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Mathematics and Statistics Theses and Dissertations

Theses/dissertations from 2024 2024.

The Effect of Fixed Time Delays on the Synchronization Phase Transition , Shaizat Bakhytzhan

On the Subelliptic and Subparabolic Infinity Laplacian in Grushin-Type Spaces , Zachary Forrest

Utilizing Machine Learning Techniques for Accurate Diagnosis of Breast Cancer and Comprehensive Statistical Analysis of Clinical Data , Myat Ei Ei Phyo

Quandle Rings, Idempotents and Cocycle Invariants of Knots , Dipali Swain

Comparative Analysis of Time Series Models on U.S. Stock and Exchange Rates: Bayesian Estimation of Time Series Error Term Model Versus Machine Learning Approaches , Young Keun Yang

Theses/Dissertations from 2023 2023

Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani

Applied Analysis for Learning Architectures , Himanshu Singh

Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze

Theses/Dissertations from 2022 2022

New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana

Advances and Applications of Optimal Polynomial Approximants , Raymond Centner

Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty

On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly

Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He

Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias

Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi

A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman

Theses/Dissertations from 2021 2021

Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri

Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi

Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou

Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando

Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu

Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang

Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang

Theses/Dissertations from 2020 2020

Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi

Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun

Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu

On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman

Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek

Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen

Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan

Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop

On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink

Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang

Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala

Theses/Dissertations from 2019 2019

Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian

Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar

Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil

Fractional Random Weighted Bootstrapping for Classification on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter

Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz

Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi

Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi

Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos

The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva

Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak

Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich

An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado

Power Graphs of Quasigroups , DayVon L. Walker

Theses/Dissertations from 2018 2018

Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed

Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai

A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah

Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa

Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields

Generalizations of Quandles and their cohomologies , Matthew J. Green

Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu

Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou

Human Activity Recognition Based on Transfer Learning , Jinyong Pang

Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham

Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel

Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova

Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang

Theses/Dissertations from 2017 2017

Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack

Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon

On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill

Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill

Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly

Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera

Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao

Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati

Dynamics of Multicultural Social Networks , Kristina B. Hilton

Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi

Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally

Patterns in Words Related to DNA Rearrangements , Lukas Nabergall

Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na

Schreier Graphs of Thompson's Group T , Allen Pennington

Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya

Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo

Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi

Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou

Theses/Dissertations from 2016 2016

A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea

Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery

Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman

On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr

Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim

Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano

Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure

Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru

Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park

Leonard Systems and their Friends , Jonathan Spiewak

Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun

Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu

Global Attractors and Random Attractors of Reaction-Diffusion Systems , Junyi Tu

Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classification and Clustering , Xing Wang

On Spectral Properties of Single Layer Potentials , Seyed Zoalroshd

Theses/Dissertations from 2015 2015

Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach , Wei Chen

Active Tile Self-assembly and Simulations of Computational Systems , Daria Karpenko

Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance , Vindya Kumari Pathirana

Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data , Taysseer Sharaf

Radial Versus Othogonal and Minimal Projections onto Hyperplanes in l_4^3 , Richard Alan Warner

Ensemble Learning Method on Machine Maintenance Data , Xiaochuang Zhao

Theses/Dissertations from 2014 2014

Properties of Graphs Used to Model DNA Recombination , Ryan Arredondo

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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Thank you. I will try my best to write my results.

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Statistical Methods in Theses: Guidelines and Explanations

Signed August 2018 Naseem Al-Aidroos, PhD, Christopher Fiacconi, PhD Deborah Powell, PhD, Harvey Marmurek, PhD, Ian Newby-Clark, PhD, Jeffrey Spence, PhD, David Stanley, PhD, Lana Trick, PhD

Version:  2.00

This document is an organizational aid, and workbook, for students. We encourage students to take this document to meetings with their advisor and committee. This guide should enhance a committee’s ability to assess key areas of a student’s work. 

In recent years a number of well-known and apparently well-established findings have  failed to replicate , resulting in what is commonly referred to as the replication crisis. The APA Publication Manual 6 th Edition notes that “The essence of the scientific method involves observations that can be repeated and verified by others.” (p. 12). However, a systematic investigation of the replicability of psychology findings published in  Science  revealed that over half of psychology findings do not replicate (see a related commentary in  Nature ). Even more disturbing, a  Bayesian reanalysis of the reproducibility project  showed that 64% of studies had sample sizes so small that strong evidence for or against the null or alternative hypotheses did not exist. Indeed, Morey and Lakens (2016) concluded that most of psychology is statistically unfalsifiable due to small sample sizes and correspondingly low power (see  article ). Our discipline’s reputation is suffering. News of the replication crisis has reached the popular press (e.g.,  The Atlantic ,   The Economist ,   Slate , Last Week Tonight ).

