. There are plethora\u2019s of explanations that can be
\noffered for the failure of small businesses. As the researcher, you have the choice of lens for
\nwhich to view the problem. For example, you might hypothesize or rationalize that
\ntransformational leadership characteristics offer a systematic view for the failure of small
\nbusinesses. Specifically, you hypothesize or rationalize that a leaders transformational leadership
\ncharacteristics are influential in the success of small businesses. As such, your study would be
\ngrounded in transformational leadership theory or transformational leadership conceptual
\nframework.
\nOr perhaps, you hypothesize or rationalize that servant leadership characteristics offer a
\nsystematic view for the failure of small businesses. Specifically, you hypothesize or rationalize
\nthat a leaders servant leadership characteristics are influential in the success of small businesses.
\nAs such, your study would be grounded in transformational leadership theory or transformational
\nleadership conceptual framework. Hence, the number of lenses through which a problem or
\nphenomena can be viewed is limitless. Only your imagination stands between you and selecting
\nthe theory or conceptual framework that can be used to connect your study to existing
\nknowledge.
\nPerhaps, one of the most misunderstood aspects of theory is how to apply it in the
\ndoctoral study. Researchers utilizing a quantitative study grounded in transformational leadership
\ntheory must measure or assess the constructs underlying the theory. The broad constructs of
\ntransformational leadership theory are idealized attributes, idealized behaviors, inspirational
\nmotivation, stimulation, and idealized consideration.
\nTherefore, an instrument such as the Multifaceted Leadership Questionnaire (MLQ) is
\nappropriate to measure the underlying constructs of transformational leadership theory. Any
\ninstrument not proven to assess transformational leadership cannot be approved for use in a study
\ngrounded in transformational leadership theory. If you (inappropriately) used a nonvalidated
\ninstrument, you would not be testing the proposed transformational leadership theory, and your
\nIdealized Attributes
\nTurnover Intention
\nIdealized Behavior
\nIntellectual Stimulation
\nInspirational Motivation
\nMoral Integrity
\nMarch 2016
\n46
\nstudy would not have construct validity. For example, the Servant Leadership Survey (SLS)
\ninstrument could not be approved for use in a study grounded in transformational leadership
\ntheory, as the SLS was validated for use in measuring constructs underlying servant leadership
\ntheory.
\nQualitative Example
\nExample research question. What strategies do department store managers use to
\nmotivate their sales associates?
\nExample conceptual framework. Vroom (1959) developed the expectancy-valence
\ntheory, which he later called the expectancy motivation theory (Vroom, 1964). The expectancy
\nmotivation theory suggests that employees will exhibit positive performance behaviors when
\nthey believe that their work will result in certain rewards (Vroom, 1964). Building upon Vroom\u2019s
\nexpectancy motivation theory, Gilbert (1978, 2013) published his behavioral engineering model
\nthat provided a motivational foundation for the inputs that can lead to specific employee motives.
\nGilbert identified three categories covering information, instrumentation, and motivation. Within
\nthe manager\u2019s scope of control are data, resources, and incentives. Within the employee\u2019s scope
\nof control are knowledge, capacity, and motives. Gilbert argued that if managers improved the
\navailability of data access, provided the tools and equipment, or incentives to perform,
\nemployees would exhibit a change in willingness to participate. Likewise, if employees have a
\nchange in knowledge or capacity to perform, employees would exhibit a change in willingness to
\nparticipate (Gilbert, 1978, 2013). Vroom\u2019s (1964) expectancy motivation theory and Gilbert\u2019s
\n(1978) behavioral engineering model both align with this study exploring the strategies that
\ndepartment store managers use to motivate their sales associates.
\n1.11 – Operational Definitions
\nDo not include terms found in a basic academic dictionary (i.e. Webster\u2019s). List only
\nterms than might not be understood by the reader. All definitions should be sourced from
\nprofessional\/scholarly sources and in alphabetical order. Do not include more than 10 key
\noperational definitions. Although one can use a maximum of 10 terms, there may only be a few
\nterms pertinent to the study. Listing a specific term that only one or two sources in the literature
\nreview introduce is likely not pertinent to the study and should not be listed in the operational
\ndefinitions.
\n1.12 – Assumptions, Limitations, and Delimitations49
\nAssumptions are facts considered to be true, but which cannot actually be verified by the
\nresearcher. Assumptions carry risk and should be treated as such. A mitigation discussion would
\nbe appropriate. Identify all assumptions associated with the study. Limitations refer to potential
\nstudy weaknesses, which cannot be addressed by the researcher. Identify all limitations<\/p>\n
49 Review the following resource for more detailed information: Ellis, T. J., & Levy, Y. (2009). Towards a guide for
\nnovice researchers on research methodology: Review and proposed methods. Issues in Informing Science and
\nInformation Technology, 6, 323-337. Retrieved from https:\/\/www.tikessays.com\/write-my-paper\/informingscience.org\/Journals\/IISIT\/Overview
\nMarch 2016
\n47
\nassociated with the study. Delimitations refer to the bounds or scope of the study. Describe the
\nboundaries and what is in and out of your study\u2019s scope.
\n1.13 – Significance of the Study
\nContribution to Business Practice
\nDiscuss how the findings, conclusions, and recommendations from your study could fill
\ngaps in the understanding and effective practice of business.
\nImplications for Social Change
\nProvide a statement of the your study\u2019s potential for effecting positive social change or
\nthe improvement of human or social conditions by promoting the worth, dignity, and
\ndevelopment of individuals, communities, organizations, institutions, cultures, or societies.
\n1.14 – Review of the Professional and Academic Literature
\nThe literature review content needs to be a comprehensive and critical analysis and
\nsynthesis of the literature related to the theory and\/or conceptual model from the
\nTheoretical\/Conceptual Framework as well as the existing body of knowledge regarding the
\nresearch topic. What a literature review should not be is an amalgamation of essays on the topic.
\nThe approach to this heading may vary by authors\u2019 specific purpose. For example, if your study
\nis to be grounded in the transformational leadership theoretical or conceptual framework, you
\nwill be examining or exploring your phenomenon through a leadership lens. You want to report
\non extant research that was grounded in the transformational leadership theoretical\/conceptual
\nframework. You would want to report on the literature that is as close to your topic\/phenomenon
\nas possible. In addition, if you are conducting a quantitative study, you need to include the
\nliterature for any other key variables. A basic outline is presented at Appendix A.50
\nCritical analysis and synthesis of the relevant literature will be an important element of
\nthe literature review. The review of the literature is not to be a regurgitation of what you have
\nread. It is also not to teach about a topic; rather, it is to show your mastery of the previous and
\nrecent research on your topic and provide a comprehensive up-to-date literature review on your
\ntopic. Start with an introductory heading and then report the literature. This should be an
\nexhaustive review of the literature using the chosen theoretical\/conceptual framework and
\nconsist of the key and recent writings in the field. Repeat this approach if there are any additional
\ntheories. In addition, in quantitative studies, there must be a critical analysis and synthesis for
\neach variable.
\nThere are three questions that students typically ask about the literature review: (a)
\nlength, (b) organizational structure, and (c) content. The length will depend upon the theoretical
\nfoundation related to the topic and scholarly studies related to the theory. Typically, for a
\ndoctoral study, a literature review will average 35-40 pages. However, demonstrating a rich and<\/p>\n
50 Literature reviews will vary by topic, author, etc. However, Appendix A presents the minimum requirements for a
\nquantitative study.
