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DoctoralStudyRubricandResearchHandbook.pdf
Doctor of Business
Administration
Doctoral Study Rubric and
Research Handbook
March 2016
2016
ii
FOREWORD
Walden University
DBA Doctoral Study Rubric and Research Handbook 1
March 2016
This document consists of two components: the Doctoral Study Rubric 2 and the Research
Handbook. Thus, the purpose of this document is two-fold. First, the purpose of the rubric is to
guide DBA students and DBA Doctoral Study supervisory committees as they work together to
develop high-quality proposals and Doctoral Study research. The committee will use the rubric
to provide on-going and flexible evaluation and reevaluation of the proposal and DBA Doctoral
Study drafts. The University Research Reviewer (URR), who reviews the proposal/DBA
Doctoral Study on behalf of the University, will also use this rubric to communicate feedback
and any required revisions.
Second, the Research Handbook is an accompanying guide to the rubric that provides detailed
instructions and knowledge pertaining to corresponding rubric components. The doctoral student
is still responsible for utilizing self-identified resources to aid in the understanding and
presentation of the rubric requirements. Elements in the Doctoral Study rubric correspond to
elements in the Research Handbook. For example, one will find more detailed information on the
Problem Statement (Heading # 1.3 in the DBA Rubric) in Heading # 1.3 (Problem Statement) of
the Research Handbook. Using the Doctoral Study Rubric in conjunction with the Research
Handbook when writing the proposal/Doctoral Study is highly recommended.
In the writing process, use the DBA Template and Rubric as a suggested outline for the DBA
Proposal and Doctoral Study and as a basis for feedback on early drafts.
Before the Proposal Oral Conference or DBA Doctoral Study Oral Conference, the
committee and URR will complete the rubric in MyDR and upload the proposal per the process
checklist. Find the MyDR Process Checklist at
http://academicguides.waldenu.edu/researchcenter/osra/dba.The guidance on orals is located at
http://academicguides.waldenu.edu/researchcenter/osra/oraldefense.
After the Proposal Oral Conference or DBA Doctoral Study Oral Conference, and once the
student completes any committee or methodologist revision requests for the proposal/Doctoral
Study, the committee will review the proposal/Doctoral Study and make any needed
modifications. When the committee members agree that the student met all of the rubric
requirements for the proposal and passed the oral defense, the chair then notes in MyDR that the
student passed the oral defense.
1 The DBA Rubric and Research Handbook video tutorial can be viewed at: http://youtu.be/KiiDGmLbRN0.
2 The guidance in the rubric supersedes any guidance you might see depicted elsewhere. For example, the Problem
Statement video tutorial on YouTube depicts a maximum word count of 250 for the Problem Statement. The
Problem Statement is recommended not to be too lengthy (recommended not to exceed 150 words). It is
recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as
appropriate).
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About consensus: For the final copy of the proposal or DBA Doctoral Study, there must be
unanimous agreement by the DBA Doctoral Study supervisory committee before the student
proceeds to the next step in the process checklist.
Note: Students must use a minimum of 85% peer-reviewed sources from the total number of
sources. Students should use a minimum of 85% of sources from the total sources that were
published within 5-years from the date of the anticipated completion date (date the CAO
approves the final study). Other than data collected from the study site, students cannot use
magazines, trade publications, summary textbooks, websites, and blogs as references.
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TABLE OF CONTENTS
FOREWORD................................................................................................................................ II
DBA DOCTORAL STUDY RUBRIC ......................................................................................... 1
DBA RESEARCH HANDBOOK .............................................................................................. 26
SECTION 1: FOUNDATION OF THE STUDY ................................................................ 27
1.1 - Abstract ..................................................................................................................... 28
1.2 - Background of the Problem ...................................................................................... 28
Applied DBA Versus a Speculative/Theoretical PhD .......................................... 28
Preparing the Background of the Problem ............................................................ 29
1.3 - Problem Statement .................................................................................................... 30
Avoiding Rubric Creep ......................................................................................... 31
Strategy for Mapping to the Rubric ...................................................................... 31
Specific Business Problem .................................................................................... 31
Aligning the Specific Business Problem With the Purpose Statement and RQ ... 33
1.4 - Purpose Statement .................................................................................................... 35
Six Elements of the Purpose Statement ................................................................ 35
1.5 - Nature of the Study ................................................................................................... 37
Hypothetical Quantitative Example ...................................................................... 38
Hypothetical Qualitative Example ........................................................................ 38
1.6 - Research Question (Quantitative Only) .................................................................... 39
1.7 - Hypotheses (Quantitative/Mixed-Method Only) ...................................................... 40
Hypotheses ............................................................................................................ 40
Correlation ............................................................................................................ 40
Quasi-experimental ............................................................................................... 40
1.8 - Research Question (Qualitative Only) ...................................................................... 40
1.9 - Interview Questions (Qualitative Only) ................................................................... 42
Example Research Question ................................................................................. 43
Example Applied DBA Interview Questions ........................................................ 43
1.10 - Theoretical/Conceptual Framework ....................................................................... 43
1.11 - Operational Definitions .......................................................................................... 46
1.12 - Assumptions, Limitations, and Delimitations ........................................................ 46
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1.13 - Significance of the Study ........................................................................................ 47
1.14 - Review of the Professional and Academic Literature ............................................ 47
1.15 – Transition ............................................................................................................... 49
SECTION 2: THE PROJECT .............................................................................................. 50
2.1 - Purpose Statement .................................................................................................... 51
2.2 - Role of the Researcher .............................................................................................. 51
2.3 - Participants ............................................................................................................... 52
2.4 - Research Method ...................................................................................................... 53
2.5 - Research Design ....................................................................................................... 53
Data Saturation in Qualitative Study Designs ...................................................... 53
How to Use Multiple Sources to Support Claims and Decisions ......................... 54
2.6 - Population and Sampling (Quantitative Only) ......................................................... 54
Population ............................................................................................................. 54
Sampling ............................................................................................................... 55
2.7 - Population and Sampling (Qualitative Only) ........................................................... 55
Defining the Population ........................................................................................ 55
Sampling ............................................................................................................... 55
Data Saturation and Sampling .............................................................................. 56
2.8 - Ethical Research ....................................................................................................... 56
2.9 - Data Collection—Instruments (Quantitative) ........................................................... 57
2.10 - Data Collection – Instruments (Qualitative) ........................................................... 57
2.11 - Data Collection Technique ..................................................................................... 60
Quantitative Studies .............................................................................................. 60
Qualitative Studies ................................................................................................ 60
2.12 - Data Organization Technique (Qualitative Only) .................................................. 60
2.13 - Data Analysis (Quantitative Only) ......................................................................... 60
2.14 - Data Analysis (Qualitative Only) ........................................................................... 61
2.15 - Study Validity (Quantitative Only) ........................................................................ 63
Internal Validity .................................................................................................... 63
External Validity ................................................................................................... 65
2.16 - Reliability and Validity (Qualitative Only) ............................................................ 65
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Reliability .............................................................................................................. 65
Validity ................................................................................................................. 65
2.17 - Transition and Summary ........................................................................................ 66
SECTION 3: APPLICATION TO PROFESSIONAL PRACTICE AND
IMPLICATIONS FOR CHANGE ........................................................................... 67
3.1 - Introduction .............................................................................................................. 68
Quantitative Example............................................................................................ 68
Qualitative Example.............................................................................................. 68
3.2 - Presentation of Findings (Quantitative) .................................................................... 68
Quantitative Example............................................................................................ 68
Tests of Assumptions ............................................................................................ 69
Descriptive Statistics ............................................................................................. 71
Inferential Results ................................................................................................. 71
3.3 - Presentation of Findings (Qualitative) ...................................................................... 74
3.4 - Application to Professional Practice ......................................................................... 74
3.5 - Implications for Social Change ................................................................................ 74
3.6 - Recommendations for Action ................................................................................... 75
3.7 - Recommendations for Further Research .................................................................. 75
3.8 - Reflections ................................................................................................................ 75
3.9 - Conclusion ................................................................................................................ 75
3.10 - Appendices/Table of Contents................................................................................ 75
APPENDIX A: WALDEN UNIVERSITY DOCTOR OF BUSINESS ADMINISTRATION
PROGRAM VIDEO TITLES AND URL ADDRESSES ............................................ 76
APPENDIX B: QUANTITATIVE RESEARCH PRIMER: PROBLEM STATEMENT,
PURPOSE STATEMENT, RESEARCH QUESTION(S), AND HYPOTHESES .... 77
APPENDIX C: MAJOR QUANTITATIVE DESIGNS .......................................................... 83
APPENDIX D: SAMPLING TYPOLOGIES ........................................................................... 84
APPENDIX E: SAMPLE POWER ANALYSIS ...................................................................... 85
APPENDIX F: SAMPLE QUANTITATIVE LITERATURE REVIEW OUTLINE........... 86
APPENDIX G: SAMPLE APA TABLES ................................................................................. 89
APPENDIX H: SAMPLE INTERVIEW PROTOCOL .......................................................... 95
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BIBLIOGRAPHY: SUGGESTED READINGS LISTS .......................................................... 97
Assumptions, Limitations, and Delimitations ................................................................... 98
Assumptions .......................................................................................................... 98
Limitations .......................................................................................................... 100
Delimitations ....................................................................................................... 103
Case Study Sources ......................................................................................................... 105
Case Study Seminal Books ............................................................................................. 110
Data Saturation and Data Collection Sources ................................................................. 111
Ethical Considerations/IRB ............................................................................................ 117
Ethnography Sources ...................................................................................................... 129
Focus Groups .................................................................................................................. 137
Interview Protocol Sources ............................................................................................. 142
Interviews Sources .......................................................................................................... 144
Journaling Sources .......................................................................................................... 155
Member Checking Sources ............................................................................................. 158
Mixed Methods Research ............................................................................................... 160
Notetaking and Fieldwork............................................................................................... 166
Phenomenological Sources ............................................................................................. 168
Pilot Studies .................................................................................................................... 174
Qualitative Research Foundation .................................................................................... 175
Qualitative and Quantitative Sources.............................................................................. 180
Reliability, Validity, Transferability, and Generalizability Sources............................... 189
Sampling and Incentives ................................................................................................. 196
Sensemaking ................................................................................................................... 202
Qualitative Software Analysis Sources ........................................................................... 205
Triangulation Sources ..................................................................................................... 210
Foreword
2016
1
The Rubric – Student, Committee, and Evaluation
DBA DOCTORAL STUDY RUBRIC
2016
2
Student and Committee Information 3
Student’s Name (Last, First):
Student ID (For office use only):
Chairperson:
Second Committee Member:
University Research Reviewer:
Student to provide total number of references:
(As you consider your references, it is recommended that in business 85%
should be within the past 5 years).
Note: Provide the required information in the yellow highlighted column.
3 Chair will complete the yellow highlighted fields in this section before submitting the rubric. Be sure to include the
names of all members of the committee.
March 2016
3
Evaluation 4
5 Date/Stage of the Rubric:
Date of Review
Before Proposal Oral Defense
Before Proposal Oral (Revised) 6
Before Doctoral Study Oral Defense
Before Doctoral Study Oral (Revised) 7
Note: Place an “X” in column (yellow highlight) associated with the appropriate stage.
Evaluation of State of the DBA Doctoral Study or Proposal:
No changes required, advance to next step; rubric requirements met
Changes required for resubmission; rubric requirements not met
Note: Place an “X” in the column (yellow highlight) associated with the appropriate evaluation
decision.
Member Information:
Name of member providing this review
Role of the member providing this review
Note: Enter the information in the yellow highlighted column.
4 Each member of the committee completes the evaluation.
5 Be sure to follow the Process Checklist (located at http://academicguides.waldenu.edu/researchcenter/osra) naming
convention when sending the document through the review process. Following the naming convention is vital for
tracking student progress throughout the doctoral study process.
6 Check when second and subsequent rubrics are needed if previous proposal defense was not passed.
7 Check when second and subsequent rubrics are needed if previous Doctoral Study defense was not passed.
March 2016
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Section 1 Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(1.1) Abstract (To be completed only after completion of Section 3)
a. Includes a WOW statement illuminating the problem under study.
b. Identifies the design (i.e., case study, phenomenological, quasi-experimental, correlation, etc.) NOTE: Do not mention the method (qualitative/quantitative)
in the abstract.
c. Identifies the study’s population and geographical location.
d. Identifies theoretical (quantitative) or conceptual framework (qualitative) that grounded the study; theory/conceptual framework names are lower case.
e. Describes the data collection process (e.g., interviews, surveys, questionnaires, etc.).
f. Describes the data analysis process (e.g., modified van Kaam method) to identify themes; in qualitative studies (e.g., t test, ANOVA, or multiple
regression), to report statistical data in a quantitative study.) Omit SW Titles.
g. h. mentioned are used.
i. Identifies two or three themes that morphed from the study (qualitative).
j. Presents the statistical results for each research question (quantitative studies).
k. Describes how these data may contribute to social change (use the word social change and be specific on who specifically may benefit).
8
l. Ensures the first line in the abstract is not indented.
m. Ensures Abstract does not exceed one page.
n. Use plural verbs with data (e.g., the data were - the word data is the plural of datum).
8 Begin this section as follows: The implications for positive social change include the potential to…”.
March 2016
5
Section 1 Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
o. Ensures all numbers are expressed in digits (i.e., 1, 2, 10, 20, etc.) and not spelled out unless beginning a sentence; Ensures Abstract does not include
seriation (i.e., (a), (b), (c), etc.).
(1.2) Background of the Problem 9
Provides a brief and concise overview of the context or background of the problem.
DBA Doctoral Studies are focused on applied business research. This sets the stage
for the study. This heading should comprise no more than one page in length.
(1.3) Problem Statement
Please review the video tutorial located @: http://youtu.be/IYWzCYyrgpo to aid you in preparing the
Problem Statement.
a. Provides a hook 10
supported by peer- reviewed or government citation 5 or
less years old from anticipated completion date (CAO approval).
b. Provides an anchor 11
supported by peer- reviewed or government citation 5 or
less years old from anticipated completion date (CAO approval).
c. States the general business problem Note: This element should start as follows: The general business problem is…
d. States 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)…
9 Include an introductory paragraph before the Background of the Problem component. However, do not label this
introductory paragraph with a L1 APA heading. The purpose of the background is to introduce the topic and
problem you will address. Briefly indicate why the problem deserves new research. More important, the Doctoral
Study must address applied research, so you will want to identify the need to solve an applied business problem. The
goal of this section is to encourage readers to continue reading, to generate interest in the study, and provide an
initial frame of reference for understanding the entire research framework
10 The hook should be a succinct WOW statement to catch the reader’s attention.
11 An anchor comprises a number, percentage, dollar value, ratio, index, etc.
March 2016
6
Section 1 Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
e. Ensures the specific business problem aligns with the research question and purpose statement.
f. Problem Statement should be clear and succinct (It is recommended not to be approximately 150 words).
Check with Ulrich’s Periodical Directory http://library.waldenu.edu/728.htm to ensure citations are peer reviewed.
12
See Problem Statement Video Tutorial at: http://youtu.be/IYWzCYyrgpo.
(1.4) Purpose Statement Describes the intent of the research
13 . The Purpose Statement is a ministory and it should be
approximately 200 words. The Purpose Statement must address the following six elements:
a. Identifies the research method as qualitative 14
, quantitative 15
, or mixed-
method.
b. Identifies research design 16
(i.e., case study, phenomenological, quasi-
experimental, correlational, etc.).
c. If quantitative or mixed method: Identifies a minimum of two 17
independent
(experimental/quasi-experimental designs) or predictor (correlational designs)
and at least one dependent variable 18
. Note: The quantitative study must
include at least two independent/predictor variables. 19
Ensures the independent
12
Ulrich’s is not 100% correct; the student must verify peer review status via the journal home page.
13 The first sentence of the purpose statement must align with the research question and specific business problem in
the problem statement.
14 Visit the Center for Research Quality qualitative methodology tutorial at:
http://academicguides.waldenu.edu/researchcenter/resources/Design
15 See the quantitative Research Primer located at Appendix B; Visit the Center for Research Quality quantitative
methodology tutorial at: http://academicguides.waldenu.edu/researchcenter/resources/Design
16 See Appendix C for a depiction of basic quantitative designs and their characteristics.
17 Covariates, mediator, and moderator variables are types of independent/predictor variables; be sure to clearly
identify these types of variables as applicable.
18 The terms “independent” and “predictor variables are often used interchangeably in correlation studies. Please be
consistent with the chosen terminology. 19
See Heading 1.6, Research Questions (Quantitative Only), in the Research Handbook.
March 2016
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Section 1 Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
variables appropriately align with the variables/constructs identified in
component 1.10, Theoretical/Conceptual Framework.
d. Identifies specific population group for proposed study.
e. Identifies geographic location of the study.
f. Identifies contribution to social change.
g. Ensures the first sentence links/aligns directly with the specific business problem.
See Purpose Statement Video Tutorial at: http://youtu.be/pLP4r0mfT9A.
Section 1
Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(1.5) Nature of the Study 20
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 detail in Section 2).
Note: A single paragraph is sufficient for each component: one for the method and one for the design.
a. Identifies the selection of one method (qualitative, quantitative, or mixed method) and why other methods would not work (cite a minimum of one
source).
b. Identifies the selection of the design 21
(within the method) and why it was
selected over other designs (cite a minimum of one source).
(1.6) Research Questions (Quantitative Only)
a. Lists research question(s) in about 10-15 words.
20
A single paragraph can be used for each component: one for the method and one for the design.
21 See Appendix C for a brief depiction of the major research designs.
March 2016
8
b. Ensures research question(s) 22
align(s) with the specific business problem and
first line of the Purpose Statement.
c. Includes the independent/predictor and dependent/criterion variables as identified in the Purpose Statement; ensures the independent/predictor
variables appropriately align with the constructs/variables identified in
component 1.10, Theoretical/Conceptual Framework.
(1.7) Hypotheses (Quantitative/Mixed-Method Only)
States, in accurate format, the null and alternative hypotheses for each research
question 23
.
(1.8) Research Question - Qualitative Only
a. Lists overarching research question in approximately 10-15 words.
b. Ensures research question aligns with the specific Business Problem and Purpose Statement.
Section 1
Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(1.9) Interview Questions - Qualitative Only
a. Lists each interview or focus group question. Questions must contribute knowledge to the research question. Questions must be open-ended, and
cannot be answered with a Yes or No.
b. Ensures interview/focus group questions align with the research question.
22
The research question(s) must contain the independent/predictor and dependent/criterion variables identified in the
Purpose Statement.
23 Hypotheses must include the variables identified in the research question.
March 2016
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(1.10) Theoretical/Conceptual Framework 24
Clearly and concisely identify the theoretical/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.
This 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
a. Identifies and describes the theory or conceptual model for theoretical/conceptual framework.
b. Identifies theorist(s) of the theory or conceptual model for theoretical/conceptual framework.
c. Identifies date of the theory or conceptual model for theoretical/conceptual framework (if applicable).
25
d. Identifies key concepts/propositions/tenets of the theory or conceptual model for theoretical/conceptual framework
26 .
e. Quantitative only - Ensures the theoretical constructs/variables underlying the theory are clearly identified and align with the constructs/variables
(independent variables) identified in the Purpose Statement and Research
Question(s).
Note: The independent variables/constructs represent the underlying concepts of
the theoretical framework in quantitative research.
Identifies how/why the theory or conceptual model for theoretical/conceptual framework is applicable and fits/applies to the study.
Section 1
Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(1.11) Operational Definitions
24
The theoretical/conceptual framework informs the research (quantitative) and interview (qualitative) questions. Be
sure to review the Theoretical/Conceptual Framework Video Tutorial at: http://youtu.be/P-01xVTIVC8
25 Some literature identifies the specific date the theorist introduced the theory; provide this date if this is the case. If
date is missing, then requirement (c) is not applicable.
