doctoral dissertation research model

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• Dissertation Proposal

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Proposal Overview and Format

Proposal committee, proposal hearing or meeting.

• Printing Credit for Use in School of Education Labs

Students are urged to begin thinking about a dissertation topic early in their degree program. Concentrated work on a dissertation proposal normally begins after successful completion of the Second-Year Review, which often includes a “mini” proposal, an extended literature review, or a theoretical essay, plus advancement to doctoral candidacy. In defining a dissertation topic, the student collaborates with their faculty advisor or dissertation advisor (if one is selected) in the choice of a topic for the dissertation.

The dissertation proposal is a comprehensive statement on the extent and nature of the student’s dissertation research interests. Students submit a draft of the proposal to their dissertation advisor between the end of the seventh and middle of the ninth quarters. The student must provide a written copy of the proposal to the faculty committee no later than two weeks prior to the date of the proposal hearing. Committee members could require an earlier deadline (e.g., four weeks before the hearing).

The major components of the proposal are as follows, with some variations across Areas and disciplines:

• A detailed statement of the problem that is to be studied and the context within which it is to be seen. This should include a justification of the importance of the problem on both theoretical and educational grounds.
• A thorough review of the literature pertinent to the research problem. This review should provide proof that the relevant literature in the field has been thoroughly researched. Good research is cumulative; it builds on the thoughts, findings, and mistakes of others.
• its general explanatory interest
• the overall theoretical framework within which this interest is to be pursued
• the model or hypotheses to be tested or the research questions to be answered
• a discussion of the conceptual and operational properties of the variables
• an overview of strategies for collecting appropriate evidence (sampling, instrumentation, data collection, data reduction, data analysis)
• a discussion of how the evidence is to be interpreted (This aspect of the proposal will be somewhat different in fields such as history and philosophy of education.)
• If applicable, students should complete a request for approval of research with human subjects, using the Human Subjects Review Form ( http://humansubjects.stanford.edu/ ). Except for pilot work, the University requires the approval of the Administrative Panel on Human Subjects in Behavioral Science Research before any data can be collected from human subjects.

Registration (i.e., enrollment) is required for any quarter during which a degree requirement is completed, including the dissertation proposal. Refer to the Registration or Enrollment for Milestone Completion section for more details.

As students progress through the program, their interests may change. There is no commitment on the part of the student’s advisor to automatically serve as the dissertation chair. Based on the student’s interests and the dissertation topic, many students approach other GSE professors to serve as the dissertation advisor, if appropriate.

A dissertation proposal committee is comprised of three academic council faculty members, one of whom will serve as the major dissertation advisor. Whether or not the student’s general program advisor serves on the dissertation proposal committee and later the reading committee will depend on the relevance of that faculty member’s expertise to the topic of the dissertation, and their availability. There is no requirement that a program advisor serve, although very often they do. Members of the dissertation proposal committee may be drawn from other area committees within the GSE, from other departments in the University, or from emeriti faculty. At least one person serving on the proposal committee must be from the student’s area committee (CTE, DAPS, SHIPS). All three members must be on the Academic Council; if the student desires the expertise of a non-Academic Council member, it may be possible to petition. After the hearing, a memorandum listing the changes to be made will be written and submitted with the signed proposal cover sheet and a copy of the proposal itself to the Doctoral Programs Officer.

Review and approval of the dissertation proposal occurs normally during the third year. The proposal hearing seeks to review the quality and feasibility of the proposal. The Second-Year Review and the Proposal Hearing are separate milestones and may not occur as part of the same hearing or meeting.

The student and the dissertation advisor are responsible for scheduling a formal meeting or hearing to review the proposal; the student and proposal committee convene for this evaluative period. Normally, all must be present at the meeting either in person or via conference phone call.

At the end of this meeting, the dissertation proposal committee members should sign the Cover Sheet for Dissertation Proposal and indicate their approval or rejection of the proposal. This signed form should be submitted to the Doctoral Programs Officer. If the student is required to make revisions, an addendum is required with the written approval of each member of the committee stating that the proposal has been revised to their satisfaction.

After submitting the Proposal Hearing material to the Doctoral Programs Officer, the student should make arrangements with three faculty members to serve on their Dissertation Reading Committee. The Doctoral Dissertation Reading Committee form should be completed and given to the Doctoral Programs Officer to enter in the University student records system. Note: The proposal hearing committee and the reading committee do not have to be the same three faculty members. Normally, the proposal hearing precedes the designation of a Dissertation Reading Committee, and faculty on either committee may differ (except for the primary dissertation advisor). However, some students may advance to Terminal Graduate Registration (TGR) status before completing their dissertation proposal hearing if they have established a dissertation reading committee. In these cases, it is acceptable for the student to form a reading committee prior to the dissertation proposal hearing. The reading committee then serves as the proposal committee.

The proposal and reading committee forms and related instructions are on the GSE website, under current students>forms.

Printing Credit for Use in GSE Labs

Upon completion of their doctoral dissertation proposal, GSE students are eligible for a $300 printing credit redeemable in any of the GSE computer labs where students are normally charged for print jobs. Only one $300 credit per student will be issued, but it is usable throughout the remainder of her or his doctoral program until the balance is exhausted. The print credit can be used only at the printers in Cubberley basement and CERAS, and cannot be used toward copying.

After submitting the signed dissertation proposal cover sheet to the Doctoral Programs Officer indicating approval (see above), students can submit a HELP SU ticket online at helpsu.stanford.edu to request the credit. When submitting the help ticket, the following should be selected from the drop-down menus for HELP SU:

Request Category :  Computer, Handhelds (PDAs), Printers, Servers Request Type :  Printer Operating System : (whatever system is used by the student, e.g., Windows XP.)

The help ticket will be routed to the GSE's IT Group for processing; they will in turn notify the student via email when the credit is available.

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Handbook Contents

• Timetable for the Doctoral Degree
• Degree Requirements
• Registration or Enrollment for Milestone Completion
• The Graduate Study Program
• Student Virtual and Teleconference Participation in Hearings
• First Year (3rd Quarter) Review
• Second Year (6th Quarter) Review
• Committee Composition for First- and Second-Year Reviews
• Advancement to Candidacy
• Academic Program Revision
• Dissertation Content
• Dissertation Reading Committee
• University Oral Examination
• Submitting the Dissertation
• Registration and Student Statuses
• Graduate Financial Support
• GSE Courses
• Curriculum Studies and Teacher Education (CTE)
• Developmental and Psychological Sciences (DAPS)
• Learning Sciences and Technology Design (LSTD)
• Race, Inequality, and Language in Education (RILE)
• Social Sciences, Humanities, and Interdisciplinary Policy Studies in Education (SHIPS)
• Contact Information
• Stanford University Honor Code
• Stanford University Fundamental Standard
• Doctoral Programs Degree Progress Checklist
• GSE Open Access Policies

PhD students, please contact

MA POLS and MA/PP students, please contact

EDS, ICE/IEPA, Individually Designed, LDT, MA/JD, MA/MBA students, please contact

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What’s Included: The Dissertation Template

If you’re preparing to write your dissertation, thesis or research project, our free dissertation template is the perfect starting point. In the template, we cover every section step by step, with clear, straightforward explanations and examples .

The template’s structure is based on the tried and trusted best-practice format for formal academic research projects such as dissertations and theses. The template structure reflects the overall research process, ensuring your dissertation or thesis will have a smooth, logical flow from chapter to chapter.

The dissertation template covers the following core sections:

• The title page/cover page
• Abstract (sometimes also called the executive summary)
• Table of contents
• List of figures /list of tables
• Chapter 1: Introduction  (also available: in-depth introduction template )
• Chapter 2: Literature review  (also available: in-depth LR template )
• Chapter 3: Methodology (also available: in-depth methodology template )
• Chapter 4: Research findings /results (also available: results template )
• Chapter 5: Discussion /analysis of findings (also available: discussion template )
• Chapter 6: Conclusion (also available: in-depth conclusion template )
• Reference list

Each section is explained in plain, straightforward language , followed by an overview of the key elements that you need to cover within each section. We’ve also included practical examples to help you understand exactly what’s required in each section.

The cleanly-formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

FAQs: Dissertation Template

What format is the template (doc, pdf, ppt, etc.).

The dissertation template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

What types of dissertations/theses can this template be used for?

The template follows the standard best-practice structure for formal academic research projects such as dissertations or theses, so it is suitable for the vast majority of degrees, particularly those within the sciences.

Some universities may have some additional requirements, but these are typically minor, with the core structure remaining the same. Therefore, it’s always a good idea to double-check your university’s requirements before you finalise your structure.

Will this work for a research paper?

A research paper follows a similar format, but there are a few differences. You can find our research paper template here .

Is this template for an undergrad, Masters or PhD-level thesis?

This template can be used for a dissertation, thesis or research project at any level of study. It may be slight overkill for an undergraduate-level study, but it certainly won’t be missing anything.

How long should my dissertation/thesis be?

This depends entirely on your university’s specific requirements, so it’s best to check with them. As a general ballpark, Masters-level projects are usually 15,000 – 20,000 words in length, while Doctoral-level projects are often in excess of 60,000 words.

What about the research proposal?

If you’re still working on your research proposal, we’ve got a template for that here .

We’ve also got loads of proposal-related guides and videos over on the Grad Coach blog .

How do I write a literature review?

We have a wealth of free resources on the Grad Coach Blog that unpack how to write a literature review from scratch. You can check out the literature review section of the blog here.

How do I create a research methodology?

We have a wealth of free resources on the Grad Coach Blog that unpack research methodology, both qualitative and quantitative. You can check out the methodology section of the blog here.

Can I share this dissertation template with my friends/colleagues?

Yes, you’re welcome to share this template. If you want to post about it on your blog or social media, all we ask is that you reference this page as your source.

Can Grad Coach help me with my dissertation/thesis?

Within the template, you’ll find plain-language explanations of each section, which should give you a fair amount of guidance. However, you’re also welcome to consider our dissertation and thesis coaching services .

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Required sections, guidelines, and suggestions.

Beyond those noted on the Formatting Requirements page , the Graduate School has no additional formatting requirements. The following suggestions are based on best practices and historic requirements for dissertations and theses but are not requirements for submission of the thesis or dissertation. The Graduate School recommends that each dissertation or thesis conform to the standards of leading academic journals in your field.

For both master’s and doctoral students, the same basic rules apply; however, differences exist in some limited areas, particularly in producing the abstract and filing the dissertation or thesis.

• Information in this guide that pertains specifically to doctoral candidates and dissertations is clearly marked with the term “ dissertation ” or “ doctoral candidates .”
• Information pertaining specifically to master’s candidates and theses is clearly marked with the term “ thesis ” or “ master’s candidates .”
• All other information pertains to both.

Examples of formatting suggestions for both the dissertation and thesis are available as downloadable templates .

Required? Yes.

Suggested numbering: Page included in overall document, but number not typed on page.

The following format for your title page is suggested, but not required.

• The title should be written using all capital letters, centered within the left and right margins, and spaced about 1.5 inches from the top of the page. (For an example, please see the template .)
• Carefully select words for the title of the dissertation or thesis to represent the subject content as accurately as possible. Words in the title are important access points to researchers who may use keyword searches to identify works in various subject areas.
• Use word substitutes for formulas, symbols, superscripts, Greek letters, etc.
• Below the title, at the vertical and horizontal center of the margins, place the following five lines (all centered):

Line 1: A Dissertation [or Thesis]

Line 2: Presented to the Faculty of the Graduate School

Line 3: of Cornell University

Line 4: in Partial Fulfillment of the Requirements for the Degree of

Line 5: Doctor of Philosophy [or other appropriate degree]

• Center the following three lines within the margins:

Line 2: Primary or Preferred Name [as registered with the University Registrar’s Office and displayed in Student Center]

Line 3: month and year of degree conferral [May, August, December; no comma between month and year]

Copyright Page

Suggested numbering: Page included in overall document, but number not typed on page

The following format for your copyright page is suggested, but not required.