\nMarch 2016
\n48
\ncomprehensive review of the topic is more important than the number of pages in a literature
\nreview.
\nThe most common ways that one may organize the literature review are to use a
\nchronological, topical, or combination of chronological and topical structure. The literature
\nreview should be a succinct yet in-depth critical analysis of scholarly studies and authoritative
\nseminal work. The literature review should not be a summary of one\u2019s reading or an
\namalgamation of essays on the topic.
\nThe literature review content needs to be a comprehensive and critical analysis and
\nsynthesis of the literature related to the theory and\/or conceptual model that one identified in the
\nTheoretical\/Conceptual Framework as well as the existing body of knowledge regarding the
\nresearch topic. Typically one half to two thirds of a good literature review will relate the theory
\nor conceptual models to a critical analysis and synthesis about the topic and problem. One
\norganizational strategy for the literature review is (a) one third discussing the theory or
\nconceptual model (see figure below), (b) one third topical foundation, and (c) one third
\ndiscussing the topic in relation to the theory.<\/p>\n
March 2016
\n49
\n1.15 \u2013 Transition
\nThis heading summarizes the key contents of Section 1. Do not introduce any new
\nmaterial in the summary, but do provide an overview of the primary objectives and contents of
\nSections 2 and 3.
\nMarch 2016
\n50
\nSECTION 2: THE PROJECT
\nSection 2 \u2013 The Project
\nMarch 2016
\n51
\n2.1 – Purpose Statement
\nSimply cut-and-paste the Purpose Statement from Section 1.
\n2.2 – Role of the Researcher
\nThe Role of the Researcher is an important part of your proposal and study. The content
\nthat you present in this subheading is important because it demonstrates that a) you have done
\nthe research that is required, b) that you understand what your role is in the study design, and 3)
\nyou understand the limitations and challenges in this type of role, and how any concerns may be
\nmitigated to enhance the reliability and validity of your work.
\nOne of the most challenging parts to write in this subheading is about the use of a
\npersonal lens primarily because novice researchers (like students) assume that they have no bias
\nin their data collection. However, it is important to remember that a participant\u2019s as well as the
\nresearcher\u2019s bias\/worldview is present in all social research, both intentionally and
\nunintentionally which is why it is important to address strategies to mitigate bias.
\nTo address the concept of a personal lens, remember that in qualitative research, the
\nresearcher is the data collection instrument and cannot separate themselves from the research,
\nwhich brings up special concerns. Remember that the researcher operates among multiple worlds
\nwhile engaging in research, which include the cultural world of the study participants as well as
\nthe world of one\u2019s own perspective. A researcher’s cultural and experiential background will
\ncontain biases, values, and ideologies that can affect the interpretation of a study\u2019s findings.
\nTherefore, researcher bias is a concern because the data can reflect the researcher\u2019s personal bias
\nand concerns. It becomes imperative that the interpretation of the phenomena represent that of
\nparticipants and not of the researcher. Hearing and understanding the perspective of others may be
\none of the most difficult dilemmas the researcher must address. The better a researcher is able to
\nrecognize his\/her personal view of the world and to discern the presence of a personal lens, the
\nbetter one is able to hear and interpret the behavior and reflections of others.
\nHow you address and mitigate a personal lens\/worldview during your data collection and
\nanalysis is important and a key component in the Role of the Researcher subheading. It is
\nimportant that a novice researcher recognizes their own personal role in the study and mitigates
\nany concerns during data collection. Part of your discussion in this subheading should address
\nhow this is demonstrated through using an interview protocol, member checking, transcript
\nvalidation and review, reaching data saturation, enabling sense making, facilitating epoch\u00e9,
\ncareful construction of interview questions, and other strategies to mitigate the use of one\u2019s
\npersonal lens during the data collection process of the study.
\nIt would be impossible to remove all bias because you are a human being. Rather, one
\nmitigates bias as best as one can. This is demonstrated via using an interview protocol, member
\nchecking, data saturation, and other strategies to mitigate the use of one personal lens during the
\ndata collection process of your study. Inadvertently driving participants to predetermined
\nconclusions speaks to the same concepts.
\nMarch 2016
\n52
\n2.3 – Participants
\nThe requirements are straight forward but often missed in the Participants heading.
\nConsider the explanations in the following table.
\nRubric Requirement Explanation
\na. Describes the eligibility
\ncriteria for study
\nparticipants.
\nThe participants must meet the eligibility requirement
\nwithin the scope of the population. Consider the research
\nquestion: What strategies do department store managers
\nuse to motivate their sales associates? If one identified the
\npopulation as department store managers who have worked
\nin the field for 8-years and have a minimum of 5-years
\nsupervising sales associates, one would not be necessarily
\naddressing the requirement.
\nThe criteria for the example research question would be
\ndepartment store managers who have successful strategies
\nthat they are using to motivate sales associates. The
\ndepartment store manager may have been in the field for
\n20-years or 1-month\u2014the time in position has nothing to
\ndo with the study. Likewise, working with the employees
\ndoes not mean that the department store manager is using a
\nstrategy to motivate the sales associates.
\nb. Discusses strategies for
\ngaining access to
\nparticipants.
\nExplain your plan for gaining access to participants. In a
\nquantitative survey, one may use a professional association
\nmembership list or other types of list to access participants
\nvia email, phone, etc.
\nFor a qualitative study, one may also use professional
\nassociations, trade affiliations, etc. for gaining access. One
\nmay also be using rosters inside the company(ies) and
\nemailing, calling, or visiting in person for a case study.
\nIt is vital that you develop a strategy to determine that
\nparticipants meet the study criteria before inviting
\nparticipation.
\nc. Identifies strategies for
\nestablishing a working
\nrelationship with
\nparticipants.
\nOnce one gains access, one needs to develop a working
\nrelationship with the participants. This may be as simple as
\nsending a survey link via email in a quantitative study to
\nhow you will cover the informed consent form and set the
\nMarch 2016
\n53
\nstage for a qualitative interview (often referencing the
\ninterview protocol).
\nd. The participants must align
\nwith the overarching
\nresearch question.
\nThis requirement is a reminder that one must have the
\ncorrect criteria for selecting the participants and that the
\ncriteria must align with the research question\u2014nothing else
\nshould be included in the criteria.
\ne. Supports every decision
\nwith a minimum of three
\nscholarly peer-reviewed or
\nseminal sources.
\nDuring planning the study, one will make several
\ndecisions. In this heading, there is a decision for the
\nparticipant criteria, how one will gain access to the
\nparticipants, and how one will build a working relationship
\nwith the participants. Each decision will need a synthesis
\nfrom a minimum of three scholarly peer-reviewed or
\nseminal sources to support the decision. This means that
\none may have nine citations for this section. Fortunately,
\nyou have an annotated bibliography with peer-reviewed
\nstudies where others have made similar decisions as well as
\nseminal sources on methodology.
\nTip: To represent your sources correctly: Write about what
\nyou will do in one sentence and synthesize your sources
\nsupporting your decision in a separate sentence.
\n2.4 – Research Method
\nThis heading is an extension of the Nature of the Study. The first paragraph of the Nature
\nof the Study required a description of and justified the methodology. Here you will extend that
\ndiscussion by providing more information and additional resources. Remember to use multiple
\nsources to support claims and decisions. It is important to have a strong case to support the
\nrationale for research design.