26 Ensures the independent variables appropriately align with the theoretical framework(s) identified in component
1.10, Theoretical/Conceptual Framework.
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10
a. Presents technical terms, jargon, or special word used in the study.
b. Lists in alphabetical order. Formats in italics followed by an italicized colon. The definition follows on the same line. (This is similar to an APA Level 5
heading with a colon replacing the period.)
c. Provides citations (for each definition) from credible sources (peer-reviewed, seminal work/text, government sites, etc).
d. Does not include terms found in a basic academic dictionary (i.e., Webster’s).
e. Does not exceed 10 key operational definitions.
(1.12) Assumptions, Limitations, and Delimitations
a. Defines the term Assumptions and provides citation; lists facts that the student assumes to be true but cannot actually be verified.
b. Defines the term Limitations and provides citation; lists potential weaknesses of the study that are not within the control of the researcher.
c. Defines the term Delimitations and provides citation; identifies the bounds of the study.
(1.13) Significance of the Study 27
a. States why the study findings may be of value to businesses.
b. States how this study may contribute to effective practice of business (improvement of business practice).
c. Identifies how the results might contribute to positive social change.
27
This area is important in determining Doc Study of the Year Award-justify well.
March 2016
11
Section 1
Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(1.14) Review of the Professional and Academic Literature 28
A. Literature Review Opening Narrative
i. Contains a brief discussion of the content of the literature that includes a critical analysis and synthesis of various sources/content of the literature
(journals, reports, and scholarly seminal books, etc.) to convince readers of
depth of inquiry.
ii. Explains the organization of the review.
iii. Explains the strategy for searching the literature.
iv. The majority of references should be from peer-reviewed sources. (Consider 85% of the total sources should be peer-reviewed.)
v. The majority of references should be current. (As you consider your references, it is recommended that in business around 85% should be within
the past 5 years).
B. Application to the Applied Business Problem
i. Introduces the purpose of the study.
ii. Identifies hypotheses if a quantitative/mixed method study.
iii. Contains a critical analysis and synthesis of literature pertaining to the theoretical/conceptual framework the student identified in item #1.10,
Theoretical/Conceptual Framework, above 29
. The student includes a critical
analysis with supporting and contrasting theories/conceptual models for the
theory in the theoretical/conceptual framework.
Section 1
Foundation of the Study
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
28
The average length of substantive literature review is between 30 to 40 pages (25 pages minimum). However, the
need for depth and breadth is required. See quantitative example at Appendix F and visit the Writing Center at:
http://writingcenter.waldenu.edu/50.htm for more information on writing the literature review.
29 A key portion of the Review of the Literature must focus on the specific theoretical/conceptual framework you
are using in your study. This is a “ key requirement for you to be able to adequately address items 3.2g, Presentation
of Findings (quantitative studies) and 3.3c, Presentation of Findings (qualitative studies).
March 2016
12
iv. Contains a critical analysis and synthesis of literature pertaining to the independent variables (quantitative/mixed-method studies) the student
identified in item # 4c (Purpose Statement).
v. Contains a critical analysis and synthesis of literature pertaining to the dependent variable(s) (quantitative/mixed-method studies) the student
identified in item # 4c (Purpose Statement).
vi. Discusses measurement of variables (quantitative/mixed-method studies) the student identified in item # 4c (Purpose Statement).
vii. Contains a critical analysis and synthesis of literature pertaining to potential themes and phenomena (qualitative studies) the student identified in the
Purpose Statement.
viii. Compares and contrasts different points of view, and the relationship of the study to previous research and findings (sample size/geographical location
variance, etc.).
ix. Provides a comprehensive critical analysis and synthesis of the literature.
C. Relevancy of the Literature
The literature review is well organized. Introduce the purpose of the study.
Include hypotheses if a quantitative/mixed method study) in the opening
narrative.
D. Literature Review Organization
i. Presented in a well-organized manner.
ii. Adheres to APA formatting standards.
(1.15) Transition
a. Ends with a Transition Heading that contains a concise summary 30
of key
points of Section 1.
b. Provides an overview introducing Sections 2 and 3.
30
A concise summary recaps the major elements of the review of the literature and does not introduce new
information.
March 2016
13
Section 2 The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(2.1) Purpose Statement
Begins Section 2 with a restatement of the Purpose Statement presented in
Section 1.
Note: Copy-and paste the purpose statement from Section 1
(2.2) Role of the Researcher
Describes the role of the researcher in the data collection process and provides a peer-reviewed or
seminal source. Describes any relationship the researcher may have had with the topic, participants,
or research area.
a. Describes the role of the researcher in the data collection process and provides a peer-reviewed or seminal source.
b. Describes any relationship the researcher may have had with the topic, participants, or research area.
c. Provides a brief description of the researcher’s role related to ethics and the Belmont Report
31 protocol.
d. Qualitative studies: Describes how the student will mitigate bias and avoid viewing data through a personal lens/or perspective.
e. Qualitative studies with interviews: Briefly describes the rationale for an interview protocol.
f. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.3) Participants 32
a. Describes the eligibility criteria for study participants.
b. Discusses strategies for gaining access to participants.
c. Identifies strategies for establishing a working relationship with participants.
d. The participants’ characteristics must align with the overarching research question.
31
See Belmont Report at: http://www.hhs.gov/ohrp/humansubjects/guidance/belmont.html.
32 Select “N/A” and explain why if participants are not used in the study.
March 2016
14
Section 2 The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
e. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
March 2016
15
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(2.4) Research Method
Expands on the discussion in Heading 1.5 (Nature of the Study).
a. Identifies the use of a specific research method by indicating whether the proposed study is quantitative, qualitative, or mixed methods.
b. Justifies the use of the research method over the other research methods.
c. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.5) Research Design
Expands on the discussion in Heading 1.5 (Nature of the Study).
a. Identifies the use of a specific research design.
b. Justifies the use of the research design over other key designs for the study.
c. For qualitative studies, identifies how the student will ensure data saturation.
d. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.6) Population and Sampling (Quantitative Only)
a. Describes the population from which the sample will come.
b. Demonstrates that population aligns with the overarching research question.
c. Describes and justifies the sampling method (i.e., probabilistic or nonprobabilistic) and specific subcategory (i.e., simple random or
convenience). Addresses the strength and weaknesses associated with the
chosen sampling method and subcategory ( Appendix C.)
d. Justifies sample size via power analysis (see example in Appendix E). Provides justification for the proposed effect size, alpha, and power levels.
e. Cites the source for calculating or the tool used to calculate the sample size.
f. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
March 2016
16
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(2.7) Population and Sampling (Qualitative Only)
a. Justifies the number of participants 33
Describes and justifies the sampling method (e.g., purposeful, snowball, etc.).
Describes and justifies the number of participants.
Identifies how the student will ensure data saturation.
b. Demonstrates criteria for selecting participants and interview setting are appropriate to the study. (Rich descriptions are encouraged.)
c. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.8) Ethical Research
a. Discusses the informed consent process. Includes informed consent form in an appendix and lists in the Table of Contents.
b. Discusses participant procedures for withdrawing from the study.
c. Describes any incentives for participating.
d. Clarifies measures that the student will use to assure that the ethical protection of participants is adequate.
e. Refers to agreement documents in the (a) appendices, and (b) Table of Contents.
f. Includes statement that the student will store the data securely for 5 years to protect confidentiality of participants.
g. Final Doctoral Study includes the Walden IRB approval number.
h. Identifies how the student will protect names of individuals or organizations to keep the participants and organizations confidential.
i. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.9) Instrumentation (Quantitative Only)
33
The DBA policy for phenomenological studies is a minimum of 20 participants.
March 2016
17
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
a. States the name of the instrument(s).
b. Identifies name of publisher/developer(s) and year of development (if applicable).
c. Discusses concept(s) measured by the instrument(s).
d. Includes a detailed description of data that comprise each construct/variable measured by the instrument(s).
e. Identifies scale of measurement (i.e., nominal, ordinal, interval, ratio) for each construct/variable measured by the instrument. Please see Scales of
Measurement video tutorial at: http://youtu.be/PDsMUlexaMY.
f. Discusses appropriateness to the current study (i.e., why is this the best instrument to use for measuring the variables/constructs?)
g. Discusses instrument administration (e.g., how long, any special requirements/tools, special instructions, pencil and paper, online, etc.).
h. Describes how scores are calculated and what the scores mean; identifies items to be reverse- coded (if applicable).
i. Identifies where and/or with what populations the instrument was normed; identifies where and with what populations other researchers have used the
instrument(s) for collecting data.
j. Identifies published reliability (e.g., test-retest reliability, internal consistency, split-half, etc.) and validity properties (e.g., construct validity, concurrent
validity, convergent validity, and discriminant validity) of the instrument(s) 34
.
k. Identifies strategies used to assess validity (e.g., construct validity, concurrent validity, convergent validity, discriminant validity) and reliability (e.g., test-
retest reliability, internal consistency, split-half, etc.).
l. Discusses and justifies any adjustments or revisions to the use of standardized research instruments.
m. Identifies where in appendices the instrument(s) (or copy of permission to use instrument or purchase is (are) located). Ensures Table of Contents lists
appendices. [Copies of the instrument may not be reproduced in an Appendix
without written permission.]
34
Published reliability and validity properties might be found in the test review and in other studies where
the instrument was used to collect data.
March 2016
18
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
n. Describes where raw data will be available (appendices, tables, or by request from the researcher).
o. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.10) Data Collection Instruments (Qualitative Studies Only)
a. In addition to identifying the student as the primary data collection instrument, identifies the data collection instrument/process (e.g., informal interview,
semistructured interviews, phenomenological in-depth interviews, focus
groups, company/archival documents, etc.).
b. Clarifies how the student will use the data collection instrument/technique (the process/protocol).
c. Identifies how the student will enhance the reliability and validity of the data collection instrument/process (e.g., member checking, transcript review, pilot
test, etc.).
d. Identifies where in appendices the instrument (e.g., interview protocol, focus group protocol, interview questions, etc.) is (are) located. Ensures Table of
Contents lists appendices.
e. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.11) Data Collection Technique
a. Describes the technique used to collect data such as an online/paper survey, interview, observation, site visit, video recording (think recipe card—step-by-
step-process and describe richly. Provides abridged interview protocol (see
Appendix H), focus group protocol, observation protocol, etc. and identifies
location in an appendix.
b. Describes advantages and disadvantages of data collection technique.
c. As applicable, describes the process for conducting a pilot study after IRB approval.
d. For qualitative studies, identifies how the student will use member checking of the data interpretation or transcript review (if applicable).
e. Supports every decision with a minimum of three scholarly peer-reviewed or seminal sources.
March 2016
19
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(2.12) Data Organization Techniques (Qualitative Only).
a. Describes the systems for keeping track of data, emerging understandings such as research logs, reflective journals, and cataloging/labeling systems.
b. Reminds readers all raw data will be stored securely for 5 years.
c. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.13) Data Analysis (Quantitative Only)
a. Restates the research questions and hypotheses from Section 1.
b. Describes and defends, in detail, the statistical analyses that the student will conduct (e.g., multiple regression, two-way ANOVA, etc.).
c. Describes and defends, in detail, why other statistical analyses are not appropriate.
d. Provides explanation of data cleaning and screening procedures as appropriate to the study.
e. Provides explanation for addressing missing data.
f. Identifies and explains the assumptions pertaining to the statistical analyses.
g. Identifies the process for testing/assessing the assumptions.
h. Identifies appropriate actions to be taken take if the assumptions are violated 35
.
i. Describes how the student will interpret inferential results (i.e. key parameter estimates, effect sizes, confidence intervals, probability values, odds ratios,
etc.).
j. Identifies statistical software and version that the student will use in the data analysis process (e.g., SPSS, Excel, R, etc.).
k. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
35
Bootstrapping can be used as an effective method for addressing violations of assumptions.
March 2016
20
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(2.14) Data Analysis (Qualitative Studies Only)
a. Identifies the appropriate data analysis process for the research design (e.g., one of the four types of triangulation for case study; modified van Kaam, van
Maanen, etc. for phenomenology).
b. Provides a logical and sequential process for the data analysis.
c. Details the student’s conceptual plan or software (e.g., NVivo, Atlasti, Ethnograph, Excel, etc.) for coding, mind-mapping, and identifying themes.
d. Identifies how the student will focus on the key themes, correlate the key themes with the literature (including new studies published since writing the proposal)
and the conceptual framework.
e. It is recommended to support claims and decisions with multiple scholarly peer- reviewed or seminal sources (as appropriate).
(2.15) Study Validity (Quantitative Only) 36
a. Experimental/quasi-experimental designs only: Describes threats to external validity (e.g., testing reactivity, interaction effects of selection and experimental
variables, specificity of variables, reactive effects of experimental arrangements,
and multiple-treatment interference, as appropriate to the study) and how the
student will address the threats to external validity.
b. Experimental/quasi-experimental designs only: Describes threats to internal validity (e.g., history, maturation, testing, instrumentation, statistical regression,
experimental mortality, and selection-maturation interaction, as appropriate to
the study) and how the student will address the threats to internal validity.
c. Discusses threats to statistical conclusion validity 37
(e.g., factors that affect the
alpha/Type I error rate) and how the student will address the threats to statistical
conclusion validity.
d. Describes the extent to which, and rationale for justifying if, and if so why, research findings can be generalized to larger populations (external validity) and
applied to different settings.
36
Items “a” and “b” pertain to experimental and quasi-experimental designs only. Item “c” pertains to all quantitative
designs. Discuss validity as it pertains to the study outcomes. This component is not to address the reliability and
validity of the study instruments. The reliability and validity of the study instruments is addressed in item 2.9
(quantitative) and 2.10 (qualitative). Item “d”, external validity, pertains to all quantitative designs.
37 The three factors to be discussed are (a) reliability of the instrument, (b) data assumptions, and (c) sample size.
March 2016
21
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
e. It is recommended to support claims and decisions with multiple scholarly peer-reviewed or seminal sources (as appropriate).
(2.16) Reliability and Validity (Qualitative Only): A key difference from quantitative research is
the reliability and validity headings. The analogous criteria for qualitative studies are credibility,
transferability, dependability, and confirmability. These criteria are not measurable and need to be
established using qualitative methods such as member checking--Marshall and Rossman (2016) have
a good definition, and triangulation (data triangulation, investigator triangulation, theoretical
triangulation, and methodological triangulation). See Norman Denzin’s (1978, 2009) works on
triangulation). Please review more detailed information on qualitative validity at:
http://www.socialresearchmethods.net/kb/qualval.php
Reliability
a. Identifies how the student will address dependability. (i.e., member checking of data interpretation, transcript review, pilot test, etc.).
b. It is recommended to support claims and decisions with multiple scholarly peer- reviewed or seminal sources (as appropriate)..
Validity
c. Identifies how the student will ensure credibility (i.e., member checking of the data interpretation, participant transcript review, triangulation, etc.).
d. Identifies how the student will address transferability in relation to the reader and future research.
e. Identifies how the student will address confirmability.
f. Identifies how the student will ensure data saturation.
g. It is recommended to support claims and decisions with multiple scholarly peer- reviewed or seminal sources (as appropriate).
(2.17) Transition and Summary
a. Ends with a Transition Statement that contains a summary of key points.
b. Includes an overview of what the student will cover in Section 3.
Proposal Stage. Before IRB approval, the paper is written in future tense and after
IRB approval, the paper is changed to past tense.
Writing Style. The paper is written in predominantly active voice without slang,
euphemisms, or anthropomorphisms.
March 2016
22
Section 2
The Project
(FOR PROPOSAL & DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
Follows APA 6 th
edition in the text and in the reference list
References: Of the total sources cited, a minimum of 85% must be peer reviewed (it is recommended that in business 85% should be within the past 5 years of anticipated completion date);
ensures there is a match between citations and reference list.
Congratulations! This ends the Proposal section. See the Process Checklist located
at the Center for Research Quality website (see URL below).
http://researchcenter.waldenu.edu/Documents/DBA_Process_Checklist.pdf
March 2016
23
Section 3 Application for Professional Practice and Implications for Social Change (FOR
DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
(3.1) Introduction
a. Begins with the purpose of the study. Do not repeat the entire purpose statement. Typically, the first sentence of the purpose statement will suffice.
b. Provides a brief summary of the findings (do not exceed one page).
(3.2) Presentation of Findings (Quantitative Only)
a. Describes the statistical test(s), the variables, and the purpose of the test(s) and how they relate to the hypotheses.
b. Presents relevant descriptive statistics 38
(i.e. mean, standard deviation for scale
variables; frequencies and percentages for nominal variables).
c. Provides evaluation of statistical assumptions from Heading 2.13e.
d. Reports inferential statistical analyses results, organized by research question, in proper APA statistical notation/format. Includes the alpha level chosen for
the test, test value, p (significance level) values, effect size, degrees of
freedom, confidence intervals (when appropriate), etc.
e. Includes appropriate tables 39
and figures to illustrate results, as per the current
edition of the Publication Manual of the American Psychological Association.
f. Summarizes answers to research questions.
38
See the following link for further information on descriptive statistics:
http://www.socialresearchmethods.net/kb/statdesc.php
39 See Appendix E for basic formatted descriptive and inferential statistic tables.
March 2016
24
Section 3
Application for Professional Practice and Implications for Social Change (FOR
DBA DOCTORAL STUDY DOCUMENTS)
Quality Indicators
Type Met,
Not Met, or
N/A in Each
Cell
g. 40
Describes in what ways findings confirm, disconfirm, or extend knowledge
of the theoretical framework and relationship(s) among variables by
comparing the findings with other peer- reviewed studies from the literature
review that includes studies addressed during the proposal stage and new
studies since writing the proposal. Ties findings or disputes findings to the
existing literature on effective business practice.
h. Analyzes and interprets the findings in the context of the theoretical framework, as appropriate.
i. Ensures interpretations do not exceed the data, findings, and scope.
(3.3) Presentation of Findings (Qualitative Only)
a. Lists the overarching research question.
b. Identifies each theme. Analyzes and discusses findings in relation to the themes.
c. 41
Describes in what ways findings confirm, disconfirm, or extend knowledge
in the discipline by comparing the findings with other peer-reviewed studies
from the literature review that includes new studies since writing the proposal.
d. Ties findings to the conceptual framework
e. Ties findings or disputes findings to the existing literature on effective business practice.
(3.4) Application to Professional Practice 42
Provides a detailed discussion on the applicability of the findings with respect to the
professional practice of business. This major subsection provides a rich academic
argument for why and how the findings are relevant to improved business practice.
40
It is important to ensure the review of the literature is a critical analysis and synthesis of the theory and variables
identified in the study.
41 It is important the student includes a critical analysis and synthesis of the new literature (studies) published since
the proposal and correlates the literature with the findings in the study.
42 This is an important area for Doctoral Study of the Year Award.
March 2016
25
(3.5) Implications for Social Change 43
Expresses implications in terms of tangible improvements to individuals,
communities, organizations, institutions, cultures, or societies as the findings could
beneficially affect social change/behaviors.
(3.6) Recommendations for Action
a. Ensures recommendations flow logically from the conclusions and contain steps to useful action.
b. States who needs to pay attention to the results.
c. Indicates how the results might be disseminated via literature, conferences, training, etc.
(3.7) Recommendations for Further Research 44
Lists recommendations for further study related to improved practice in business.
Identifies how limitations identified in Section 1.12b, Limitations, can be addressed in
future research.
(3.8) Reflections
Includes a reflection on the researcher's experience within the DBA Doctoral Study
process, in which the researcher discusses possible personal biases or preconceived
ideas and values, the possible effects of the researcher on the participants or the
situation, and any changes to the researcher’s thinking after completing the study.
(3.9) Conclusion
Closes with a strong concluding statement making the take-home message clear to
the reader.
(3.10) Appendices/Table of Content
a. Consent form(s) attached. (Redact/blackout all personal or identifying data.) information.)
b. Organizational permission (Blackout name).
c. Sample of Instrument (i.e., survey, interview protocol with interview questions, observation protocol, etc.; copyrighted surveys cannot be included
w/o written permissions.)