• A notice of copyright should appear as the sole item on the page centered vertically and horizontally within the margins: © 20__ [Primary or Preferred Name [as registered with the University Registrar’s Office]. Please note that there is not usually a page heading on the copyright page.
• The copyright symbol is a lowercase “c,” which must be circled. (On Macs, the symbol is typed by pressing the “option” and “g” keys simultaneously. If the font does not have the © symbol, type the “c” and circle it by hand. On PCs, in the insert menu, choose “symbol,” and select the © symbol.)
• The date, which follows the copyright symbol, is the year of conferral of your degree.
• Your name follows the date.

Required?  Yes.

Suggested numbering: Page(s) not counted, not numbered

Abstract formats for the doctoral dissertation and master’s thesis differ greatly. The Graduate School recommends that you conform to the standards of leading academic journals in your field.

Doctoral candidates:

• TITLE OF DISSERTATION
• Student’s Primary or Preferred Name, Ph.D. [as registered with the University Registrar’s Office]
• Cornell University 20__ [year of conferral]
• Following the heading lines, begin the text of the abstract on the same page.
• The abstract states the problem, describes the methods and procedures used, and gives the main results or conclusions of the research.
• The abstract usually does not exceed 350 words in length (about one-and-one-half correctly spaced pages—but not more than two pages).

Master’s candidate:

• In a thesis, the page heading is simply the word “ABSTRACT” in all capital letters and centered within the margins at the top of the page. (The thesis abstract does not display the thesis title, author’s name, degree, university, or date of degree conferral.)
• The abstract should state the problem, describe the methods and procedures used, and give the main results or conclusions.
• The abstract usually does not exceed 600 words in length, which is approximately two-and-one-half to three pages of correctly spaced typing.
• In M.F.A. theses, an abstract is not required.

Biographical Sketch

Suggested numbering: iii (may be more than one page)

• Type number(s) on page(s).

The following content and format are suggested:

• The biographical sketch is written in third-person voice and contains your educational background. Sometimes additional biographical facts are included.
• As a page heading, use “BIOGRAPHICAL SKETCH” in all capital letters, centered on the page.
• Number this page as iii.

Required? Optional.

Suggested numbering: iv (may be more than one page)

The dedication page is not required and can contain whatever text that you would like to include. Text on this page does not need to be in English.

Acknowledgements

Suggested numbering: v (may be more than one page)

The following content and format are suggested, not required.

• The acknowledgements may be written in first-person voice. If your research has been funded by outside grants, you should check with the principal investigator of the grant regarding proper acknowledgement of the funding source. Most outside funding sources require some statement of acknowledgement of the support; some also require a disclaimer from responsibility for the results.
• As a page heading, use “ACKNOWLEDGEMENTS” in all capital letters, centered on the page.

Table of Contents

Suggested numbering: vi (may be more than one page)

The following are suggestions.

• As a page heading, use “TABLE OF CONTENTS” in all capital letters and centered on the page.
• List the sections/chapters of the body of the dissertation or thesis. Also, list preliminary sections starting with the biographical sketch. (Title page, copyright page, and abstract are not listed.)
• For theses and dissertations, the conventional format for page numbers is in a column to the right of each section/chapter title. The first page of each chapter/section is stated with a single number. Table of contents usually do not include a range of page numbers, such as 7-22.
• The table of contents is often single-spaced.

Two-Volume Theses or Dissertations

If the dissertation or thesis consists of two volumes, it is recommended, but not required, that you list “Volume II” as a section in the table of contents.

List of Figures, Illustrations, and Tables

Suggested numbering: vii (may be more than one page)

• If included, type number(s) on page(s).

As described in the formatting requirements above, figures and tables should be consecutively numbered. The Graduate School recommends that you conform to the styles set by the leading academic journals in your field. The items below are formatting suggestions based on best practices or historic precedents.

Table of contents format:

• As a page heading, use “LIST OF FIGURES,” “LIST OF ILLUSTRATIONS,” or “LIST OF TABLES” in all capital letters, centered on the page.
• There should be separate pages for “LIST OF FIGURES,” “LIST OF ILLUSTRATIONS,” or “LIST OF TABLES” even if there is only one example of each.
• The list should contain enough of the titles or descriptions so readers can locate items using the list. (It may not be necessary to include entire figure/illustration/table captions.)
• The list should contain the page number on which each figure, illustration, or table is found, as in a table of contents.
• The list of figures/illustrations/tables may be single-spaced.

Page format:

• Figures/illustrations/tables should be placed as close as possible to their first mention in the text. They may be placed on a page with no text above or below, or placed directly into the text. If a figure/illustration/table is placed directly into the text, text may appear above or below the figure/illustration/table; no text may wrap around the figure/illustration/table.
• If a figure/illustration/table appears on a page without other text, it should be centered vertically within the page margins. Figures/illustrations/tables should not be placed at the end of the chapter or at the end of the dissertation or thesis.
• Figure/illustration/table numbering should be either continuous throughout the dissertation or thesis, or by chapter (e.g. 1.1, 1.2; 2.1, 2.2, etc.). The word “Figure,” “Illustration,” or “Table” must be spelled out (not abbreviated), and the first letter must be capitalized.
• A caption for a figure/illustration should be placed at the bottom of the figure/illustration. However, a caption for a table must be placed above the table.
• If the figure/illustration/table, not including the caption, takes up the entire page, the figure/illustration/table caption should be placed alone on the preceding page and centered vertically and horizontally within the margins. (When the caption is on a separate page, the List of Figures or List of Illustrations or List of Tables can list the page number containing the caption.)
• If the figure/illustration/table, not including the caption, takes up more than two pages, it should be preceded by a page consisting of the caption only. The first page of the figure/illustration/table must include the figure/illustration/table (no caption), and the second and subsequent pages must also include, at the top of the figure/illustration/table, words that indicate its continuance—for example, “Figure 5 (Continued)”—and on these pages the caption is omitted.
• If figures/illustrations/tables are too large, they may be reduced slightly so as to render a satisfactory product or they must either be split into several pages or be redone. If a figure/illustration/table is reduced, all lettering must be clear, readable, and large enough to be legible. All lettering, including subscripts, must still be readable when reduced 25% beyond the final version. All page margin requirements must be maintained. Page numbers and headings must not be reduced.
• While there are no specific rules for the typographic format of figure/illustration/table captions, a consistent format should be used throughout the dissertation or thesis.
• The caption of a figure/illustration/table should be single-spaced, but then captions for all figures/illustrations/tables must be single-spaced.
• Horizontal figures/illustrations/tables should be positioned correctly—i.e., the top of the figure/illustration/table will be at the left margin of the vertical page of the dissertation or thesis (remember: pages are bound on the left margin). Figure/illustration/table headings/captions are placed with the same orientation as the figure/illustration/table when they are on the same page as the figure/illustration/table. When they are on a separate page, headings and captions are always placed in vertical orientation, regardless of the orientation of the figure/illustration/table. Page numbers are always placed as if the figure/illustration/table was vertical on the page.

Photographs should be treated as illustrations. To be considered archival, photographs must be black-and-white. (If actual color photographs are necessary, they should be accompanied by black-and-white photographs of the same subject.) Color photos obtained digitally do not need to be accompanied by a black-and-white photograph. Make a high-resolution digital version of each photograph and insert it into your electronic document, following the guideline suggestions for positioning and margins.

Optional Elements

List of abbreviations.

As a page heading, use “LIST OF ABBREVIATIONS” in all capital letters, centered on the page.

List of Symbols

As a page heading, use “LIST OF SYMBOLS” in all capital letters, centered on the page.

Suggested numbering: xi (may be more than one page)

As a page heading, use “PREFACE” in all capital letters, centered on the page.

Body of the Dissertation or Thesis: Text

Suggested numbering: Begin page number at 1

• Text (required)
• Appendix/Appendices (optional)
• Bibliography, References, or Works Cited (required)

Please note that smaller font size may be appropriate for footnotes or other material outside of the main text. The following suggestions are based on best practice or historic precedent, but are not required.

• Chapter headings may be included that conform to the standard of your academic field.
• Textual notes that provide supplementary information, opinions, explanations, or suggestions that are not part of the text must appear at the bottom of the page as footnotes. Lengthy footnotes may be continued on the next page. Placement of footnotes at the bottom of the page ensures they will appear as close as possible to the referenced passage.

Appendix (or Appendices)

An appendix (-ces) is not required for your thesis or dissertation. If you choose to include one, the following suggestions are based on best practice or historic precedent.

• As a page heading, use “APPENDIX” in all capital letters, centered on the page.
• Place in an appendix any material that is peripheral, but relevant, to the main text of the dissertation or thesis. Examples could include survey instruments, additional data, computer printouts, details of a procedure or analysis, a relevant paper that you wrote, etc.
• The appendix may include text that does not meet the general font and spacing requirements of the other sections of the dissertation or thesis.

Bibliography (or References or Works Cited)

A bibliography, references, or works cited is required for your thesis or dissertation. Please conform to the standards of leading academic journals in your field.

• As a page heading, use “BIBLIOGRAPHY” (or “REFERENCES” or “WORKS CITED”) in all capital letters, centered on the page. The bibliography should always begin on a new page.
• Bibliographies may be single-spaced within each entry but should include 24 points of space between entries.

Suggested numbering: Continue page numbering from body

If you choose to include a glossary, best practices and historic precedent suggest using a page heading, use “GLOSSARY” in all capital letters, centered on the page.

Suggested numbering: Continue page numbering from glossary

If you choose to include one, best practices and historic precedent suggest using a page heading, use “INDEX” in all capital letters, centered on the page.

Font Samples

Sample macintosh fonts.

• Palatino 12
• Garamond 14
• New Century School Book
• Helvetica 12 or Helvetica 14
• Times New Roman 12
• Times 14 (Times 12 is not acceptable)
• Symbol 12 is acceptable for symbols

Sample TeX and LaTeX Fonts

• CMR 12 font
• Any font that meets the above specifications

Sample PC Fonts

• Helvetica 12

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The Top 3 Types of Dissertation Research Explained

Preparing for your doctoral dissertation takes serious perseverance. You’ve endured years of studies and professional development to get to this point. After sleepless nights and labor-intensive research, you’re ready to present the culmination of all of your hard work. Even with a strong base knowledge, it can be difficult — even daunting — to decide how you will begin writing.

By taking a wide-lens view of the dissertation research process , you can best assess the work you have ahead of you and any gaps in your current research strategy. Subsequently, you’ll begin to develop a timeline so you can work efficiently and cross that finish line with your degree in hand.

What Is a Dissertation?

A dissertation is a published piece of research on a novel topic in your chosen field. Students complete a dissertation as part of a doctoral or PhD program. For most students, a dissertation is the first substantive piece of academic research they will write. 

Because a dissertation becomes a published piece of academic literature that other academics may cite, students must defend it in front of a board of experts consisting of peers in their field, including professors, their advisor, and other industry experts. 

For many students, a dissertation is the first piece of research in a long career full of research. As such, it’s important to choose a topic that’s interesting and engaging.

Types of Dissertation Research

Dissertations can take on many forms, based on research and methods of presentation in front of a committee board of academics and experts in the field. Here, we’ll focus on the three main types of dissertation research to get you one step closer to earning your doctoral degree.

1. Qualitative

The first type of dissertation is known as a qualitative dissertation . A qualitative dissertation mirrors the qualitative research that a doctoral candidate would conduct throughout their studies. This type of research relies on non-numbers-based data collected through things like interviews, focus groups and participant observation. 

The decision to model your dissertation research according to the qualitative method will depend largely on the data itself that you are collecting. For example, dissertation research in the field of education or psychology may lend itself to a qualitative approach, depending on the essence of research. Within a qualitative dissertation research model, a candidate may pursue one or more of the following:

• Case study research
• Autoethnographies
• Narrative research 
• Grounded theory 

Although individual approaches may vary, qualitative dissertations usually include certain foundational characteristics. For example, the type of research conducted to develop a qualitative dissertation often follows an emergent design, meaning that the content and research strategy changes over time. Candidates also rely on research paradigms to further strategize how best to collect and relay their findings. These include critical theory, constructivism and interpretivism, to name a few. 

Because qualitative researchers integrate non-numerical data, their methods of collection often include unstructured interview, focus groups and participant observations. Of course, researchers still need rubrics from which to assess the quality of their findings, even though they won’t be numbers-based. To do so, they subject the data collected to the following criteria: dependability, transferability and validity. 