An increasing number of psychologists have responded by promoting new research standards that involve open science and the elimination of  Questionable Research Practices . The open science perspective is made manifest in the  Transparency and Openness Promotion (TOP) guidelines  for journal publications. These guidelines were adopted some time ago by the  Association for Psychological Science . More recently, the guidelines were adopted by American Psychological Association journals ( see details ) and journals published by Elsevier ( see details ). It appears likely that, in the very near future, most journals in psychology will be using an open science approach. We strongly advise readers to take a moment to inspect the  TOP Guidelines Summary Table . 

A key aspect of open science and the TOP guidelines is the sharing of data associated with published research (with respect to medical research, see point #35 in the  World Medical Association Declaration of Helsinki ). This practice is viewed widely as highly important. Indeed, open science is recommended by  all G7 science ministers . All Tri-Agency grants must include a data-management plan that includes plans for sharing: “ research data resulting from agency funding should normally be preserved in a publicly accessible, secure and curated repository or other platform for discovery and reuse by others.”  Moreover, a 2017 editorial published in the  New England Journal of Medicine announced that the  International Committee of Medical Journal Editors believes there is  “an ethical obligation to responsibly share data.”  As of this writing,  60% of highly ranked psychology journals require or encourage data sharing .

The increasing importance of demonstrating that findings are replicable is reflected in calls to make replication a requirement for the promotion of faculty (see details in  Nature ) and experts in open science are now refereeing applications for tenure and promotion (see details at the  Center for Open Science  and  this article ). Most dramatically, in one instance, a paper resulting from a dissertation was retracted due to misleading findings attributable to Questionable Research Practices. Subsequent to the retraction, the Ohio State University’s Board of Trustees unanimously revoked the PhD of the graduate student who wrote the dissertation ( see details ). Thus, the academic environment is changing and it is important to work toward using new best practices in lieu of older practices—many of which are synonymous with Questionable Research Practices. Doing so should help you avoid later career regrets and subsequent  public mea culpas . One way to achieve your research objectives in this new academic environment is  to incorporate replications into your research . Replications are becoming more common and there are even websites dedicated to helping students conduct replications (e.g.,  Psychology Science Accelerator ) and indexing the success of replications (e.g., Curate Science ). You might even consider conducting a replication for your thesis (subject to committee approval).

As early-career researchers, it is important to be aware of the changing academic environment. Senior principal investigators may be  reluctant to engage in open science  (see this student perspective in a  blog post  and  podcast ) and research on resistance to data sharing indicates that one of the barriers to sharing data is that researchers do not feel that they have knowledge of  how to share data online . This document is an educational aid and resource to provide students with introductory knowledge of how to participate in open science and online data sharing to start their education on these subjects. 

Guidelines and Explanations

In light of the changes in psychology, faculty members who teach statistics/methods have reviewed the literature and generated this guide for graduate students. The guide is intended to enhance the quality of student theses by facilitating their engagement in open and transparent research practices and by helping them avoid Questionable Research Practices, many of which are now deemed unethical and covered in the ethics section of textbooks.

This document is an informational tool.

How to Start

In order to follow best practices, some first steps need to be followed. Here is a list of things to do:

• Get an Open Science account. Registration at  osf.io  is easy!
• If conducting confirmatory hypothesis testing for your thesis, pre-register your hypotheses (see Section 1-Hypothesizing). The Open Science Foundation website has helpful  tutorials  and  guides  to get you going.
• Also, pre-register your data analysis plan. Pre-registration typically includes how and when you will stop collecting data, how you will deal with violations of statistical assumptions and points of influence (“outliers”), the specific measures you will use, and the analyses you will use to test each hypothesis, possibly including the analysis script. Again, there is a lot of help available for this. 

Exploratory and Confirmatory Research Are Both of Value, But Do Not Confuse the Two

We note that this document largely concerns confirmatory research (i.e., testing hypotheses). We by no means intend to devalue exploratory research. Indeed, it is one of the primary ways that hypotheses are generated for (possible) confirmation. Instead, we emphasize that it is important that you clearly indicate what of your research is exploratory and what is confirmatory. Be clear in your writing and in your preregistration plan. You should explicitly indicate which of your analyses are exploratory and which are confirmatory. Please note also that if you are engaged in exploratory research, then Null Hypothesis Significance Testing (NHST) should probably be avoided (see rationale in  Gigerenzer  (2004) and  Wagenmakers et al., (2012) ). 

This document is structured around the stages of thesis work:  hypothesizing, design, data collection, analyses, and reporting – consistent with the headings used by Wicherts et al. (2016). We also list the Questionable Research Practices associated with each stage and provide suggestions for avoiding them. We strongly advise going through all of these sections during thesis/dissertation proposal meetings because a priori decisions need to be made prior to data collection (including analysis decisions). 

To help to ensure that the student has informed the committee about key decisions at each stage, there are check boxes at the end of each section.

How to Use This Document in a Proposal Meeting

• Print off a copy of this document and take it to the proposal meeting.
• During the meeting, use the document to seek assistance from faculty to address potential problems.
• Revisit responses to issues raised by this document (especially the Analysis and Reporting Stages) when you are seeking approval to proceed to defense.