43
This is an important area for Doctoral Study of the Year Award.
44 Limitations identified in section 1.12b, as a minimum, are ideal sources for future studies.
March 2016
26
DBA RESEARCH HANDBOOK
Doctor of Business
Administration
Research Handbook
March 2016
27
SECTION 1: FOUNDATION OF THE STUDY
Section 1 – Foundation of
the Study
March 2016
28
Note: This handbook is not in the DBA Doctoral Study Template. Make certain that the proposal
and study conform to DBA Doctoral Study Template heading sequencing, and formatting with
the correct margins and line spacing.
1.1 - Abstract
The abstract must not exceed one page. The abstract text must be double-spaced with no
paragraph breaks. The first line must not be indented. Describe the overall research problem
being addressed in the first couple of sentences and indicate why it is important (e.g., who would
care if the problem were solved). You can include a general introduction of the issue in the first
sentence, but you need to move to a clear statement of the research problem. Identify the purpose
and theoretical foundations, summarize the key research question(s), and briefly describe the
overall research design and data analytic procedures. Identify the key results, themes, one or two
conclusions, and recommendations that capture the heart of the research. Conclude with a
statement on the implications for positive social change. Here are some form and style tips: (a)
limit the abstract to one page; (b) maintain the scholarly language used throughout the doctoral
study; (c) keep the abstract concise, accurate, and readable; (d) use correct English; one may use
passive voice in the abstract; (e) ensure each sentence adds value to the reader’s understanding of
the research; (f) use the full name of any term and if the acronym is used more than once in the
abstract include the acronym in parentheses. Do not include references or citations in the
abstract. Per APA style, unless at the beginning of a sentence, use numerals in the abstract, and
don’t identify the titles of any software. Do not include seriation (i.e., (a), (b), (c), etc.)
1.2 - Background of the Problem
The purpose of the background is to introduce the topic and problem you will address.
Briefly, you want to indicate why the problem deserves new research. More important, the
Doctoral Study must address applied research, so you will want to identify the need to study how
some business leaders are solving or have solved an applied business problem. The goal of this
heading is to encourage readers to continue reading, to generate interest in the study, and provide
an initial frame of reference for understanding the entire research framework.
Applied DBA Versus a Speculative/Theoretical PhD
A DBA study is an applied business study linking theory to professional practice.
Students can use the following criteria to ensure that they have a clear DBA business study or a
DBA business study rather than a PhD business study. In contrast to a DBA study, a PhD study
is a hypothetical/theoretical study that leads to expanding or creating theory rather than solving a
business problem.
Qualitative studies. A qualitative study about people’s perceptions on how to address a
business problem is hypothetical and is a PhD study. In contrast, a qualitative study is about a
strategy that a business leader or manager has implemented /is implementing to solve a business
problem or a strategy that a business leader or manager has implemented to solve a business
problem is an applied DBA study.
March 2016
29
Quantitative studies. A quantitative study that includes one or more variables in which
the leader or manager cannot change to solve a business problem is a hypothetical/theoretical
PhD study. Whereas, a quantitative study that includes only variables which business leaders or
business managers can manipulate or change to solve a business problem is an applied DBA
study.
Preparing the Background of the Problem
The Background of the Problem can be effectively accomplished in no more than one
page; brevity and clarity are essential. The Review of the Literature will provide a more detailed
discussion on the literature pertaining to the topic/problem. Immersing yourself in the literature
on your topic/problem is crucial to uncovering a viable business problem. Do not underestimate
the importance of the literature in helping identifying a viable business problem.
The research topic is broad in nature; do not narrow the focus too quickly. You want to
provide the reader, especially those not familiar with the topic, time to become familiar with the
topic. Transition the reader to a more a concise presentation of the specific business
topic/problem under study. This component focuses on identifying why the study is important,
how the study relates to previous research on the topic/problem, and gives the reader a firm sense
of what your study is going to address and why. The Background of the Problem contains
information supporting the business problem. Do not describe, explain, justify, etc., the need for
the study in the Problem Statement. Provide these critical elements (description, explanation,
justification, etc.) in the Background of the Problem component. As such, the Problem Statement
can be written effectively in as little as four sentences: (a) hook, (b) anchor (c) general business
problem, and (d) specific business problem. Transfer the supporting references in the
Background of the Problem to the Problem Statement, but submit in a concise manner. For
example, the hook and anchor reference provided in the Background of the Problem should be
used in the Problem Statement.
March 2016
30
Include a transition statement that leads to problem statement that will provide more
specificity regarding the problem identified in the Background or the Problem component. A
well-written transition signals a change in content. It tells your reader that they have finished one
main unit and are moving to the next, or it tells them that they are moving from a general
explanation to a specific example or application. A transition form the background to the
Problem Statement is often as brief as one sentence, as follows: The background to the problem
has been provided; the focus will now shift to the Problem Statement. Tip: Many potential
business topics/problems can be found in the Area for Future Research heading of most peer-
reviewed journal articles.
1.3 - Problem Statement
As shown in the following graphic, the Problem Statement must include four specific
components the (a) hook, (b) anchor, (c) general business problem, and (d) specific business
problem. The Problem Statement is not to exceed 150 words. One should utilize the Tool/Word
Count feature in Microsoft Word to ensure the word count does not exceed the 150 maximum
word requirement. More important, ensure the problem statement reflects an applied business
problem; avoid Rubric Creep 45
. You must ensure you map to the rubric requirements. This is the
most critical component of the doctoral study and will be highly scrutinized in the review
process. Again, the Problem Statement is not to identify causes for the problem, solutions to the
problem, or any other superfluous information. A well-written problem statement can be
presented in four to five sentences. Please review the training video (see link below) developed
by the DBA methodology team to aid in writing your problem statement. The video will help
add clarity and save you time. The Problem Statement Video Tutorial can be found at:
http://youtu.be/IYWzCYyrgpo.
45
Rubric creep occurs when the problem statement does not reflect an applied business problem.
March 2016
31
DBA students are seeking a degree in business and must ensure the problem statement is
business focused. The problem statement must not represent a problem that has a social,
psychological, educational, or other discipline specific emphasis. A business problem is
something that is a problem for a business from the perspective of the business managers or the
industry’s leaders. Therefore, it is important to adopt a management perspective, and not that of
social advocates. The perspective must be from the position of the managers and leaders of
business who can address the problem.
Avoiding Rubric Creep
To ascertain if a problem addresses a business issue or has Rubric creep/Rubric drift,
please consider the following:
An important indicator that a business related problem is a specific business problem is that the problem statement relates to a key business process that organizational leaders
need to address and effectively meet the organization’s mission.
A business problem relates to one or more critical success factors (CSFs). Business leaders use business processes to function effectively to complete one or more CSF’s
needed to carry out their business mission.
A business problem is one that a business manager/leader can solve.
Conduct a final check of the problem statement by putting the hook, anchor, general business
problem, and specific business problem in bullet form and check for alignment among the four
bullets. When you can ensure that the problem statement aligns throughout, write in scholarly
narrative form (no bullets).
Strategy for Mapping to the Rubric
Read the rubric requirements for a heading.
Read what you wrote in the heading.
Read the rubric requirements for a heading again.
Read what you wrote in the section and highlight (in the proposal and the rubric) the rubric elements that you addressed in the heading.
Revise the heading as needed to include the rubric elements that you missed and eliminate superfluous narrative.
Start the process at the top again until you have mastered the rubric elements in the heading.
Specific Business Problem
The specific business problem is the genesis of one’s study. It is vital that one has a clear
and precise specific business problem. One will align the contents of the Research Question and
Purpose Statement with the specific business problem.
The qualitative specific business problem. The qualitative specific business problem
must be well defined and not contain multiple issues (variables in quantitative studies). The
March 2016
32
following graphic depicts how to include the elements needed in a qualitative specific business
problem.
The quantitative specific business problem. The quantitative specific business problem must be well defined and contain the key variables. The following graphic depicts how to include
the elements needed in a qualitative specific business problem.
March 2016
33
Aligning the Specific Business Problem With the Purpose Statement and RQ
Make certain that the specific business problem, Purpose Statement, and Research
Question (RQ) align. A good technique to use to enhance the alignment is to put the specific
business problem, RQ, and first sentence of the Purpose Statement together on a blank document
to ensure that you are using the same words. Notice the suggested order differs from the order
the headings appear in the study.
Qualitative alignment example. The graphic below provides an example of alignment among the Specific Business Problem, Research Question, and first sentence of the Purpose
Statement using the same key words. Pay attention to the words one uses in identifying the issue
that the leader lacks or has in limited supply. The word determines how one can collect data.
Some business leaders lack understanding… To ascertain what one understands will require a quantitative design.
Some business leaders lack knowledge… To ascertain a business leader’s knowledge will require a quantitative design.
Some business leaders lack strategies (or have limited plans, processes, procedures)… To ascertain a business leader’s strategies may involve interviews, focus groups, company
archival records and documents, company policies and procedures, company
intranet/Internet site, and direct/participant observation (in some cases) to collect data.
Usually interviews or focus groups are the primary data collection method.
Some business leaders lack skills… To ascertain a business leader’s skills will involve direct/participant observation as the primary data collection method.
March 2016
34
Quantitative alignment example. Notice how the Specific Business Problem, Research
Question, and first sentence of the Purpose Statement use the same key words with the exception
that the research question and subsequent first sentence in the purpose statement do not address
the business leader—this is a difference between qualitative and quantitative studies. The
following is an example of alignment for a quantitative correlational study.
March 2016
35
1.4 - Purpose Statement
There is a difference in the rubric requirements for a quantitative versus a qualitative
study. The Purpose Statement must include the following components: (a) methodology, (b)
design, (c) independent and dependent variables (for quantitative studies only), (d) specific
population and justification for using the chosen population, (e) geographical location, and (f)
the study’s potential for effecting social change. The Purpose Statement is not to exceed 200
words. One should utilize the Tool/Word Count feature in Microsoft Word to ensure the word
count does not exceed 200 words. The Purpose Statement is to be a concise statement and must
not include detailed design information (sample size, data collection, etc.). Please be sure to map
to the rubric. Please review the purpose statement video at: http://youtu.be/pLP4r0mfT9A. This
video tutorial will be helpful to you in preparing your Purpose Statement.
Six Elements of the Purpose Statement
As mentioned above, the Purpose Statement consists of six elements. These six elements,
and their contents, are:
Methodology. The first element to be presented in the Purpose Statement is the research
methodology. The methodology is the overall philosophical assumption the researcher uses for
designing and developing the study. In other words, the methodology is a worldview of how
knowledge is acquired. The qualitative method is a means for exploring and understanding the
meaning individuals or groups ascribe to a business problem. The qualitative method involves
researchers using open-ended questions to learn what a business leader is doing or has done to
solve a business problem. The quantitative method involves researchers using closed-ended
questions to test hypotheses. Mixed-method studies contain a qualitative study methodology and
a quantitative study methodology and must meet the requirements of both methodologies.
Mixed-method studies are rarely conducted in the DBA program. You simply need to identify
the methodology for or your study in a single sentence. There is no other information required
other than this single statement.
Design. The second element to be presented in the Purpose Statement is the research
design. While there are numerous designs, the most common qualitative designs seen in DBA
doctoral studies are the case study design, miniethnography, focus group, and the
phenomenological design. The correlational design is the most common design for quantitative
studies. You simply need to identify the design of your study. There is no other information
required other than this single statement.
Variables (quantitative study only) 46
. A variable is any entity that can take on different
values. Another definition of a variable is that it is a characteristic or condition that changes or
has different values for different individuals or units of analyses (i.e. sample units). More so,
variables are the corner stone of quantitative research, where the researcher seeks to explain the
relationships among variables or to compare group differences regarding a variable or variables
46
See section 1.6 “Research Questions” for more information on variable requirements.
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of interest. Another important distinction for term variable is the distinction between an
independent and dependent variable.
An independent variable is the variable you have control over (experimental designs),
what you can choose and manipulate. A dependent variable is also known as a response variable
or explained variable. The independent variable is usually what you think will affect the
dependent variable. In some cases, you may not be able to manipulate the independent variable.
It may be something that is already there and is fixed (i.e. company size), something you would
like to evaluate with respect to how it predicts, influences, impacts, or causes a change in the
dependent variable (i.e. employee satisfaction).
As it applies to your research, the dependent variable is normally the problematic
variable in DBA studies where the researcher it trying to explain what influences, affects, causes
or can predict the problem. For example, if the specific business problem is low employee
satisfaction then employee satisfaction is the dependent variable. The researcher then selects
independent variables that are thought to predict, influence, impact, or cause the dependent
variable, in this case, employee satisfaction.
Thus, it is extremely important to identify clearly the independent and dependent
variables in the Purpose Statement component of the proposal. Identification of the variables
informs other research components such as sample size and type of statistical analysis that is to
be conducted. See more on variables at: http://www.socialresearchmethods.net/kb/variable.php
Targeted population. A population is the larger group that you are studying. The
population is not to be misconstrued as the sample, or your study’s participants. You will select
your sample, or study participants from the larger population. For example, your population
might be all small business leaders in New York. You will however, select a subset of small
business leaders in New York to serve as your sample or participants. Remember, you are to
address the broader population in this component of the Purpose Statement.
In a qualitative ethnographic or case study, you will need to define the population with
the scope of the study. For example, if you are conducting a single case study, the population
will be people that meet the participant criteria within that organization/company. Likewise, in a
multiple case study the population will be the people that meet the participant criteria within the
organizations/companies in the study.
Examples for a case study with the following research question: What strategies do
department store managers use to motivate their sales associates?
Single case study example. The population will be department store managers in one
New England department store who have a strategy to motivate their sales associates.
Multiple case study example. The population will be department store managers in four
New England department stores who have a strategy to motivate their sales associates.
Geographical location. The geographical location simply identifies the geographical
location of your study’s participants. The participants might be in a particular country, region,
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state, or city. Of course, this may vary based upon the purpose of your study. In the decision to
identify the geographic location, one must ensure that the confidentiality of the company(ies) and
participants. If one is conducting a study in an automotive manufacturing facility and there are
only one or two companies in the city or state (i.e. Alabama), one should define the geographic
location to avoid the specific sample units being easily identifiable (i.e., southern United States).
Social change. The final element of your Purpose Statement requires you to provide a
positive social change statement. Positive social change involves improvement of human or
social conditions by promoting the worth, dignity, and development of individuals, communities,
organizations, institutions, cultures, or societies. Focus on explaining “WHO” may benefit, and
“HOW” the “WHO” may benefit from your study’s findings and recommendations.
Quantitative hypothetical example. The purpose of this quantitative correlation study is
to examine the relationship between leadership styles, size of business, and business revenue.
The independent variables are leadership style and size of business size. The dependent variable
is business revenue. The targeted population will consist of business leaders of microelectronic
companies in the southeast United States. The implications for positive social change include the
potential to (provide social change statement).
Note: DBA doctoral studies require the highest level or rigor and scholarship. One
focus of rigor and scholarship is that of the number of predictor or independent variables
examined in quantitative doctoral studies. Nonexperimental research (i.e. correlation, quasi-
experimental, etc.) requires the use of at least two independent or predictor variables.
Qualitative hypothetical example (case study). The purpose of this qualitative multiple
case study is to explore the strategies that department store managers use to motivate their sales
associates. The targeted population will comprise of department store managers form one of the
three department stores in the southeast region of the United States who have implemented
strategies to motivate their sales associates. The implication for positive social change includes
the potential to (provide social change statement).
Note: In a case study, and often in ethnographic studies, the population is limited to those
people meeting the participant criteria in the company or companies being studies. In a
phenomenological or narrative study, the population includes all people who meet the participant
criteria.
1.5 - Nature of the Study
The Nature of the Study component serves two purposes (a) describing and justifying the
methodology (i.e. quantitative, qualitative, mixed-method) and (b) describing and justifying the
design (i.e. case study, phenomenological, correlation, sequential explanatory, etc.). Therefore, a
well-crafted Nature of the Study can be presented in two paragraphs and not exceed one page.
The first paragraph describes and justifies the methodology and the second paragraph describes
and justifies the design. These two components should not be intermingled. A common error in
this heading is to restate the purpose, identify variables, analyses, etc. and include other
superfluous information. Again, map to the rubric and only include the required content!
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Remember that the Nature of the Study succinctly represents your defense of your choice
of method and design; therefore, it must have depth. You must demonstrate to the reviewers
that you have done the reading and research needed to support your research method and design.
That evidence also includes discussing why you did not choose other methods and designs.
Keep this heading deep yet brief. You will have time to expand upon the Nature of the Study
later in the Research Method and Design heading.
Hypothetical Quantitative Example 47
I chose a quantitative methodology for this study. Using a quantitative study enables one
to identify results that can be used to describe or note numerical changes in numerical
characteristics of a population of interest; generalize to other, similar situations; provide
explanations of predictions, and explain casual relationships (cite). Thus, the quantitative method
is appropriate for this study because the purpose of the study is to analyze numerical data and
infer the results to a larger population. A mixed methods study contains the attributes of both
quantitative and qualitative methods (cite). The qualitative method is appropriate when the
research intent is to explore business processes, how people make sense and meaning, and what
their experiences are like (cite). Therefore, the qualitative and qualitative portions of a mixed-
method approach are not appropriate for this study.
Specifically, the correlation design is chosen for this study. A correlation researcher
examines the relationship between or among two or more variables (cite). The correlation design
is appropriate for this study because a key objective for this study is to predict the relationship
between a set of predictor variables (leadership style and size of business) and a dependent
variable (company revenue). Other designs, such as experimental and quasi-experimental designs
are appropriate when the researcher seeks to assess a degree of cause and effect (cite). This
principal objective for this study is to identify a predictive model; thus the experimental and
quasi-experimental designs are not appropriate.
Hypothetical Qualitative Example
The three research methods include qualitative, quantitative, and mixed methods (cite). I
selected the qualitative method to use open-ended questions. Qualitative researchers use open-
ended questions to discover what is occurring or has occurred (cite). In contrast, quantitative
researchers use closed ended questions to test hypotheses (cite). Mixed methods research
includes both a qualitative element and quantitative element (cite). To explore (your topic), I
will not be testing hypotheses which is part of a quantitative study or the quantitative portion of a
mixed methods study.
47
Note: As you can see, the example clearly starts with topic sentences (red text) that foreshadow what is to be
addressed in the paragraph. Notice the quantitative method paragraph does not address the design, as the topic
sentence does not suggest the design is the focus of the paragraph. The design is not foreshadowed in the topic
sentence. Remember, a topic sentence alerts the reader to the main topic of the paragraph.
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I considered four research designs that one could use for a qualitative study on (2-3
words identifying your topic): (a) miniethnography, (b) focus group, (c) narrative, and (d) case
study. (Note: Select the designs that you considered and are applicable to an applied qualitative
study.) Miniethnography involves… (Briefly discuss miniethnography, 1-sentence defining with
a citation, 1-sentence if needed why it is or is not the optimal choice). Business researchers use
focus groups to… (Briefly discuss focus groups, 1-sentence defining with a citation, 1-sentence
if needed why it is or is not the optimal choice). A narrative design entails… (Briefly discuss
narrative designs, 1-sentence defining with a citation, 1-sentence if needed for why it is or is not
the optimal choice). Case study researchers… (Briefly discuss case study, 1-sentence defining
with a citation, 1-sentence is needed why it is or is not the optimal choice).
1.6 - Research Question (Quantitative Only)
DBA doctoral studies require the highest level or rigor and scholarship. One focus
of rigor and scholarship is that of the number of predictor or independent variables
examined in quantitative doc studies. Non-experimental research (i.e. correlation, quasi-
experimental, etc.) requires the use of at least two independent or predictor variables. This
is due to the “third variable” problem. A third variable also known as a confounding or
mediator variable can confound the relationship between the independent and dependent
variable. This confounding can lead the researcher to incorrectly interpret the results,
leading to an incorrect rejection of the null hypothesis.