When it comes time to present their findings, doctoral candidates who produce qualitative dissertation research have several options. Some choose to include case studies, personal findings, narratives, observations and abstracts. Their presentation focuses on theoretical insights based on relevant data points. 

2. Quantitative

Quantitative dissertation research, on the other hand, focuses on the numbers. Candidates employ quantitative research methods to aggregate data that can be easily categorized and analyzed. In addition to traditional statistical analysis, quantitative research also hones specific research strategy based on the type of research questions. Quantitative candidates may also employ theory-driven research, replication-based studies and data-driven dissertations. 

When conducting research, some candidates who rely on quantitative measures focus their work on testing existing theories, while others create an original approach. To refine their approach, quantitative researchers focus on positivist or post-positivist research paradigms. Quantitative research designs focus on descriptive, experimental or relationship-based designs, to name a few. 

To collect the data itself, researchers focus on questionnaires and surveys, structured interviews and observations, data sets and laboratory-based methods. Then, once it’s time to assess the quality of the data, quantitative researchers measure their results against a set of criteria, including: reliability, internal/external validity and construct validity. Quantitative researchers have options when presenting their findings. Candidates convey their results using graphs, data, tables and analytical statements.

3. Mixed-Method

Many PhD candidates also use a hybrid model in which they employ both qualitative and quantitative methods of research. Mixed dissertation research models are fairly new and gaining traction. For a variety of reasons, a mixed-method approach offers candidates both versatility and credibility. It’s a more comprehensive strategy that allows for a wider capture of data with a wide range of presentation optimization. 

In the most common cases, candidates will first use quantitative methods to collect and categorize their data. Then, they’ll rely on qualitative methods to analyze that data and draw meaningful conclusions to relay to their committee panel. 

With a mixed-method approach, although you’re able to collect and analyze a more broad range of data, you run the risk of widening the scope of your dissertation research so much that you’re not able to reach succinct, sustainable conclusions. This is where it becomes critical to outline your research goals and strategy early on in the dissertation process so that the techniques you use to capture data have been thoroughly examined. 

How to Choose a Type of Dissertation Research That’s Right for You

After this overview of application and function, you may still be wondering how to go about choosing a dissertation type that’s right for you and your research proposition. In doing so, you’ll have a couple of things to consider: 

• What are your personal motivations? 
• What are your academic goals? 

It’s important to discern exactly what you hope to get out of your doctoral program . Of course, the presentation of your dissertation is, formally speaking, the pinnacle of your research. However, doctoral candidates must also consider:

• Which contributions they will make to the field
• Who they hope to collaborate with throughout their studies
• What they hope to take away from the experience personally, professionally and academically

Personal Considerations

To discern which type of dissertation research to choose, you have to take a closer look at your learning style, work ethic and even your personality. 

Quantitative research tends to be sequential and patterned-oriented. Steps move in a logical order, so it becomes clear what the next step should be at all times. For most candidates, this makes it easier to devise a timeline and stay on track. It also keeps you from getting overwhelmed by the magnitude of research involved. You’ll be able to assess your progress and make simple adjustments to stay on target. 

On the other hand, maybe you know that your research will involve many interviews and focus groups. You anticipate that you’ll have to coordinate participants’ schedules, and this will require some flexibility. Instead of creating a rigid schedule from the get-go, allowing your research to flow in a non-linear fashion may actually help you accomplish tasks more efficiently, albeit out of order. This also allows you the personal versatility of rerouting research strategy as you collect new data that leads you down other paths. 

After examining the research you need to conduct, consider more broadly: What type of student and researcher are you? In other words, What motivates you to do your best work? 

You’ll need to make sure that your methodology is conducive to the data you’re collecting, and you also need to make sure that it aligns with your work ethic so you set yourself up for success. If jumping from one task to another will cause you extra stress, but planning ahead puts you at ease, a quantitative research method may be best, assuming the type of research allows for this. 

Professional Considerations

The skills you master while working on your dissertation will serve you well beyond the day you earn your degree. Take into account the skills you’d like to develop for your academic and professional future. In addition to the hard skills you will develop in your area of expertise, you’ll also develop soft skills that are transferable to nearly any professional or academic setting. Perhaps you want to hone your ability to strategize a timeline, gather data efficiently or draw clear conclusions about the significance of your data collection. 

If you have considerable experience with quantitative analysis, but lack an extensive qualitative research portfolio, now may be your opportunity to explore — as long as you’re willing to put in the legwork to refine your skills or work closely with your mentor to develop a strategy together. 

Academic Considerations

For many doctoral candidates who hope to pursue a professional career in the world of academia, writing your dissertation is a practice in developing general research strategies that can be applied to any academic project. 

Candidates who are unsure which dissertation type best suits their research should consider whether they will take a philosophical or theoretical approach or come up with a thesis that addresses a specific problem or idea. Narrowing down this approach can sometimes happen even before the research begins. Other times, candidates begin to refine their methods once the data begins to tell a more concrete story.

Next Step: Structuring Your Dissertation Research Schedule

Once you’ve chosen which type of dissertation research you’ll pursue, you’ve already crossed the first hurdle. The next hurdle becomes when and where to fit dedicated research time and visits with your mentor into your schedule. The busyness of day-to-day life shouldn’t prevent you from making your academic dream a reality. In fact, search for programs that assist, not impede, your path to higher levels of academic success. 

Find out more about SNU’s online and on-campus education opportunities so that no matter where you are in life, you can choose the path that’s right for you.

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How do I formulate the research design for my PhD thesis?

The research design must be done correctly otherwise the dissertation will not be successful. But how do I create a suitable research design for my topic? How can I even know that my approach is appropriate? How can I be sure that I will gain the right insight? Question after question. Here are the answers.

What is the research design anyway and why do I need it?

The research design is a detailed and systematic description of all steps needed to answer the research question. It shows your way of discovering new knowledge and consists of many elements. It helps to have the design handy when you start to organize your project. The more detailed the better and the more concrete your design, the easier it will be to implement.

What belongs in the research design?

• The research question, detailed questions and possibly hypotheses, the design of YOUR research model with relevant variables and factors
• Methods of data collection such as interviews, surveys, observations, documentations, measurements etc.,
• Decisions on the type of data, data sources and description of the samples,
• Tools for data collection such as interview guidelines or questionnaires etc.,
• Methods and tools for data evaluation such as statistical methods, content analysis etc.,
• Timetable for implementation.

When and how do I develop the appropriate research design?

You start with the design already in the proposal. This is your first draft. Prerequisites for the research design are the subject, research question and goal. They are your compass. You also need models on the topic for the classification of your variables and thus of the data. Here is an example: digitization in SMEs. How is digitization evolving inside SMEs? Operationalized research question: Which concepts do SMEs use when digitizing their business processes? You can create case studies and conduct an expert survey. You need models on SMEs, digitization and business processes. These models contain players, data, processes, activities, regulations, IT system and much more. Then you can collect data on the concrete concepts the SMEs studies used for digitization.

How do I check the research design for completeness and correctness?

Your design must meet 3 requirements:

• The methodology must definitely work and it must lead to an insight. A good test is whether the methods have already worked in other studies. Completely new methods are risky.
• The data must be obtainable, in quality and scope.
• The aids must be able to produce good results.

How do I test the research design?

• Simulate your research process. Are these the right detailed questions or sub questions?
• Do the answers to the detailed questions close the gap in research? Have I defined the correct data?
• Does this data match the objects in the detailed questions? Do I have the right data sources?
• Are there perhaps still better sources than the one I already found? Can the methods be used to evaluate the collected data?
• Has anyone ever described this in an article?
• How do I structure and describe the research design in the text?

Describe the research design in the text as a separate chapter in your dissertation.

To do so, you should answer about 20 questions. Here are the first so-called micro questions:

• What exactly is analyzed? Who or what is the focus of the analysis?
• What is the aim of the analysis? What insight will be gained?
• Which methods can be used for the analysis?
• What are the decision criteria for choosing a method?
• Why am I using a particular method?

Additional micro questions can be found in the 200 Days Dissertation Guide. Answer all these questions and your research design will be ready!

What sources do I need for the research design?

The best papers are those that have dealt with a related question. The methods are described there but are usually not comprehensive so you will also need method books. However, maybe an article is good but quite old. Don’t worry because methods are quite timeless. Papers on other issues are also interesting for the design if they have used certain suitable methods.

Method books are useful when it comes to the description. They are also more general and not just focused on one case.

How can I tell that my research design is "complete"?

The design is complete when the questions in the chapter ‘Research Design’ are answered and your supervisor knows the procedure and considers it useful. Actually this question is only finally answered when you have collected and evaluated all the data. Until then, minor changes in your research design are still necessary.

How do I work with the research design?

Just go for it! That's it! Let's go! Good luck writing your text!

Silvio and the Aristolo Team

PS: Check out the PhD Guide for writing a PhD in 200 days .

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Thesis and Dissertation: Getting Started

Welcome to the Purdue OWL

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The resources in this section are designed to provide guidance for the first steps of the thesis or dissertation writing process. They offer tools to support the planning and managing of your project, including writing out your weekly schedule, outlining your goals, and organzing the various working elements of your project.

Weekly Goals Sheet (a.k.a. Life Map) [Word Doc]

This editable handout provides a place for you to fill in available time blocks on a weekly chart that will help you visualize the amount of time you have available to write. By using this chart, you will be able to work your writing goals into your schedule and put these goals into perspective with your day-to-day plans and responsibilities each week. This handout also contains a formula to help you determine the minimum number of pages you would need to write per day in order to complete your writing on time.

Setting a Production Schedule (Word Doc)

This editable handout can help you make sense of the various steps involved in the production of your thesis or dissertation and determine how long each step might take. A large part of this process involves (1) seeking out the most accurate and up-to-date information regarding specific document formatting requirements, (2) understanding research protocol limitations, (3) making note of deadlines, and (4) understanding your personal writing habits.

Creating a Roadmap (PDF)

Part of organizing your writing involves having a clear sense of how the different working parts relate to one another. Creating a roadmap for your dissertation early on can help you determine what the final document will include and how all the pieces are connected. This resource offers guidance on several approaches to creating a roadmap, including creating lists, maps, nut-shells, visuals, and different methods for outlining. It is important to remember that you can create more than one roadmap (or more than one type of roadmap) depending on how the different approaches discussed here meet your needs.

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The Dissertation House Model: Doctoral Student Experiences Coping and Writing in a Shared Knowledge Community

Wendy y. carter-veale.

† Graduate School, University of Maryland, Baltimore County, Baltimore, MD 21250

Renetta G. Tull

Janet c. rutledge, lenisa n. joseph.

‡ Division of Education, Downstate Graduate Programs, Alfred University, Alfred, NY 14802

Associated Data

The Dissertation House model provides a voluntary, supplementary professional development activity that expands single-mentor and single-department approaches to create shared learning communities with multiple mentors across several academic disciplines. We find that participating in the Dissertation House increases the likelihood of retention and graduation for PhD candidates.

The problem of PhD attrition, especially at the dissertation-writing stage, is not solely related to mentoring, departments, or disciplines; it is a problem that affects the entire institution. As such, solutions require collaborative efforts for student success. Building on Yeatman’s master–apprentice model, which assumes mastering disciplinary writing in singular advisor–student contexts, and Burnett’s collaborative cohort model, which introduced doctoral dissertation supervision in a collaborative-learning environment with several faculty mentors in a single discipline, the Dissertation House model (DHM) introduces a model of doctoral dissertation supervision that involves multiple mentors across several disciplines. On the basis of more than 200 students’ reflections, we find that challenges in completing the dissertation extend beyond departmental and disciplinary boundaries. The DHM’s multidisciplinary approach preserves the traditional master–apprentice relationship between faculty and students within academic departments while providing an additional support mechanism through interdisciplinary collaborative cohorts. Using Thoits’s coping assistance theory and data from DH students over a 10-year period, the DHM incorporates Hoadley’s concept of knowledge communities to establish a successful dissertation-writing intervention for graduate students across doctoral programs. Using propensity score analysis, we provide in this study an empirical assessment of the benefits and efficacy of the DHM.