\n2.5 – Research Design
\nThis section is an extension of the Nature of the Study. The second paragraph of the
\nNature of the Study required a description of and justified the design. Here you will extend that
\ndescription by providing more information and additional resources. Be sure to include at least
\nthree sources for each decision you make.
\nData Saturation in Qualitative Study Designs
\nA vital prerequisite for a valid qualitative study is having a plan to ensure data saturation.
\nData saturation in qualitative research ensures the validity in a qualitative study similar to a
\nstatistically valid sample in a quantitative study. See more on data saturation in the Population
\nand Sampling heading below.
\nMarch 2016
\n54
\nHow to Use Multiple Sources to Support Claims and Decisions
\nSpecifically stating multiple sources is one way to make it clear to the reviewers that you
\nhave mapped to the Rubric. However, what the reviewers are looking for is that students have
\ndone the required reading to justify the choice of research design that will best assist collecting
\ndata to answer the research question. Rather than list name-date, name-date, name-date
\nrepeatedly, one would synthesize the concepts into one cohesive whole supported by sources in a
\nsomewhat indirect manner. For example:
\nCase studies are the preferred strategy researchers employ when asking how or
\nwhat questions (Amerson, 2011; Andrade, 2009; Yin, 2009). These types of studies
\nidentify operational links among events over time (Andrade, 2009; Baxter & Jack, 2008;
\nYin, 2009). Case studies may be exploratory, explanatory, or descriptive and may involve
\none organization and location or multiple organizations and locations for a comparative
\ncase study (Amerson, 2011; Stake, 1995; Yin, 2009).
\nIn other words, you are supporting your synthesis with multiple sources. Another way to
\nsupport your design with a source is:
\nEthnographic study is unique in that it includes fieldwork where all relevant
\nparticipants are observed and interviewed informally rather than a specified number as
\nin phenomenology (Fusch, 2001; Wolcott, 2011). Bernard (2012) stated that the number
\nof participants needed for a qualitative study was a number he could not quantify, but
\nthat the researcher takes what he can get it.
\nIn other words, you support your synthesis in a more direct way. Note that Bernard’s
\nentire work is not within the text, but, rather, one important statement that he did make is and it
\nsupports the chosen research design.
\nIn both examples, the synthesis demonstrated depth of knowledge that is supported by
\npublished peer-reviewed work, which is what reviewers want to see in your work. Moreover, it is
\na demonstration of your scholarly research abilities. Note, you may use the same source to
\nsupport more than one decision if applicable.
\n2.6 – Population and Sampling (Quantitative Only)
\nPopulation
\nStart by describing the population from which the sample will be drawn. Include any
\npertinent demographic variables (e.g., CEO, senior executive, mid-level manager, sales
\nprofessional, front-line supervisor, etc.). Refer to pg. 29 (Participant Characteristics) of the APA
\nManual (American Psychological Association, 2010) for other appropriate characteristics when
\nappropriate.
\nMarch 2016
\n55
\nSampling
\nThe two broad categories of sampling methods are probabilistic sampling (random
\nsampling) and non-probabilistic sampling (non-random sampling)51. Identify and defend your
\nsampling method. You must address the strengths and weaknesses of your chosen sampling
\nmethod. For example, if you will utilize a stratified random technique defend your reason for
\ndoing so. Also note why stratified sampling is more appropriate for your research situation than
\nanother sampling technique. You will need to refer to the literature pertaining to sampling
\ntechniques.
\nDescribe and defend the sample size. This is where you discuss conducting a power
\nanalysis to determine the appropriate sample size. You will present your power analysis in this
\ncomponent. G*Power3 is an excellent power analysis software tool and can be downloaded at:
\nhttps:\/\/www.tikessays.com\/write-my-paper\/gpower.hhu.de\/en.html. You will find a user\u2019s manual and short tutorial at the same
\nwebsite. See Appendix B for an example power analysis.
\nDescribe the eligibility criteria for inclusion in the study. Discuss any exclusion criteria.
\nMake the eligibility criteria clear, as the results of the study cannot be generalized beyond your
\ntargeted population. You need to make it clear as to who can, and who cannot, participate in your
\nstudy.
\n2.7 – Population and Sampling (Qualitative Only)
\nDefining the Population
\nIn this heading, one needs to define the scope of the study. For example, in a
\nphenomenological study, the population will be all the people within the scope of the study (i.e.,
\na specific industry) that meet the participant criteria noted in the participant section 2.3 above. In
\nan ethnographic study or case study, the population would comprise all people that meet the
\nparticipant criteria in one company for an ethnographic study or single case study and multiple
\ncompanies for a multiple case study. One should identify the number of companies in a multiple
\ncase study. Likewise, one should identify the approximate number of people (that meet the
\nparticipant criteria) within your study\u2019s population.
\nSampling
\nOne must describe and justify the sampling method (census, convenience, criterion,
\npurposeful, quota, snowball, etc.). Once one defines the total population meeting the participant
\ncriteria within the scope of the study, one must identify the sample size that has the best
\nopportunity for the researcher to reach data saturation. A large sample size does not guarantee
\nthat one will reach data saturation, nor does a small sample size\u2014rather, it is what constitutes the
\nsample size. One must also select a sampling technique that supports the research design.<\/p>\n
51 See Appendix B for a typology of sampling strategies.
\nMarch 2016
\n56
\nFor example, one may use a census sample for a single or multiple case study with a
\nsmall population versus a convenience sample in an ethnographic study. A census sample is
\nactually a census, which means that the study participants will include 100% of the population.
\nFor example, as depicted in the following graphic, if one identified the scope of a multiple case
\nstudy to include five companies and the people that meet the participant criteria for the
\npopulation as the CEOs of the five companies, there would be a census sample if all five of the
\nCEOs participated.
\nData Saturation and Sampling
\nIn the Population and Sampling heading (as well as the Research Design and the Validity
\nheadings), one must define how one will ensure data saturation. Although data saturation in
\nqualitative research ensures the validity in a qualitative study similar to a statistically valid
\nsample in a quantitative study, there is no direct correlation between the sample size and
\nreaching data saturation. Data saturation in qualitative research is a way to ensure that one
\nobtained accurate and valid data. Using too small of a sample or too large of a sample will not
\nensure data saturation. One should critically read and obtain a clear understanding of data
\nsaturation before writing a qualitative proposal. Fusch and Ness (2015) synthesized the literature
\nto identify some key characteristics of reaching data saturation which include no new data, no
\nnew themes, no new coding, and ability to replicate the study (providing one asks the same
\nparticipants the same questions in the same timeframe). The study design (case study,
\nminiethnography, phenomenological, etc.) will affect when and how one reaches data saturation.
\nOne may be conducting interviews only in a phenomenological study, whereas one would use
\nmultiple data collection methods in a case study.
\nAlthough the DBA leadership requires a minimum of 20-participants in a
\nphenomenological study and although one may use member checking to enhance the richness of
\nthe data, one may have to interview many more participants to reach data saturation. In contrast,
\nin a case study using a small census sample and multiple data collection methods, one may reach
\ndata saturation with one or a few participants. In qualitative studies, quality (rich data) is more
\nimportant than quantity (thick data).