As such, all DBA quantitative studies require the examination of at least two predictor, or
independent variables. This affects the statistical analysis, as simple bivariate correlations
(correlation designs) or one-way ANOVAs cannot be used as inferential statistical tests. Other
statistical procedures, such as partial correlation, semipartial correlation, mediation and
moderation, and multiple regression analyses, as a minimum must be used for correlation studies.
Quasi-experimental, causal comparative, etc., designs must employ statistical analyses (i.e.
factorial ANOVAs), as a minimum, which examines more than one independent variable.
Below are appropriate and inappropriate examples of correlation and quasi-experimental
research questions. These examples depict predictor/independent variables, which are (a)
employee job satisfaction and (b) leadership experience. The dependent variable is company
gross revenue.
Appropriate Correlation Example (two predictor variables): Does a linear combination of employee job satisfaction and leadership experience significantly
predict employee productivity?
Inappropriate Correlation Example (only one predictor variable): Does employee job satisfaction significantly predict employee productivity?
Appropriate Quasi-experimental Example (two independent variables): Do employee job satisfaction and leadership experience significantly influence employee
productivity?
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Inappropriate Quasi-experimental Example (only one independent variable): Does employee job satisfaction significantly influence employee productivity?
1.7 - Hypotheses (Quantitative/Mixed-Method Only)
Hypotheses
Two major elements in the research design are the hypotheses and the variables used to
test them. A hypothesis is a provisional idea whose merit deserves further evaluation. Two
hypotheses, the null (H0) and alternative (H1), are to be stated for each research question. Below
are appropriate examples of correlation and quasi-experimental/casual comparative null and
alternative hypotheses; note how they mirror the research questions identified above in the
Quantitative Research Questions heading. These examples depict predictor/independent
variables, which are (a) employee job satisfaction and (b) leadership experience. The dependent
variable is company gross revenue. The H0 and H1 reflect the appropriate statistical notation and
are to be included. See more on hypotheses at:
http://www.socialresearchmethods.net/kb/hypothes.php
Correlation
Null Hypothesis (H0): The linear combination of employee job satisfaction and leadership experience will not significantly predict employee productivity.
Alternative Hypothesis (H1): The linear combination of employee job satisfaction and leadership experience will significantly predict employee productivity.
Quasi-experimental
Null Hypothesis (H0): Employee job satisfaction and leadership experience do not significantly influence employee productivity.
Alternative Hypothesis (H1): Employee job satisfaction and leadership experience significantly influence employee productivity.
1.8 - Research Question (Qualitative Only) In a qualitative study, the Research Question uses the same words as in the Specific
Business Problem to identify the specific business leader and identify what the leader has limited
supply of or is lacking. The following examples demonstrate how to align the research question
with the specific business problem.
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1.9 - Interview Questions (Qualitative Only)
In qualitative studies, the researcher must first identify the population for the study
(business leaders that have solved or are solving the specific business problem) and align the
interview questions with the population and the research question. Interview questions must (a)
provide answers to the research question, (b) not go beyond the research question (i.e., no
demographics if not part of the research question), (c) be in the language (word choice) that the
participant will understand, (d) be open-ended questions (no Yes or No answerable questions),
and (e) be applied DBA rather than speculative PhD questions (see the example below).
Interview questions should be straightforward and ask what or how the business leader
has addressed the research problem. Typically, case study and ethnographic interviews will be
semistructured, semiformal, unstructured, or informal. Phenomenological studies use the
phenomenological long interview with only one to three questions to have a longer discussion
getting in depth data and reaching a state of epoché. Students should critically read about the
different interviewing techniques and select the best technique for the study design.
Semistructured and semiformal interviews frequently include six to ten interview
questions to allow time for probing questions. The final interview question in a semistructured or
informal interview frequently asks the participant to share any additional information for
addressing the research question(s): What additional information would you like to share about
XYZ? One typically uses an unstructured or informal interview technique when having a more
casual discussion often spreading the interview questions out over time during field visits (i.e.,
during a direct observation or participant observation phase in data collection).
In contrast, the phenomenological long interview typically has one or two interview
questions. Although phenomenological interview questions are written as a question, the
interview protocol involves creating an in depth discussion (typically 1-2 hours) and reaching a
state of epoché. The phenomenological long interview requires more study and preparation as
compared to more traditional interviewing techniques used in ethnography and case study
designs.
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Be cautious not to confuse the interviewing process with the interviewing questions. The
concept of semistructured questions or semistructured interview questions does not exist.
Semistructured interviews (semiformal, unstructured, or informal interviews) are a specific
interviewing technique/process. All qualitative interview questions are open-ended. However,
the interview questions are not semistructured.
Example Research Question
What strategies do department store managers use to motivate their sales associates?
Example Applied DBA Interview Questions
1. What strategies are you using to motivate your sales associates? 2. What method did you find worked best to motivate your sales associates? 3. How did your sales associates respond to your different motivation techniques?
Example Speculative/Theoretical PhD Questions (do not use)
1. What strategies should managers use to motivate sales associates? 2. What method do you think will work best to motivate sales associates? 3. How do you feel your sales associates respond to other motivation techniques?
1.10 - Theoretical/Conceptual Framework
A theoretical (for quantitative studies) or conceptual framework (for qualitative studies)
offers a systematic view of a phenomenon. In other words, the framework provides a lens
through which to view a phenomenon.
Identifying the Best Theory or Conceptual Model
Make certain that the theory aligns with the research question. Consider the following when
searching for a theory or conceptual model for the conceptual framework.
Critically read peer- reviewed studies related to your topic and identify the theories that the sources found aligned with their studies. After one has read and synthesized
numerous peer-reviewed studies related to the topic for the annotated bibliography, one
will notice a few theories (or conceptual models) that aligned with several studies.
Critically read the seminal work on the theories (or conceptual models) that you found in peer-reviewed studies related to your topic.
Related studies may be about the concept and not the specific industry.
For example, if one is studying how the family owned wrecking yard leaders succession plan, one could look at studies on leadership training and development in other types of
organizations.
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Quantitative. Select the theory or conceptual model that best aligns with the research question and provides an interrelated set of constructs, variables, hypotheses, or
propositions that offer an explanation for phenomenon.
Qualitative. Select the theory or conceptual model that best aligns with the research question.
As you can see, it is important to immerse yourself in the literature pertaining to your
conceptual framework to gain a good understanding of the framework. More important, your
literature review must include an exhaustive review of the literature pertaining to the conceptual
framework you are proposing for your study. This is extremely important, as you will be
required to discuss your findings as they confirm, disconfirm, extend, etc., the extant literature
on your conceptual framework. You must critically analyze and synthesize the studies where
your conceptual framework has been the lens through which the phenomenon has been viewed.
As outlined in the DBA Rubric, you are required to present a brief overview of your
theory or conceptual framework in Section one of the proposal. Please note this is not to be a
detailed review of your theory or framework. The detailed review is required in the Review of the
Literature heading. Here, a model for presenting the theory or framework heading is offered.
You will want to state the name of the theory or identify the conceptual framework, identify the
theorist if applicable, list key concepts of the theory or framework, identify any propositions or
hypotheses, and identify how the theory or framework applies to your study. Please note there
are obvious variations to this model depending upon your particular study and topic. However,
the intent is to briefly present the key aspects of your theory and or framework and show how it
fits into your study.
Quantitative Example
Burns (1978) developed the transformational leadership theory. 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). The
following figure 48
is a graphical depiction of the transformational leadership theory as it applies
to examining turnover intentions.
48
Graphical models are useful for depicting the theoretical framework in quantitative studies.
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Let’s examine the theoretical framework from the perspective of possible lenses through
which to view phenomena. Assume the business problem or phenomenon is the failure rate of
small businesses, an obvious business concern. There are plethora’s of explanations that can be
offered for the failure of small businesses. As the researcher, you have the choice of lens for
which to view the problem. For example, you might hypothesize or rationalize that
transformational leadership characteristics offer a systematic view for the failure of small
businesses. Specifically, you hypothesize or rationalize that a leaders transformational leadership
characteristics are influential in the success of small businesses. As such, your study would be
grounded in transformational leadership theory or transformational leadership conceptual
framework.
Or perhaps, you hypothesize or rationalize that servant leadership characteristics offer a
systematic view for the failure of small businesses. Specifically, you hypothesize or rationalize
that a leaders servant leadership characteristics are influential in the success of small businesses.
As such, your study would be grounded in transformational leadership theory or transformational
leadership conceptual framework. Hence, the number of lenses through which a problem or
phenomena can be viewed is limitless. Only your imagination stands between you and selecting
the theory or conceptual framework that can be used to connect your study to existing
knowledge.
Perhaps, one of the most misunderstood aspects of theory is how to apply it in the
doctoral study. Researchers utilizing a quantitative study grounded in transformational leadership
theory must measure or assess the constructs underlying the theory. The broad constructs of
transformational leadership theory are idealized attributes, idealized behaviors, inspirational
motivation, stimulation, and idealized consideration.
Therefore, an instrument such as the Multifaceted Leadership Questionnaire (MLQ) is
appropriate to measure the underlying constructs of transformational leadership theory. Any
instrument not proven to assess transformational leadership cannot be approved for use in a study
grounded in transformational leadership theory. If you (inappropriately) used a nonvalidated
instrument, you would not be testing the proposed transformational leadership theory, and your
Idealized Attributes
Turnover Intention
Idealized Behavior
Intellectual Stimulation
Inspirational Motivation
Moral Integrity
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study would not have construct validity. For example, the Servant Leadership Survey (SLS)
instrument could not be approved for use in a study grounded in transformational leadership
theory, as the SLS was validated for use in measuring constructs underlying servant leadership
theory.
Qualitative Example
Example research question. What strategies do department store managers use to
motivate their sales associates?
Example conceptual framework. Vroom (1959) developed the expectancy-valence
theory, which he later called the expectancy motivation theory (Vroom, 1964). The expectancy
motivation theory suggests that employees will exhibit positive performance behaviors when
they believe that their work will result in certain rewards (Vroom, 1964). Building upon Vroom’s
expectancy motivation theory, Gilbert (1978, 2013) published his behavioral engineering model
that provided a motivational foundation for the inputs that can lead to specific employee motives.
Gilbert identified three categories covering information, instrumentation, and motivation. Within
the manager’s scope of control are data, resources, and incentives. Within the employee’s scope
of control are knowledge, capacity, and motives. Gilbert argued that if managers improved the
availability of data access, provided the tools and equipment, or incentives to perform,
employees would exhibit a change in willingness to participate. Likewise, if employees have a
change in knowledge or capacity to perform, employees would exhibit a change in willingness to
participate (Gilbert, 1978, 2013). Vroom’s (1964) expectancy motivation theory and Gilbert’s
(1978) behavioral engineering model both align with this study exploring the strategies that
department store managers use to motivate their sales associates.
1.11 - Operational Definitions
Do not include terms found in a basic academic dictionary (i.e. Webster’s). List only
terms than might not be understood by the reader. All definitions should be sourced from
professional/scholarly sources and in alphabetical order. Do not include more than 10 key
operational definitions. Although one can use a maximum of 10 terms, there may only be a few
terms pertinent to the study. Listing a specific term that only one or two sources in the literature
review introduce is likely not pertinent to the study and should not be listed in the operational
definitions.
1.12 - Assumptions, Limitations, and Delimitations 49
Assumptions are facts considered to be true, but which cannot actually be verified by the
researcher. Assumptions carry risk and should be treated as such. A mitigation discussion would
be appropriate. Identify all assumptions associated with the study. Limitations refer to potential
study weaknesses, which cannot be addressed by the researcher. Identify all limitations
49
Review the following resource for more detailed information: Ellis, T. J., & Levy, Y. (2009). Towards a guide for
novice researchers on research methodology: Review and proposed methods. Issues in Informing Science and
Information Technology, 6, 323-337. Retrieved from http://www.informingscience.org/Journals/IISIT/Overview
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associated with the study. Delimitations refer to the bounds or scope of the study. Describe the
boundaries and what is in and out of your study’s scope.
1.13 - Significance of the Study
Contribution to Business Practice
Discuss how the findings, conclusions, and recommendations from your study could fill
gaps in the understanding and effective practice of business.
Implications for Social Change
Provide a statement of the your study’s potential for effecting positive social change or
the improvement of human or social conditions by promoting the worth, dignity, and
development of individuals, communities, organizations, institutions, cultures, or societies.
1.14 - Review of the Professional and Academic Literature
The literature review content needs to be a comprehensive and critical analysis and
synthesis of the literature related to the theory and/or conceptual model from the
Theoretical/Conceptual Framework as well as the existing body of knowledge regarding the
research topic. What a literature review should not be is an amalgamation of essays on the topic.
The approach to this heading may vary by authors’ specific purpose. For example, if your study
is to be grounded in the transformational leadership theoretical or conceptual framework, you
will be examining or exploring your phenomenon through a leadership lens. You want to report
on extant research that was grounded in the transformational leadership theoretical/conceptual
framework. You would want to report on the literature that is as close to your topic/phenomenon
as possible. In addition, if you are conducting a quantitative study, you need to include the
literature for any other key variables. A basic outline is presented at Appendix A. 50
Critical analysis and synthesis of the relevant literature will be an important element of
the literature review. The review of the literature is not to be a regurgitation of what you have
read. It is also not to teach about a topic; rather, it is to show your mastery of the previous and
recent research on your topic and provide a comprehensive up-to-date literature review on your
topic. Start with an introductory heading and then report the literature. This should be an
exhaustive review of the literature using the chosen theoretical/conceptual framework and
consist of the key and recent writings in the field. Repeat this approach if there are any additional
theories. In addition, in quantitative studies, there must be a critical analysis and synthesis for
each variable.
There are three questions that students typically ask about the literature review: (a)
length, (b) organizational structure, and (c) content. The length will depend upon the theoretical
foundation related to the topic and scholarly studies related to the theory. Typically, for a
doctoral study, a literature review will average 35-40 pages. However, demonstrating a rich and
50
Literature reviews will vary by topic, author, etc. However, Appendix A presents the minimum requirements for a
quantitative study.
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comprehensive review of the topic is more important than the number of pages in a literature
review.
The most common ways that one may organize the literature review are to use a
chronological, topical, or combination of chronological and topical structure. The literature
review should be a succinct yet in-depth critical analysis of scholarly studies and authoritative
seminal work. The literature review should not be a summary of one’s reading or an
amalgamation of essays on the topic.
The literature review content needs to be a comprehensive and critical analysis and
synthesis of the literature related to the theory and/or conceptual model that one identified in the
Theoretical/Conceptual Framework as well as the existing body of knowledge regarding the
research topic. Typically one half to two thirds of a good literature review will relate the theory
or conceptual models to a critical analysis and synthesis about the topic and problem. One
organizational strategy for the literature review is (a) one third discussing the theory or
conceptual model (see figure below), (b) one third topical foundation, and (c) one third
discussing the topic in relation to the theory.
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1.15 – Transition
This heading summarizes the key contents of Section 1. Do not introduce any new
material in the summary, but do provide an overview of the primary objectives and contents of
Sections 2 and 3.
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SECTION 2: THE PROJECT
Section 2 – The Project
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2.1 - Purpose Statement
Simply cut-and-paste the Purpose Statement from Section 1.
2.2 - Role of the Researcher
The Role of the Researcher is an important part of your proposal and study. The content
that you present in this subheading is important because it demonstrates that a) you have done
the research that is required, b) that you understand what your role is in the study design, and 3)
you understand the limitations and challenges in this type of role, and how any concerns may be
mitigated to enhance the reliability and validity of your work.
One of the most challenging parts to write in this subheading is about the use of a
personal lens primarily because novice researchers (like students) assume that they have no bias
in their data collection. However, it is important to remember that a participant’s as well as the
researcher’s bias/worldview is present in all social research, both intentionally and
unintentionally which is why it is important to address strategies to mitigate bias.
To address the concept of a personal lens, remember that in qualitative research, the
researcher is the data collection instrument and cannot separate themselves from the research,
which brings up special concerns. Remember that the researcher operates among multiple worlds
while engaging in research, which include the cultural world of the study participants as well as
the world of one’s own perspective. A researcher's cultural and experiential background will
contain biases, values, and ideologies that can affect the interpretation of a study’s findings.
Therefore, researcher bias is a concern because the data can reflect the researcher’s personal bias
and concerns. It becomes imperative that the interpretation of the phenomena represent that of
participants and not of the researcher. Hearing and understanding the perspective of others may be
one of the most difficult dilemmas the researcher must address. The better a researcher is able to
recognize his/her personal view of the world and to discern the presence of a personal lens, the
better one is able to hear and interpret the behavior and reflections of others.
How you address and mitigate a personal lens/worldview during your data collection and
analysis is important and a key component in the Role of the Researcher subheading. It is
important that a novice researcher recognizes their own personal role in the study and mitigates
any concerns during data collection. Part of your discussion in this subheading should address
how this is demonstrated through using an interview protocol, member checking, transcript
validation and review, reaching data saturation, enabling sense making, facilitating epoché,
careful construction of interview questions, and other strategies to mitigate the use of one’s
personal lens during the data collection process of the study.
It would be impossible to remove all bias because you are a human being. Rather, one
mitigates bias as best as one can. This is demonstrated via using an interview protocol, member
checking, data saturation, and other strategies to mitigate the use of one personal lens during the
data collection process of your study. Inadvertently driving participants to predetermined
conclusions speaks to the same concepts.
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2.3 - Participants
The requirements are straight forward but often missed in the Participants heading.
Consider the explanations in the following table.
Rubric Requirement Explanation
a. Describes the eligibility criteria for study
participants.
The participants must meet the eligibility requirement
within the scope of the population. Consider the research
question: What strategies do department store managers
use to motivate their sales associates? If one identified the
population as department store managers who have worked
in the field for 8-years and have a minimum of 5-years
supervising sales associates, one would not be necessarily
addressing the requirement.
The criteria for the example research question would be
department store managers who have successful strategies
that they are using to motivate sales associates. The
department store manager may have been in the field for
20-years or 1-month—the time in position has nothing to
do with the study. Likewise, working with the employees
does not mean that the department store manager is using a
strategy to motivate the sales associates.
b. Discusses strategies for gaining access to
participants.
Explain your plan for gaining access to participants. In a
quantitative survey, one may use a professional association
membership list or other types of list to access participants
via email, phone, etc.
For a qualitative study, one may also use professional
associations, trade affiliations, etc. for gaining access. One
may also be using rosters inside the company(ies) and
emailing, calling, or visiting in person for a case study.
It is vital that you develop a strategy to determine that
participants meet the study criteria before inviting
participation.
c. Identifies strategies for establishing a working
relationship with
participants.
Once one gains access, one needs to develop a working
relationship with the participants. This may be as simple as
sending a survey link via email in a quantitative study to
how you will cover the informed consent form and set the
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stage for a qualitative interview (often referencing the
interview protocol).
d. The participants must align with the overarching
research question.
This requirement is a reminder that one must have the
correct criteria for selecting the participants and that the
criteria must align with the research question—nothing else
should be included in the criteria.
e. Supports every decision with a minimum of three
scholarly peer-reviewed or
seminal sources.
During planning the study, one will make several
decisions. In this heading, there is a decision for the
participant criteria, how one will gain access to the
participants, and how one will build a working relationship
with the participants. Each decision will need a synthesis
from a minimum of three scholarly peer-reviewed or
seminal sources to support the decision. This means that
one may have nine citations for this section. Fortunately,
you have an annotated bibliography with peer-reviewed
studies where others have made similar decisions as well as
seminal sources on methodology.
Tip: To represent your sources correctly: Write about what
you will do in one sentence and synthesize your sources
supporting your decision in a separate sentence.