INTRODUCTION

The United States’ commitment to the advancement of research in science, technology, engineering, and mathematics (STEM) fields and broadening participation of people from underrepresented groups in those areas is evident in the number of initiatives developed by federal agencies such as the National Science Foundation (NSF) and the National Institutes of Health (NIH) over the past several years. In a recent press release from the Council of Graduate Schools ( CGS, 2015 ) on the findings from the Doctoral Initiative of Minority Attrition and Completion (DIMAC), the president of CGS remarked, “One of the striking lessons from this study is that the dissertation phase is particularly a critical time for students. Our country’s STEM workforce will lose a great deal of potential talent if we don’t help underrepresented doctoral students cross the finish line” (p. 2). As with the PhD Completion Project ( CGS, 2009 ) and the DIMAC Project ( Sowell et al ., 2015 ), the CGS website boasts “CGS Best Practice initiatives address common challenges in graduate education by supporting institutional innovations and sharing effective practices with the graduate community. Our programs have provided millions of dollars of support for improvement and innovation projects at member institutions” ( CGS, 2016 ).

Although CGS has labeled these practices “effective,” without rigorous theory-driven evaluations, these practices can simply be labeled exploratory, descriptive, and explanatory case studies. Evaluation of the impact of these supplemental professional development graduate degree–completion programs most often focuses on enrollment and graduation data of students who are directly funded by a particular grant-funded program, in comparison with overall student performance or performance before implementation of a given program. Many of the initiatives, such as establishing a doctoral student writing room, offering summer dissertation-writing residency fellowships, and hosting dissertation boot camps for students at the dissertation stage, have been described in the CGS Ph.D. Completion and Attrition: Policies and Practices to Promote Student Success ( 2010 ). While programs can present information on the successes of their student participants, it is challenging to address the counterfactual question that asks about levels of success without such programs or the levels of success of similar students who did not participate in the program.

According to Simpson (2012) , graduate education relies heavily on mentoring as the “engine” for teaching, especially in the sciences. Still, many agree that mentoring and thereby student-learning experiences vary by field and even within graduate programs. Where some mentors are extremely hands-on, others are hands-off or extremely busy. The challenge, as Simpson points out, is to create sources of inputs that strengthen (but do not compete with) the mentoring relationship and connect students to existing resources in their fields. We believe that mentoring is not the sole responsibility of the research advisor. We have previously addressed the concept of the “it takes a village” approach to mentoring in The University as Mentor: Lessons Learned from UMBC Inclusiveness Initiatives ( Bass et al ., 2007 ).

The objectives of this study are threefold. First, we provide a description of the application of a model of collaborative learning and mentoring that is distinguishable from both the traditional dyadic mentor–protégé (master–apprentice and advisor–student) relationship ( Yeatman, 1995 ) and the collaborative cohort model (CCM) of doctoral supervision ( Burnett, 1999 ) that have been applied within a single discipline. Second, we employ a mixed-methods approach to evaluate the effectiveness of the intervention on retention and PhD completion. Third, we provide a qualitative look at the impact of the intervention on students’ perceptions of the collaborative-learning and mentoring experience. Although dissertation advisors maintain primary responsibility for the supervision of doctoral students’ research, the findings are suggestive of the advantages of using a collaborative, interdisciplinary approach as a supplement to—not a replacement for—the traditional independent dyadic advisor–student supervision model.

Moreover, for policy makers within NSF and the federal government, state and local officials, and decision makers in the educational community, our mixed-methods approach provides empirical evidence of the effectiveness of the Dissertation House model (DHM) for collaborative learning and mentoring during the doctoral dissertation–writing stage.

The Dissertation House (DH) is a program of NSF’s PROMISE–Maryland’s Alliance for Graduate Education and the Professoriate (AGEP) developed to help underrepresented minority (URM) graduate students in STEM transition from PhD candidacy to completion. The NSF’s PROMISE AGEP is a University System of Maryland–wide program that includes a large number of students at the master’s and doctoral levels from the life sciences and research areas that link to the biomedical sciences in other STEM disciplines (e.g., bioinformatics in computer science, biomechanics in engineering, and mathematical biology). This study looks at the effectiveness of the DH program’s primary goal of achieving greater retention and PhD completion. As a research partner of CGS PhD Completion Project, we established the DH as a promising practice in the area of mentoring and advising for URM students in STEM doctoral programs who have reached the dissertation stage.

Historically, the success of the cohort model of doctoral supervision over the master–apprentice model (AMM; Yeatman, 1995 ) has been well documented ( Samuel and Vithal, 2011 ; Bista and Cox, 2014 ). Therefore, we will not explore the merits of the AMM in depth. Instead, we address the question of collaborative supervision and the development of communities of practice without the use of a predefined structured cohort. Moreover, in this study, we use the terms “knowledge community” and “communities of practice” interchangeably. According to Hoadley (2012) , a knowledge community will have a learning goal at the outset. A community of practice occurs naturally and typically will not have a learning goal; learning will emerge depending on the community’s function and role in society (p. 292).

Researchers have documented the use of cohort models for supervision of doctoral degrees within non-STEM disciplines such as education ( Norris and Barnett, 1994 ; Burnett, 1999 ; Mather and Hanley, 1999 ; Graduate Institute, 2006 ; Bista and Cox, 2014 ) and counseling education ( Burnett, 1999 ). Lewis et al . (2010) provide a comprehensive review of the literature and identify three well-documented trends in PhD attrition that may lead a university to implement a cohort model: 1) poor completion rates of doctoral studies ( Lovitts, 2001 ; CGS, 2004 ; Smallwood, 2004 ; Academy of Science of South Africa, 2010 ); 2) lack of support and feelings of isolation among doctoral students ( Ali and Kohun, 2006 , 2007 ; Unzueta et al ., 2008 ); and 3) pressure on students, faculty, and administrators to meet academic expectations ( Unzueta et al ., 2008 ; Lewis et al ., 2010 ; Bista and Cox, 2014 ). Most cohort models describe an organizational structure in which groups of students are bound together by a program of study within a single academic department. An inherent characteristic of cohort models is that students take the majority of their course work together ( Miller and Irby, 1999 ; Barnett et al ., 2000 ; Potthoff et al ., 2001 ). Cohort models that are built on the Huey eight-factor framework (as cited in Potthoff et al ., 2001 ) suggest that that there are eight dimensions to cohorts: 1) social interaction, 2) common mission, 3) group and individual learning, 4) cohesiveness, 5) collaboration, 6) academic success, 7) interaction with professors, and 8) retention. Bista and Cox (2014) already provided a comprehensive review of the literature on the advantages and disadvantages of cohort-based doctoral programs, and we refer the reader to that article for a more in-depth view. Burnett (1999) introduced the use of the CCM to the supervision of doctoral dissertations within one academic discipline, counseling education. In the CCM model, graduate students in counseling education who had completed their comprehensive exams enrolled in a semester-long faculty-guided support group. Results from the students’ evaluations showed that the structure and regular communication with faculty through meetings, emails, and a cohort newsletter were beneficial to their success ( Burnett, 1999 ). Rather than restructuring an entire discipline or university system, a new model based on the DH program offers a hybrid approach by suggesting that the supervision of doctoral dissertations can be accomplished by capitalizing on the positive aspects of cohort-based models ( Bista and Cox, 2014 ; Hartmann et al ., 2015 ). We introduce here a more organic, self-selected, and diverse multidisciplinary model of doctoral dissertation supervision, the DHM, which expands the single-mentor and single-department AMM and CCM approaches to develop shared learning communities with multiple mentors across several academic disciplines. This approach focuses on the social shared learning experiences of doctoral students in a broader context, beyond their specific academic discipline.

Bowen and Rudenstine (1992) and Liechty et al . (2009) provide a comprehensive literature review of nationwide trends of doctoral attrition and focus on both the barriers and facilitators that affect dissertation completion. They note that much of the attrition (17%) from doctoral programs occurs when students are at the “all but dissertation (ABD)” stage, described as the time after the course work is finished but before and during the dissertation-writing process ( Di Pierro, 2007 ). Challenges in the area of doctoral writing range from the unstructured nature of the dissertation stage ( Davis, 2000 ; Hockey, 1994 ) and social isolation ( Ali and Kohun, 2007 ; Jones, 2013 ), to the inability to make intellectual progress when the student becomes stuck ( Johnson, 2015 ). From the faculty advisor’s perspective, the greatest challenge to successfully completing a dissertation rests on students’ lack of knowledge about how to plan, implement, and write up a large-scale independent project ( D’Andrea, 2002 ). Liechty and colleagues suggest that “the dissertation phase of a doctoral program is a high-risk period for attrition and that targeted interventions at this juncture are warranted” ( Liechty et al ., 2009 , p. 482). The same authors document some of the interventions instituted in universities in the United States, Australia ( Burnett, 1999 ), and New Zealand ( Johnson and Conyers, 2001 ). They provide a description of the CCM model of supervision by Burnett (1999) and support group programs with counseling services ( Johnson and Conyers, 2001 ), and they even include institutional-level support programs such as the “campus-wide workshops for students on motivational strategies on time-management relevant to the dissertation” ( Liechty et al ., 2009 , p. 490) offered at the University of Maryland, Baltimore County (UMBC). Despite a comprehensive review of both the challenges and the interventions, the authors did not document any empirical evidence regarding the effectiveness of the interventions mentioned. At the end of the review, they find that, despite the growing body of research on the factors that affect overall PhD completion, few studies focus on the dissertation process. In addition, the authors directly called for “rigorous theory-driven evaluations, including experimental designs, to determine the effectiveness and “active ingredients” of specific individual, relational, and departmental/institutional interventions to promote dissertation completion” ( Liechty et al ., 2009 , p. 494). An empirical evaluation of the effectiveness of a dissertation-writing initiative, the DHM of doctoral dissertation supervision that we introduce here, answers that call and addresses the paucity of literature on the dissertation process.

Much of the literature on the dissertation process has provided case study explorations of dissertation-writing interventions that include dissertation-writing boot camps ( CGS, 2010 ; Simpson, 2012 ; Peters et al ., 2015 ; Sowell et al ., 2015 ). At best, the authors provide examples of promising practices to identify the institutional patterns and support structures that should enhance the dissertation process. This study extends the existing literature on this topic by providing a rigorous, theory-driven evaluation of one institutional-level intervention at the UMBC. The DH promotes dissertation completion with underrepresented students, many of whom are conducting research in the biomedical sciences, across academic departments. We begin with a detailed description of the DHM, followed by a propensity score analysis of the effectiveness of the DHM, and conclude with a qualitative look at the students’ feedback on their experiences with the DHM.

In an attempt to maximize social support for doctoral students, PROMISE AGEP introduced the DHM in 2006 to a group of African-American and Hispanic graduate students from several academic disciplines who were working on either their master’s theses, PhD proposals, or dissertations. A faculty member/administrator, who is also a member of an underrepresented group, served as a primary PhD facilitator, coordinator, and mentor (hereafter referred to as the “dissertation coach”) to the group of underrepresented students. The first DH pilot was held in 2006 during a 3-day weekend retreat in a rustic, remote, and rural mountain conference center in Berkeley Springs, West Virginia, with limited access to the Internet and cell phone reception. The name “Dissertation House” was based on the students’ cabin-style living quarters, with students working on their dissertations grouped together in selected cabins.

The DH project used as a foundation the successful Scholar’s Retreat at the University of Colorado at Denver (described in Smallwood’s 2004 article in the Chronicle of Higher Education titled “A Week at Camp Dissertation”), which provides the opportunity for intensive, focused, distraction-free, supervised writing time so that writers (with or without a PhD) can make significant progress toward the completion of their dissertations, theses, or writing projects. Although the DH began as a full-weekend experience in a remote area, current implementations take on different formats. These formats include DH sessions that are concurrent with multiday conferences at hotels, sessions designed for 2–4 days on campuses, and/or sessions that take place online. All versions of the DH facilitate students’ progression through the dissertation-writing process by providing the professional consultation, guidance, and support necessary for scholarly research and writing. The program focuses on reducing isolation by having students work as a cohort toward shared goals. Since its inception, more than 200 graduate students from three Maryland universities have participated in the DH; the majority ( n = 154) of participants attended UMBC.