Consultation and Help Line

Note that the Center for Open Science now has a help line (for individual researchers and labs) you can call for help with open science issues. They also have training workshops. Please see their  website  for details.

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What do senior theses in Statistics look like?

This is a brief overview of thesis writing; for more information, please see our website here . Senior theses in Statistics cover a wide range of topics, across the spectrum from applied to theoretical. Typically, senior theses are expected to have one of the following three flavors:                                                                                                            

1. Novel statistical theory or methodology, supported by extensive mathematical and/or simulation results, along with a clear account of how the research extends or relates to previous related work.

2. An analysis of a complex data set that advances understanding in a related field, such as public health, economics, government, or genetics. Such a thesis may rely entirely on existing methods, but should give useful results and insights into an interesting applied problem.                                                                                 

3. An analysis of a complex data set in which new methods or modifications of published methods are required. While the thesis does not necessarily contain an extensive mathematical study of the new methods, it should contain strong plausibility arguments or simulations supporting the use of the new methods.

A good thesis is clear, readable, and well-motivated, justifying the applicability of the methods used rather than, for example, mechanically running regressions without discussing the assumptions (and whether they are plausible), performing diagnostics, and checking whether the conclusions make sense. 

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• Master's Thesis

As an integral component of the Master of Science in Statistical Science program, you can submit and defend a Master's Thesis. Your Master's Committee administers this oral examination. If you choose to defend a thesis, it is advisable to commence your research early, ideally during your second semester or the summer following your first year in the program. It's essential to allocate sufficient time for the thesis writing process. Your thesis advisor, who also serves as the committee chair, must approve both your thesis title and proposal. The final thesis work necessitates approval from all committee members and must adhere to the  Master's thesis requirements  set forth by the Duke University Graduate School.

Master’s BEST Award 

Each second-year Duke Master’s of Statistical Science (MSS) student defending their MSS thesis may be eligible for the  Master’s BEST Award . The Statistical Science faculty BEST Award Committee selects the awardee based on the submitted thesis of MSS thesis students, and the award is presented at the departmental graduation ceremony. 

Thesis Proposal

All second-year students choosing to do a thesis must submit a proposal (not more than two pages) approved by their thesis advisor to the Master's Director via Qualtrics by November 10th.  The thesis proposal should include a title,  the thesis advisor, committee members, and a description of your work. The description must introduce the research topic, outline its main objectives, and emphasize the significance of the research and its implications while identifying gaps in existing statistical literature. In addition, it can include some of the preliminary results. 

Committee members

MSS Students will have a thesis committee, which includes three faculty members - two must be departmental primary faculty, and the third could be from an external department in an applied area of the student’s interest, which must be a  Term Graduate Faculty through the Graduate School or have a secondary appointment with the Department of Statistical Science. All Committee members must be familiar with the Student’s work.  The department coordinates Committee approval. The thesis defense committee must be approved at least 30 days before the defense date.

Thesis Timeline and  Departmental Process:

Before defense:.

Intent to Graduate: Students must file an Intent to Graduate in ACES, specifying "Thesis Defense" during the application. For graduation deadlines, please refer to https://gradschool.duke.edu/academics/preparing-graduate .

Scheduling Thesis Defense: The student collaborates with the committee to set the date and time for the defense and communicates this information to the department, along with the thesis title. The defense must be scheduled during regular class sessions. Be sure to review the thesis defense and submission deadlines at https://gradschool.duke.edu/academics/theses-and-dissertations/

Room Reservations: The department arranges room reservations and sends confirmation details to the student, who informs committee members of the location.

Defense Announcement: The department prepares a defense announcement, providing a copy to the student and chair. After approval, it is signed by the Master's Director and submitted to the Graduate School. Copies are also posted on department bulletin boards.

Initial Thesis Submission: Two weeks before the defense, the student submits the initial thesis to the committee and the Graduate School. Detailed thesis formatting guidelines can be found at https://gradschool.duke.edu/academics/theses-and-dissertations.

Advisor Notification: The student requests that the advisor email [email protected] , confirming the candidate's readiness for defense. This step should be completed before the exam card appointment.

Format Check Appointment: One week before the defense, the Graduate School contacts the student to schedule a format check appointment. Upon approval, the Graduate School provides the Student Master’s Exam Card, which enables the student to send a revised thesis copy to committee members.

MSS Annual Report Form: The department provides the student with the MSS Annual Report Form to be presented at the defense.

Post Defense:

Communication of Defense Outcome: The committee chair conveys the defense results to the student, including any necessary follow-up actions in case of an unsuccessful defense.

In Case of Failure: If a student does not pass the thesis defense, the committee's decision to fail the student must be accompanied by explicit and clear comments from the chair, specifying deficiencies and areas that require attention for improvement.

Documentation: The student should ensure that the committee signs the Title Page, Abstract Page, and Exam Card.

Annual Report Form: The committee chair completes the Annual Report Form.

Master's Director Approval: The Master's director must provide their approval by signing the Exam Card.

Form Submission: Lastly, the committee chair is responsible for returning all completed and signed forms to the Department.