\n2.8 – Ethical Research
\nEach research study comes with its own set of specific ethical issues. Thus, a rubric
\ncannot address all possible scenarios. Therefore, it will be helpful to review the IRB Application
\nForm before you complete this component to ensure you address any requirements not identified
\nin the rubric or Research Handbook. However, as a minimum, discuss the informed consent
\nprocess. Include a copy of the informed consent form in an appendix and list the informed
\nMarch 2016
\n57
\nconsent form in the Table of Contents. Discuss participant procedures for withdrawing from the
\nstudy. Describe any applicable incentives. Clarify measures for assuring the ethical protection of
\nparticipants is adequate. Agreement documents are to be listed in the (a) text of the study, (b)
\nappendices and (c) Table of Contents. Include a statement that data will be maintained in a safe
\nplace for 5-years to protect rights of participants. Ensure you indicate that the final doctoral
\nmanuscript will include the Walden IRB approval number. Ensure the document does not include
\nnames or any other identifiable information of individuals or organizations.
\nEach participant in your study must give written consent to take part in the data collection
\nphase of the work. Moreover, as a researcher following the protocols of the Belmont Report, you
\nmust ensure that your participants have a full understanding of their part in the study. Finally,
\nyou must ensure that participants understand that they may withdraw from your study at any time
\nwithout penalty, and how to withdraw from the study.
\nIt is a good practice to complete the first draft of your IRB application while completing
\nthe ethics section as well as Section 2. Consider: (a) writing a sentence about your plan to share
\na summary of the findings with the study participants, and (b) do not use the term anonymous for
\nqualitative studies if you will be interviewing or knowing whom the participants are. Qualitative
\nresearchers can protect the confidentiality but not the anonymity of participants because the
\nresearcher will know who the participants are. Depending upon the data collection method,
\nquantitative researchers may be able to protect participants\u2019 anonymity.
\n2.9 – Data Collection\u2014Instruments (Quantitative)
\nYou will describe each instrument\u2019s purpose, intended populations, scales, scoring
\nprocess, time needed to complete, etc. This heading will also address the psychometric issues
\nsurrounding the instrument, reliability and validity\u2014this is very important. You will need to
\nreport the reliability and validity coefficients. Where possible, include the details of the
\nreliability measures employed (e.g. test-retest, equivalent or alternate form, split-half, and
\ninternal consistency). Validity should include content validity, criterion-related validity, and
\nconstruct validity. State briefly what these measures of validity are, and report their
\nIntercorrelation coefficients.
\nYou will need to address any special requirements of the publisher. You will need to gain
\npermission from the test publisher to use some instruments. This can be requested by sending a
\nformal letter or email to the publisher. Alternatively, you may need to complete a training course
\nor require your chair\u2019s signature to acquire the instrument\u2014be sure to include this information if
\napplicable.
\n2.10 – Data Collection \u2013 Instruments (Qualitative)
\nThe requirements are straight forward but often missed in the Participants heading.
\nConsider the explanations in the following table.
\nMarch 2016
\n58
\nRubric requirement Explanation
\na. In addition to identifying
\nthe student as the primary
\ndata collection instrument,
\nidentifies the data collection
\ninstrument\/process (i.e.,
\ninformal interview,
\nsemistructured interviews,
\nphenomenological in-depth
\ninterviews, focus groups,
\ncompany\/archival
\ndocuments, etc.).
\nRubric requirement has two parts and students sometimes
\nmiss one of them, which can lead to a revision request.
\n1. Identifying that you are the primary data collection
\ninstrument.
\n2. Identifying all of the secondary, tertiary, etc. data
\ncollection instruments. Although common in
\nethnographic research, in case studies, students
\nmust have a minimum of two data collection
\nmethods.
\nb. Clarifies how the student
\nwill use the data collection
\ninstrument\/technique (the
\nprocess\/protocol).
\nDescribe how you will use the instrument(s) by providing a
\nbrief definition of each instrument and referencing
\ninterview or focus group protocols, etc.
\nThe focus here should be more on defining and using the
\ninstrument. For example, if you are using a specific type of
\ninterview, what is the interviewing technique specific to
\nyour chosen approach (i.e., unstructured or semistructured
\ninterviews). Keep this brief; however, be sure to define the
\ndifferent data collection methods (with scholarly support).
\nIn the Data Collection Technique Heading, where you will
\nexpand upon the process.
\nc. Identifies how the student
\nwill enhance the reliability
\nand validity of the data
\ncollection
\ninstrument\/process (i.e.,
\nmember checking, transcript
\nreview, pilot test, etc.).
\nClarify how you will enhance the reliability and validity of
\nthe instruments such as using an expert panel to validate
\ninterview questions, member checking follow up
\ninterviews after semistructured interviews, triangulation of
\nmultiple data collection methods (during the data analysis
\nas applicable to the research design), etc.
\nd. Identifies where in
\nappendices the instrument
\n(i.e., interview protocol,
\nfocus group protocol,
\ninterview questions, etc.) is
\n(are) located. Ensures Table
\nof Contents lists
\nappendices.
\nAs applicable, include interview protocols, focus group
\nprotocols, direct\/participant observation protocols in the
\nappendices.
\ne. Supports every decision
\nwith a minimum of three
\nscholarly peer-reviewed or
\nseminal sources.
\nDuring the study plan, one will make several decisions. In
\nthis heading there are several decisions to make and
\nsupport. Each decision such as the following will need
\nscholarly support:
\n\uf0b7 Identifying that you are the primary data collection
\nMarch 2016
\n59
\ninstrument.
\n\uf0b7 Identifying all of the secondary, tertiary, etc. data
\ncollection instruments such as type of interviews,
\nfocus groups, company\/archival documents,
\ncompany marketing materials, etc.).
\n\uf0b7 Identifying how you will use the instruments by
\nproviding a brief definition of the instrument and
\nreferencing interview or focus group protocols, etc.
\n\uf0b7 Identifying how you will enhance the reliability and
\nvalidity of the instruments such as by using
\nmember-checking follow up interviews after a
\nsemistructured interview.
\nTip to represent your sources correctly: Write about what
\nyou will do in one sentence and synthesize your sources
\nsupporting your decision in a separate sentence. See the
\nfollowing examples:
\nAcademic integrity code of conduct violation
\n(misrepresenting sources) example 1: I will use
\nsemistructured to explore the strategies that department
\nstore managers use to motive their sales associates
\n(Johnson & Williams, 2013; Rubin & Rubin, 2012; Smith,
\n2014). Note that the sources did not discuss the student\u2019s
\nstudy in their publications and the example is a
\nmisrepresentation of the sources.
\nCorrectly supporting a decision example 1. Cite (2014)
\nused semistructured interviews to determine how sales
\nmanagers motivate sales associates. Likewise, Cite (2013)
\nfound that semistructured interviews were a good approach
\nto learn how department store managers motivate sales
\nclerks. Rubin and Rubin (2012) argued that semistructured
\ninterviews are a good way for the researcher to focus on the
\ndetails that address the research question. Therefore, I will
\nuse semistructured to explore the strategies that department
\nstore managers use to motivate their sales associates. Note:
\nplease be sure to synthesize your sources to support your
\ndecisions.