Currently, the most common format is a 4-day session on a university campus (DH on campus). Students come to campus for consecutive days and stay from 9:00 am until 5:00 pm. Orientation on the morning of the first day includes introductions and goal-setting activities. Students share their goals in three different formats: written on posters that are affixed to the wall and viewable by all, orally through a shared goals session, and virtually via the DH website. In their introductions to the other members of their DH cohort, students must describe the project that will be tackled during the session and their goals for the full DH session. The facilitator provides feedback on the feasibility of each participant’s listed goals. Each student then logs into the DH website to post the revised, measurable goals for the day. The public declaration of the goals introduces positive peer pressure and accountability to the group and to the dissertation coach.

Each day, DH sessions include goal setting, two minilectures/professional development exercises, one-on-one coaching, and at least 5 h of uninterrupted writing (2 h in the morning and 3 h in the afternoon). Writing activities in the DH include writing sections of the proposal or dissertation, working on data analysis or data coding, reading and summarizing journal articles, writing up experimental results, deciphering feedback from their advisors, and addressing necessary revisions for resubmission. Overall, students are either working on their own while seated together in one room, or they are in one-on-one meetings with the dissertation coach, who serves as the facilitator for the DH.

The one-on-one meetings with the dissertation coach are confidential discussions focused on anything that prevents a student from writing. Discussions can involve personal issues at home, situations that occur in the laboratory, difficulties with graduate school, or lack of vision for managing the entire project. It is rare that a student is looking for an editor or feedback on the writing itself. The dissertation coach focuses on big picture items: moving the student from a stagnant position, slow or nonexistent writing, lack of clarity, or indecision. Since these DH discussions occur outside the student’s academic department, with the dissertation coach serving as an external mentor, discussions can gravitate from research or writing to honest issues about what might be stalling work on the dissertation.

Students receive additional support via 30-min minilectures after lunch and in the morning. These minilectures provide resources, tips, and strategies for PhD completion and are delivered by the director of the DH program, the dean of the graduate school, the university counseling services, the writing center, or others. Guest DH alumni, whose past circumstances mirrored current participants’ challenges, return to the DH to support current students. Topics may include managing stress, preparing for a dissertation/proposal defense, managing the relationship with your advisor, writing a literature review, updates on new policies, guidelines and graduation requirements, time management, and public speaking. These additional supports or minilectures provide students with information that may not be readily available within their academic departments.

The DH provides breakfast, lunch, and snacks at the same time every day and adheres to a strict schedule for the duration. This schedule helps students understand the integrity of a writing schedule and the value of time management. Providing food helps create ideal conditions for working on the document without the distractions of responsibilities such as preparing meals, cleaning, and washing dishes. The last DH day ends with a roundtable discussion/assessment of what students have learned and what tools they will use in the coming weeks when they are not writing under these ideal conditions.

The DH occurs during winter or summer breaks when the students most likely will not have other academic or teaching responsibilities. Because they are on campus, the participants can check in on their experiments either before the 9:00 am start of the DH, at lunch hour, or after the DH is finished at 5:00 pm. When time permits during the DH, students are able to take time away to meet with mentors on campus. After the workshop ends, some students elect to continue communicating online with the dissertation coach or continue to set goals and keep daily accountability via the DH website.

Using the framework established by Hoadley (2012) , the DHM could be considered a shared knowledge community using his description of a “model of learning—learning in which people, through a process of legitimate peripheral participation, take up membership and identity with a community which serves as the home of the shared practices” (p. 299). In an attempt to maximize support for its doctoral students without changing the overall structure of any graduate programs at UMBC, the DHM embraced the traditional AMM and supplemented the supervisor’s role with a concurrent voluntary support mechanism for students based on a CCM. The traditional learning AMM model characterizes the master–apprentice relationship as the central component of the learning process. Specifically, for graduate students who are developing a dissertation proposal or writing a dissertation, the dissertation chair/advisor/supervisor’s involvement in the dyadic process is essential. During the writing process, the document is closely monitored to its completion. Student experiences with the DH are not in competition with the role of the advisor, but act as other positive components in the learning community available for student success.

Eligibility

The DH is free and open to graduate students from all disciplines. The applicant must be an advanced PhD graduate student enrolled either full-time or part-time at the institution where the campus event is being hosted. Graduate students must have already selected a research topic before they can participate in the DH (a requirement that was instituted early on, because we found that, without it, students could not fully benefit from the experience). Thus, each applicant submits a written application, documenting his or her current stumbling blocks along with a brief synopsis (maximum two pages) of the research project he or she wishes to work on during the DH period.

Selection Process

Up to 18 students are selected to participate in each DH. Applicants are preferentially selected if they 1) plan to finish the PhD within the next 6 months; 2) are working on a dissertation rather than a proposal; and 3) meet the application requirements and deadline. Early on, we found that students who already had a dissertation topic, had started writing the proposal or dissertation, were willing to commit to attend the entire 4 days of the DH, and were willing to write the two-page application proved most able to benefit from the services offered at the DH.

ASSESSING THE DHM

This research draws on two sources of data: 1) institutional data from UMBC ( N = 1890) for PhD cohorts from 2000 to 2012 (see Supplemental Material Table A2), some of whom have participated in the DH ( n = 154) and others who have not ( n = 1736); and 2) written evaluations from DH participants who were students enrolled in three AGEP Alliance institutions from 2007 to 2013 ( N = 267). The UMBC PeopleSoft database contains information on the demographic and academic data for each participant, including race, gender, citizenship, graduate program, cumulative grade point average, date entered, and date departed.

Study 1: Retention and Graduation

Study 1 addresses the primary research question as to whether an intervention such as the DH was able to meet its main objective of helping students persist until they received their PhDs. As with many graduate program interventions, the challenge has been to answer the following question: Would individuals who did not participate in the broadening participation programs also likely achieve similar results?

We did not randomly assign students to the DH, so we are unable to address this question without using some statistical techniques. In other words, in a true randomized experiment, students who participated in the DH would be considered the treatment group and would have been randomly assigned to the DH. The students who were not assigned to attend would be considered the control group. The voluntary nature of program participation complicates the issue of identifying the true “treatment effect,” that is, the effect of participation. Because students apply and self-select to participate in the DH, a failure to adequately control for preexisting differences between retained and graduating students leaves open the possibility that preexisting characteristics rather than DH may be the cause of the postintervention outcomes. Simply comparing the percentages of graduates in each group or comparing the mean difference in time to degree does not adequately tell us much about the effectiveness of the program. Table 1 shows that 42% of the non-DH group graduated compared with 76% of the DH group. Likewise, the mean time to degree was calculated to be M = 5.4 (SD = 1.89) and M = 5.8 (SD = 1.90), respectively.

Comparison of baseline characteristics between DH and non-DH participants at UMBC (original data set)

a Not included in the covariates for propensity score.

Social scientists prefer regression methods for estimating intervention impacts using comparison group data. However, the use of propensity score matching (PSM) as originally proposed by Rosenbaum and Rubin (1983 , 1985 ) has become increasingly popular over the past few decades (see Thoemmes and Kim, 2011 ), notably in the areas of education ( Hong and Raudenbush, 2005 ; Wu et al ., 2008 ; Hughes et al ., 2010 ) and evaluation research ( Hong and Raudenbush, 2005 ; Hughes et al ., 2010 ). As in the medical literature, PSM is a tool for approximation of a randomized trial and for reducing selection bias in observed studies. PSM is a nonexperimental evaluation method used to compare what would have happened to similar graduate students who did not participate in the DH. In this case, PSM techniques are designed to measure the impact of participation among similar individuals for whom the only difference between them is the treatment outcome, which is retention or graduation.

Heinrich et al . (2010) note that, to determine whether matching is likely to reduce selection bias, it is crucial to understand under what conditions it is most likely to work. First, the variables for both treated and untreated must be observable to the researcher (p. 15). This assumption is known as the conditional independence assumption, meaning that “the potential outcomes are independent of the treatment status, given X. Or in other words, after controlling for X, the treatment assignment is as good as random” (p. 16). Second, the common support condition must be met, meaning that, to calculate the difference in mean outcomes for each value of X, there must be a positive probability of finding both a treated and untreated unit to ensure that each treated unit can be matched with an untreated unit (pp. 15–16). As with many causal inference methodologies, PSM also requires a large sample size to gain statistically reliable results. When the treatment group in relatively small, this is particularly true for PSM due to the tendency to discard many observations that do fall under the common support.

PSM balances out the covariates across the treatment and control group based on a single dimension. The single dimension is the probability that a unit in the combined sample of treated and untreated units receives the treatment, given a set of observed variables. If all information relevant to participation and outcomes is observable to the researcher, the propensity score (or probability of participation) will produce valid matches for estimating the impact of an intervention. Therefore, rather than attempting to match on all values of the variables, cases can be compared on the basis of propensity scores alone ( Thoemmes, 2012 , p. 7). Zanutto (2006) suggests that, when compared with regression analysis, “this ability to easily check that the data can support comparisons between the two groups is one of the advantages of a propensity score analysis over a regression analysis” (p. 81). According to Titus (2007) , unlike the more popular ordinary least-squares regression, PSM addresses the issue of self-selection bias and allows for a decomposition of treatment effects into three different components: 1) the average treatment effect (ATE), which measures the mean impact of the program across all individuals in the population; 2) the average treatment effect on the treated (ATT), which measures the impact of the program on those who participated in the treatment; and 3) the average treatment effect for the untreated, which evaluates the impact that the program would have had on those who did not participate.

This study focuses on a quantitative analysis of the institutional data provided to us by the Office of Institutional Research, Analysis and Decision Support at UMBC. The data set contains demographic PhD graduate student information tied to measures of PhD completion and attrition for cohorts of graduate student enrolled from 2000 to 2012. DH participation/attendance lists matched to these data confirmed the number of distinct UMBC DH participants ( n = 154).

Table 1 presents a distribution of the doctoral student cohorts entering UMBC from 2000 to 2012. More importantly, it presents a comparison of the baseline characteristics between DH and non-DH participants at UMBC. To be clear, students starting in 2000 would have been in their seventh year when the on-campus DH program started in 2007 at UMBC. The study population ( N = 1890) consists of doctoral students, a majority (87%) of whom were enrolled in STEM programs. Each student was defined as either a PhD graduate ( n = 853), a PhD continuing student ( n = 451), or a student who left his or her graduate program and/or the university ( n = 586). These categories are mutually exclusive; students who transferred from one program to another within the university were counted as continuing. The majority of students were well distributed across STEM programs (17.5% engineering, 10.8% life sciences, 40.7% physical sciences, 24.5% social sciences). The physical sciences category includes computer science, information science, and human-centered computing. A small percentage of doctoral students (6.5%) were in the humanities (only one of the 24 doctoral programs at UMBC is considered non-STEM based on NSF classification). Approximately 14% of the students belonged to a URM group (9.7% black/African American, 1.3% Native American, 2.5% Hispanic). Males constituted a slight majority at 52%.

Our first task was to calculate propensity scores and establish a control group. We used binary logistic regression to generate propensity scores predicting the likelihood of participating in the DH (see Supplemental Material Table A.1). Afterward, to reduce the effects of confounding factors, we matched each DH participant to a nonparticipant based on the probability or likelihood that the non-DH student would have participated in the program. The specified logistic model is designed to capture the propensity to participate in the DH program; DH participation was the dependent variable coded 1 for DH participation and 0 for nonparticipation. The included covariates were based on the selection criteria, which gave priority to advanced graduate students (cohort year = categorical), STEM (dichotomous; 1 = STEM, 0 = non-STEM), gender (dichotomous; 1 = female, 0 = male), and URM students (dichotomous; 1 = underrepresented, 0 = not underrepresented). Also included among the covariates was a series of dummy variables (life sciences, humanities, physical sciences, engineering, and social sciences) to account for differences in completion rates by broad fields. Note that, when an outcome is measured using propensity scoring, the covariates must be common across both groups (participants and nonparticipants). Using student information data, we provide information on the observed characteristics for both groups. Matching by propensity scores assumes that all differences between individuals affecting treatment and outcome can be captured by observable pretreatment characteristics.