Final Thesis Submission: The student must meet the Graduate School requirement by submitting the final version of their Thesis to the Graduate School via ProQuest before the specified deadline. For detailed information, visit https://gradschool.duke.edu/academics/preparinggraduate .

• The Stochastic Proximal Distance Algorithm
• Logistic-tree Normal Mixture for Clustering Microbiome Compositions
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• Bayesian Modeling for Identifying Selection in B Cell Maturation
• Differentially Private Verification with Survey Weights
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• A Differentially Private Bayesian Approach to Replication Analysis
• Numerical Approximation of Gaussian-Smoothed Optimal Transport
• Computational Challenges to Bayesian Density Discontinuity Regression
• Hierarchical Signal Propagation for Household Level Sales in Bayesian Dynamic Models
• Logistic Tree Gaussian Processes (LoTgGaP) for Microbiome Dynamics and Treatment Effects
• Bayesian Inference on Ratios Subject to Differentially Private Noise
• Multiple Imputation Inferences for Count Data
• An Euler Characteristic Curve Based Representation of 3D Shapes in Statistical Analysis
• An Investigation Into the Bias & Variance of Almost Matching Exactly Methods
• Comparison of Bayesian Inference Methods for Probit Network Models
• Differentially Private Counts with Additive Constraints
• Multi-Scale Graph Principal Component Analysis for Connectomics
• MCMC Sampling Geospatial Partitions for Linear Models
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• Analyzing Amazon CD Reviews with Bayesian Monitoring and Machine Learning Methods
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• Forecasting the Term Structure of Interest Rates: A Bayesian Dynamic Graphical Modeling Approach
• Testing Between Different Types of Poisson Mixtures with Applications to Neuroscience
• Multiple Imputation of Missing Covariates in Randomized Controlled Trials
• A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems
• Applied Factor Dynamic Analysis for Macroeconomic Forecasting
• A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
• Bayesian Inference Via Partitioning Under Differential Privacy
• A Bayesian Forward Simulation Approach to Establishing a Realistic Prior Model for Complex Geometrical Objects
• Two Applications of Summary Statistics: Integrating Information Across Genes and Confidence Intervals with Missing Data
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International Students blog

Thesis life: 7 ways to tackle statistics in your thesis.

By Pranav Kulkarni

Thesis is an integral part of your Masters’ study in Wageningen University and Research. It is the most exciting, independent and technical part of the study. More often than not, most departments in WU expect students to complete a short term independent project or a part of big on-going project for their thesis assignment.

Source : www.coursera.org

This assignment involves proposing a research question, tackling it with help of some observations or experiments, analyzing these observations or results and then stating them by drawing some conclusions.

Since it is an immitigable part of your thesis, you can neither run from statistics nor cry for help.

The penultimate part of this process involves analysis of results which is very crucial for coherence of your thesis assignment.This analysis usually involve use of statistical tools to help draw inferences. Most students who don’t pursue statistics in their curriculum are scared by this prospect. Since it is an immitigable part of your thesis, you can neither run from statistics nor cry for help. But in order to not get intimidated by statistics and its “greco-latin” language, there are a few ways in which you can make your journey through thesis life a pleasant experience.

Make statistics your friend

The best way to end your fear of statistics and all its paraphernalia is to befriend it. Try to learn all that you can about the techniques that you will be using, why they were invented, how they were invented and who did this deed. Personifying the story of statistical techniques makes them digestible and easy to use. Each new method in statistics comes with a unique story and loads of nerdy anecdotes.

If you cannot make friends with statistics, at least make a truce

If you cannot still bring yourself about to be interested in the life and times of statistics, the best way to not hate statistics is to make an agreement with yourself. You must realise that although important, this is only part of your thesis. The better part of your thesis is something you trained for and learned. So, don’t bother to fuss about statistics and make you all nervous. Do your job, enjoy thesis to the fullest and complete the statistical section as soon as possible. At the end, you would have forgotten all about your worries and fears of statistics.

Visualize your data

The best way to understand the results and observations from your study/ experiments, is to visualize your data. See different trends, patterns, or lack thereof to understand what you are supposed to do. Moreover, graphics and illustrations can be used directly in your report. These techniques will also help you decide on which statistical analyses you must perform to answer your research question. Blind decisions about statistics can often influence your study and make it very confusing or worse, make it completely wrong!

Simplify with flowcharts and planning

Similar to graphical visualizations, making flowcharts and planning various steps of your study can prove beneficial to make statistical decisions. Human brain can analyse pictorial information faster than literal information. So, it is always easier to understand your exact goal when you can make decisions based on flowchart or any logical flow-plans.

Source: www.imindq.com

Find examples on internet

Although statistics is a giant maze of complicated terminologies, the internet holds the key to this particular maze. You can find tons of examples on the web. These may be similar to what you intend to do or be different applications of the similar tools that you wish to engage. Especially, in case of Statistical programming languages like R, SAS, Python, PERL, VBA, etc. there is a vast database of example codes, clarifications and direct training examples available on the internet. Various forums are also available for specialized statistical methodologies where different experts and students discuss the issues regarding their own projects.

