Doing Your Master’s Dissertation Review

Doing Your Master’s Dissertation Review

Table of Contents

Ever found yourself staring at your master’s dissertation wondering where to start—or even how to handle the entire process from beginning to end? Us too. It’s as if we’re standing at the base of Mount Everest, unsure whether we’re impressed or just plain terrified. That’s where “Doing Your Master’s Dissertation: From Start to Finish Paperback – 25 March 2013” comes in. This book promises to be our climbing guide, helping us navigate the steep, icy, sometimes treacherous path to the summit of academic writing.

Doing Your Master′s Dissertation: From Start to Finish     Paperback – 25 March 2013

Discover more about the Doing Your Master′s Dissertation: From Start to Finish     Paperback – 25 March 2013.

What’s in a Name?

The title “Doing Your Master’s Dissertation: From Start to Finish” makes a pretty ambitious claim. We can almost feel the weight lifting off our shoulders as we hold the book, relieved by the thought that there’s a structured pathway to follow. Right from the title, it’s like the author is reaching out to us, saying, “Don’t worry, we’ve got this.”

Expectations vs Reality

First impressions might lead us to believe that this book contains every secret we need to complete our dissertation. And while it doesn’t literally write our paper for us (bummer), it does offer a fascinating roadmap filled with actionable advice.

Aspect Our Expectation The Reality
Content Step-by-step instructions Offers detailed insights and structured plans
Usability Easy to navigate User-friendly with clear headings
Guidance Comprehensive and thorough Practical and in-depth
Language Academic and complex Conversational and engaging
Additional Resources Bibliography and examples Examples, templates, and exercises

Doing Your Master′s Dissertation: From Start to Finish Paperback – 25 March 2013

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Unpacking the Structure

Let’s break this down the way we wish all dissertation topics could be – step by step, layer by layer, like a gourmet onion. You won’t find it peeling your soul apart, but maybe you’ll shed fewer tears.

Getting Started

This is often the hardest part. We know what it feels like to have a blank document and 10,000 words to fill it with, wondering if you should start with a brilliant, ground-breaking thesis statement or a polite ‘Dear Reader’. The book kicks off by helping us narrow down a topic and formulate a research question.

Research and Literature Review

Ugh, research. Sound familiar? It’s the part where we have to dive into mountains of academic papers and pretend we understand all those big, fancy words. What’s neat about this guide is it simplifies this daunting task. It’s pretty much our best research buddy—pointing out where to find sources, how to critique these sources critically, and then taking all these sources and turning them into a killer literature review. All without making our brains melt.

Methodology

Methodology sounds like one of those terms that’s easy to throw out in conversation to sound smart but is hard to define without stuttering. Here, the book explains various research methods in a way that even those who are allergic to the word ‘data’ can grasp. Quantitative, qualitative, mixed methods—it’s like tasting the ice cream flavors before deciding which one makes your ears perk up. And it even gives advice on choosing the right methodology for our specific research.

Crafting the Dissertation

Now for the fun part, right? Okay, maybe it’s not exactly “fun,” but at least we’re onto writing. This section covers everything from the introduction to the conclusion, with explicit pointers on how to structure each chapter. We particularly enjoy how it turns what seems like a colossal task into digestible chunks. It’s like taking baby steps, except each step gets you closer to that finish line without feeling like you’re about to stumble and fall.

Chapters Breakdown

Chapter Focus Key Takeaways
Introduction Setting the stage Clarity and purpose are key
Literature Review Previous work in the field Synthesizing sources and identifying gaps
Methodology How research will be conducted Justify your choices
Findings/Results Presenting data and discoveries Be clear and concise
Discussion Analysis of findings Link back to research question and theory
Conclusion Summarizing the study Emphasize contributions and limitations
References and Appendices All the extra information Follow citation rules meticulously

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Real Talk: Strengths and Weaknesses

We like to believe every product review should give the good, the bad, and the ugly, right? Let’s dive into what makes “Doing Your Master’s Dissertation: From Start to Finish” a potential lifesaver, and where it might have tripped a bit.

Strengths

Practical Advice

The actionable tips are incredibly helpful. They’re not just fluffy, motivational quotes that make us feel good for a second. Instead, they provide concrete steps and strategies. Think of it as a mentor handing us a toolkit and saying, “Here, this is exactly what you need for this specific task.”

User-Friendly

The book is laid out in a practical manner, making it easy to navigate. We can jump straight to the section we need without wading through irrelevant chapters. It’s like those really well-organized kitchen drawers where everything has its place—even the garlic press you rarely use.

Engaging Tone

Okay, so textbooks are not typically what we’d call ‘page-turners,’ but this one manages to be that rare exception that doesn’t put us to sleep. The conversational language makes it less of a snooze-fest and more like a friendly chat over coffee. Well, strong coffee.

Weaknesses

Not One-Size-Fits-All

The book tries to cater to a broad audience, but the downside to this is that it might not cover the extreme specificity of some dissertations. It’s fantastic for general guidance, but we might still need to consult more specialized texts or advisors for niche topics.

Assumes Basic Research Knowledge

While it’s beginner-friendly, it does assume we have a foundational understanding of research methods and academic writing. Sure, it gives us a refresher, but if we’re absolute newbies, we might find ourselves feeling a tad lost at times.

Limited Access to Resources

While the book provides templates and examples, it doesn’t come with an online portal for supplementary resources. Having access to digital worksheets or additional reading lists could have been a nice touch. Let’s be honest, our generation’s comfort zone lies somewhere between printed paper and high-speed Wi-Fi.

Final Verdict

Does “Doing Your Master’s Dissertation: From Start to Finish Paperback – 25 March 2013” defy the usual pitfalls of academic self-help books? Yes, to a large extent. It won’t do our research or write our paper, but it sure does its best to ensure we know exactly how to tackle each step.

Should We Recommend It?

Absolutely. For many of us struggling to get past that blinking cursor in our Word document, this book is like finding a map when you’re lost in a maze. It doesn’t throw jargon at us without explanation and it doesn’t expect us to know everything from the get-go. Instead, it patiently walks us through every step, turning the mountainous task of writing a dissertation into a manageable journe

Doing Your Master′s Dissertation: From Start to Finish     Paperback – 25 March 2013

Final Thoughts

In the end, while no book can promise 100% success without effort on our part, “Doing Your Master’s Dissertation: From Start to Finish” gets pretty darn close. It equips us with not just the tools but the confidence to make it through. If nothing else, it offers the comforting reassurance that we are not climbing this mountain alone. And sometimes, that’s all we need to keep moving forward.

So, let’s crack open that book, take a deep breath, and start our climb. We’ve got this!

See the Doing Your Master′s Dissertation: From Start to Finish     Paperback – 25 March 2013 in detail.

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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.