\nAcademic integrity code of conduct violation
\n(misrepresenting sources) example 2: I will be the
\nprimary data collection instrument in this study (Denzin,
\n2014; Marshall & Rossman, 2016; Wolcott, 2005). Note
\nthat the sources did not discuss the student\u2019s study in their
\npublications and the example is a misrepresentation of the
\nMarch 2016
\n60
\nsources.
\nCorrectly supporting a decision example 2. I will be the
\nprimary data collection instrument in this study. In
\nqualitative research, the researcher is the primary data
\ncollection instrument because the researcher hears, sees,
\nand interprets the data (Denzin, 2014; Marshall &
\nRossman, 2016; Wolcott, 2005). Note: please be sure to
\nsynthesize your sources to support your decisions.
\n2.11 – Data Collection Technique
\nDo not confuse the purpose of this heading with that for the explanation of procedures.
\nYou want to discuss the main approach to collecting your data. It is a good idea to restate the
\nresearch question and then address the data collection process. Depending upon whether you are
\nusing a quantitative or qualitative method, you should discuss and support your decision for
\ncollecting the data.
\nQuantitative Studies
\nIn a quantitative study one would discuss: (a) surveys, (b) structured record reviews to
\ncollect data (e.g., sales data, performance records, government databases, etc.), and (d) structured
\nobservations. Self-administered questionnaires and structured records are more prevalent with
\nquantitative research. Indicate the process you will use to collect your data. State your rationale
\nfor selecting the process (e.g., in terms of strengths and weaknesses, cost, data availability,
\nconvenience, etc.).
\nQualitative Studies
\nDescribe the process for collecting the data (i.e., interviews, focus groups, direct or
\nparticipant observations, and review of company\/archival documents, performance indicators,
\nsales reports, business plans, etc.) Provide an abridged interview protocol, focus group protocol,
\nobservation protocol, etc., and identify the location of the protocols in an appendix.
\n2.12 – Data Organization Technique (Qualitative Only)
\nThe Data Organization Technique can often be a short paragraph where students address
\nall of the data that they collected in this heading. There are typically two decisions in this
\nsection: (a) about how one will securely store the data (electronic and hard copies) and (b) that
\nthe data will be destroyed after 5 years.
\n2.13 – Data Analysis (Quantitative Only)
\nData analysis involves discussing the statistical test(s) you will use to answer each
\nresearch question, and justify the tests\u2019 selection. Indicate the nature of the scale for each
\nMarch 2016
\n61
\nvariable (e.g., nominal, ordinal, interval, and ratio). Why is the selected statistical test more
\nappropriate than another? (Hint: The statistical test is usually selected due to the nature of the
\nquestion and scale of measurement of the variables you defined). Describe how you will deal
\nwith discrepant cases (missing data, data that cannot be interpreted, etc.). Identify the software
\nthat will be used to analyze the data. Be sure to discuss the data assumptions, how they will be
\nassessed, and how you will address any violations (e.g., using Bootstrapping).
\n2.14 – Data Analysis (Qualitative Only)
\nThe qualitative data analysis heading is critical for demonstrating doctoral level
\ncompetence and will help you prepare for Section 3. This heading must be deep yet can be
\ncovered in one or two succinct paragraphs. Reviewing the following table\u2019s contents will help
\nyou develop and write your data analysis plan.
\nRubric requirement Explanation
\na. Identifies the appropriate
\ndata analysis process for the
\nresearch design (i.e., one of
\nthe four types of
\ntriangulation for case
\nstudies; modified van
\nKaam, van Maanen, etc. for
\nphenomenology).
\nDifferent qualitative research designs require different data
\nanalysis processes. Critically read seminal works and other
\nstudies using your research design to be able to
\ndemonstrate that you are prepared to conduct a data
\nanalysis. For example, case study researchers will use
\nmethodological triangulation. Ethnographic researchers
\nwill likely use methodological triangulation. However
\nethnographers may also use data triangulation.
\nb. Provides a logical and
\nsequential process for the
\ndata analysis.
\nStudents must succinctly describe how they will perform
\nthe data analysis. Students must use all the data for the
\nanalysis. Often students planning case studies or
\nethnographic studies discuss the data collection instruments
\nand techniques above, but forget everything but the
\ninterview data in the data analysis section.
\nStudents should begin their data analysis heading by noting
\nthe data from the planned collection methods and how they
\nwill use the data analysis process (in either order). For a
\ncase study, one would start by discussing how one will use
\nmethodological triangulation for the information from the
\ndifferent data collection methods.
\nMarch 2016
\n62
\nc. Details the student\u2019s
\nconceptual plan or software
\n(i.e., NVivo, Atlasti,
\nEthnograph, Excel, etc.) for
\ncoding, mind-mapping, and
\nidentifying themes.
\nOr is the key word in this requirement. Explain the classic
\ndata analysis method or qualitative software analysis
\nmethod (how you will do it).
\nClassic Data Analysis Method
\nFor the classic data analysis method, discuss sorting all of
\nthe concepts and ideas on separate sheets of paper into
\ncategorized piles\u2014be sure to support your decision.
\nCritically analyze the data using a large physical mind map
\n(i.e., stacks, piles, or clusters of concepts and ideas on a
\nwall or large room floor) for the classic data analysis
\nmethod.
\nQualitative Software Analysis Method
\nFor the qualitative software analysis method, code all of
\nthe concepts and ideas (all of the data and not just the
\ninterview questions)\u2014be sure to support your decision.
\nCritically analyze the data in a graphical portrayal of
\ncategorized and coded concepts and ideas using the
\nqualitative software analysis method.
\nThemes
\nQuestion the meaning of the reoccurring concepts and
\nideas to identify the themes.
\nIn effect, the compiling phase involves organizing the data
\nin an order, to create a database, while disassembling
\nphases involves dividing the complied data into fragments
\nand labels. The reassembling process involves clustering
\nand categorizing the labels into sequences and groups. The
\ninterpretation stage requires creating narratives from the
\nsequences and groups including conclusions.
\nd. Identifies how the student
\nwill focus on the key
\nthemes, correlate the key
\nthemes with the literature
\n(including new studies
\npublished since writing the
\nproposal) and the
\nconceptual framework.
\nThis should be a one or two sentence plan on how you will
\ncorrelate the key themes with recent studies and the theory
\nor conceptual models from your conceptual framework.
\nThis will help you prepare for the presentation of findings
\nin Section 3.
\ne. Supports every decision Critically reading seminal and authoritative work for data
\nMarch 2016
\n63
\nwith a minimum of three
\nscholarly peer-reviewed or
\nseminal sources.