Comparative studies

Much unlike blindly searching the internet for examples and taking word of advice from online faceless people, you can systematically learn which quantitative tests to perform by rigorously studying literature of relevant research. Since you came up with a certain problem to tackle in your field of study, chances are, someone else also came up with this issue or something quite similar. You can find solutions to many such problems by scouring the internet for research papers which address the issue. Nevertheless, you should be cautious. It is easy to get lost and disheartened when you find many heavy statistical studies with lots of maths and derivations with huge cryptic symbolical text.

When all else fails, talk to an expert

All the steps above are meant to help you independently tackle whatever hurdles you encounter over the course of your thesis. But, when you cannot tackle them yourself it is always prudent and most efficient to ask for help. Talking to students from your thesis ring who have done something similar is one way of help. Another is to make an appointment with your supervisor and take specific questions to him/ her. If that is not possible, you can contact some other teaching staff or researchers from your research group. Try not to waste their as well as you time by making a list of specific problems that you will like to discuss. I think most are happy to help in any way possible.

Talking to students from your thesis ring who have done something similar is one way of help.

Sometimes, with the help of your supervisor, you can make an appointment with someone from the “Biometris” which is the WU’s statistics department. These people are the real deal; chances are, these people can solve all your problems without any difficulty. Always remember, you are in the process of learning, nobody expects you to be an expert in everything. Ask for help when there seems to be no hope.

Apart from these seven ways to make your statistical journey pleasant, you should always engage in reading, watching, listening to stuff relevant to your thesis topic and talking about it to those who are interested. Most questions have solutions in the ether realm of communication. So, best of luck and break a leg!!!

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There are 4 comments.

A perfect approach in a very crisp and clear manner! The sequence suggested is absolutely perfect and will help the students very much. I particularly liked the idea of visualisation!

You are write! I get totally stuck with learning and understanding statistics for my Dissertation!

Statistics is a technical subject that requires extra effort. With the highlighted tips you already highlighted i expect it will offer the much needed help with statistics analysis in my course.

this is so much relevant to me! Don’t forget one more point: try to enrol specific online statistics course (in my case, I’m too late to join any statistic course). The hardest part for me actually to choose what type of statistical test to choose among many options

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Home > Sciences > Mathematics & Statistics > ETDs

Mathematics & Statistics Theses & Dissertations

Theses and dissertations published by graduate students in the Department of Mathematics and Statistics, College of Sciences, Old Dominion University, since Fall 2016 are available in this collection. Backfiles of all dissertations (and some theses) have also been added.

In late Fall 2023 or Spring 2024, all theses will be digitized and available here. In the meantime, consult the Library Catalog to find older items in print.

Theses/Dissertations from 2023 2023

Dissertation: Inference for Multiple Utility in Time-Dependent Choice Pairs Under Copula-Based Models , Sasanka Adikari

Dissertation: Copula Based Models for Bivariate Zero-Inflated Count Time Series Data , Dimuthu Fernando

Theses/Dissertations from 2022 2022

Dissertation: A Direct Method for Modeling and Simulations of Elliptic and Parabolic Interface Problems , Kumudu Janani Gamage

Dissertation: Statistical Methods for Meta-Analysis in Large-Scale Genomic Experiments , Wimarsha Thathsarani Jayanetti

Dissertation: Inexact Fixed-Point Proximity Algorithms for Nonsmooth Convex Optimization , Jin Ren

Dissertation: Kinetic Simulations of Active Nematic Polymers in Channel Flow , Lacey Savoie Schenk

Theses/Dissertations from 2021 2021

Dissertation: Electrohydrodynamic Simulations of Capsule Deformation Using a Dual Time-Stepping Lattice Boltzmann Scheme , Charles Leland Armstrong

Dissertation: Finite Difference Schemes for Integral Equations with Minimal Regularity Requirements , Wesley Cameron Davis

Dissertation: On the p -Inner Functions of ℓ p A , James G. Dragas

Dissertation: A Copula Model Approach to Identify the Differential Gene Expression , Prasansha Liyanaarachchi

Dissertation: High-Order Positivity-Preserving L 2 -Stable Spectral Collocation Schemes for the 3-D Compressible Navier-Stokes Equations , Johnathon Keith Upperman

Theses/Dissertations from 2020 2020

Dissertation: Inference and Estimation in Change Point Models for Censored Data , Kristine Gierz

Dissertation: D-Vine Pair-Copula Models for Longitudinal Binary Data , Huihui Lin

Dissertation: Investigating the Feasibility and Stability for Modeling Acoustic Wave Scattering Using a Time-Domain Boundary Integral Equation with Impedance Boundary Condition , Michelle E. Rodio

Theses/Dissertations from 2019 2019

Dissertation: Copula-Based Zero-Inflated Count Time Series Models , Mohammed Sulaiman Alqawba

