Demystifying Dissertation Writing Review

Demystifying Dissertation Writing Review

Table of Contents

Do you remember the anxiety of staring at a blank page, knowing you need to write a dissertation? Ah, dissertation writing. It’s that final boss battle in the game of academic life. We’ve all been there, caffeinated and sleep-deprived, wrestling with abstract ideas that barely make sense. Enter “Demystifying Dissertation Writing: A Streamlined Process from Choice of Topic to Final Text” by Peg Boyle Single. This book is like having a wise mentor whispering in your ear, helping you navigate the labyrinth of dissertation writing.

Demystifying Dissertation Writing: A Streamlined Process from Choice of Topic to Final Text     Paperback – 28 September 2009

Check out the Demystifying Dissertation Writing: A Streamlined Process from Choice of Topic to Final Text     Paperback – 28 September 2009 here.

Why We Needed This Book

The Academic Jigsaw Puzzle

Ever feel like writing a dissertation is like assembling a puzzle with no edge pieces? Every piece looks the same, and none seem to fit. Peg Boyle Single gets it. She realizes the emotional and psychological rollercoaster this task puts us through. What she offers is a strategy, not just tips and tricks but a comprehensive approach that breaks down the whole ordeal into manageable chunks. This approach transforms the dissertation process into a series of small, daily battles, making the monumental task a bit less terrifying.

The Time Trap

Time management during dissertation writing is an oxymoron. We’re eternally caught between procrastination and the guilt of procrastination. Single’s strategy incorporates time-specific tactics that encourage us to tackle our dissertation one bite at a time. Imagine scheduling your dissertation like a series of dentist appointments—not exactly fun, but definitely necessary.

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What Sets This Book Apart

Streamlined Process

Think of Single as your personal academic project manager. Her book isn’t an endless manifesto but a streamlined guide. She details a step-by-step method, avoiding the curse of ambiguity. The sequencing is intuitive, moving from topic selection to the final text without assuming we’re endowed with any supernatural abilities. It’s almost like following a well-designed IKEA manual—minus the Allen wrench.

Table: Breaking Down the Process

Step Key Activities Emotional State
Choosing a Topic Brainstorming, Narrowing down Excited, Overwhelmed
Planning Outlining, Literature Review Anxious, Hopeful
Writing First Draft Freewriting, Drafting Sections Stressed, Determined
Revising Peer Feedback, Editing Relieved, Exhausted
Finalizing Formatting, Proofreading Elated, Nervous

This structure helps us keep our sanity intact by knowing exactly what to expect and when.

Voice of Compassion

The tone of the book is compassionate without being patronizing. Single acknowledges our dread but doesn’t indulge it. Her empathy shines through while maintaining an authoritative voice—like that friend who brings soup when you’re sick but won’t let you wallow in self-pity for too long.

Academic and Psychological Insight

It’s refreshing to find a resource that marries academic rigor with psychological insight. Single understands that writing isn’t just a technical skill; it’s an emotional endeavor, fraught with self-doubt and existential crises. She addresses these emotional roadblocks with the same detail as the methodological ones, making her advice holistic and deeply humane.

Demystifying Dissertation Writing: A Streamlined Process from Choice of Topic to Final Text     Paperback – 28 September 2009

Learn more about the Demystifying Dissertation Writing: A Streamlined Process from Choice of Topic to Final Text     Paperback – 28 September 2009 here.

From Theory to Practice

Choosing a Topic: The Rubik’s Cube

Selecting a dissertation topic often feels like solving a Rubik’s Cube blindfolded. Single’s approach demystifies this stage by offering practical advice on how to choose a topic that is both interesting and viable. She recommends generating a list of potential ideas, then filtering them through criteria such as relevance, feasibility, and personal interest. We end up with a topic that not only serves academic requirements but also keeps us engaged through the long haul.

Planning: The Architectural Phase

Once the topic is nailed down, the next step is planning, or as Single calls it, “The Architectural Phase.” Here, we’re directed to outline the entire dissertation, chapter by chapter. The key is to create a roadmap that balances depth with clarity. Planning isn’t just about laying bricks; it’s about envisioning the entire building. This phase feels less daunting when we have a blueprint to guide us.