After calculating the predicted probabilities, we conducted a matched analysis to estimate the program effect. Researchers have several balancing techniques (conditional, stratified, matching) to choose from. Herbert and Yao (2009) provide a review of each technique, along with the advantages and disadvantages of each option. We selected matching because of the interest in comparing the likelihood of graduating between doctoral students who participated in the DH and those who did not. Without going into the subtleties of each technique, we chose the simplest case of matching (1:1), wherein one DH student is matched to one non-DH student with similar characteristics. While there are many possible matching techniques that could have been selected, we used Dattalo’s (2010) on-line syntax for 1:1 matching (nearest-neighbor matching) in SPSS. The large sample size of the nontreatment group allowed for matching without replacement, which means that one score in the treatment group could match one person in the nontreatment group with the nearest matched score. For a more detailed discussion on PSM using SPSS, see Thoemmes (2012) or Dattalo (2010) .

To assess our matching results (see Supplemental Material Appendix 3), we calculated standardized differences for each variable in the logistic model ( Austin, 2011 ). Tables 1 and ​ and2 2 present a comparison of the distribution covariates for both the control group (non-DH participants) and the treatment group (DH participants) before and after being matched, respectively. In both tables, the absolute standardized difference (ASD) column provides a measure of the balance of covariates. Researchers suggest that ASD values greater than 10% indicate the presence of imbalance ( Linden et al ., 2005 ; Austin, 2011 ; Rankin, 2014 ). Table 1 provides a comparison of the distribution of DH participants to the entire distribution of doctoral students in the same cohorts. For example, 14.9% of the DH participants majored in life sciences compared with 10.54% of non-DH participants. Thus, the standardized difference is 0.11 (or 11%), which suggests that the distribution is imbalanced. After matching, the results in Table 2 show much lower absolute values on each covariate. For example, the same covariate for life sciences now has an ASD value of 0.08 (or 8%), reducing the selection bias by 32%. The last columns in Table 2 show what percentage of the selection bias is being reduced by introducing matched data.

Comparison of baseline characteristics between DH and non-DH participants at UMBC (matched data)

a Not included as a covariate for PSM.

Once the covariates’ balance had been established, we measured what researchers in the PSM literature refer to as the ATT. As noted earlier, the ATT is the average gain from treatment for those who actually were treated. The results in Table 2 show that ∼76% of DH participants graduated with a PhD compared with 40% of non-DH participants. To measure the ATT, we used the relative risk (RR) ratio, which assesses the difference in outcomes for DH participants and non-DH participants (see results marked with an asterisk in Table 2 ). For our purposes, the RR tells how much more likely a student is to graduate based on whether he or she participated in the DH. An RR of 1 indicates that the outcomes did not differ in the two groups. Our calculated RR of 1.92 indicates that the DH participants had a probability of graduating at least 1.92 times that of nonparticipants (the control group). In other words, participating in the DH increases the likelihood of graduating by 92%. Similarly, we calculated a RR for retention to be 1.64, which increases the likelihood of retention by 64%.

Whereas RR reflects a ratio between two conditions, we wanted to know what the RR increase is for students who participated. In other words, what is the effect of the treatment? To find this answer, we look to the attributable risk (AR), calculated as the ratio or percentage of an event in one group minus the same within a comparison group. Thus, the AR for this study was 0.479 for graduation and 0.392 for retention. In sum, 47.9% of graduation and 39.2% of retention is attributable to the DH experience.

Limitations of PSM

As with any evaluation using observational data, or “ex post facto data” (after the fact data), we highlight some common limitations. First, in the absence of experimental data, the researcher presumes that all biases and confounding variables have been adjusted for in the model. However, this assumption cannot be truly tested absent a randomized study; biased estimates are the norm for observational data. Although random assignment is considered the gold standard research design tool for evaluation, it is not always feasible, politically expedient, or ethical to implement.

Similar to omitted variable bias in regression analysis, one limitation specific to PSM is that the researcher cannot balance the groups based on unobserved (unmeasured) factors. For example, we do not have measures of household living arrangements or writing anxiety, nor do we have measures of other responsibilities such as employment, children, or teaching. These factors could influence likelihood of participation in the DH. Nonetheless, these unobserved factors could occur randomly in both groups.

According to the CGS DIMAC Report , women and URM students have high levels of attrition from STEM doctoral programs. Some might argue that students with greater motivation to finish are more likely to participate in the DH. Thus, unmeasurable variables such as motivation and grit might be the “hidden bias” missing from this study. From experience, we find that the most motivated URM students do not rush to sign up to participate in the DH. The opposite is true; application essays and summative program evaluations indicate that those who are “stuck” or struggling with a lack of motivation tend to participate in the DH. All the same, Heinrich et al . (2010) , among others, suggest that the programs’ more explicit eligibility criteria should be used as variables under consideration for matching purposes.

Second, for researchers using ex post facto data, the analysis could be hindered by missing data. The deletion of cases with at least one missing response (e.g., listwise deletion) could result in a further reduction of available cases. Other issues of residual bias, precision, and lack of independence across units are beyond the scope of this study and have been addressed by Hill (2008) .

Study 2: Student Qualitative Perspectives on Social Support and Coping Assistance

To understand the role of social support in completing a doctoral degree, the authors culled information from open-ended questions on 267 evaluations completed by students who participated in the DH program. We analyzed answers of three open-ended questions to explore the shared experiences of doctoral students involved in DH and to investigate whether these successful experiences could be understood using the coping assistance conceptual framework ( Thoits, 1986 ). Beyond gathering basic demographic and programmatic information, the evaluation had a series of yes/no questions followed by open-ended questions that asked graduate students about their experiences in the DH:

• Did the Dissertation House experience help you to progress with your dissertation? If yes, how significant was your progress? If no, what could have been done differently?
• Was having the “Dissertation House” a good use of funding? If yes, please explain. If no, please provide ideas for better ways to use the money that was spent on this experience.
• What are suggestions for improvement that will reduce your “time to degree”?

The participants in the study were graduate students from three University of Maryland campuses: University of Maryland, College Park ( n = 87), University of Maryland, Baltimore ( n = 19), and UMBC ( n = 157). Based on the number of evaluations returned, Table 3 presents a profile of the DH participants that includes cohorts who participated in DH sessions across Maryland between 2007 and 2013. Whereas the other institutions in the PROMISE AGEP sponsored a DH sporadically or once a year, UMBC sponsors the event twice or sometimes three times a year based on funding availability. At UMBC, beyond the regular twice-yearly DH, we introduced in October of 2010 the Employee DH for employees of the university and nontraditional students who were working on their dissertations and seeking help and time to write. This explains the variation in number of participants from each campus. The majority of participants who returned evaluations were women (79%) and members of an underrepresented group (55%). These percentages reflect a convenience sample to provide contextual information from those who experienced the DH. That the majority are women and come from underrepresented groups reflects targeted groups the DH was designed to attract. For the qualitative portion of the study, our findings reflect the experiences of DH participants and do not necessarily generalize to the experiences of nonparticipants. It is important to note that the numbers in Table 3 are based on the number of evaluations returned, not necessarily the number of students who participated. Moreover, because students are allowed to participate more than once, and evaluations are anonymous, these numbers may not reflect unique individuals.

Demographics of DH participants from 2007 to 2013 ( N = 267)

These evaluations represented doctoral student assessments from a variety of academic disciplines that could generally be classified under STEM ( n = 133, 49.8%) and non-STEM ( n = 59, 22.1%) for those who specified a discipline on the evaluation form. Twenty-seven percent of students did not account for their academic disciplines on the evaluation forms. The respondents were primarily female ( n = 209) with a small group of male ( n = 56) participants. The majority (55%) of the respondents were members of underrepresented racial minority groups (black/African American = 121, Hispanic = 24). Results from evaluations show that nearly all the students affirmed that the DH was helpful in making progress with the dissertation (98.1%), that it was a good use of funding (98.9%), and that they would recommend having the DH again (99.2%). (See Table 3 .)

The first question, on how the DH experience helped progress with the dissertation, resulted in the largest number of responses. One of the most important themes to emerge from the evaluations was that DH helped students work through a state of writing paralysis, lack of direction, or lack of any significant progress. Progress means different things to different students; however, most of the focus is either on time management, project management, or writing. To answer the question, one student wrote, “I had nothing on paper, I now have an overall outline and I feel that I will be able to get finished now. This workshop has helped me become ‘unblocked.’ I have been at UMBC for 4 ½ years and was in danger of just quitting.” An important outcome of the daily goal-setting exercise is learning how to set measurable goals and celebrate small daily successes. One student who had been stuck for 2 months said, “It was good to see others struggling and I started making more realistic expectations for myself. The advice was invaluable.” In response to this same question, another student shared a similar sentiment about making significant progress: “I have achieved more this week than I would have in a typical month. Having professionals on-hand to assist me, and having structured time to do the work has been invaluable.” Commenting on the shared experience, one student noted that “the opportunity to share our progress with other students” helped a lot and singled out the on-site coach as a valuable aspect: “It helps a lot for guidance for sure. The positive encouragement while we are working together brings a lot of benefit.”

Although issues such as writer’s block might seem to have an obvious solution, graduate students are sometimes reluctant to reach out and ask for help. Issues with writer’s block might be research related, related to the anxiety of writing itself ( John-Steiner and Mahn, 2003 ) or perfectionism, or related to the fear of being wrong. The DH provides coping assistance by providing a safe place and a shared knowledge community of doctoral students all facing the similar challenge of making progress on the dissertation. In AMM, the struggling graduate student might be forced to acknowledge to his or her advisor that he or she is stuck or uncertain about what to write. AMM relies on the advisor’s ability to motivate and encourage the student to write. Coping assistance theory ( Thoits, 1986 ) argues that the student might be unwilling to ask for help under this circumstance for a number of reasons, including fear that the advisor might be an unsympathetic helper. That advisors had to write their own dissertations in the past does not provide a shared situational experience whereby graduate students might willingly seek help with dissertation writing. The advisor is seen as an expert in the student’s research area, not an expert in writing or dealing with emotional issues like stress, family troubles, or burnout. Moreover, the student might not be able to identify the underlying shared stressor unless the advisor had revealed his/her own struggle with writing the dissertation. Although students are from different disciplines, they identify with one another.

Coping assistance theory ( Thoits, 1986 ) suggests that misery loves company. Thus, the second theme to emerge was social interaction and social support, especially when students were able to meet students from other campuses in the PROMISE AGEP at a weekend retreat. Sometimes students in STEM from URM groups are “the only one” in their departments or colleges. In addition to coping assistance based on a shared experience, students suggested that DH made plain what is otherwise a tacit understanding between the faculty and the student about what he or she should be doing to complete the degree. A student commented by writing, “Dissertation House provides students with access to information, resources, tips etc. that is usually not readily available to a student. There seems to be an assumption among faculty, across disciplines and campuses that student should just know ‘these things.’ … And because most participants are so productive during the program [ sic ] they are more likely to continue to utilize these techniques in the DH to achieve or accelerate their progress.”

Many students commented that the program provided them with valuable tips about organizing their dissertations. While students may have presented parts of their dissertation topic at a conference or prepared chapters for submission, perceiving it as one cohesive document can become overwhelming without some form of organization, be it for the references or the document itself. A student wrote, “I did not have my thesis organized into one location or structured into a format resembling a thesis, now I have a solid first draft.” Further assistance came in areas beyond the nuts-and-bolts writing phase. Another student said DH taught him useful tips “about defending, [and] interacting with the committee.” One part-time student wrote, “I am now more motivated to write a contract with myself for finishing and creating a schedule.”

Much of a STEM student’s time in graduate school is spent in the lab conducting experiments, and many advisors might think that conducting successful experiments is the most time-consuming and challenging aspect of graduate research. The underlying assumption is that once the student is able to get successful results, the student will be able to write up the results quickly and turn in a comprehensible document in the form of a publishable paper or doctoral dissertation. The challenge of writing itself is rarely considered a hindrance to getting results. Nonetheless, students in the DH provide feedback about the writing process and the lack of time to write. The DH provides a space to write, but support from the student’s advisor/supervisor provides the student with the opportunity to write. Several students discuss the benefits of getting time away from the lab to concentrate on writing. One student wrote, “This program was very significant to me in helping me realize how important writing every day is to the completion of my degree.”