Dissertation: Spatio-Temporal Cluster Detection and Local Moran Statistics of Point Processes , Jennifer L. Matthews

Dissertation: Electrohydrodynamic Simulations of the Deformation of Liquid-Filled Capsules , Pai Song

Theses/Dissertations from 2018 2018

Dissertation: Extended Poisson Models for Count Data With Inflated Frequencies , Monika Arora

Dissertation: Approximation of Quantiles of Rank Test Statistics Using Almost Sure Limit Theorems , Mark Ledbetter

Theses/Dissertations from 2017 2017

Dissertation: A Partitioned Approach for Computing Fluid-Structure Interaction, With Application to Tumor Modeling and Simulation , Asim Timalsina

Dissertation: Methods for Analyzing Attribute-Level Best-Worst Discrete Choice Experiments , Amanda Faye Working

Theses/Dissertations from 2016 2016

Dissertation: Analysis off Dependent Discrete Choices Using Gaussian Copula , Arjun Poddar

Theses/Dissertations from 2015 2015

Dissertation: Wavelet Collocation Method for Hammerstein Integral Equations of High Dimension , Xingwang Chen

Dissertation: Modeling and Simulation of Molecular Couette Flows and Related Flows , Wei Li

Dissertation: Supervised Classification Using Copula and Mixture Copula , Sumen Sen

Dissertation: Zero-Inflated Models to Identify Transcription Factor Binding Sites in ChIP-seq Experiments , Sameera Dhananjaya Viswakula

Theses/Dissertations from 2014 2014

Dissertation: Modeling and Simulation of Shape Changes of Red Blood Cells in Shear Flow , John Gounley

Dissertation: Computational Solutions of the Forward and Adjoint Euler Equations with Application to Duct Aeroacoustics , Ibrahim Kocaogul

Dissertation: Ray- and Wave-Theoretic Approach to Electromagnetic Scattering from Radially Inhomogeneous Spheres and Cylinders , Michael A. Pohrivchak

Dissertation: Analyzing Cholera Dynamics in Homogeneous and Heterogeneous Environments , Drew Posny

Dissertation: Bivariate Doubly Inflated Poisson and Related Regression Models , Pooja Sengupta

Theses/Dissertations from 2013 2013

Dissertation: Modelling Locally Changing Variance Structured Time Series Data By Using Breakpoints Bootstrap Filtering , Rajan Lamichhane

Dissertation: Optimal Control Modeling and Simulation, with Application to Cholera Dynamics , Chairat Modnak

Dissertation: Analysis of Continuous Longitudinal Data with ARMA(1, 1) and Antedependence Correlation Structures , Sirisha Mushti

Dissertation: Topics in Electromagnetic, Acoustic, and Potential Scattering Theory , Umaporn Nuntaplook

Dissertation: Analysis and Simulation of Kinetic Model for Active Suspensions , Panon Phuworawong

Theses/Dissertations from 2012 2012

Dissertation: A Statistical Model to Determine Multiple Binding Sites of a Transcription Factor on DNA Using ChIP-seq Data , Rasika Jayatillake

Dissertation: Analysis of Discrete Choice Probit Models with Structured Correlation Matrices , Bhaskara Ravi

Theses/Dissertations from 2011 2011

Dissertation: An Extensible Mathematical Model of Glucose Metabolism , Caleb L. Adams

Dissertation: Perfectly Matched Layer Absorbing Boundary Conditions for the Discrete Velocity Boltzmann-BGK Equation , Elena Craig

Dissertation: A Three Dimensional Green's Function Solution Technique for the Transport of Heavy Ions in Laboratory and Space , Candice Rockell Gerstner

Dissertation: Modeling and Analysis of Repeated Ordinal Data Using Copula Based Likelihoods and Estimating Equation Methods , Raghavendra Rao Kurada

Dissertation: The Doubly Inflated Poisson and Related Regression Models , Manasi Sheth-Chandra

Dissertation: A Least Squares Closure Approximation for Liquid Crystalline Polymers , Traci Ann Sievenpiper

Theses/Dissertations from 2010 2010

Dissertation: A Solution of the Heat Equation with the Discontinuous Galerkin Method Using a Multilivel Calculation Method That Utilizes a Multiresolution Wavelet Basis , Robert Gregory Brown

Dissertation: Semi-Parametric Likelihood Functions for Bivariate Survival Data , S. H. Sathish Indika

Dissertation: Mathematical Models and Stability Analysis of Cholera Dynamics , Shu Liao

Dissertation: Canonical Correlation Analysis for Longitudinal Data , Raymond McCollum

Dissertation: Post-Processing Techniques and Wavelet Applications for Hammerstein Integral Equations , Khomsan Neamprem

Dissertation: A Study of Relationships Between Family Members Using Familial Correlations , Corinne Wilson

Dissertation: Analysis of Models for Longitudinal and Clustered Binary Data , Weiming Yang

Dissertation: Rao's Quadratic Entropy and Some New Applications , Yueqin Zhao

Theses/Dissertations from 2009 2009

Dissertation: An Adaptive Method for Calculating Blow-Up Solutions , Charles F. Touron