\nanalysis in your selected research design is vital at this
\nstage of your doctoral journey. You should have ample
\nsources to support your decisions\u2014there are some
\nsuggested readings lists in the Bibliography-Suggested
\nReadings Lists
\n2.15 – Study Validity (Quantitative Only)
\nInternal Validity52
\nInternal validity is the approximate truth about inferences regarding cause-effect or
\ncausal relationships. Thus, internal validity is only relevant in studies in which researchers seek
\nto examine causal relationships (i.e., experiments or quasi-experimental designs). Internal
\nvalidity is not relevant in observational (i.e., correlation designs or descriptive studies, for
\ninstance.) However, for studies in which researchers seek to assess the effects of programs or
\ninterventions, internal validity is perhaps the primary consideration. In those contexts, you would
\nlike to be able to conclude that your program or treatment made a difference — it improved a
\nbusiness process or outcome
\nExperiments\/quasiexperiments. Experimental and quasi-experimental designs are
\nsusceptible to up to 8 threats to internal validity, depending upon the specific design. These eight
\nthreats are (a) selection, (b) selection by maturation, (c) statistical regression, (d) mortality, (e)
\nmaturation, (f) history, (g) testing, and (h) instrumentation. You need to address each of these
\nthreats by briefly mentioning what they are, and, as relevant, the steps you will take in your study
\nto address each of these threats. Again, some of the threats may not be applicable, depending
\nupon your specific design. You can refer to a basic research design textbook to obtain a better
\nunderstanding of these threats and how to combat them. Be sure to cite your sources. See the
\nfollowing link for further information: https:\/\/www.tikessays.com\/write-my-paper\/socialresearchmethods.net\/kb\/causeeff.php
\nIf you are not conducting an experiment then indicate that this is a nonexperimental
\ndesign (i.e. correlation) and threats to internal validity are not applicable. However, indicate that
\nthreats to statistical conclusion validity are of concern, and then address threats to statistical
\nconclusion validity.
\nThreats to statistical conclusion validity. Start by explaining what these threats are.
\nThreats to statistical conclusion validity are conditions that inflate the Type I error rates,
\n(rejecting the null hypothesis when it is in fact true), and Type II error rates (accepting the null
\nhypothesis when it is false.) The three conditions that you need to cover here are: (a) reliability
\nof the instrument, (b) data assumptions, and (c) sample size.<\/p>\n
52 See more on internal validity @ https:\/\/www.tikessays.com\/write-my-paper\/socialresearchmethods.net\/kb\/intval.php
\nMarch 2016
\n64
\nReliability of the instrument. You already reported the reliability properties of your
\ninstrument in the Instrumentation heading. However, you need to determine how reliable the
\ninstrument is for your specific sample. Here you will indicate you will conduct an internal
\nconsistency reliability check of the instrument against your specific sample. The intent is to see
\nhow close the reported reliability coefficient (in section 2.9 – Instrumentation) is and your
\ncalculated reliability coefficient. State what an acceptable value is (i.e. >.7) and how you will
\ncheck your instrument\u2019s reliability. There is a procedure (Analyze\/Scale\/Reliability Analysis) in
\nSPSS that will allow you to compute Cronbach\u2019s alpha, one of several reliability coefficients.
\nYou will report the results of the reliability analysis in Section 3, Presentation of Findings
\nheading. The degree of agreement\/disagreement can provide information for your discussion,
\nespecially in the event of a nonsignificant result.
\nData assumptions53 (varies by statistical test). You will state what the assumptions are
\npertaining to your tests and the effects violation of the assumptions can have on your results.
\nIndicate how you will check these assumptions. Refer to a basic statistics textbook for
\nassumptions regarding various tests. For example, the Green and Salkind text used in the DDBA
\n8438 course is an excellent resource for identifying assumptions for most basic statistical tests.
\nPallant (2010)54 is an excellent text for instruction on performing parametric assumption testing.
\nThe following Table contains the major assumptions and procedures for testing the assumptions
\nfor multiple linear regression and for ANOVA tests.
\nTable X
\nStatistical Test, Assumptions, and Procedures for Testing Assumptions
\nStatistical test Assumptions Testing
\nMultiple Regression
\nOutliers Scatterplot
\nMulticollinearity Normal Probability Plot (PP)
\nof the Regression
\nStandardized Residual
\nNormality \u201c
\nLinearity \u201c
\nHomoscedasticity \u201c
\nIndependence of Residuals \u201c
\nANOVA
\nNormality Histograms
\nEquality of Variances Levene\u2019s Test of Equality
\nof Variances<\/p>\n
53 Data assumptions vary by statistical test.
\n54 Pallant, J. (2010). SPSS survivor manual (4th ed.). Berkshire. England: McGraw-Hill.
\nMarch 2016
\n65
\nSample size. Include a brief explanation of the effects of using too small a sample size
\ncould have on your study\u2019s outcomes (refer to any basic statistics textbook). However, you will
\nindicate this threat has been met by conducting a power analysis to ensure you have a sufficient
\nsample size. Be sure to cite your work.
\nExternal Validity
\nExternal validity refers to the extent the study findings can be generalized to larger
\npopulations and applied to different settings. External validity is related to the sampling strategy
\n(identified in Heading 2.6, Population and Sampling). Probability sampling strategies (random
\nsampling) enhances external validity. Conversely, nonprobabilistic sampling strategies hinder
\nexternal validity. This relationship is to be discussed in this heading.
\n2.16 – Reliability and Validity (Qualitative Only)
\nA key difference from quantitative research is the reliability and validity headings. The
\nanalogous criteria for qualitative studies are dependability, credibility, transferability, and
\nconfirmability. These criteria are not measurable and need to be established using qualitative
\nmethods such as member checking [Marshall and Rossman (2016) provide a good definition.]
\nand triangulation (Data triangulation, investigator triangulation, theoretical triangulation, and
\nmethodological triangulation). See Norman Denzin\u2019s work on triangulation). Please review more
\ndetailed information on qualitative validity at:
\nhttps:\/\/www.tikessays.com\/write-my-paper\/socialresearchmethods.net\/kb\/qualval.php
\nReliability
\nReliability refers to how one will address dependability. Some of the ways to enhance the
\ndependability of the study are member checking of data interpretation, transcript review, pilot
\ntest, expert validation of the interview questions, interview protocol, focus group protocol, direct
\nor participant observation protocol, etc. Reaching data saturation will help assure the
\ndependability of the findings. See the seminal literature on reliability bring in a minimum of
\nthree scholarly sources to support every decision.
\nValidity
\nQualitative study validity refers to the credibility, transferability, and confirmability of
\nthe findings. Reaching data saturation will help assure the credibility, transferability, and
\nconfirmability of the findings. Please see seminal work on qualitative validity to ensure that you
\nhave a valid study. Support every decision with a minimum of three scholarly peer-reviewed or
\nseminal sources.
\nCredibility. One can enhance credibility by member checking of the data interpretation,
\nparticipant transcript review, triangulation, interview protocol, focus group protocol, direct or
\nparticipant observation protocol, etc. Demonstrating qualitative credibility ensures the
\nreviewers that one is addressing the findings from the perspective of the participants.
\nMarch 2016
\n66
\nConfirmability. One can enhance the confirmability by ensuring that the results can be
\nconfirmed or supported by others. Probing during interviews and follow up member checking
\ninterviews, questioning from different perspectives, triangulation, etc. are techniques one may
\nuse to enhance the confirmability.
\nTransferability. Be sure to demonstrate how you will enable others to determine the
\ntransferability of the findings (i.e., meticulously adhering to the data collection and analysis
\ntechniques for the research design, using interview protocol, focus group protocol, direct or
\nparticipant observation protocol, reaching data saturation, etc.). In contrast to quantitative
\nstudies where the researcher generalizes the findings, qualitative researchers do not generalize
\nand do not state that the findings are transferable.
\n2.17 – Transition and Summary
\nEnd with a transaction heading that contains a summary of key points and provides an
\noverview introducing Section 3. Do not include any new information in the summary.