2.4 - Research Method
This heading is an extension of the Nature of the Study. The first paragraph of the Nature
of the Study required a description of and justified the methodology. Here you will extend that
discussion by providing more information and additional resources. Remember to use multiple
sources to support claims and decisions. It is important to have a strong case to support the
rationale for research design.
2.5 - Research Design
This section is an extension of the Nature of the Study. The second paragraph of the
Nature of the Study required a description of and justified the design. Here you will extend that
description by providing more information and additional resources. Be sure to include at least
three sources for each decision you make.
Data Saturation in Qualitative Study Designs
A vital prerequisite for a valid qualitative study is having a plan to ensure data saturation.
Data saturation in qualitative research ensures the validity in a qualitative study similar to a
statistically valid sample in a quantitative study. See more on data saturation in the Population
and Sampling heading below.
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How to Use Multiple Sources to Support Claims and Decisions
Specifically stating multiple sources is one way to make it clear to the reviewers that you
have mapped to the Rubric. However, what the reviewers are looking for is that students have
done the required reading to justify the choice of research design that will best assist collecting
data to answer the research question. Rather than list name-date, name-date, name-date
repeatedly, one would synthesize the concepts into one cohesive whole supported by sources in a
somewhat indirect manner. For example:
Case studies are the preferred strategy researchers employ when asking how or
what questions (Amerson, 2011; Andrade, 2009; Yin, 2009). These types of studies
identify operational links among events over time (Andrade, 2009; Baxter & Jack, 2008;
Yin, 2009). Case studies may be exploratory, explanatory, or descriptive and may involve
one organization and location or multiple organizations and locations for a comparative
case study (Amerson, 2011; Stake, 1995; Yin, 2009).
In other words, you are supporting your synthesis with multiple sources. Another way to
support your design with a source is:
Ethnographic study is unique in that it includes fieldwork where all relevant
participants are observed and interviewed informally rather than a specified number as
in phenomenology (Fusch, 2001; Wolcott, 2011). Bernard (2012) stated that the number
of participants needed for a qualitative study was a number he could not quantify, but
that the researcher takes what he can get it.
In other words, you support your synthesis in a more direct way. Note that Bernard's
entire work is not within the text, but, rather, one important statement that he did make is and it
supports the chosen research design.
In both examples, the synthesis demonstrated depth of knowledge that is supported by
published peer-reviewed work, which is what reviewers want to see in your work. Moreover, it is
a demonstration of your scholarly research abilities. Note, you may use the same source to
support more than one decision if applicable.
2.6 - Population and Sampling (Quantitative Only)
Population
Start by describing the population from which the sample will be drawn. Include any
pertinent demographic variables (e.g., CEO, senior executive, mid-level manager, sales
professional, front-line supervisor, etc.). Refer to pg. 29 (Participant Characteristics) of the APA
Manual (American Psychological Association, 2010) for other appropriate characteristics when
appropriate.
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Sampling
The two broad categories of sampling methods are probabilistic sampling (random
sampling) and non-probabilistic sampling (non-random sampling) 51
. Identify and defend your
sampling method. You must address the strengths and weaknesses of your chosen sampling
method. For example, if you will utilize a stratified random technique defend your reason for
doing so. Also note why stratified sampling is more appropriate for your research situation than
another sampling technique. You will need to refer to the literature pertaining to sampling
techniques.
Describe and defend the sample size. This is where you discuss conducting a power
analysis to determine the appropriate sample size. You will present your power analysis in this
component. G*Power3 is an excellent power analysis software tool and can be downloaded at:
http://www.gpower.hhu.de/en.html. You will find a user’s manual and short tutorial at the same
website. See Appendix B for an example power analysis.
Describe the eligibility criteria for inclusion in the study. Discuss any exclusion criteria.
Make the eligibility criteria clear, as the results of the study cannot be generalized beyond your
targeted population. You need to make it clear as to who can, and who cannot, participate in your
study.
2.7 - Population and Sampling (Qualitative Only)
Defining the Population
In this heading, one needs to define the scope of the study. For example, in a
phenomenological study, the population will be all the people within the scope of the study (i.e.,
a specific industry) that meet the participant criteria noted in the participant section 2.3 above. In
an ethnographic study or case study, the population would comprise all people that meet the
participant criteria in one company for an ethnographic study or single case study and multiple
companies for a multiple case study. One should identify the number of companies in a multiple
case study. Likewise, one should identify the approximate number of people (that meet the
participant criteria) within your study’s population.
Sampling
One must describe and justify the sampling method (census, convenience, criterion,
purposeful, quota, snowball, etc.). Once one defines the total population meeting the participant
criteria within the scope of the study, one must identify the sample size that has the best
opportunity for the researcher to reach data saturation. A large sample size does not guarantee
that one will reach data saturation, nor does a small sample size—rather, it is what constitutes the
sample size. One must also select a sampling technique that supports the research design.
51
See Appendix B for a typology of sampling strategies.
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For example, one may use a census sample for a single or multiple case study with a
small population versus a convenience sample in an ethnographic study. A census sample is
actually a census, which means that the study participants will include 100% of the population.
For example, as depicted in the following graphic, if one identified the scope of a multiple case
study to include five companies and the people that meet the participant criteria for the
population as the CEOs of the five companies, there would be a census sample if all five of the
CEOs participated.
Data Saturation and Sampling
In the Population and Sampling heading (as well as the Research Design and the Validity
headings), one must define how one will ensure data saturation. Although data saturation in
qualitative research ensures the validity in a qualitative study similar to a statistically valid
sample in a quantitative study, there is no direct correlation between the sample size and
reaching data saturation. Data saturation in qualitative research is a way to ensure that one
obtained accurate and valid data. Using too small of a sample or too large of a sample will not
ensure data saturation. One should critically read and obtain a clear understanding of data
saturation before writing a qualitative proposal. Fusch and Ness (2015) synthesized the literature
to identify some key characteristics of reaching data saturation which include no new data, no
new themes, no new coding, and ability to replicate the study (providing one asks the same
participants the same questions in the same timeframe). The study design (case study,
miniethnography, phenomenological, etc.) will affect when and how one reaches data saturation.
One may be conducting interviews only in a phenomenological study, whereas one would use
multiple data collection methods in a case study.
Although the DBA leadership requires a minimum of 20-participants in a
phenomenological study and although one may use member checking to enhance the richness of
the data, one may have to interview many more participants to reach data saturation. In contrast,
in a case study using a small census sample and multiple data collection methods, one may reach
data saturation with one or a few participants. In qualitative studies, quality (rich data) is more
important than quantity (thick data).
2.8 - Ethical Research
Each research study comes with its own set of specific ethical issues. Thus, a rubric
cannot address all possible scenarios. Therefore, it will be helpful to review the IRB Application
Form before you complete this component to ensure you address any requirements not identified
in the rubric or Research Handbook. However, as a minimum, discuss the informed consent
process. Include a copy of the informed consent form in an appendix and list the informed
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consent form in the Table of Contents. Discuss participant procedures for withdrawing from the
study. Describe any applicable incentives. Clarify measures for assuring the ethical protection of
participants is adequate. Agreement documents are to be listed in the (a) text of the study, (b)
appendices and (c) Table of Contents. Include a statement that data will be maintained in a safe
place for 5-years to protect rights of participants. Ensure you indicate that the final doctoral
manuscript will include the Walden IRB approval number. Ensure the document does not include
names or any other identifiable information of individuals or organizations.
Each participant in your study must give written consent to take part in the data collection
phase of the work. Moreover, as a researcher following the protocols of the Belmont Report, you
must ensure that your participants have a full understanding of their part in the study. Finally,
you must ensure that participants understand that they may withdraw from your study at any time
without penalty, and how to withdraw from the study.
It is a good practice to complete the first draft of your IRB application while completing
the ethics section as well as Section 2. Consider: (a) writing a sentence about your plan to share
a summary of the findings with the study participants, and (b) do not use the term anonymous for
qualitative studies if you will be interviewing or knowing whom the participants are. Qualitative
researchers can protect the confidentiality but not the anonymity of participants because the
researcher will know who the participants are. Depending upon the data collection method,
quantitative researchers may be able to protect participants’ anonymity.
2.9 - Data Collection—Instruments (Quantitative)
You will describe each instrument’s purpose, intended populations, scales, scoring
process, time needed to complete, etc. This heading will also address the psychometric issues
surrounding the instrument, reliability and validity—this is very important. You will need to
report the reliability and validity coefficients. Where possible, include the details of the
reliability measures employed (e.g. test-retest, equivalent or alternate form, split-half, and
internal consistency). Validity should include content validity, criterion-related validity, and
construct validity. State briefly what these measures of validity are, and report their
Intercorrelation coefficients.
You will need to address any special requirements of the publisher. You will need to gain
permission from the test publisher to use some instruments. This can be requested by sending a
formal letter or email to the publisher. Alternatively, you may need to complete a training course
or require your chair’s signature to acquire the instrument—be sure to include this information if
applicable.
2.10 - Data Collection – Instruments (Qualitative)
The requirements are straight forward but often missed in the Participants heading.
Consider the explanations in the following table.
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Rubric requirement Explanation
a. In addition to identifying the student as the primary
data collection instrument,
identifies the data collection
instrument/process (i.e.,
informal interview,
semistructured interviews,
phenomenological in-depth
interviews, focus groups,
company/archival
documents, etc.).
Rubric requirement has two parts and students sometimes
miss one of them, which can lead to a revision request.
1. Identifying that you are the primary data collection instrument.
2. Identifying all of the secondary, tertiary, etc. data collection instruments. Although common in
ethnographic research, in case studies, students
must have a minimum of two data collection
methods.
b. Clarifies how the student will use the data collection
instrument/technique (the
process/protocol).
Describe how you will use the instrument(s) by providing a
brief definition of each instrument and referencing
interview or focus group protocols, etc.
The focus here should be more on defining and using the
instrument. For example, if you are using a specific type of
interview, what is the interviewing technique specific to
your chosen approach (i.e., unstructured or semistructured
interviews). Keep this brief; however, be sure to define the
different data collection methods (with scholarly support).
In the Data Collection Technique Heading, where you will
expand upon the process.
c. Identifies how the student will enhance the reliability
and validity of the data
collection
instrument/process (i.e.,
member checking, transcript
review, pilot test, etc.).
Clarify how you will enhance the reliability and validity of
the instruments such as using an expert panel to validate
interview questions, member checking follow up
interviews after semistructured interviews, triangulation of
multiple data collection methods (during the data analysis
as applicable to the research design), etc.
d. Identifies where in appendices the instrument
(i.e., interview protocol,
focus group protocol,
interview questions, etc.) is
(are) located. Ensures Table
of Contents lists
appendices.
As applicable, include interview protocols, focus group
protocols, direct/participant observation protocols in the
appendices.
e. Supports every decision with a minimum of three
scholarly peer-reviewed or
seminal sources.
During the study plan, one will make several decisions. In
this heading there are several decisions to make and
support. Each decision such as the following will need
scholarly support:
Identifying that you are the primary data collection
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instrument.
Identifying all of the secondary, tertiary, etc. data collection instruments such as type of interviews,
focus groups, company/archival documents,
company marketing materials, etc.).
Identifying how you will use the instruments by providing a brief definition of the instrument and
referencing interview or focus group protocols, etc.
Identifying how you will enhance the reliability and validity of the instruments such as by using
member-checking follow up interviews after a
semistructured interview.
Tip to represent your sources correctly: Write about what
you will do in one sentence and synthesize your sources
supporting your decision in a separate sentence. See the
following examples:
Academic integrity code of conduct violation
(misrepresenting sources) example 1: I will use
semistructured to explore the strategies that department
store managers use to motive their sales associates
(Johnson & Williams, 2013; Rubin & Rubin, 2012; Smith,
2014). Note that the sources did not discuss the student’s
study in their publications and the example is a
misrepresentation of the sources.
Correctly supporting a decision example 1. Cite (2014)
used semistructured interviews to determine how sales
managers motivate sales associates. Likewise, Cite (2013)
found that semistructured interviews were a good approach
to learn how department store managers motivate sales
clerks. Rubin and Rubin (2012) argued that semistructured
interviews are a good way for the researcher to focus on the
details that address the research question. Therefore, I will
use semistructured to explore the strategies that department
store managers use to motivate their sales associates. Note:
please be sure to synthesize your sources to support your
decisions.
Academic integrity code of conduct violation
(misrepresenting sources) example 2: I will be the
primary data collection instrument in this study (Denzin,
2014; Marshall & Rossman, 2016; Wolcott, 2005). Note
that the sources did not discuss the student’s study in their
publications and the example is a misrepresentation of the
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sources.
Correctly supporting a decision example 2. I will be the
primary data collection instrument in this study. In
qualitative research, the researcher is the primary data
collection instrument because the researcher hears, sees,
and interprets the data (Denzin, 2014; Marshall &
Rossman, 2016; Wolcott, 2005). Note: please be sure to
synthesize your sources to support your decisions.
2.11 - Data Collection Technique
Do not confuse the purpose of this heading with that for the explanation of procedures.
You want to discuss the main approach to collecting your data. It is a good idea to restate the
research question and then address the data collection process. Depending upon whether you are
using a quantitative or qualitative method, you should discuss and support your decision for
collecting the data.
Quantitative Studies
In a quantitative study one would discuss: (a) surveys, (b) structured record reviews to
collect data (e.g., sales data, performance records, government databases, etc.), and (d) structured
observations. Self-administered questionnaires and structured records are more prevalent with
quantitative research. Indicate the process you will use to collect your data. State your rationale
for selecting the process (e.g., in terms of strengths and weaknesses, cost, data availability,
convenience, etc.).
Qualitative Studies
Describe the process for collecting the data (i.e., interviews, focus groups, direct or
participant observations, and review of company/archival documents, performance indicators,
sales reports, business plans, etc.) Provide an abridged interview protocol, focus group protocol,
observation protocol, etc., and identify the location of the protocols in an appendix.
2.12 - Data Organization Technique (Qualitative Only)
The Data Organization Technique can often be a short paragraph where students address
all of the data that they collected in this heading. There are typically two decisions in this
section: (a) about how one will securely store the data (electronic and hard copies) and (b) that
the data will be destroyed after 5 years.
2.13 - Data Analysis (Quantitative Only)
Data analysis involves discussing the statistical test(s) you will use to answer each
research question, and justify the tests’ selection. Indicate the nature of the scale for each
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variable (e.g., nominal, ordinal, interval, and ratio). Why is the selected statistical test more
appropriate than another? (Hint: The statistical test is usually selected due to the nature of the
question and scale of measurement of the variables you defined). Describe how you will deal
with discrepant cases (missing data, data that cannot be interpreted, etc.). Identify the software
that will be used to analyze the data. Be sure to discuss the data assumptions, how they will be
assessed, and how you will address any violations (e.g., using Bootstrapping).
2.14 - Data Analysis (Qualitative Only)
The qualitative data analysis heading is critical for demonstrating doctoral level competence and will help you prepare for Section 3. This heading must be deep yet can be
covered in one or two succinct paragraphs. Reviewing the following table’s contents will help
you develop and write your data analysis plan.
Rubric requirement Explanation
a. Identifies the appropriate data analysis process for the
research design (i.e., one of
the four types of
triangulation for case
studies; modified van
Kaam, van Maanen, etc. for
phenomenology).
Different qualitative research designs require different data
analysis processes. Critically read seminal works and other
studies using your research design to be able to
demonstrate that you are prepared to conduct a data
analysis. For example, case study researchers will use
methodological triangulation. Ethnographic researchers
will likely use methodological triangulation. However
ethnographers may also use data triangulation.
b. Provides a logical and sequential process for the
data analysis.
Students must succinctly describe how they will perform
the data analysis. Students must use all the data for the
analysis. Often students planning case studies or
ethnographic studies discuss the data collection instruments
and techniques above, but forget everything but the
interview data in the data analysis section.
Students should begin their data analysis heading by noting
the data from the planned collection methods and how they
will use the data analysis process (in either order). For a
case study, one would start by discussing how one will use
methodological triangulation for the information from the
different data collection methods.
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c. Details the student’s conceptual plan or software
(i.e., NVivo, Atlasti,
Ethnograph, Excel, etc.) for
coding, mind-mapping, and
identifying themes.
Or is the key word in this requirement. Explain the classic
data analysis method or qualitative software analysis
method (how you will do it).
Classic Data Analysis Method
For the classic data analysis method, discuss sorting all of
the concepts and ideas on separate sheets of paper into
categorized piles—be sure to support your decision.
Critically analyze the data using a large physical mind map
(i.e., stacks, piles, or clusters of concepts and ideas on a
wall or large room floor) for the classic data analysis
method.
Qualitative Software Analysis Method
For the qualitative software analysis method, code all of
the concepts and ideas (all of the data and not just the
interview questions)—be sure to support your decision.
Critically analyze the data in a graphical portrayal of
categorized and coded concepts and ideas using the
qualitative software analysis method.
Themes
Question the meaning of the reoccurring concepts and
ideas to identify the themes.
In effect, the compiling phase involves organizing the data
in an order, to create a database, while disassembling
phases involves dividing the complied data into fragments
and labels. The reassembling process involves clustering
and categorizing the labels into sequences and groups. The
interpretation stage requires creating narratives from the
sequences and groups including conclusions.
d. Identifies how the student will focus on the key
themes, correlate the key
themes with the literature
(including new studies
published since writing the
proposal) and the
conceptual framework.
This should be a one or two sentence plan on how you will
correlate the key themes with recent studies and the theory
or conceptual models from your conceptual framework.
This will help you prepare for the presentation of findings
in Section 3.
e. Supports every decision Critically reading seminal and authoritative work for data
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with a minimum of three
scholarly peer-reviewed or
seminal sources.
analysis in your selected research design is vital at this
stage of your doctoral journey. You should have ample
sources to support your decisions—there are some
suggested readings lists in the Bibliography-Suggested
Readings Lists
2.15 - Study Validity (Quantitative Only)
Internal Validity 52
Internal validity is the approximate truth about inferences regarding cause-effect or
causal relationships. Thus, internal validity is only relevant in studies in which researchers seek
to examine causal relationships (i.e., experiments or quasi-experimental designs). Internal
validity is not relevant in observational (i.e., correlation designs or descriptive studies, for
instance.) However, for studies in which researchers seek to assess the effects of programs or
interventions, internal validity is perhaps the primary consideration. In those contexts, you would
like to be able to conclude that your program or treatment made a difference -- it improved a
business process or outcome
Experiments/quasiexperiments. Experimental and quasi-experimental designs are
susceptible to up to 8 threats to internal validity, depending upon the specific design. These eight
threats are (a) selection, (b) selection by maturation, (c) statistical regression, (d) mortality, (e)
maturation, (f) history, (g) testing, and (h) instrumentation. You need to address each of these
threats by briefly mentioning what they are, and, as relevant, the steps you will take in your study
to address each of these threats. Again, some of the threats may not be applicable, depending
upon your specific design. You can refer to a basic research design textbook to obtain a better
understanding of these threats and how to combat them. Be sure to cite your sources. See the
following link for further information: http://www.socialresearchmethods.net/kb/causeeff.php
If you are not conducting an experiment then indicate that this is a nonexperimental
design (i.e. correlation) and threats to internal validity are not applicable. However, indicate that
threats to statistical conclusion validity are of concern, and then address threats to statistical
conclusion validity.
Threats to statistical conclusion validity. Start by explaining what these threats are.
Threats to statistical conclusion validity are conditions that inflate the Type I error rates,
(rejecting the null hypothesis when it is in fact true), and Type II error rates (accepting the null
hypothesis when it is false.) The three conditions that you need to cover here are: (a) reliability
of the instrument, (b) data assumptions, and (c) sample size.