Theses/Dissertations from 2008 2008

Dissertation: DGM-FD: A Finite Difference Scheme Based on the Discontinuous Galerkin Method , Anne Marguerite Fernando

Dissertation: Improved Constrained Global Optimization for Estimating Molecular Structure From Atomic Distances , Terri Marie Grant

Dissertation: Analysis and Application of Perfectly Matched Layer Absorbing Boundary Conditions for Computational Aeroacoustics , Sarah Anne Parrish

Theses/Dissertations from 2007 2007

Dissertation: A Technique for Solving the Singular Integral Equations of Potential Theory , Brian George Burns

Dissertation: The Computation of Exact Green's Functions in Acoustic Analogy By a Spectral Collocation Boundary Element Method , Andrea D. Jones

Dissertation: Modeling and Efficient Estimation of Intra-Family Correlations , Roy Sabo

Dissertation: Three Methods for Solving the Low Energy Neutron Boltzmann Equation , Tony Charles Slaba

Dissertation: Canonical Correlation and Correspondence Analysis of Longitudinal Data , Jayesh Srivastava

Dissertation: On the Use of Quasi-Newton Methods for the Minimization of Convex Quadratic Splines , William Howard Thomas II

Theses/Dissertations from 2006 2006

Dissertation: Efficient Unbiased Estimating Equations for Analyzing Structured Correlation Matrices , Yihao Deng

Dissertation: Estimating Familial Correlations Using a Kotz Type Density , Amal Helu

Dissertation: An Implicit Level Set Model for Firespread , Pallop Huabsomboon

Dissertation: Hessian Matrix-Free Lagrange-Newton-Krylov-Schur-Schwarz Methods for Elliptic Inverse Problems , Widodo Samyono

Theses/Dissertations from 2005 2005

Dissertation: Statistical Analysis of Longitudinal and Multivariate Discrete Data , Deepak Mav

Dissertation: Principal Component Regression for Construction of Wing Weight Estimation Models , Humberto Rocha

Dissertation: The Straggling Green's Function Method for Ion Transport , Steven Andrew Walker

Theses/Dissertations from 2003 2003

Dissertation: A Forward-Backward Fluence Model for the Low-Energy Neutron Boltzmann Equation , Gary Alan Feldman

Dissertation: Multi-Symplectic Integrators for Nonlinear Wave Equations , Alvaro Lucas Islas

Dissertation: Superconvergence of Iterated Solutions for Linear and Nonlinear Integral Equations: Wavelet Applications , Boriboon Novaprateep

Dissertation: Analysis of Multivariate Data Using Kotz Type Distribution , Kusaya Plungpongpun

Dissertation: Estimation of Parameters in Replicated Time Series Regression Models , Genming Shi

Theses/Dissertations from 2002 2002

Dissertation: Nearly Balanced and Resolvable Block Designs , Brian Henry Reck

Theses/Dissertations from 2001 2001

Dissertation: Mathematical Models of Quiescent Solar Prominences , Iain McKaig

Theses/Dissertations from 2000 2000

Dissertation: Diffusion Problems in Wound Healing and a Scattering Approach to Immune System Interactions , Julia Suzanne Arnold

Theses/Dissertations from 1999 1999

Dissertation: A Numerical Solution of Low-Energy Neutron Boltzmann Equation , Martha Sue Clowdsley

Theses/Dissertations from 1998 1998

Dissertation: Mathematical Models of Tumors and Their Remote Metastases , Carryn Bellomo

Dissertation: Error-Correcting Codes Associated With Generalized Hadamard Matrices Over Groups , Iem H. Heng

Dissertation: Superconvergence in Iterated Solutions of Integral Equations , Peter A. Padilla

Dissertation: The Solution of Hypersingular Integral Equations With Applications in Acoustics and Fracture Mechanics , Richard S. St. John

Dissertation: Reaction-Diffusion Models of Cancer Dispersion , Kim Yvette Ward

Theses/Dissertations from 1997 1997

Dissertation: Analysis of Repeated Measures Data Under Circular Covariance , Andrew Montgomery Hartley

Dissertation: Reverse Engineering of Aircraft Wing Data Using a Partial Differential Equation Surface Model , Jacalyn M. Huband

Dissertation: High-Order Finite-Difference Schemes and Their Application to Computational Acoustics , Joe Leo Manthey

Dissertation: Mark-Recapture Creel Survey and Survival Models , Shampa Saha

Theses/Dissertations from 1996 1996

Dissertation: Exact Solutions for Orthogonal and Non-Orthogonal Magnetohydrodynamic Stagnation-Point Flow , Shahrooz Moosavizadeh

Dissertation: Linear Models for Multivariate Repeated Measures Data , Shantha S. Rao

Dissertation: Optimality and Construction of Designs with Generalized Group Divisible Structure , Sudesh K. Srivastav

Theses/Dissertations from 1995 1995

Dissertation: Thermal Ignition Analysis in the Laminar Boundary Layer Behind a Propagating Shock Front , Mushtaq Ahmed Khan

Dissertation: Studies of Mixing Processes in Gases and Effects on Combustion and Stability , Frank Paul Kozusko Jr.