Responses to the question concerning whether the DH was a good use of funding echoed many of the positive benefits that have already been discussed earlier. There were no specific suggestions on better use of the funding; however; the main theme emerging to justify the use of funding is that DH helps students complete the degree. One student in particular wrote, “Providing funding for a dissertation house was a great idea. The outcome of ensuring all PhD students that participated in the program will complete their dissertation is priceless.” A subtheme is the time the DH is estimated to save in completing the degree. Another student concurs: “I was able to do in one day of DH what it would have taken me at least a whole week to accomplish.… it helped us to speed up and clarify our thinking so we can finish ASAP ‘an investment in our progress.’” Others commented on the personal value of a program that helps them achieve their goals. A student who is working in another state while completing her dissertation summed up her experience by writing: “It has helped to know that I am part of a ‘group.’ Knowing that there are other students going through the exact same experience has made me feel that this is doable! I know I can get that PhD!! I strongly recommend Diss House to everyone! I even flew 3000 mile and paid a lot of money to be here, flight, hotel, rent-a-car, food etc) and it was absolutely worth it.”

Information on what students think could reduce their time to degree provides the staff with new ideas that can be incorporated into future DHs. Some students wanted the same services offered at the department level ( Golde, 2005 ), and others wanted more of the program, from extended time each day to offering the program on a more frequent basis. We have since provided the opportunity for students who want to stay in the room after the mentored program ends at 5:00 pm. We found that students established social and collegial networks that extend beyond the 4 days of the DH. Small groups of three to four students often continue to meet regularly on their own, (on or off campus) to work on their dissertations until completion.

The challenges that students face in completing the dissertation often extend beyond department and disciplinary boundaries. Whereas the CCM introduced doctoral dissertation supervision in a collaborative-learning environment with several faculty mentors in a single non-STEM discipline, the DHM extends this model across several disciplines by introducing a model of doctoral dissertation supervision that involves an external dissertation coach and multiple mentors. Unlike the CCM, this multidisciplinary approach of doctoral dissertation supervision preserves the traditional master–apprentice relationship between faculty and students within academic departments while providing an additional support mechanism through interdisciplinary collaborative cohorts. Social isolation is common at the dissertation-writing stage ( Golde, 1998 ; Burnett, 1999 ; Lovitts, 2001 ; Ali and Kohun, 2007 ). However, for underrepresented students working in laboratories, social isolation might be the norm for their entire graduate careers. Hortulanus et al . (2006) described social isolation as a “lack of meaningful relationship” that negatively impacts an individual’s quality of life. Ali and Kohun (2007) suggest that, in graduate school, “meaningful relationship” might refer to a social contract among students as well as with faculty members. The DHM builds on lessons learned through the PROMISE program ( Tull et al ., 2012 ) on the importance of creating an environment of inclusiveness that promotes social support and connections. Moreover, the ongoing support of the dissertation coach and the online DH website blog help to reduce the sense of isolation well after the DH event ends.

Given a dearth of scholarship on both PhD completion at the dissertation phase and the lack of rigorous evaluation of programs designed to enhance STEM education for URM doctoral students who are at the dissertation phase, this article seeks to provide some insights into the potential for mixed-methods approaches. First, using a counterfactual analytical framework, this investigation revealed how the use of PSM techniques can be used by institutional and other investigators to help address growing concerns about the lack of “evidence-based” policies and practices in higher education. The study described here provides support for the DHM as an effective intervention that combines one-to-one doctoral dissertation supervision with an interdisciplinary support learning community for students from underrepresented groups.

Second, growing concerns about constrained resources and the lack of empirical evidence to justify institutional support to sustain some policies and practices in higher education that might have been established with grant funding require a mixed-methods approach. This approach provides researchers with a wider array of analytical tools for conducting comparison studies with observational data. Whereas the results of propensity score analysis indicate that those who attended the DH had higher rates of graduation, the qualitative results provide further support for the effectiveness of the DHM, the value that students place on the DH experience, and the impact it has on both their progression and satisfaction.

Supplementary Material

Acknowledgments.

We recognize support from the staff researchers in the Office of Institutional Research, Analysis and Decision Support at the UMBC. We acknowledge funding and support from the CGS PhD Completion Project and the DIMAC Project. The DH was developed as a project for PROMISE–Maryland’s AGEP. PROMISE is funded by the NSF Directorate for Education and Human Resources (EHR), Division of Human Resource Development (HRD). Current projects are supported by Collaborative Research: AGEP-T: PROMISE AGEP Maryland Transformation 1309290, 1309264, and 1309256. Foundational projects were developed and implemented under HRD grant 0202169: AGEP: Maryland’s Alliance for Graduate Education and the Professoriate; HRD grant 0639698: PROMISE: Maryland’s AGEP; and HRD grant 1111217: PROMISE Pathways.

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Starting Doctoral Dissertation Journey with a Solid Research Problem Statement – A Four Stage Framework

2021, International Journal of Doctoral Studies

Aim/Purpose: Provide methodology suggesting steps to doctoral mentors to work with students in constructing their research problem statement in their dissertation. Background: Doctoral students face difficulties writing their dissertation and they begin by writing the research problem statement. Methodology: This paper uses a framework widely used to describe student adjustment to graduate studies in general and to doctoral program in particular. Contribution: This study provides a framework to mentors/advisors that is helpful in guiding the students to writing their research problem statement. Findings: Writing a research problem statement is difficult by itself. Following a methodological approach suggested in this study could help with writing it. Recommendations for Practitioners: A methodological approach in writing the dissertation is helpful to mitigate the difficulties of writing the dissertation. Our study tackles difficulties with writing the research problem statement. Re...

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D. Anthony Miles

Most researchers and doctoral students have considerable trouble writing a problem statement with their research projects dissertations, and theses. We have to ask ourselves, why that is? Why are we encountering doctoral students and researchers that have trouble writing the problem statement? Why the disconnect? This is a common occurrence with doctoral students. We would think the required research methods course would address this. However, that is not case. Thus, this article addresses, discusses and illustrates how to develop a problem statement. This article will provide a model and template for developing a problem statement. As basis for a research study, it is important the researcher and doctoral student know how to construct a problem statement. First, this article discusses the problem statement. Second, this article provides a model as a for developing a problem statement. Last, the article provides examples of problem statements using the conceptual template. Doctoral Student Workshop: Problem Statement Development and Strategies

InSITE Conference

shardul pandya

Aim/Purpose: Develop instructional rubrics that help in writing and evaluating the doctoral dissertation research problem statement. Background: This is a follow-up study. In the first paper, we introduced a model for writing a research problem statement that takes the students through four phases to complete their writing. In this paper, we introduce an instructional rubric to be used for helping to write the research problem statement. Methodology: This paper builds on the previous model and adds Socratic questions to trigger critical thinking to help with writing the research problem statement. Contribution: Developing the instructional rubrics is the contribution of this study. The instructional rubrics can help with writing the research problem statement. Findings: Writing a research problem statement is difficult by itself. Following the methodological approach suggested in this study will help students with the task of writing their own. Following this instructional rubric wi...

Scientific Research Publishing: Creative Education.

Dr. Qais Faryadi

Thesis writing is a skill that every PhD candidate must acquire to convey his or her research findings clearly. The main objective of this paper is to facili- tate the thesis writing process so that PhD candidates understand what a PhD thesis is and can write their thesis correctly and scientifically. The methodol- ogy used in this research was descriptive as it discusses and describes the var- ious parts of thesis writing process and explains how to do it in a very simple and understanding language. As thus, this article outlines the various steps of thesis writing to guide the PhD candidate so that the task of PhD thesis writ- ing becomes manageable and less daunting. This research is a useful roadmap especially for students of the social sciences studies. Further, in this paper, research procedure and thesis writing strategies are explained in a simple manner. This paper adopts a how-to approach when discussing a variety of relevant topics, such as thesis introduction, types of introductions, introduc- tion statements, problem statement, research questions, hypothesis and con- tributions of the study. This paper has 5 parts: Introduction, Literature Re- view, Methodology, Results and Conclusion. The introduction chapter is dis- cussed in this paper. I will discuss the rest as a series in the future.

Dr Dare E Ajayi

The complexities and diversities of human nature and challenges necessitated the need to discover and identify ways to solving and meeting human and academic problem needs. The existence of problems gave rise to the the need for research. The book takes researchers and students through the latest and best research practice through the adoption of simple, adoptable and practicable research models for academic and contemporary research writing.

Families in Society: The Journal of Contemporary Social Services

Joanne Riebschleger

An author with a new doctorate shares lessons learned about writing a dissertation. Lessons include (1) there are few sources to guide one on how to write a dissertation; (2) it is easier to critique research than to create research; (3) dissertation writing is an evolutionary communication process; (4) criticism is good; (5) dissertation writing produces a product; (6) hypotheses rule and methods matter most; and (7) less is more. Additionally, the author asserts that (8) writing for dissertation is an apprenticeship experience that prepares one for writing for publication.

instituteonteachingandmentoring.org

David Schuldberg

The Teachers College Record

Abdul Hakeem

Abdilahi Adam Mohamoud

Journal of Information Management and Practices

Nareen Hashmi

The purpose of this study was to investigate the challenges faced by research students during thesis writing. Qualitative research methodology was adopted to conduct this study. Research questions were developed to achieve the research objectives. The population of the study was comprised of research students working on their thesis or recently passed out. Purposive sampling technique was adopted. The research tool of the study was interview guide. Interviews were conducted with a sample of 40 research students studying in Library and Information Science/Information Management Departments in public and private sector universities of Punjab province. Thematic approach was adopted to analyze the interview data. The results showed that it is challenging for research students to select the topic for research, develop focus on study, acquiring knowledge of information sources, developing online searching skills, developing data analysis skills and time management skills. They also faced ...

Otolaryngology online

Balasubramanian Thiagarajan

This book has been authored with PhD scholars in mind. The author believes that this would be a good starting point for these scholars. The following chapters have been included: Chapters: 1. Introduction to Thesis Writing 2. Choosing a Topic and Developing a Thesis Statement 3. Conducting Literature Review 4. Methodology and Data Collection 5. Writing the Introduction and Background of Your Thesis 6. Presenting Your Findings and Analysis 7. Writing the Discussion and Conclusion of Your Thesis 8. Formatting and Structuring Your Thesis 9. Referencing and Citations 10. Defending Your Thesis: Preparing for the Viva Voce 11. Revising and Editing Your Thesis 12. Time Management and Staying on Track 13. Overcoming Writer's Block and Staying Motivated 14. Using Technology and Tools to Enhance Your Thesis Writing Process 15. Publishing Your Thesis and Next Steps. 16. Data visualization 17. Statistical tools This book also contains tips about choosing an ideal thesis topic. It also warns the student about the various pitfalls involved in choosing a research topic. The topic on referencing citations would be very useful for even a novice researcher. This book also introduces the researcher to the myriad of software tools that are available to the scholar. Using these software tools would make the life of the researcher that much easier.

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• Design and Development Research (DDR) For Instructional Design

Overview of Design and Development Research (DDR) For Applied Doctorate Students in the Instructional Design Program

Types of design and development research, 3 stages in design and development research, data collection methods and sources of data in ddr.

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The purpose of this quick guide is to assist Applied Doctorate students in the Instructional Design Program in determining the best methodology and design for their Applied Doctorate Experience (ADE) dissertation. The guide covers intended target audience, an overview of Design and Development Research (DDR), types of DDR research including product, program, tool research, and model research, 3 stages providing alignment of DDR with NUs Applied Doctoral Record (DDR) deliverables, examples of problem, purpose, and research questions for DDR research, and suggested references. 

Target Audience: Doctoral Students in Instructional Design in the ADE program

This quick reference guide will aid doctoral students in instructional design challenged with deciding on what type of applied research study they want to do for their dissertation.

Overview of Design and Development Research (DDR) Methods

At the core of the instructional design and instructional technology and media field, is the design, development, implementation and evaluation of instructional products, tools, programs, models, and frameworks.  In many ways DDR is like Action Research (Goldkuhl, 2012), however, there are many differences. DDR research allows instructional designers a pathway to test theory, models, and frameworks and to authenticate practice. The focus of DDR is on the use, design, development, implementation, and evaluation of products, tools, programs, and models using instructional design models and frameworks. Richey and Klein (2007) defined DDR as “the systematic study of design, development, and evaluation processes with the aim of establishing an empirical basis for the creation of instructional and non-instructional products and tools and new or enhanced models that govern their development” (p. xv). Often the models and frameworks are validated and/or further developed and enhanced through the DDR. DDR is applied research. An area of DDR research that is particularly applicable to ADE students is the creation, implementation, and evaluation of one or more artifacts, such as products, tools, models, new technologies, and learning objects that will aid in solving a complex problem in practice that can be addressed through human imagination, creativity, engagement, and interaction (Ellis & Levy, 2010). These types of problems are found in K-12 education, higher education, corporations, not-for-profits, healthcare, and the military. 