52
See more on internal validity @ http://www.socialresearchmethods.net/kb/intval.php
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Reliability of the instrument. You already reported the reliability properties of your
instrument in the Instrumentation heading. However, you need to determine how reliable the
instrument is for your specific sample. Here you will indicate you will conduct an internal
consistency reliability check of the instrument against your specific sample. The intent is to see
how close the reported reliability coefficient (in section 2.9 - Instrumentation) is and your
calculated reliability coefficient. State what an acceptable value is (i.e. >.7) and how you will
check your instrument’s reliability. There is a procedure (Analyze/Scale/Reliability Analysis) in
SPSS that will allow you to compute Cronbach’s alpha, one of several reliability coefficients.
You will report the results of the reliability analysis in Section 3, Presentation of Findings
heading. The degree of agreement/disagreement can provide information for your discussion,
especially in the event of a nonsignificant result.
Data assumptions 53
(varies by statistical test). You will state what the assumptions are
pertaining to your tests and the effects violation of the assumptions can have on your results.
Indicate how you will check these assumptions. Refer to a basic statistics textbook for
assumptions regarding various tests. For example, the Green and Salkind text used in the DDBA
8438 course is an excellent resource for identifying assumptions for most basic statistical tests.
Pallant (2010) 54
is an excellent text for instruction on performing parametric assumption testing.
The following Table contains the major assumptions and procedures for testing the assumptions
for multiple linear regression and for ANOVA tests.
Table X
Statistical Test, Assumptions, and Procedures for Testing Assumptions
Statistical test Assumptions Testing
Multiple Regression
Outliers Scatterplot
Multicollinearity Normal Probability Plot (P-
P) of the Regression
Standardized Residual
Normality “
Linearity “
Homoscedasticity “
Independence of Residuals “
ANOVA
Normality Histograms
Equality of Variances Levene’s Test of Equality
of Variances
53
Data assumptions vary by statistical test.
54 Pallant, J. (2010). SPSS survivor manual (4th ed.). Berkshire. England: McGraw-Hill.
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Sample size. Include a brief explanation of the effects of using too small a sample size
could have on your study’s outcomes (refer to any basic statistics textbook). However, you will
indicate this threat has been met by conducting a power analysis to ensure you have a sufficient
sample size. Be sure to cite your work.
External Validity
External validity refers to the extent the study findings can be generalized to larger
populations and applied to different settings. External validity is related to the sampling strategy
(identified in Heading 2.6, Population and Sampling). Probability sampling strategies (random
sampling) enhances external validity. Conversely, nonprobabilistic sampling strategies hinder
external validity. This relationship is to be discussed in this heading.
2.16 - Reliability and Validity (Qualitative Only)
A key difference from quantitative research is the reliability and validity headings. The
analogous criteria for qualitative studies are dependability, credibility, transferability, and
confirmability. These criteria are not measurable and need to be established using qualitative
methods such as member checking [Marshall and Rossman (2016) provide a good definition.]
and triangulation (Data triangulation, investigator triangulation, theoretical triangulation, and
methodological triangulation). See Norman Denzin’s work on triangulation). Please review more
detailed information on qualitative validity at:
http://www.socialresearchmethods.net/kb/qualval.php
Reliability
Reliability refers to how one will address dependability. Some of the ways to enhance the
dependability of the study are member checking of data interpretation, transcript review, pilot
test, expert validation of the interview questions, interview protocol, focus group protocol, direct
or participant observation protocol, etc. Reaching data saturation will help assure the
dependability of the findings. See the seminal literature on reliability bring in a minimum of
three scholarly sources to support every decision.
Validity
Qualitative study validity refers to the credibility, transferability, and confirmability of
the findings. Reaching data saturation will help assure the credibility, transferability, and
confirmability of the findings. Please see seminal work on qualitative validity to ensure that you
have a valid study. Support every decision with a minimum of three scholarly peer-reviewed or
seminal sources.
Credibility. One can enhance credibility by member checking of the data interpretation,
participant transcript review, triangulation, interview protocol, focus group protocol, direct or
participant observation protocol, etc. Demonstrating qualitative credibility ensures the
reviewers that one is addressing the findings from the perspective of the participants.
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Confirmability. One can enhance the confirmability by ensuring that the results can be
confirmed or supported by others. Probing during interviews and follow up member checking
interviews, questioning from different perspectives, triangulation, etc. are techniques one may
use to enhance the confirmability.
Transferability. Be sure to demonstrate how you will enable others to determine the
transferability of the findings (i.e., meticulously adhering to the data collection and analysis
techniques for the research design, using interview protocol, focus group protocol, direct or
participant observation protocol, reaching data saturation, etc.). In contrast to quantitative
studies where the researcher generalizes the findings, qualitative researchers do not generalize
and do not state that the findings are transferable.
2.17 - Transition and Summary
End with a transaction heading that contains a summary of key points and provides an
overview introducing Section 3. Do not include any new information in the summary.
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SECTION 3: APPLICATION TO PROFESSIONAL PRACTICE AND
IMPLICATIONS FOR CHANGE
Section 3 –Application to
Professional Practice and
Implications for Change
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3.1 - Introduction
Reacquaint the reader to the purpose of the study. For quantitative studies, simply restating the
first two sentences of the Purpose Statement followed by a brief summary of the study findings.
For qualitative studies simply restate the first sentence of the purpose statement and briefly
summarize the findings.
Quantitative Example
The purpose of this quantitative correlation study was to examine the relationship
between employee job satisfaction, employee motivation, and employee turnover intention. The
independent variables were employee job satisfaction and employee motivation. The dependent
variable was employee turnover intention. The null hypothesis was rejected and the alternative
hypothesis was accepted. Employee job satisfaction and employee motivation significantly
predicted employee turnover.
Qualitative Example
The purpose of this qualitative multiple case study was to explore the strategies that
department store managers used to motivate their sales associates. The data came from manager
interviews, manager-employee observations, and company documentation at five department
stores in Texas. The findings showed methods that the managers used to motivate their sales
employees to provide better customer service and increase sales.
3.2 - Presentation of Findings (Quantitative)
An example of an APA results write-up for a multiple regression analysis is provided.
Assumptions vary by statistical test. Therefore, ensure you address the appropriate assumptions
for your statistical test.
Quantitative Example
In this subheading, I will discuss testing of the assumptions, present descriptive statistics,
present inferential statistic results, provide a theoretical conversation pertaining to the findings,
and conclude with a concise summary. I employed Bootstrapping, using 1,000 samples, to
address the possible influence of assumption violations. Thus, bootstrapping 95% confidence
intervals are presented where appropriate.
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Tests of Assumptions
The assumptions of multicollinearity, outliers, normality, linearity, homoscedasticity, and
independence of residuals were evaluated. Bootstrapping, using 1,000 samples, enabled
combating the influence of assumption violations.
Multicollinearity. Multicollinearity was evaluated by viewing the correlation
coefficients among the predictor variables. All bivariate correlations were small to medium
(Table X); therefore the violation of the assumption of multicollinearity was not evident. The
following table contains the correlation coefficients.
Table X
Correlation Coefficients Among Study Predictor Variables
Variable Age Weight Height
Age 1.00 .151 -.010
Weight .151 1.00 .562
Height -.010 .562 1.00
Note. N = 204.
Outliers, normality, linearity, homoscedasticity, and independence of residuals 55
.
Outliers, normality, linearity, homoscedasticity, and independence of residuals were evaluated by
examining the Normal Probability Plot (P-P) of the Regression Standardized Residual (Figure 1)
and the scatterplot of the standardized residuals (Figure 2). The examinations indicated there
were no major violations of these assumptions. The tendency of the points to lie in a reasonably
straight line (Figure 1), diagonal from the bottom left to the top right, provides supportive
evidence the assumption of normality has not been grossly violated (Pallant, 2010). The lack of a
clear or systematic pattern in the scatterplot of the standardized residuals (Figure 2) supports the
tenability of the assumptions being met. However, 1,000 bootstrapping samples were computed
to combat any possible influence of assumption violations and 95% confidence intervals based
upon the bootstrap samples are reported where appropriate.
55
These are the same assumptions discussed in Section 2; the results of the assumption testing are now
discussed. These assumptions differ by statistical test and the appropriate assumptions are to be discussed. Note,
your specific discussion might differ. For example, there may be severe data assumption violations in the data you
collected. Therefore, you would discuss appropriately.
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Figure 1. Normal probability plot (P-P) of the regression standardized residuals.
Figure 2. Scatterplot of the standardized residuals.
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Descriptive Statistics
In total, I received 207 surveys. Three records were eliminated due to missing data,
resulting in 204 records for the analysis. Table X contains descriptive statistics of the study
variables.
Table X
Means and Standard Deviations for Quantitative Study Variables
Variable M SD Bootstrapped 95% CI (M) 56
Sleep Index 26.36 10.56 [24.80, 27.94]
Age 43.60 12.51 [41.90, 45.28]
Weight 72.34 15.21 [70.23, 74.51]
Height 169.12 10.00 [167.68, 170.44]
Note: N = 204.
Inferential Results
Standard multiple linear regression, 57
α = .05 (two-tailed), was used to examine the
efficacy of age, weight, and height in predicting sleep index. The independent variables were
age, weight, and height 58
. The dependent variable was sleep index 59
. The null hypothesis was
that age, weight, and height would not significantly predict sleep index. The alternative
hypothesis was that age, weight, and height would significantly predict sleep index. Preliminary
analyses were conducted to assess whether the assumptions of multicollinearity, outliers,
normality, linearity, homoscedasticity, and independence of residuals 60
were met; no serious
violations were noted (see Tests of Assumptions). The model as a whole was able to significantly
predict sleep index, F(3, 200) = 4.778, p < .003, R 2 = .067
61 . The R
2 (.067)
value indicated that
approximately 7% of variations in sleep index is accounted for by the linear combination of the
predictor variables (sex, weight, and height). In the final model, age and height were statistically
56
The 95% Bootstrap confidence intervals are produced when the bootstrapping procedure is selected in the SPSS
regression process. See regression video tutorial located at: https://www.youtube.com/watch?v=1ItFMKlPG5k
57 Identify the test and of purpose of the test.
58 Restate the independent variables as presented in the purpose statement and research question; there is to be no
deviation.
59 Restate the dependent variables as presented in the purpose statement and research question; there is to be no
deviation.
60 Identify the assumptions and state they how were assessed.
61 State whether the model as a whole was able to predict (or not) the dependent variable. Report the appropriate
statistics.
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significant with age (t= -3.892, p < .01) accounting for a higher contribution to the model than
height (t = -2.595, p < .05). Weight did not explain any significant variation in sleep index. The
final predictive equation was:
Sleep Index = 70.205 -.148(Age) + .109(Weight) –2.303(Height).
Age. The negative slope for age (-.148) as a predictor of sleep index indicated there was
about a .148 decrease in sleep index for each one-point increase in age. In other words, sleep
index tends to decrease as age increases. The squared semi-partial coefficient (sr 2 ) 62
that
estimated how much variance in sleep index was uniquely predictable from age was .03,
indicating that 3% of the variance in sleep index is uniquely accounted for by age, when weight
and height are controlled.
Height. The negative slope for height (-2.303) as a predictor of sleep index indicated
there was a 2.303 decrease in sleep index for each additional one-unit increase in height,
controlling for age and weight. In other words, sleep index tends to decrease as height increases.
The squared semi-partial coefficient (sr 2 ) that estimated how much variance in sleep index was
uniquely predictable from height was .04, indicating that 4% of the variance in sleep is uniquely
accounted for by height, when age and weight are controlled. The following Table depicts the
regression summary table.
Table X
62
Derived from the SPSS output.
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Regression Analysis Summary for Predictor Variables
Variable Β 63
SE Β β 64
t 65
p 66
B 95% 67
Bootstrap CI
Age -.148 0.054 -.393 -3.892 <. 01 [-.262, -.025]
Weight .109 3.770 -.038 0.371 .712 [-.008, .245]
Height -2.303 .888 -.268 -2.595 .011 [-.442, -.081]
Note. N= 204.
Analysis summary. The purpose of this study was to examine the efficacy of age, weight,
and height in predicting sleep index. I used standard multiple linear regression to examine the
ability of age, weight, and height to predict the value of sleep index. Assumptions surrounding
multiple regression were assessed with no serious violations noted. The model as a whole was
able to significantly predict sleep index, F(3, 200) = 4.778, p < .003, R 2 = .067. Both age and
height provide useful predictive information about sleep index. The conclusion from this analysis
is that age and height are significantly associated with sleep index, even when weight is
controlled (e.g. held constant).
Theoretical conversation on findings. 68
Describe in what ways findings confirm,
disconfirm, or extend knowledge of the theoretical framework and relationship(s) among
variables by comparing the findings with other peer-reviewed studies 69
from the literature review
that includes studies addressed during the proposal stage and new studies since writing the
proposal. 70
Ties findings or disputes findings to the existing literature on effective business
63
Β values are to be used in the regression equation. These are the unstandardized coefficients in the SPSS output.
64 The beta weights identify which variables contribute more to the model. These are the standardized coefficients in
the SPSS output.
65 The test statistic for the hypothesis test for the slope (Β); derived from the SPSS output; used to evaluate the
significance of the Β weights, where p ≤ .05 is significant.
66 The test statistic for the hypothesis test for the slope (Β); derived from the SPSS output; used to evaluate the
significance of the Β weights, where p ≤ .05 is significant.
67 The 95% Bootstrap confidence intervals are produced when the bootstrapping procedure is selected in the SPSS
regression process. See regression video tutorial located at: https://www.youtube.com/watch?v=1ItFMKlPG5k
68 Rubric item 3.2g
69 This rubric requirement substantiates the requirement to critically analyze, synthesize and “report” the results of
the literature (studies) pertaining to the theory and variables (see rubric component 1.14, Review of the Professional
and Academic Literature).
70 Rubric item 3.2h
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practice. Analyzes and interpret the findings in the context of the theoretical framework, as
appropriate. 71
Ensures interpretations do not exceed the data, findings, and scope.
3.3 - Presentation of Findings (Qualitative)
There is a common misconception about Section 3. Reporting the results of the study
findings is more complicated than it first appears to be. This is because the findings must be
related back to the body of knowledge as well as the conceptual framework. It is not a matter of
telling the reader who-said-what-and-when, one must present an in-depth scholarly discussion of
how the study findings contribute to the field.
Do not be misled or fail to understand that reporting the findings is not about listing the
answers to the interview questions. The answers to the interview questions are your evidence, not
the answer to the research question. Moreover, one should never list the interview questions in
the presentation of findings.
Remember that the rubric asks about the research question, not the interview questions.
The research question is the overarching question that your study answers.
Also, remember that you are presenting your findings as themes—major, minor,
unexpected, and/or serendipitous that are a result of your data—answers to interview questions,
document review, journaling, observation notes, focus group answers, etc. Also, remember that it
is a good practice when using a qualitative data analysis software program to include at least one
table per theme from NVivo, Atlasti, Ethnograph, or others. that illustrates the frequencies.
Finally, when appropriate, remember to integrate member checking.
To sum up: Present the theme, present the evidence from the findings that support the
theme (including tables), then support both from the body of knowledge/conceptual framework.
3.4 - Application to Professional Practice
Discuss how business leaders can apply the findings to aid in solving the specific
business problem. Do not repeat literature review; rather focus on application. Often researchers
can use this heading to help gain access by offering potentially participating company leaders a
summary of the findings including suggestions for professional practice.
3.5 - Implications for Social Change
Now that you have analyzed and discussed the findings, suggest potential implications in
terms of tangible improvements for individuals, communities, organizations, institutions,
cultures, or societies as the findings could catalyze beneficial social change/behaviors.
71
Rubric item 3.2i
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3.6 - Recommendations for Action
This is where you can create a win-win for companies and individuals participating in
your study. The rubric requires the following: (a) that you ensure the recommendations flow
logically from the conclusions and contain steps to useful action, (b) that you state who needs to
pay attention to the results (this can help you with a win-win to discuss when gaining access for
the study), and (c) that you indicate how the results might be disseminated via literature,
conferences, training, etc.
3.7 - Recommendations for Further Research
Discuss areas for future research. A starting point is to identify how the limitations
(weaknesses) identified in Heading 1.12, Assumptions, Limitations, Delimitations, can be
improved upon in future studies. Follow up this conversation by identifying other research
possibilities illuminated while conducting the study. Do not repeat literature; rather provide
future researchers (e.g., other DBA students) with potential research agenda for furthering the
scholarly conversation pertaining to the business problem.
This is a good section to discuss serendipitous results, unanswered new questions that
arose, and a finding that does not seem to align with a theory or conceptual model warranting a
recommendation for further research. Often this section can lead to postdoc research.
3.8 - Reflections
Per the rubric, this short heading includes a reflection on the researcher's experience
within the DBA Doctoral Study process in which the researcher discusses possible personal
biases or preconceived ideas and values, the possible effects of the researcher on the participants
or the situation, and her/his changes in thinking after completing the study.
3.9 - Conclusion Per the rubric, students should close with a strong concluding statement making the take-
home message clear to the reader. This should be a conclusion and not a summary.
3.10 - Appendices/Table of Contents
Ensure all appendices appear in the order they are referenced in the proposal/doctoral
study.
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APPENDIX A: WALDEN UNIVERSITY DOCTOR OF BUSINESS
ADMINISTRATION PROGRAM VIDEO TITLES AND URL ADDRESSES
Title URL Address 1 Walden DBA Rubric and
Handbook Video Tutorial https://www.youtube.com/watch?v=KiiDGmLbRN0
2 Walden DBA Problem
Statement Tutorial https://www.youtube.com/watch?v=IYWzCYyrgpo
3 Walden DBA Purpose
Statement Tutorial https://www.youtube.com/watch?v=pLP4r0mfT9A
4 Walden DBA
Theoretical/Conceptual
Framework
http://youtu.be/P-01xVTIVC8
5 Scales of Measurement https://www.youtube.com/watch?v=PDsMUlexaMY 6 DDBA Week One Application https://www.youtube.com/watch?v=pRnTKU913IM 7 DDBA 8438 Week Two
Application Video – Part 1 https://www.youtube.com/watch?v=yEc7bzEsFH0
8 Week Two Application Video –
Part 2 https://www.youtube.com/watch?v=WqgA36uXK2g
9 Part 1: Independent Samples T -
Test https://www.youtube.com/watch?v=r2hxzEcglsY
10 Part 2: Independent Samples T -
Test https://www.youtube.com/watch?v=dXmINdmgX0g
11 Part 1: Week Five One-way
ANOVA https://www.youtube.com/watch?v=cnhlXya-YR8
12 Part 2: Week Five One-way
ANOVA https://www.youtube.com/watch?v=V5pJosM-yWU
13 Walden University Doctor of
Business Administration
Multiple Linear Regression –
Part 1
https://www.youtube.com/watch?v=1ItFMKlPG5k
14 Walden University Doctor of
Business Administration
Multiple Linear Regression –
Part 2
http://youtu.be/Pyz6E26joU0
Note: Titles in green are used in DDBA 8438 but can be applicable in the research process.
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APPENDIX B: QUANTITATIVE RESEARCH PRIMER: PROBLEM
STATEMENT, PURPOSE STATEMENT, RESEARCH
QUESTION(S), AND HYPOTHESES
Doctor of Business
Administration
Quantitative Research Primer:
Problem Statement, Purpose
Statement, Research Question, and
Hypotheses
Prepared by the DBA Methodology Team: June 2014
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78
DBA doctoral studies require the highest level of rigor and scholarship. One focus of
rigor and scholarship is the number of predictor or independent variables 72
examined in
quantitative doc studies. Nonexperimental research (i.e. correlation 73
, quasi-
experimental 74
, etc.) requires the use of at least two independent or predictor variables.
This is due to the third variable problem. A third variable, also known as a confounding
or mediator variable, can confound the relationship between the independent and
dependent variable. This compounding effect can lead the researcher to incorrectly
interpret the results, leading to an incorrect rejection of the null hypothesis (Type I error).
