Help Application of Multiple imputation in Analysis of missing data in a study of Health-related quality of life Zhu, Chunming Application of Multiple imputation in Analysis of missing data in a study of Health-related quality of life.
She was always ready to learn new techniques, and she diversified her skill set by working in the animal facility, cell culture room, and also in a mass spectrometry lab. As the years went on, Jesse was spread thin between her own PhD thesis project, mentoring, and collaborations.
Jesse tried to reduce her load by asking her advisor to take her off collaborations. Over the course of 7 years Jesse had collected a lot of data, but most of the projects were dead-end or too small for a publication. As a 7th year student, Jesse had only one publication, and she was only 2nd author on it.
Nevertheless, Jesse scheduled what she hoped to be her final committee meeting. Given her diverse skill set, companies started to approach her with employment opportunities but she could not start interviewing until she had a defense date scheduled.
Motivated by the companies that were trying to hire her, Jesse became laser-focused on how to write a PhD thesis.
She had several heated meetings with her supervisor to get off collaborations that were not supporting her thesis, and she also asked for clarification on what she had to do in order to be allowed to defend.
Over the next few months, Jesse collected enough data to complete and defend her PhD thesis, which allowed her to interview for jobs and get an offer. She thought she was doing Thesis on missing data the right things for seven years working long hours and pleasing her supervisor, collaborators and group membersyet her committee did not approve her PhD thesis, until she started doing things differently.
Perhaps you will recognize some of these patterns in your own workflow. Do what you think your advisor and PhD thesis committee wants you to do, and avoid conflict at all cost Miscommunication is the 1 reason for unpleasant surprises at committee meetings.
|My Account||These missing values can occur for a number of reasons, including equipment malfunctions and, more typically, subjects recruited to a study not participating fully.|
Many students think they know what they need to do to graduate. They put a lot of work into collecting and analyzing data without communicating frequently enough with their supervisor to see whether they are on the right track. The frequency of meetings with your supervisor depends on his or her management style hands-off vs.
In some cases, getting clarification will involve disagreements and heated discussions.
Good practice for working with others in your career. As research evolves, expectations will change over time, but you always need to know what you are supposed to be working on now.
What if the expectations of your supervisor are not clear? Assume that all the hard work that you do will turn into a PhD thesis eventually Jesse collected lots of data, but she was missing the most important ingredient of a finished thesis: With her time scattered among collaborations and mentoring other students, Jesse lost focus.
Her projects were related, but not closely enough for a comprehensive doctoral thesis. Define clearly the question that your PhD thesis will answer.
Once you have a question, you can set up a long-term research plan, with well-defined milestones and deadlines.
Students are hesitant to this approach because the thesis question sometimes changes as more data is collected. Check this post on how to manage a large research project.
Given the uncertain nature of research, your initial plan will most certainly change. However, you always need to have a plan to start with, and milestones to measure your progress. More hours at work do not automatically translate into a finished thesis.
Do research that you think is interesting This is related to 1 and 2, but it is so common that it deserves a category of its own. Students pour in a lot of their resources without checking whether it complements their PhD thesis research. If you come across a novel idea that you think could complement your thesis, run it by your advisor before spending a significant amount of time or money on it.
You might need to do literature research or collect preliminary data before presenting your idea to your supervisor. There are two problems with this approach.
The second one is that you cannot dictate how your results turn out — your data is what it is. In fact, sometimes unexpected results are more interesting and can lead to new research directions. If you doubt your own methods and data, your committee will probably pick up on your lack of self-confidence and ask you to repeat your studies until your data is more robust.
Think about possible outcomes in advance. How will each outcome effect the interpretation of your results? Many successful graduate students also have several backup plans in case they reach a dead-end, either in the direction of their research or in the development of their methods. Jump into conclusions or the next phase of research before rigorous data analysis Did you ever make preliminary conclusions by eye-balling your results?
Unfortunately many students jump into conclusions too soon, go off in a certain direction, and then realize that they are back to square one. I learned this lesson the hard way in graduate school when I had to determine whether certain conditions improved the survival of cell in my culture system.
The plots in Excel suggested that one experimental setup was superior to the other.Abstract Missing Data Problems in Machine Learning Benjamin M. Marlin Doctor of Philosophy Graduate Department of Computer Science University of Toronto.
Abstract Missing Data Problems in Machine Learning Benjamin M. Marlin Doctor of Philosophy Graduate Department of Computer Science University of Toronto. Gordon, Claire Ann () Investigating statistical approaches to handling missing data in the context of the Gateshead Millennium Study.
MSc(R) thesis, University of Glasgow. Full text available as. ROBUST LOW-RANK MATRIX FACTORIZATION WITH MISSING DATA BY MINIMIZING L1 LOSS APPLIED TO COLLABORATIVE FILTERING by Shama Mehnaz Huda Bachelor of Science in Electrical Engineering, University of Arkansas, This thesis uses MovelLens data provided by GroupLens which consists of explicit ratings.
To the Graduate Council: I am submitting herewith a thesis written by Yan Zeng entitled "A Study of Missing Data Imputation and Predictive Modeling of Strength Properties of Wood Composites.". Handling Data with Three Types of Missing Values Jennifer A.
Boyko, Ph.D. University of Connecticut, ABSTRACT Missing values present challenges in .