• Example Design and Development Research

The field of DDR is constantly evolving and expanding as technology and media are changing at exponential rates.  Richey and Klein (2007) in their seminal work divided DDR into two major categories:

• Product and Tool Research and
• Model Research.

Table 1 provides a summary of common designs used in DDR. Most DDR work falls under the qualitative research category of qualitative case study, however, methodologies such as quantitative and mixed method have been used as well as other qualitative designs, including Delphi.

Product, Program, and Tool Research

Ellis and Levy (2010) asserted that DDR must go beyond commercial product development by determining a research problem, based on existing research literature and gaps in the literature that researchers assert must be studied to add to the instructional design knowledgebase.

Product and Tool Research can be further divided into:

• Comprehensive Design and Development Projects covering all phases of the instructional design process,
• Specific Project Phases (such as those in the ADDIE model: Analysis, Design, Development, Implementation, and Evaluation), and
• Design, Development, and Use of tools (Richey & Klein, 2007).

Model Research

Instructional designers and instructional technologists have focused on model research since the emergence of the field.

Model research can be broken into three types:

• Model Development,
• Model Validation and

Model development can focus on a comprehensive model design or on part of a process. Model validation research uses empirical processes to prove the effectiveness of a model in practice. Finally, model use research addresses usability typically from the perspective of instructional designers and stakeholder experts.

3 Stages in Design and Development Research for the ADE Doctoral Student’s Dissertation

NU doctoral students in the Instructional Design Program can use one of the various types of DDR research to complete their doctoral dissertation using the NU ADE template. There will be three stages in this process and in each stage the student will have one or more deliverables using the NU template and posting in the ADR on NU One.

Stage 1: Design and Development Research aligned with the NU ADE Template Process

• Identify a research worthy problem which is expressed by researchers in peer reviewed research literature. Ask yourself, what is going wrong? What do researchers say is known about the problem? And what is needed to be known to address the problem?
• Describe the purpose of your research ensuring that it aligns with your problem statement. In the description state your methodology and design and which DDR type of research you will do. Be sure to include a description of your target population (audience), the size of your sample and the sampling strategy you will use to access your sample. What permissions do you need? Site permission? Other IRB permission?
• Write your research questions to align with your problem and purpose statements.
• Complete Section 1 of your Applied Doctoral Experience (ADE) template securing all necessary approvals in the Applied Doctoral Record (ADR).
• Needs Assessment
• Measurable Goals and Objectives
• Sample size and Access to the sample
• Sampling strategy
• Content analysis (course, program, product, or tool descriptions)
• Technology and media analysis/selection
• Learning management system(s)
• Asynchronous
• Synchronous
• Evaluation Plan
• Complete Section 2, Proposal Draft, Proposal for AR, and Final Proposal of the ADE securing all necessary approvals in the ADR.
• Submit Proposal and IRB Application to secure IRB approval.

Stage 2: Design and Development Research aligned with the NU ADE Template Process

After receiving IRB approval of your ADE Proposal, it is time to design, develop, test, validate, and/or evaluate your artifacts. Below are example steps:

• Review and Finalize Design Document
• Recruit Expert Participants, if required
• Recruit Artifact User/Participants, if required
• Lesson plan or syllabus
• Instructional strategies and activities
• Participant materials
• Trainer materials
• Storyboards and scripts
• Other media
• Create model, tool, product, or program.
• Validate model, if required
• Evaluation plan (Kirkpatrick Levels 1, 2, 3, 4)
• Alpha test, Beta test, Pilots.
• Rapid Prototyping
• Participant reaction
• Trainer/facilitator reaction
• Were Design Goals met?
• Were Design Objectives met?
• Revise artifact(s), Retest, if necessary.

Stage 3: Design and Development Research aligned the NU ADE Template Process

Complete Section 3 of the ADE template presenting the study findings, conclusions, and implications. Next pull all three sections into a dissertation manuscript for approval in the ADR.

While DDR covers a wide variety of approaches, most doctoral students in the ADE program will find case study to be the preferred design. To strengthen trustworthiness of the data, multiple sources of data will typically be used.  Using multiple sources of data is called triangulation in research. Figure 1 shows examples of sources of data for DDR.

The goal is to create, use, and/or validate New Artifacts by collecting and analyzing various sources of data including:

• Existing artifacts,
• Expert individual and focus group interviews,
• Participant/user individual interviews, talk aloud-think aloud interviews, focus group interviews,
• Research observation and participant observation,
• Evaluation, Kirkpatrick Levels 1-4, and
• Needs assessment and design documents.

The new artifacts may be lesson plans, student guides, facilitator/teacher guides, learning objects, tools, models, programs, and/or products.

Figure:  Sources of Data in DDR

Ellis, T.J. & Levy, Y. (2010). A guide for novice researchers: Design and development research methods. Proceedings of Informing Science & IT Education Conference (InSITE) 2010, pp. 108-118. http://proceedings.informingscience.org/InSITE2010/InSITE10p107-118Ellis725.pdf

Goldkuhl, G. (2012). From Action Research to Practice Research. Australasian Journal of Information Systems, 17 (2). https://doi.org/10.3127/ajis.v17i2.688

Richey, R. C. & Klein, J. D. (2007). Design and Development Research. Routledge

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How to Write a Dissertation or Thesis Proposal

Published on September 21, 2022 by Tegan George . Revised on July 18, 2023.

When starting your thesis or dissertation process, one of the first requirements is a research proposal or a prospectus. It describes what or who you want to examine, delving into why, when, where, and how you will do so, stemming from your research question and a relevant topic .

The proposal or prospectus stage is crucial for the development of your research. It helps you choose a type of research to pursue, as well as whether to pursue qualitative or quantitative methods and what your research design will look like.

You can download our templates in the format of your choice below.

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Table of contents

What should your proposal contain, dissertation question examples, what should your proposal look like, dissertation prospectus examples, other interesting articles, frequently asked questions about proposals.

Prior to jumping into the research for your thesis or dissertation, you first need to develop your research proposal and have it approved by your supervisor. It should outline all of the decisions you have taken about your project, from your dissertation topic to your hypotheses and research objectives .

Depending on your department’s requirements, there may be a defense component involved, where you present your research plan in prospectus format to your committee for their approval.

Your proposal should answer the following questions:

• Why is your research necessary?
• What is already known about your topic?
• Where and when will your research be conducted?
• Who should be studied?
• How can the research best be done?

Ultimately, your proposal should persuade your supervisor or committee that your proposed project is worth pursuing.

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Strong research kicks off with a solid research question , and dissertations are no exception to this.

Dissertation research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly
• What are the main factors enticing people under 30 in suburban areas to engage in the gig economy?
• Which techniques prove most effective for 1st-grade teachers at local elementary schools in engaging students with special needs?
• Which communication streams are the most effective for getting those aged 18-30 to the polls on Election Day?

An easy rule of thumb is that your proposal will usually resemble a (much) shorter version of your thesis or dissertation. While of course it won’t include the results section , discussion section , or conclusion , it serves as a “mini” version or roadmap for what you eventually seek to write.

Be sure to include:

• A succinct introduction to your topic and problem statement
• A brief literature review situating your topic within existing research
• A basic outline of the research methods you think will best answer your research question
• The perceived implications for future research
• A reference list in the citation style of your choice

The length of your proposal varies quite a bit depending on your discipline and type of work you’re conducting. While a thesis proposal is often only 3-7 pages long, a prospectus for your dissertation is usually much longer, with more detailed analysis. Dissertation proposals can be up to 25-30 pages in length.

Writing a proposal or prospectus can be a challenge, but we’ve compiled some examples for you to get your started.

• Example #1: “Geographic Representations of the Planet Mars, 1867-1907” by Maria Lane
• Example #2: “Individuals and the State in Late Bronze Age Greece: Messenian Perspectives on Mycenaean Society” by Dimitri Nakassis
• Example #3: “Manhood Up in the Air: A Study of Male Flight Attendants, Queerness, and Corporate Capitalism during the Cold War Era” by Phil Tiemeyer

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If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

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The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A dissertation prospectus or proposal describes what or who you plan to research for your dissertation. It delves into why, when, where, and how you will do your research, as well as helps you choose a type of research to pursue. You should also determine whether you plan to pursue qualitative or quantitative methods and what your research design will look like.

It should outline all of the decisions you have taken about your project, from your dissertation topic to your hypotheses and research objectives , ready to be approved by your supervisor or committee.

Note that some departments require a defense component, where you present your prospectus to your committee orally.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The empirical study of business responses to disasters is relatively scarce, despite that businesses are a fundamental part of the cities, providing services, jobs, and taxes that are essential for urban sustainability. This dissertation research developed an agent-based simulation model to represent and understand the business reopening process in a dynamic environment in New Orleans after Hurricane Katrina. The objectives were two-fold: 1) To identify the main reopening predictors involved and estimate their relative importance through time, using an empirical data set collected from a previous study; 2) To represent the business reopening process through a computer simulation model, using the parameters derived from the first objective.

The results show that businesses located in flooded areas had lower reopening probabilities. However the effect was significant only in the first nine months after the disaster. Larger businesses had better reopening probabilities than smaller ones, although this variable stopped being significant after six months. Variables traditionally associated with higher social vulnerability, such as percent non-white population and percent population under 18 years old, had a negative effect on the business reopening probabilities at different points of time. The influence of neighboring firms using 1-km buffer was found significantly positive only immediately after the disaster; it became significantly negative one year after the disaster.

The simulation model developed proved to mimic the actual reopening process at a suitable level. The model was used to simulate two scenarios: 1) First, the flood depth was reduced by 1 meter as a way to represent the implementation of measures designed to increase the buildings and infrastructure resistance to floods. The simulation results indicate that there are specific areas that would obtain greater benefit from these measures, however ten months after the disaster the effect of the measures tends to diminish. 2) Second, the spatial effects of aids were simulated by making a limited number of businesses in specific locations totally resilient to the disaster. The results indicate that the beneficial effect is influenced by variables such as business density and socio-economic conditions of the area. The positive effect is perceivable until four months after the disaster, after this point it diminishes.

This research contributes to an increase in understanding in vulnerability and sustainability science and helps develop methods for spatial dynamic modeling. The research is innovative, as it is among the first to simulate the business recovery process using businesses as agents in a simulation model. The use of simulation has helped in understanding the complex recovery process and how a business opens in one location at one point in time will affect the business reopening probabilities in other locations in the second time period. The research is significant, because the simulation model developed has been validated by real data. A validated simulation model allows researchers to design and test recovery policies so that resources can be better allocated, as shown by the two scenarios simulated in this research. Although the simulation model is based on post-Katrina New Orleans data, the methods developed in this research could be applied to study other locations affected by disasters of the same and other nature.

Last Modified: 10/14/2011 Modified by: Nina S Lam

Please report errors in award information by writing to: [email protected] .

Doctoral Candidate Presents Dissertation Findings at National Conference

Karmen Yu’s research addresses the question: How do undergraduate Calculus I students experience and navigate their learning of calculus in the parallel spaces of coursework and inquiry-oriented complementary instruction?

Posted in: Faculty and Student Research , Mathematics Education PhD , Students and Alumni

Doctoral candidate Karmen Yu recently presented findings from her dissertation study at the annual Research in Undergraduate Mathematics Education conference in Omaha, NE. Karmen’s talk, entitled Case Studies of Undergraduate Students’ Agentive Participation in the Parallel Spaces of Calculus I Coursework and Peer-Led, Inquiry-Oriented, Complementary Instruction.  She shared findings from one case study that included characterizations of the different forms of agentive participation afforded to students in each of the two spaces, as well as their complementary nature relative to learning calculus with understanding. It was a fantastic presentation. Karmen’s advisor, Dr. Steven Greenstein, was a contributor to the presentation and was there to support her. Great work, Karmen!