As such, all DBA quantitative studies require the examination of at least two predictor
(correlation studies), or independent (i.e., quasi-experimental, causal comparative, etc.
studies) variables. This affects the statistical analysis, as simple bivariate correlations
(correlation designs) or one-way ANOVAs cannot be used as inferential statistical tests.
Other statistical procedures, such as multiple regression analyses, must be used for
correlation studies. Quasi-experimental/causal comparative designs must employ
statistical analyses (i.e. factorial ANOVAs), as a minimum capable of examining more
than one independent variable. Please be sure to discuss this with your chair!
Below are hypothetical examples of correlation and quasi-experimental research
scenarios, which include the Problem Statement, Purpose Statement, Research Question,
and Hypotheses. These examples depict two predictor (correlation studies)/independent
(quasi-experimental) variables, which are (a) employee job satisfaction and (b) employee
motivation. The dependent variable is employee turnover intentions. It may be helpful to
use this model as a script and fill in the specifics as they apply to your study. The red
underlined text is what you will need to change for your specific study. Footnotes (in red)
are included to identify the required rubric elements.
Again, map to the rubric in this component and all components of your doctoral study.
The rubric criteria are the basis for judging the quality of your study. Notice how each of
the six rubric elements is included in the purpose statement and there is no superfluous
information.
Please review the Problem Statement video tutorial at: http://youtu.be/IYWzCYyrgpo to
aid you in preparing the Problem Statement.
Please review the Purpose statement video tutorial at: http://youtu.be/pLP4r0mfT9A to
aid you in preparing the Purpose Statement.
72
Click the hyperlink to be taken to additional information.
73 Click the hyperlink to be taken to additional information.
74 Click the hyperlink to be taken to additional information.
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Hypothetical Example (Correlation Design)
Problem Statement
Organizations place great emphasis on retention because of the strategic value of
intellectual capital and the costs of replacing valued employees (cite) 75
. Research in this
domain is potentially valuable because turnover costs U.S. businesses billions of dollars
per year (cite), and practices that promote retention can save even small companies
millions of dollars annually (cite) 76
. The general business problem is that turnover
intention has been shown to be among the best predictors of turnover (cite) 77
. The
specific business problem is that some microelectronic business owners do not
understand the relationship between job satisfaction, motivation, and employee turnover
intentions 78
.
Purpose Statement
The purpose of this quantitative 79
correlation 80
study is to examine the relationship
between employee job satisfaction, employee motivation, and employee turnover
intentions. The independent variables are employee job satisfaction and employee
motivation 81
. The dependent variable is employee turnover intention 82
. The targeted
population will consist of mid-level employees of microelectronic companies 83
located in
the southeast United States. The implications for positive social change include the
potential to better understand the correlates of employee turnover, thus increasing
propensity for sustainability of the microelectronic industry 84
.
Research Question
What is the relationship between employee job satisfaction, employee motivation,
and employee turnover intentions?
Hypotheses
Null Hypothesis (H0): There is no statistically significant relationship between
employee job satisfaction, employee motivation, and employee turnover intentions.
75
Hook
76 Anchor
77 General business problem
78 Specific business problem
79 Method
80 Design
81 Independent variables
82 Dependent variable
83 Targeted population
84 Social change statement
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80
Alternative Hypothesis (H1): There is a statistically significant relationship
between employee job satisfaction, employee motivation, and employee turnover
intentions.
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Hypothetical Example (Causal-Comparative Design)
Problem Statement
Organizations place great emphasis on retention because of the strategic value of
intellectual capital and the costs of replacing valued employees (cite). Research in this
domain is potentially valuable because turnover costs U.S. businesses billions of dollars
per year (cite), and practices that promote retention can save even small companies
millions of dollars annually (cite). The general business problem is that turnover intention
have been shown to have a significant impact on employee turnover (cite). The specific
business problem is that some micro-electronic business owners do not understand the
impact of job satisfaction, motivation, on employee turnover intentions.
Purpose Statement
The purpose of this quantitative 85
correlation 86
study is to examine the impact of
employee job satisfaction and employee motivation on employee turnover intentions. The
independent variables are employee job satisfaction and employee motivation 87
. The
dependent variable is employee turnover intention 88
. The targeted population will consist
of midlevel employees of microelectronic companies 89
located in the southeast United
States. The implications for positive social change include the potential to provide a
better understanding of the correlates of employee turnover, thus increasing propensity
for sustainability of the microelectronic industry 90
.
Research Question
What is the impact of employee job satisfaction and employee motivation on
employee turnover intentions?
Hypotheses
Null Hypothesis (H0): Employee job satisfaction and employee motivation have no
significant impact on employee turnover intentions.
Alternative Hypothesis (H1): Employee job satisfaction and employee motivation
have a statistically significant impact on employee turnover intentions.
85
Method
86 Design
87 Independent variables
88 Dependent variable
89 Targeted population
90 Social change statement
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Research Tips
Correlation designs use the term relationship
Causal comparative designs use the terms impact or influence
Variables are presented in temporal order; that is the independent variables are presented
first, followed by the dependent variable
The word and (see bold text in Purpose Statement) separates the predictor variables from
the dependent variable in correlation designs
The word on (see bold text in Purpose Statement) separates the independent variables from
the dependent variable in experimental/quasi-experimental designs
The null and alternative hypotheses are almost mirror images of the research question
The null hypothesis is the hypothesis of no difference; suggesting there will not be a
significant result
The alternative hypothesis is the hypothesis of difference; suggesting there will be a
significant result
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APPENDIX C: MAJOR QUANTITATIVE DESIGNS
Research design 91
is the blueprint that enables the investigator to develop solutions to
research problems and guides the researcher in the various stages of the research (Frankfort-
Nachmias & Nachmias, 2008). The research design aids the researcher in structuring, analyzing,
and interpreting the data (Frankfort-Nachmias & Nachmias, 2008). DeForge (2010) described
research design as a plan for guiding researchers in addressing research problems and answering
research questions.
Quantitative Methodology and Associated Designs
Design Characteristics
Experimental Assess causal (cause and effect) relationships between an independent and dependent variable
Defining feature: random assignment to group condition
Manipulation of the independent variable
Strongest in terms of internal validity; greatest confidence in causal inferences
Requires power analysis to determine appropriate sample size
Analyses can include, but are not limited to, (ANOVA, ANCOVA, MANOVA, etc.)
Quasi-experimental Assess causal relationships between an independent and dependent variable.
Defining feature: lack of random assignment to group condition
Manipulation of the independent variable
Weakened ability to make causal inferences
Requires power analysis to determine appropriate sample size
Correlation Assess relationships between independent and dependent variables
Defining feature: does not imply causality
Requires power analysis to determine appropriate sample size
Analyses can include, but are not limited to, (a) multiple regression, (b) logistic regression, and (c) discriminant
analysis
Note. Correlation designs are the most common seen in DBA studies.
91
Review the Research Methods Knowledge Base at: http://www.socialresearchmethods.net/kb/design.php for more
information pertaining to research design.
84
APPENDIX D: SAMPLING TYPOLOGIES 92
Non Probabilistic Sampling (Non-Random)
Availability (Convenience) A nonprobabilistic sampling procedure in which units are
selected from the target population based on their
availability or convenience of the researcher.
Purposive A nonprobabilistic sampling procedure in which units are
selected from the target population based on their fit with
the purpose of the study and specific inclusion and
exclusion criteria.
Quota A nonprobabilistic sampling procedure in which the
population is divided into mutually exclusive
subcategories. Interviewers or other data collectors solicit
participation in the study from members of the
subcategories until a target number of elements to be
sampled from the subcategories have been met.
Snowball A nonprobabilistic sampling procedure in which elements
are selected from the target population with assistance of
previously selected populations.
Probabilistic Sampling (Random)
Simple Random Sampling A probability sampling procedure that gives every unit in
the target population, and each possible sample of a given
size, an equal chance of being selected.
Stratified Sampling A probability sampling procedure in which the target
population is first separated into mutually exclusive,
homogeneous segments (strata) and then a simple
random sample is selected from each segment (stratum)
Systematic Sampling A probability sampling procedure in which a random
selection is made of the first unit for the sample, and then
subsequent units are selected used a fixed or systematic
interval until the desired sample size is reached.
Cluster Sampling A nonprobabilistic sampling procedure in which units of
the target population are randomly selected in natural
occurring groups (clusters).
92
Adapted from Daniel, J. (2012). Sampling essentials: Practical guidelines for making sampling choices. Los
Angeles, CA: SAGE.
85
APPENDIX E: SAMPLE POWER ANALYSIS
G*Power is a statistical software package quantiative researhcers use to conduct an
apriori sample size analysis (Faul, Erdfelder, Buchner, & Lang, 2009) 93
. A power analysis, using
G*Power version 3.1.9 software, was conducted to determine the appropriate sample size for the
study. An a priori power analysis, assuming a medium effect size (f 2 = .15), α = .05, and 2
predictor variables, identified that a minumum sample size of 68 participants is required to
achieve a power of .80. Increasing the sample size to 146 will increase power to .99. Therefore,
the researcher will seek between 68 and 146 participants for the study (Figure 1).
Figure 1. Power as a function of sample size.
The use of a medium effect size (f 2 = .15) is apporiate for this proposed study. The
medium effect size was based on the analysis of X articles where (identify your variable) was the
outcome measurement.
93
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests
for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160. doi:10.3758/brm.41.4.1149
86
APPENDIX F: SAMPLE QUANTITATIVE LITERATURE REVIEW
OUTLINE
Introduction
Provide an introduction containing a discussion of the content of the literature review
(including the percentages of total references that are peer reviewed, and the percentage of total
references that are published within 5 years of the expected year of CAO approval). Also discuss
the organization of the review, and the strategy for searching the literature. The review of the
literature will follow in appropriately formatted APA headings. Do not present the literature
review in annotated bibliography format (i.e., presenting one study after another.) Rather,
provide a critical analysis and synthesis of the literature.
Transformational Leadership Theory 94
Introduce the theory. You can present the information provided in Heading 1-4,
Theoretical/Conceptual Framework. However, this heading should be expanded, providing the
reader with more depth pertaining to the theory. Descriptive information should be included
here. The critical analysis and synthesis of the literature follows below.
Main point one. 95
Conducting a good literature review involves the reader identifying
and separating literature by similar ideas, themes, topics etc. The similar ideas can be presented
using appropriate APA L2 headings; use subordinate headings as appropriate. You are not to
simply regurgitate the material you have read. The literature presented in each main topic
heading must be a critical analysis and synthesis of the empirical observations (research studies)
you have reviewed. Critical analysis and synthesis of the literature grounded in your theoretical
framework will enable you to meet the requirements in the Presentation of Findings heading.
See the Doctoral Study Rubric for more information.
Main point two. The same information presented in main point one applies for main
point two.
Main point three. The same information presented in main point three applies for Main
Point C.
Rival Theories/Opponents of the Theoretical/Conceptual Framework
There are always rival theories, that is, rival/alternate lenses for examining a
phenomenon. A good literature review comprises an inquiry into the major rival theories.
Provide a very brief overview of two to three rival theories and then shift the discussion to one
major rival theory. Questions you may consider addressing in this component are:
What are the strengths and limitations of this theory?
94
APA Level 2 heading.
95 APA Level 3 heading.
87
Why did you not choose to examine your problem through this theoretical lens?
What do opponents (other authorities) in the field identify as the limitations or weakness of this rival theory?
Measurement
A good literature review must address the measurement instruments pertaining to the
variables or constructs underlying the theoretical framework. Often times, there is more than one
measurement instrument available to measure the same variables or constructs. A review of the
measurement instruments will facilitate your identifying appropriate instruments for your
theoretical variables/constructs. Addressing, validity and reliability properties of the various
instruments is a vital component of this heading. In addition, discussing the various populations
for which the instruments were used is vital to addressing the requirements for this component.
.
For example, a study grounded in transformational leadership theory will undoubtedly
uncover a plethora of literature where previous researchers employed the Multifaceted
Leadership Questionnaire (MLQ) to measure the transformational leadership constructs. In many
cases, you will identify more than one instrument purporting to measure the same variables or
constructs. A critical analysis and synthesis will enable you to select the most appropriate
instrument to measure the constructs underlying your study. Address the strengths and
weaknesses of each instrument. The results of your critical analysis and synthesis will justify the
selection of the instrument you propose to use for your study. Remember, many decisions you
make in your study (i.e. selecting instruments) are grounded in the extant literature; these
decisions are not to be arbitrarily made.
Independent Variable A (variable not underlying the theory)
The study may contain additional variables 96
outside the umbrella of the theoretical
framework. Therefore, discussions of these variables are warranted. An informed decision must
be made to include variables in a study. As such, variables or constructs examined in a
quantitative study are derived from extant literature; they are not arbitrarily selected for inclusion
in a study. For example, assume job satisfaction is an independent or predictor variable in your
study. If so, this variable must be substantiated from the literature. Therefore, you are to conduct
a critical analysis and synthesis pertaining to the literature. This critical analysis and synthesis
must support evidence of a relationship between each potential independent variable and the
dependent variable in your study, or a variable closely related to the dependent variable in your
study. In addition, there might be inconclusive evidence and you are to provide the support for
including the independent or predictor variable in your study. Include APA sub headings for
each independent and dependent variable.
96
It is important to understand you are not addressing variables underlying the theoretical framework. Here you are
addressing any “additional” variables included in the study that are not aligned with the theoretical framework. In
essence, there will be justification for every variable measured in the study.
88
Independent Variable B (variable not underlying the theory)
The same information in Independent Variable A applies for each independent or
predictor variable in the study.
Independent Variable C (variable not underlying the theory)
The same information in Independent Variable A applies for each independent or
predictor variable in the study.
Dependent Variable
The dependent variable must also be addressed in the literature review. This is normally
the problematic variable in the study. Remember you are viewing this problematic variable
through the identified theoretical lens. Again, this component is to include a critical analysis and
synthesis of the empirical literature pertaining to the dependent variable.
Methodologies
Address they various methodologies (quantitative, qualitative, mixed-method) in the
literature through which previous researchers have addressed the dependent variable. A literature
review must not solely address the methodology that matches to intended studies design.
Remember, the literature review is to be an exhaustive review of the literature pertaining to a
topic.
Summary
End with a transition heading that contains a summary of key points and provides an
overview introducing Section 2 and Section 3. Do not include any new information in the
summary.
89
APPENDIX G: SAMPLE APA TABLES
Properly formatted APA tables are critical media for presenting descriptive and
inferential statistics results. This appendix provides templates that serve as models for
what is required for various types of statistical analyses. The examples are based on
guidelines contained in the sixth edition of the Publication Manual of the American
Psychological Association 97
. You can simply cut and paste these tables into the
appropriate section of your proposal/doctoral study. 98
97
American Psychological Association. (2010). Publication manual of the American Psychological
Association. (6 th
ed.). Washington, DC: Author.
98 Tables will need to be adjusted for your particular analyses. For example, you may need to add/delete
additional rows/columns as appropriate.
90
Basic One Group Descriptive Statistics Table for Quantitative Variables
(Example Depicting 3 Variables)
Table X
The Table Title Goes Here and Is Italicized (N = XX)
Variable n M
M 95%
Bootstrap CI
SD
SD 95%
Bootstrap CI
Variable 1 23 2.4 [1.85, 2.99] .24 [.11, .64]
Variable 2 34 2.8 [1.56, 3.94] .34 [.22, .53]
Variable 3 34 2.9 [2.05, 3.35] .28 [.25, .44]
Basic Descriptive Statistics Table for Qualitative
(Example Depicting 3 Variables)
Table X
The Table Title Goes Here and Is Italicized (N = XX)
Variable n %
Variable 1 32 32
Variable 2 34 34
Variable 3 34 34
Total 100 100
91
Simultaneous Regression Table (2 Variables)
Table X
The Table Title Goes Here and Is Italicized (N = XX)
Variable B SE Β β t p B 95%
Bootstrap CI
Variable 1 0.00 0.00 .00 .00 .00 [00.00, 00.00]
Variable 2 0.00 0.00 .00 .00 .00 [00.00, 00.00]
Note. Type any notes here.
Hierarchical Regression Table (2 Steps)
Table X
The Table Title Goes Here and Is Italicized (N = XX)
Variable B SE Β β R 2 ∆R
2
Step 1
Variable 1 0.00 0.00 .00 .00 .00
Variable 2 0.00 0.00 .00 .00 .00
Step 2 0.00 0.00 .00 .00 .00
Variable 1 0.00 0.00 .00 .00 .00
Variable 2 0.00 0.00 .00 .00 .00
Variable 3 0.00 0.00 .00 .00 .00
Note. Type any notes here.
The table above reflects a “Play it Safe 99
” hierarchical regression table with 2 variables in step
one and 3 variables in step 2. You will need to make modifications according to your specific
model.
99
The “Play It safe” table is comprehensive and thus would be appropriate if the writer wanted to be as
thorough as possible and was not concerned with brevity.
92
Two-Way ANOVA Table
Table X
The Table Title Goes Here and Is Italicized (N = XX)
Source df F η p
Between subjects
Variable 1 (A) XX 0.00 0.00 .00
Variable 2 (B) XX 0.00 0.00 .00
A x B XX .00
B within-group error XX .00
Within-subjects
XX 0.00 0.00 .00
XX 0.00 0.00 .00
XX 0.00 0.00 .00
Note. Type any notes here.
93
Correlation Table
Table X
The Table Title Goes Here and Is Italicized (N = XX)
Subscale 1 2 3 4
Students (n = XX)
1. Variable 1 1.0 .00 .00 .00
2. Variable 2 .00 1.0 .00 .00
3. Variable 3 .00 .00 1.0 .00
4. Variable 4 .00 .00 .00 1.0
Older adults (n = XX)
1. Variable 1 1.0 .00 .00 .00
2. Variable 2 .00 1.0 .00 .00
3. Variable 3 .00 .00 1.0 .00
4. Variable 4 .00 .00 .00 1.0
Note. Type any notes here.
94
Logistic Regression Table (6 Predictors)
Table X
The Table Title Goes Here and Is Italicized (N = XX)
B S.E Wald df p
Odds
Ratio
95% CI for Odds
Ratio
Lower Upper
Variable 1
Variable 2
Variable 3
Variable 4
Variable 5
Variable 6
Constant
95
APPENDIX H: SAMPLE INTERVIEW PROTOCOL
Interview Protocol
What you will do What you will say—script
Introduce the interview and set the stage—often over a meal or coffee
Script XXXXXXXXXXXXXXXXXXXXX
Watch for non-verbal queues
Paraphrase as needed
Ask follow-up probing questions to get more indepth
1. Interview question
2. Interview question
3. Interview question
4. Interview question
5. Interview question
6. Interview question
7. Interview question
8. Interview question
9. Interview question
10. Last interview question should be a wrap up question such as: What additional experiences have you had…?
Wrap up interview thanking participant
Script XXXXXXXXXXXXXXXXXXXXX
Schedule follow-up member checking interview
Script XXXXXXXXXXXXXXXXXXXXX
Follow–up Member Checking Interview
Graphic by Gene E. Fusch, Ph.D. not needed in proposal or study—just a visual reminder during proposal stage when creating interview protocol.
Introduce follow-up interview and set the stage
Script XXXXXXXXXXXXXXXXXXXXX
96
Share a copy of the succinct synthesis for each individual question
Bring in probing questions related to other information that you may have found— note the information must be related so that you are probing and adhering to the IRB approval.
Walk through each question, read the interpretation and ask:
Did I miss anything? Or, What would you like to add?
Script XXXXXXXXXXXXXXXXXXXXX
1. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
2. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
3. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
4. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
5. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
6. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
7. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
8. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
9. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
10. Question and succinct synthesis of the interpretation—perhaps one paragraph or as needed
97
BIBLIOGRAPHY: SUGGESTED READINGS LISTS
Please note that these references are an amalgamation of input and suggestions. The purpose is to provide DBA students with additional reading sources to prepare for the doctoral study. Students are responsible for correctly referencing any sources per the APA publication manual (6th ed.). The following Readings lists are in order by the following topics.