\nMarch 2016
\n67
\nSECTION 3: APPLICATION TO PROFESSIONAL PRACTICE AND
\nIMPLICATIONS FOR CHANGE
\nSection 3 \u2013Application to
\nProfessional Practice and
\nImplications for Change
\nMarch 2016
\n68
\n3.1 – Introduction
\nReacquaint the reader to the purpose of the study. For quantitative studies, simply restating the
\nfirst two sentences of the Purpose Statement followed by a brief summary of the study findings.
\nFor qualitative studies simply restate the first sentence of the purpose statement and briefly
\nsummarize the findings.
\nQuantitative Example
\nThe purpose of this quantitative correlation study was to examine the relationship
\nbetween employee job satisfaction, employee motivation, and employee turnover intention. The
\nindependent variables were employee job satisfaction and employee motivation. The dependent
\nvariable was employee turnover intention. The null hypothesis was rejected and the alternative
\nhypothesis was accepted. Employee job satisfaction and employee motivation significantly
\npredicted employee turnover.
\nQualitative Example
\nThe purpose of this qualitative multiple case study was to explore the strategies that
\ndepartment store managers used to motivate their sales associates. The data came from manager
\ninterviews, manager-employee observations, and company documentation at five department
\nstores in Texas. The findings showed methods that the managers used to motivate their sales
\nemployees to provide better customer service and increase sales.
\n3.2 – Presentation of Findings (Quantitative)
\nAn example of an APA results write-up for a multiple regression analysis is provided.
\nAssumptions vary by statistical test. Therefore, ensure you address the appropriate assumptions
\nfor your statistical test.
\nQuantitative Example
\nIn this subheading, I will discuss testing of the assumptions, present descriptive statistics,
\npresent inferential statistic results, provide a theoretical conversation pertaining to the findings,
\nand conclude with a concise summary. I employed Bootstrapping, using 1,000 samples, to
\naddress the possible influence of assumption violations. Thus, bootstrapping 95% confidence
\nintervals are presented where appropriate.
\nMarch 2016
\n69
\nTests of Assumptions
\nThe assumptions of multicollinearity, outliers, normality, linearity, homoscedasticity, and
\nindependence of residuals were evaluated. Bootstrapping, using 1,000 samples, enabled
\ncombating the influence of assumption violations.
\nMulticollinearity. Multicollinearity was evaluated by viewing the correlation
\ncoefficients among the predictor variables. All bivariate correlations were small to medium
\n(Table X); therefore the violation of the assumption of multicollinearity was not evident. The
\nfollowing table contains the correlation coefficients.
\nTable X
\nCorrelation Coefficients Among Study Predictor Variables
\nVariable Age Weight Height
\nAge 1.00 .151 -.010
\nWeight .151 1.00 .562
\nHeight -.010 .562 1.00
\nNote. N = 204.
\nOutliers, normality, linearity, homoscedasticity, and independence of residuals55
\n.
\nOutliers, normality, linearity, homoscedasticity, and independence of residuals were evaluated by
\nexamining the Normal Probability Plot (P-P) of the Regression Standardized Residual (Figure 1)
\nand the scatterplot of the standardized residuals (Figure 2). The examinations indicated there
\nwere no major violations of these assumptions. The tendency of the points to lie in a reasonably
\nstraight line (Figure 1), diagonal from the bottom left to the top right, provides supportive
\nevidence the assumption of normality has not been grossly violated (Pallant, 2010). The lack of a
\nclear or systematic pattern in the scatterplot of the standardized residuals (Figure 2) supports the
\ntenability of the assumptions being met. However, 1,000 bootstrapping samples were computed
\nto combat any possible influence of assumption violations and 95% confidence intervals based
\nupon the bootstrap samples are reported where appropriate.<\/p>\n
55 These are the same assumptions discussed in Section 2; the results of the assumption testing are now
\ndiscussed. These assumptions differ by statistical test and the appropriate assumptions are to be discussed. Note,
\nyour specific discussion might differ. For example, there may be severe data assumption violations in the data you
\ncollected. Therefore, you would discuss appropriately.
\nMarch 2016
\n70
\nFigure 1. Normal probability plot (P-P) of the regression standardized residuals.
\nFigure 2. Scatterplot of the standardized residuals.
\nMarch 2016
\n71
\nDescriptive Statistics
\nIn total, I received 207 surveys. Three records were eliminated due to missing data,
\nresulting in 204 records for the analysis. Table X contains descriptive statistics of the study
\nvariables.
\nTable X
\nMeans and Standard Deviations for Quantitative Study Variables
\nVariable M SD Bootstrapped 95% CI (M)
\n56
\nSleep Index 26.36 10.56 [24.80, 27.94]
\nAge 43.60 12.51 [41.90, 45.28]
\nWeight 72.34 15.21 [70.23, 74.51]
\nHeight 169.12 10.00 [167.68, 170.44]
\nNote: N = 204.
\nInferential Results
\nStandard multiple linear regression,57 \u03b1 = .05 (two-tailed), was used to examine the
\nefficacy of age, weight, and height in predicting sleep index. The independent variables were
\nage, weight, and height 58. The dependent variable was sleep index 59. The null hypothesis was
\nthat age, weight, and height would not significantly predict sleep index. The alternative
\nhypothesis was that age, weight, and height would significantly predict sleep index. Preliminary
\nanalyses were conducted to assess whether the assumptions of multicollinearity, outliers,
\nnormality, linearity, homoscedasticity, and independence of residuals60 were met; no serious
\nviolations were noted (see Tests of Assumptions). The model as a whole was able to significantly
\npredict sleep index, F(3, 200) = 4.778, p < .003, R
\n2
\n= .06761. The R
\n2
\n(.067) value indicated that
\napproximately 7% of variations in sleep index is accounted for by the linear combination of the
\npredictor variables (sex, weight, and height). In the final model, age and height were statistically<\/p>\n
56 The 95% Bootstrap confidence intervals are produced when the bootstrapping procedure is selected in the SPSS
\nregression process. See regression video tutorial located at: https:\/\/www.youtube.com\/watch?v=1ItFMKlPG5k
\n57 Identify the test and of purpose of the test.
\n58 Restate the independent variables as presented in the purpose statement and research question; there is to be no
\ndeviation.
\n59 Restate the dependent variables as presented in the purpose statement and research question; there is to be no
\ndeviation.
\n60 Identify the assumptions and state they how were assessed.
\n61 State whether the model as a whole was able to predict (or not) the dependent variable. Report the appropriate
\nstatistics.
\nMarch 2016
\n72
\nsignificant with age (t= -3.892, p < .01) accounting for a higher contribution to the model than
\nheight (t = -2.595, p < .05). Weight did not explain any significant variation in sleep index. The
\nfinal predictive equation was:
\nSleep Index = 70.205 -.148(Age) + .109(Weight) \u20132.303(Height).
\nAge. The negative slope for age (-.148) as a predictor of sleep index indicated there was
\nabout a .148 decrease in sleep index for each one-point increase in age. In other words, sleep
\nindex tends to decrease as age increases. The squared semi-partial coefficient (sr
\n2
\n)
\n62 that
\nestimated how much variance in sleep index was uniquely predictable from age was .03,
\nindicating that 3% of the variance in sleep index is uniquely accounted for by age, when weight
\nand height are controlled.
\nHeight. The negative slope for height (-2.303) as a predictor of sleep index indicated
\nthere was a 2.303 decrease in sleep index for each additional one-unit increase in height,
\ncontrolling for age and weight. In other words, sleep index tends to decrease as height increases.