Writing the First Draft: Embrace the Mess

Writing the first draft can feel like wrestling an octopus. Single encourages embracing the mess. She introduces the concept of freewriting, wherein we let our thoughts flow without policing our grammar or coherence. It’s liberating to realize that the first draft doesn’t have to be perfect; it just has to exist. She even gives strategies for this messy phase, such as writing in time-bound sessions and focusing on sections rather than the entire dissertation.

Revising: The Sculptor’s Touch

Revising is when the marble block becomes a statue. Single advises gathering peer feedback and editing iteratively. She breaks down the revision process into manageable tasks, focusing first on content and structure before delving into stylistic elements. This phase is all about refinement and precision—polishing the rough diamond until it shines.

Finalizing: The Last Hurdle

The final stage often feels like sprinting the last leg of a marathon. We’re almost there but not quite yet. Single’s advice on finalizing is meticulous, covering everything from formatting requirements to proofreading hacks. This section is particularly useful because it helps us avoid the minor mistakes that can undermine months’ worth of hard work.

Real-Life Application: Stories from the Trenches

Sarah’s Journey

Sarah, a grad student in Environmental Science, found herself paralyzed at the prospect of writing her dissertation. Enter Single’s book. Sarah followed the step-by-step guide religiously. She started with brainstorming and eventually chose a topic that fascinated her and was feasible in terms of research scope. The structured planning phase helped her break down her dissertation into many sections, making the task seem less like climbing Everest and more like a series of small hikes.

Paul’s Experience

Paul, a History major, swore by the messiness of the first draft phase. He used Single’s freewriting exercise religiously, setting a timer for each session and focusing solely on getting words on paper. His house looked like a scene from a conspiracy theory movie, with post-it notes and draft pages everywhere. Yet, when it came time for revision, he found that Single’s iterative approach to feedback and editing turned chaos into coherence.

Our Collective Wisdom

We’ve pooled our collective experiences, and the consensus is clear: Single’s book is a dissertation-writing lifeline! By following her methodical approach, we’ve managed to transform what seemed like an insurmountable task into a structured, manageable process. The blend of academic rigor and emotional support is what makes this resource a standout.

The Downside

Missing Voices

If we were to nitpick, the book sometimes feels too oriented towards the humanities and social sciences. STEM students might find themselves wishing for additional, field-specific advice. Single’s framework is robust, but a bit more inclusivity in terms of academic disciplines would make it perfect.

Length and Depth

Some sections, especially those on planning and revising, could benefit from a deeper dive—there could be more real-world examples or case studies to illustrate points. The existing framework is solid, but there’s room for expansion.

Our Final Take

Worth Every Penny

Despite minor drawbacks, “Demystifying Dissertation Writing: A Streamlined Process from Choice of Topic to Final Text” is a gem in the world of academic writing guides. Peg Boyle Single’s compassionate, no-nonsense approach offers invaluable support during one of the most stressful periods of our academic lives. This book transforms the overwhelming process of dissertation writing into a series of achievable tasks, making it a must-have for any graduate student facing the dissertation gauntlet. So, if you’re staring down the barrel of your dissertation and feeling queasy, this book might just be the antidote you need.

Our Vote: Highly Recommended

We firmly believe this book is a lifesaver for anyone struggling with dissertation writing. With a practical, step-by-step approach and a tone that’s both comforting and motivating, “Demystifying Dissertation Writing” earns our unwavering endorsement. If you’re in the throes of dissertation despair, give Peg Boyle Single a call—through her book, of course.

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University Student Essentials
University Student Essentials

About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (= 0.87, < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (= 0.02, = .423). Additionally, CSE (= 0.02, = .423), PC (= 0.05, = .334), ATE (= 0.00, = .983), and PP (= 0.03, = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (= 0.22, < .001) and RES (= 0.21, < .001). The results also indicated that PC was significantly correlated with ATE (= -0.79, < .001), AXY (= 0.18, < .001), and RES (= 0.20, < .001). ATE was significantly correlated with AXY (= -0.19, < .001) and RES (= -0.19, < .001). AXY was significantly correlated with RES (= 0.85, < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, < .001), ATE (F(7,194) = 2.20, = .036), Years computers (F(1,194) = 5.71, = .018), and PC (F(12,194) = 2.00, = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.