Assumptions, Limitations, and Delimitations
Case Study Sources
Case Study Seminal Books
Data Saturation and Data Collection Sources
Ethical Considerations/IRB
Ethnography Sources
Focus Groups
Interview Protocol Sources
Interviews Sources
Journaling Sources
Member Checking Sources
Mixed Methods Research
Notetaking and Fieldwork
Phenomenological Sources
Pilot Studies
Qualitative Research Foundation
Qualitative and Quantitative Sources
Reliability, Validity, Transferability, and Generalizability Sources
Sampling and Incentives
Sense-making
Qualitative Software Analysis Sources
Triangulation Sources
98
Assumptions, Limitations, and Delimitations
Assumptions Abrams, L. S. (2010). Sampling hard to reach populations in qualitative research: The
case of incarcerated youth. Qualitative Social Work, 9, 536-550. doi:10.1077/1473325010367821
Applebaum, M. (2012). Phenomenological psychological research as science. Journal
of Phenomenological Psychology, 43(1), 36-72. doi:10.1163/156916212x632952 Arghode, V. (2012). Qualitative and quantitative research: Paradigmatic differences.
Global Education Journal, 2012(4), 155-163. Retrieved from http://franklinpublishing.net/globaleducation.html
Bansal, P., & Corley, K. (2011). The coming of age for qualitative research: Embracing
the diversity of qualitative methods. Academy of Management Journal, 54, 233- 237. doi:10.5465/AMJ.2011.60262792
Bunniss, S., & Kelly, D. R. (2010). Research paradigms in medical education research.
Qualitative Research in Medical Education, 44, 358-366. doi:10.1111/j.1365- 2923.2009.03611.x
Castellan, C. M. (2010). Quantitative and qualitative research: A view for clarity.
International Journal of Education, 2(2), 1-14. Retrieved from http:// www.macrothink.org/ije
Cunliffe, A. L. (2011). Crafting qualitative research: Morgan and Smircich 30 years on.
Organizational Research Methods, 14, 647-673. doi:10.1177/1094428110373658 Diefenbach, T. (2009). Are case studies more than sophisticated storytelling?
Methodological problems of qualitative empirical research mainly based on semistructured interviews. Quality and Quantity, 43, 875-894. doi:10.1007/s11135-008-9164-0
Draper, A. A., & Swift, J. A. (2011). Qualitative research in nutrition and dietetics: Data
collection issues. Journal of Human Nutrition & Dietetics, 24(1), 3-12. doi:10.1111/j.1365-277X.2010.01117.x
Ellis, T. J., & Levy, Y. (2009). Towards a guide for novice researchers on research
methodology: Review and proposed methods. Issues in Informing Science & Information Technology, 323-337. Retrieved from http://informingscience.org/
Fan, X. (2013). “The test is reliable”; “The test is valid”: Language use, unconscious
assumptions, and education research practice. The Asia-Pacific Education Researcher, 22, 217-218. doi:10.1007/s40299-012-0036-y
99
Gallop, S. (2011). Viewpoint: Assumptions. Journal of Behavioral Optometry, 22, 158-
160. Retrieved from http://www.oepf.org/journals Grant, A. (2014). Troubling ‘lived experience’: A post-structural critique of mental health
nursing qualitative research assumptions. Journal of Psychiatric and Mental Health Nursing, 21(6), 544-549 doi:10.1111/jpm.12113
Hodges, N. (2011). Qualitative research: A discussion of frequently articulated qualms
(FAQs). Family and Consumer Sciences Research Journal, 40, 90-92. doi:10.1111/j.1552-3934.2011.02091.x
Lips-Wiersma, M., & Mills, A. J. (2013) Understanding the basic assumptions about
human nature in workplace spirituality: Beyond the critical versus positive divide. Journal of Management Inquiry, 23(2), 148-161. doi:10.1177/1056492613501227
Kirkwood, A., & Price, L. (2013). Examining some assumptions and limitations of
research on the effects of emerging technologies for teaching and learning in higher education. British Journal of Educational Technology, 44, 536-543. doi:10.1111/bjet.12049
Kouchaki, M., Okhuysen, G. A., Waller, M. J., & Tajeddin, G. (2012). The treatment of
the relationship between croups and their environments: A review and critical examination of common assumptions in research. Group & Organization Management, 37, 171-203. doi:10.1177/1059601112443850
Marshall, C., & Rossman, G. B. (2016). Designing qualitative research (6th ed.).
Thousand Oaks, CA: Sage. Martin, K., & Parmar, B. (2012). Assumptions in decision-making scholarship:
Implications for business ethics research. Journal of Business Ethics, 105, 289- 306. doi:10.1007/s10551-011-0965-z
Pratt, M. G. (2009). For the lack of a boilerplate: Tips on writing up (and reviewing)
qualitative research. Academy of Management Journal, 52, 856-862. doi:10.5465/AMJ.2009.44632557
Rocha Pereira, H. (2012). Rigour in phenomenological research: Reflections of a novice
nurse researcher. Nurse Researcher, 19(3), 16-19. Retrieved from http://nurse researcher.rcnpublishing.co.uk
Wahyuni, D. (2012). The research design maze: understanding paradigms, cases,
methods and methodologies. Journal of Applied Management Accounting Research, 10(1), 69-80. Retrieved from http://maaw.info/JAMAR.htm
100
Limitations Aastrup, J., & Halldorsson, A. (2013). Quality criteria for qualitative inquiries in logistics.
European Journal of Operational Research, 144, 321-332. doi:10.1016/S0377- 2217(02)00397-1
Anderson, C. (2010). Presenting and evaluating qualitative research. American Journal
of Pharmaceutical Education, 74(8), 1-7. doi:10.5688/aj7408141 Brutus, S., Aguinis, H., & Wassmer, U. (2012). Self-reported limitations and future
directions in scholarly reports analysis and recommendations. Journal of Management, 39(1) 48-75. doi:10.1177/0149206312455245
Brutus, S., Gill, H., & Duniewicz, K. (2010). State of science in industrial and
organizational psychology: A review of self-reported limitations. Personnel Psychology, 63, 907-936. doi:10.1111/j.1744-6570.2010.01192.x
Bunniss, S., & Kelly, D. R. (2010). Research paradigms in medical education research.
Qualitative Research in Medical Education, 44, 358-366. doi:10.1111/j.1365- 2923.2009.03611.x
Castellan, C. M. (2010). Quantitative and qualitative research: A view for clarity.
International Journal of Education, 2(2), 1-14. Retrieved from http:// www.macrothink.org/ije
Connelly, L. M. (2013). Limitation section. Medsurg Nursing, 22, 325-325, 336.
Retrieved from http://www.medsurgnursing.net/cgi- bin/WebObjects/MSNJournal.woa
Cunliffe, A. L. (2011). Crafting qualitative research: Morgan and Smircich 30 years on.
Organizational Research Methods, 14, 647-673. doi:10.1177/1094428110373658 Diefenbach, T. (2009). Are case studies more than sophisticated storytelling?
Methodological problems of qualitative empirical research mainly based on semistructured interviews. Quality and Quantity, 43, 875-894. doi:10.1007/s11135-008-9164-0
Draper, A. A., & Swift, J. A. (2011). Qualitative research in nutrition and dietetics:
Data collection issues. Journal of Human Nutrition & Dietetics, 24(1), 3-12. doi:10.1111/j.1365-277X.2010.01117.x
Ellis, T. J., & Levy, Y. (2009). Towards a guide for novice researchers on research
methodology: Review and proposed methods. Issues in Informing Science & Information Technology, 323-337. Retrieved from http://informingscience.org/
101
Fan, X. (2013). “The test is reliable”; “The test is valid”: Language use, unconscious assumptions, and education research practice. The Asia-Pacific Education Researcher, 22, 217-218. doi:10.1007/s40299-012-0036-y
Finfgeld-Connett, D. (2010). Generalizability and transferability of meta-synthesis
research findings. Journal of Advanced Nursing, 66, 246-254. doi:10.1111/j.1365-2648.2009.05250.x
Gibbs, L., Kealy, M., Willis, K., Green, J., Welch, N., & Daly, J. (2007). What have
sampling and data collection got to do with good qualitative research? Australian and New Zealand Journal of Public Health, 31, 540-544. doi:10.1111/j.1753- 6405.2007.00140.x
Hodges, N. (2011). Qualitative research: A discussion of frequently articulated qualms
(FAQs). Family and Consumer Sciences Research Journal, 40, 90-92. doi:10.1111/j.1552-3934.2011.02091.x
Houghton, C., Casey, D., Shaw, D., & Murphy, K. (2013). Rigour in qualitative case-
study research. Nurse Researcher, 20(4), 12-17. doi:10.7748/nr2013.03.20.4.12.e326
Marshall, C., & Rossman, G. B. (2016). Designing qualitative research (6th ed.).
Thousand Oaks, CA: Sage. O’Reilly, M., & Parker, N. (2012, May). Unsatisfactory saturation: A critical exploration of
the notion of saturated sample sizes in qualitative research. Qualitative Research Journal, 1-8. doi:10.1177/1468794112446106
Polit, D. F., & Beck, C. T. (2010). Generalization in quantitative and qualitative research:
Myths and strategies. International Journal of Nursing Studies, 47, 1451-1458. doi:10.1016/j.ijnurstu.2010.06.004
Pratt, M. G. (2009). For the lack of a boilerplate: Tips on writing up (and reviewing)
qualitative research. Academy of Management Journal, 52, 856-862. doi:10.5465/AMJ.2009.44632557
Prowse, M., & Camfield, L. (2013). Improving the quality of development assistance:
What role for qualitative methods in randomized experiments? Progress in Development Studies, 13(1), 51-61. doi:10.1177/146499341201300104
Rocha Pereira, H. (2012). Rigour in phenomenological research: Reflections of a novice
nurse researcher. Nurse Researcher, 19(3), 16-19. Retrieved from http://nurse researcher.rcnpublishing.co.uk
102
Sabbour, S., Lasi, H., & Tessin, P. (2012). Business intelligence and strategic decision simulation. World Academy of Science, Engineering and Technology, 6, 980-987. Retrieved from http://waset.org/Publications?p=61
103
Delimitations Barratt, M., Choi, T. Y., & Li, M. (2011). Qualitative case studies in operations
management: Trends, research outcomes, and future research implications. Journal of Operations Management, 29, 329-342. doi:10.1016/j.jom.2010.06.002
Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and
implementation for novice researchers. The Qualitative Report, 13, 544-559. Retrieved from http://www.nova.edu/ssss/QR/QR13-4/baxter
Bunniss, S., & Kelly, D. R. (2010). Research paradigms in medical education research. Qualitative Research in Medical Education, 44, 358-366. doi:10.1111/j.1365- 2923.2009.03611.x
Castellan, C. M. (2010). Quantitative and qualitative research: A view for clarity.
International Journal of Education, 2(2), 1-14. Retrieved from http:// www.macrothink.org/ije
Cunliffe, A. L. (2011). Crafting qualitative research: Morgan and Smircich 30 years on.
Organizational Research Methods, 14, 647-673. doi:10.1177/1094428110373658 Diefenbach, T. (2009). Are case studies more than sophisticated storytelling?
Methodological problems of qualitative empirical research mainly based on semistructured interviews. Quality and Quantity, 43, 875-894. doi:10.1007/s11135-008-9164-0
Draper, A. A., & Swift, J. A. (2011). Qualitative research in nutrition and dietetics:
Data collection issues. Journal of Human Nutrition & Dietetics, 24(1), 3-12. doi:10.1111/j.1365-277X.2010.01117.x
Ellis, T. J., & Levy, Y. (2009). Towards a guide for novice researchers on research
methodology: Review and proposed methods. Issues in Informing Science & Information Technology, 323-337. Retrieved from http://informingscience.org/
Fan, X. (2013). “The test is reliable”; “The test is valid”: Language use, unconscious
assumptions, and education research practice. The Asia-Pacific Education Researcher, 22, 217-218. doi:10.1007/s40299-012-0036-y
Marshall, C., & Rossman, G. B. (2016). Designing qualitative research (6th ed.).
Thousand Oaks, CA: Sage. Hodges, N. (2011). Qualitative research: A discussion of frequently articulated qualms
(FAQs). Family and Consumer Sciences Research Journal, 40, 90-92. doi:10.1111/j.1552-3934.2011.02091.x
104
Nenty, H., & Adedoyin, O. O. (2010). Research orientation and research-related behaviour of graduate education students at University of Botswana. International Research Journal, 1, 577-585. Retrieved from http://interesjournals.org
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias
in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539-569. doi:10.1146/annurev-psych- 120710-100452
Pratt, M. G. (2009). For the lack of a boilerplate: Tips on writing up (and reviewing)
qualitative research. Academy of Management Journal, 52, 856-862. doi:10.5465/AMJ.2009.44632557
Rocha Pereira, H. (2012). Rigour in phenomenological research: Reflections of a novice nurse researcher. Nurse Researcher, 19(3), 16-19. Retrieved from http://nurse researcher.rcnpublishing.co.uk
Scotland, J. (2012). Exploring the philosophical underpinnings of research: Relating
ontology and epistemology to the methodology and methods of the scientific, interpretive, and critical research paradigms. English Language Teaching, 5(9), 9-17. doi:10.5539/elt.v5n9p9
Small, M. (2009). How many cases do I need: On science and the logic of case
selection in field-based research. Ethnography, 10(1), 5-38. doi:10.1177/1466138108099586
Spitzmüller, J., & Warnke, I. H. (2011). Discourse as a “linguistic object”: Methodical
and methodological delimitations. Critical Discourse Studies, 8, 75-94. doi:10.1080/17405904.2011.558680
105
Case Study Sources Alfonso, M., Nickelson, L., & Cohen, D. (2012). Farmers’ markets in rural communities:
A case study. American Journal of Health Education, 43(3), 143-151. Retrieved from http://www.aahperd.org/aahe/publications/ajhe/
Almutairi, A. F., Gardner, G. E., & McCarthy, A. (2014). Practical guidance for the use of
pattern-matching technique in case-study research: A case presentation. Nursing & Health Sciences, 16, 239-244. doi:10.1111/nhs.12096
Amerson, R. (2011). Making a case for the case study method. Journal of Nursing
Education, 50, 427-428. doi:10.3928.01484834-20110719-01 Andrade, A. D. (2009). Interpretive research aiming at theory building: Adopting and
adapting the case study design. The Qualitative Report, 14(1), 42-60. Retrieved from http://www.nova.edu/ssss/QR/QR14-1/diaz-andrade
Ates, O. (2013). Using case studies for teaching management to computer engineering
students. International Journal of Business and Management, 8(5), 72-81. doi:10.5539/ijbm.v8n5p72
Baker, R. G., (2011). The contribution of case study research to knowledge of how to
improve the quality of care. British Medical Journal Quality and Safety, 20, 30-35. doi:10.1136/bmjqs.2010.046490
Baxter, P., & Jack, S. (2008). Qualitative case study methodology: Study design and
implementation for novice researchers. The Qualitative Report, 13, 544-559. Retrieved from http://www.nova.edu/ssss/QR/QR13-4/baxter
Beverland, M., & Lindgreen, A. (2010). What makes a good case study? A positivist
review of qualitative case research published in Industrial Marketing Management, 1971-2006. Industrial Marketing Management, 39, 56-63. doi:10.1016/j.indmarman.2008.09.005
Boblin, S. L., Ireland, S., Kirkpatrick, H., & Robertson, K. (2013). Using Stakes
qualitative case study approach to explore implementation evidence-based practice. Qualitative Health Research, 23, 1267-1275. doi:10.1177/1049732313502128
Breslin, M., & Buchanan, R. (2011). On the case study method of research and teaching
in design. Design Issues, 24(1), 36-40. Retrieved from http://www.mitjournals.org Bucic, T., Robinson, L., & Ramburuth, P. (2010). Effects of leadership style on team
learning. Journal of Workplace Learning, 22, 228-248. doi:10.1108/13665621011040680
106
Butvilas, T., & Zygmantas, J. (2011). An ethnographic case study in educational research. Acta Paedagogica Vilnensia, 27, 33-42. Retrieved from http://www.leidykla.eu/index.php?id=36
Cinneide, B. (2015). The role of effectiveness of case studies: Student performance in
case study vs. “theory” examinations. Journal of European Industrial Training, 21(1) 3-13. www.emeraldinsight.com/journal.jeit
Cronin, C. (2014). Using case study research as a rigorous form of inquiry. Nurse
Researcher, 21(5), 19-27. doi:10.7748/nr.21.5.19.e1240 Crowe, S., Cresswell, K., Robertson, A., Huby, G., Avery, A., & Sheikh, A. (2011). The
case study approach. BMC Medical Research Methodology, 11(1), 1-9. doi:10.1186/1471-2288-11-100
Da Mota Pedrosa, A., Näslund, D., & Jasmand, C. (2012). Logistics case study based
research: Towards higher quality. International Journal of Physical Distribution & Logistics Management, 42, 275-295. doi:10.1108/09600031211225963
Dasgupta, M. (2015). Exploring the relevance of case study research. Vision
(09722629), 19(2), 147-160. doi:10.1177/0972262915575661 De Massis, A., & Kotlar, J. (2014). The case study method in family business research:
Guidelines for qualitative scholarship. Journal of Family Business Strategy, 5(1), 15-29. doi:10.1016/j.jfbs.2014.01.007
Easton, G. (2010). Critical realism in case study research. Industrial Marketing
Management, 39(1), 118-128. doi:10.1016/j.indmarman.2008.06.004 Eno, M., & Dammak, A. (2014). Debating the case study dilemma: Controversies and
considerations. Veritas: The Academic Journal of St Clements Education Group, 5(3), 1-8. Retrieved from http://stclements.edu/Veritas/VERITAS%20October%202014
Gibbert, M., & Ruigrok, W. (2010). The what and how of case study rigor: Three
strategies based on published work. Organizational Research Methods, 13, 710- 737. doi:10.1177/1094428109351319
Harland, T. (2014). Learning about case study methodology to research higher
education. Higher Education Research & Development, 1-10. doi:10.1080/07294360.2014.911253
Hietanen, J., Sihvonen, A., Tikkanen, H., & Mattila, P. (2014). Managerial storytelling:
How we produce managerial and academic stories in qualitative B2B case study research. Journal of Global Scholars of Marketing, 24. doi:10.1080/21639159.2014.911496
107
Houghton, C. E., Casey, D., Shaw, D., & Murphy, K. (2010). Ethical challenges in
qualitative research: Examples from practice. Nurse Researcher, 18(1), 15-25. Retrieved from http://nurseresearcher.rcnpublishing.co.uk
Hyett, N., Kenny, A., & Dickson-Swift, V. (2014). Methodology or method? A critical
review of qualitative case study reports. International Journal of Qualitative, 9. doi:10.3402/qhw.v9.23606
Järvensivu, T., & Törnroos, J. Å. (2010). Case study research with moderate
constructionism: Conceptualization and practical illustration. Industrial Marketing Management, 39(1), 100-108. doi:10.1016/j.indmarman.2008.05.005
Ketokivi, M., & Choi, T. (2014). Renaissance of case research as a scientific method.
Journal of Operations Management, 32, 232-240. doi:10.1016/j.jom.2014.03.004 Moll, S. (2012). Navigating political minefields: Partnerships in organizational case study
research. Work, 43, 5-12. doi:10.3233/wor-2012-1442 Morse, A. L., & McEvoy, C. D. (2014). Qualitative research in sport management: Case
study as a methodological approach. The Qualitative Report, 19, 1-13. Retrieved from http://www.nova.edu/ssss/QR/QR19/morse17
Murakami, Y. (2013, March). Rethinking a case study method in educational research:
A comparative analysis method in qualitative research. Educational Studies in Japan: International Yearbook, (7), 81-96. Retrieved from http://ci.nii.ac.jp/vol_issue/nels/AA12192695_en.html
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