\nThe squared semi-partial coefficient (sr
\n2
\n) that estimated how much variance in sleep index was
\nuniquely predictable from height was .04, indicating that 4% of the variance in sleep is uniquely
\naccounted for by height, when age and weight are controlled. The following Table depicts the
\nregression summary table.
\nTable X<\/p>\n
62 Derived from the SPSS output.
\nMarch 2016
\n73
\nRegression Analysis Summary for Predictor Variables
\nVariable \u0392
\n63 SE \u0392 \u03b2
\n64 t
\n65
\np
\n66
\nB 95%67
\nBootstrap CI
\nAge -.148 0.054 -.393 -3.892 <. 01 [-.262, -.025]
\nWeight .109 3.770 -.038 0.371 .712 [-.008, .245]
\nHeight -2.303 .888 -.268 -2.595 .011 [-.442, -.081]
\nNote. N= 204.
\nAnalysis summary. The purpose of this study was to examine the efficacy of age, weight,
\nand height in predicting sleep index. I used standard multiple linear regression to examine the
\nability of age, weight, and height to predict the value of sleep index. Assumptions surrounding
\nmultiple regression were assessed with no serious violations noted. The model as a whole was
\nable to significantly predict sleep index, F(3, 200) = 4.778, p < .003, R
\n2
\n= .067. Both age and
\nheight provide useful predictive information about sleep index. The conclusion from this analysis
\nis that age and height are significantly associated with sleep index, even when weight is
\ncontrolled (e.g. held constant).
\nTheoretical conversation on findings. 68Describe in what ways findings confirm,
\ndisconfirm, or extend knowledge of the theoretical framework and relationship(s) among
\nvariables by comparing the findings with other peer-reviewed studies69 from the literature review
\nthat includes studies addressed during the proposal stage and new studies since writing the
\nproposal. 70Ties findings or disputes findings to the existing literature on effective business<\/p>\n
63 \u0392 values are to be used in the regression equation. These are the unstandardized coefficients in the SPSS output.
\n64 The beta weights identify which variables contribute more to the model. These are the standardized coefficients in
\nthe SPSS output.
\n65 The test statistic for the hypothesis test for the slope (\u0392); derived from the SPSS output; used to evaluate the
\nsignificance of the \u0392 weights, where p \u2264 .05 is significant.
\n66
\n The test statistic for the hypothesis test for the slope (\u0392); derived from the SPSS output; used to evaluate the
\nsignificance of the \u0392 weights, where p \u2264 .05 is significant.
\n67 The 95% Bootstrap confidence intervals are produced when the bootstrapping procedure is selected in the SPSS
\nregression process. See regression video tutorial located at: https:\/\/www.youtube.com\/watch?v=1ItFMKlPG5k
\n68 Rubric item 3.2g
\n69 This rubric requirement substantiates the requirement to critically analyze, synthesize and \u201creport\u201d the results of
\nthe literature (studies) pertaining to the theory and variables (see rubric component 1.14, Review of the Professional
\nand Academic Literature).
\n70 Rubric item 3.2h
\nMarch 2016
\n74
\npractice. Analyzes and interpret the findings in the context of the theoretical framework, as
\nappropriate. 71Ensures interpretations do not exceed the data, findings, and scope.
\n3.3 – Presentation of Findings (Qualitative)
\nThere is a common misconception about Section 3. Reporting the results of the study
\nfindings is more complicated than it first appears to be. This is because the findings must be
\nrelated back to the body of knowledge as well as the conceptual framework. It is not a matter of
\ntelling the reader who-said-what-and-when, one must present an in-depth scholarly discussion of
\nhow the study findings contribute to the field.
\nDo not be misled or fail to understand that reporting the findings is not about listing the
\nanswers to the interview questions. The answers to the interview questions are your evidence, not
\nthe answer to the research question. Moreover, one should never list the interview questions in
\nthe presentation of findings.
\nRemember that the rubric asks about the research question, not the interview questions.
\nThe research question is the overarching question that your study answers.
\nAlso, remember that you are presenting your findings as themes\u2014major, minor,
\nunexpected, and\/or serendipitous that are a result of your data\u2014answers to interview questions,
\ndocument review, journaling, observation notes, focus group answers, etc. Also, remember that it
\nis a good practice when using a qualitative data analysis software program to include at least one
\ntable per theme from NVivo, Atlasti, Ethnograph, or others. that illustrates the frequencies.
\nFinally, when appropriate, remember to integrate member checking.
\nTo sum up: Present the theme, present the evidence from the findings that support the
\ntheme (including tables), then support both from the body of knowledge\/conceptual framework.
\n3.4 – Application to Professional Practice
\nDiscuss how business leaders can apply the findings to aid in solving the specific
\nbusiness problem. Do not repeat literature review; rather focus on application. Often researchers
\ncan use this heading to help gain access by offering potentially participating company leaders a
\nsummary of the findings including suggestions for professional practice.
\n3.5 – Implications for Social Change
\nNow that you have analyzed and discussed the findings, suggest potential implications in
\nterms of tangible improvements for individuals, communities, organizations, institutions,
\ncultures, or societies as the findings could catalyze beneficial social change\/behaviors.<\/p>\n
71 Rubric item 3.2i
\nMarch 2016
\n75
\n3.6 – Recommendations for Action
\nThis is where you can create a win-win for companies and individuals participating in
\nyour study. The rubric requires the following: (a) that you ensure the recommendations flow
\nlogically from the conclusions and contain steps to useful action, (b) that you state who needs to
\npay attention to the results (this can help you with a win-win to discuss when gaining access for
\nthe study), and (c) that you indicate how the results might be disseminated via literature,
\nconferences, training, etc.
\n3.7 – Recommendations for Further Research
\nDiscuss areas for future research. A starting point is to identify how the limitations
\n(weaknesses) identified in Heading 1.12, Assumptions, Limitations, Delimitations, can be
\nimproved upon in future studies. Follow up this conversation by identifying other research
\npossibilities illuminated while conducting the study. Do not repeat literature; rather provide
\nfuture researchers (e.g., other DBA students) with potential research agenda for furthering the
\nscholarly conversation pertaining to the business problem.
\nThis is a good section to discuss serendipitous results, unanswered new questions that
\narose, and a finding that does not seem to align with a theory or conceptual model warranting a
\nrecommendation for further research. Often this section can lead to postdoc research.
\n3.8 – Reflections
\nPer the rubric, this short heading includes a reflection on the researcher’s experience
\nwithin the DBA Doctoral Study process in which the researcher discusses possible personal
\nbiases or preconceived ideas and values, the possible effects of the researcher on the participants
\nor the situation, and her\/his changes in thinking after completing the study.
\n3.9 – Conclusion
\nPer the rubric, students should close with a strong concluding statement making the takehome
\nmessage clear to the reader. This should be a conclusion and not a summary.
\n3.10 – Appendices\/Table of Contents
\nEnsure all appendices appear in the order they are referenced in the proposal\/doctoral
\nstudy.
\nMarch 2016
\n76
\nAPPENDIX A: WALDEN UNIVERSITY DOCTOR OF BUSINESS
\nADMINISTRATION PROGRAM VIDEO TITLES AND URL ADDRESSES
\nTitle URL Address
\n1 Walden DBA Rubric and
\nHandbook Video Tutorial
\n