Dissertation: Nozzle Flow with Vibrational Nonequilibrium , John Gary Landry

Dissertation: Mathematical Models of Chemotherapy , John Carl Panetta

Dissertation: Analysis of Growth Curves Under Some Special Covariance Structures , Shobha Prabhala

Dissertation: Elimination of Edge Effects Using Spline Wavelets Which Maintain a Uniform Two-Scale Relation , Sang Kyu Yang

Theses/Dissertations from 1994 1994

Dissertation: Some Sampling Designs and Estimation Problems , Hassan Lakkis

Dissertation: Invariant Manifolds of a Toy Climate Model , Michael Toner

Dissertation: Invariance Properties of Statistical Tests for Dependent Observations , Akhil K. Vaish

Dissertation: Rational Cubic B-Spline Interpolation and Its Applications in Computer Aided Geometric Design , Kotien Wu

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Department of Statistics – Academic Commons Link to Recent Ph.D. Dissertations (2011 – present)

2022 Ph.D. Dissertations

Andrew Davison

Statistical Perspectives on Modern Network Embedding Methods

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Nabarun Deb

Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing

Sponsor: Bodhisattva Sen / Co-Sponsor: Sumit Mukherjee

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Dissertation Statistics and Thesis Statistics

There are a dizzying number of statistical tests out there and knowing where to start can be a real problem. You probably haven’t taken a statistics class in years and now you are being called upon to recall statistical tests and details that you believe even a statistician would not know. Fear not! Read on and click through to get all the help you need.

What are dissertation statistics and thesis statistics?

Dissertation statistics and thesis statistics are the statistics used in a dissertation or thesis… just kidding – okay seriously. You have spent all kinds of time on the internet and the library completing this epic task only to hit the wall of………statistics.

There are all kinds of statistics you could use for your Master’s thesis, Master’s dissertation, Ph.D. thesis, and Ph.D. dissertation. These days, it is assumed and maybe required that you use multivariate statistics of some kind. The days of simple bivariate correlations and t -tests seem to be gone forever – depending on the area of expertise. Given the increased level of familiarity with the tests like multiple regression , logistic regression , n -way ANOVA , mixed ANOVA , ANCOVA , MANOVA , MANCOVA , and the like, institutions and committee members’ expectations for you thesis or dissertation are much higher. The individual tests, their benefits, and their uses are described in some more detail here .

What statistical analysis should I use for my thesis or dissertation?

The statistical analysis for your thesis or dissertation should be appropriate for what you are researching and should fit with your needs and capabilities. I know, that’s not saying much, but it’s important that you’re comfortable with the statistical analysis you will be conducting. An experienced dissertation consultant will help you tremendously with this. You will find a great one here .

I am going to go briefly cover why multivariate analysis are so popular with institutions and committees, but before your head explodes, keep in mind that seeking professional help will simplify this greatly by saving you the hours of researching the tests and revising if – God forbid – the tests are run incorrectly. In the end, an experienced dissertation consultant is worth his weight in gold.

What are multivariate statistics and why should I use them for my dissertation or thesis?

The main thing multivariate statistics do is protect your alpha. Something happens when you conduct multiple tests on the same dataset, e.g. t- tests and correlations: you run the risk of the statistical tests being falsely significant simply because multiple tests were conducted on the dataset. In other words, your statistical tests may show significance even though they aren’t.

There are corrections for this like the Bonferroni correction , however, a researcher can avoid the stringent thresholds that follow with implementation of the Bonferroni correction by simply conducting multivariate statistics for their dissertation or thesis. Instead of conducting multiple tests on the same dataset, maybe only one or two tests are conducted. Get help using multivariate statistics with your Master’s thesis, Master’s dissertation, Ph.D. thesis, or Ph.D. dissertation.

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Descriptive Statistics

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The mean, the mode, the median, the range, and the standard deviation are all examples of descriptive statistics. Descriptive statistics are used because in most cases, it isn't possible to present all of your data in any form that your reader will be able to quickly interpret.

Generally, when writing descriptive statistics, you want to present at least one form of central tendency (or average), that is, either the mean, median, or mode. In addition, you should present one form of variability , usually the standard deviation.

Measures of Central Tendency and Other Commonly Used Descriptive Statistics

The mean, median, and the mode are all measures of central tendency. They attempt to describe what the typical data point might look like. In essence, they are all different forms of 'the average.' When writing statistics, you never want to say 'average' because it is difficult, if not impossible, for your reader to understand if you are referring to the mean, the median, or the mode.

The mean is the most common form of central tendency, and is what most people usually are referring to when the say average. It is simply the total sum of all the numbers in a data set, divided by the total number of data points. For example, the following data set has a mean of 4: {-1, 0, 1, 16}. That is, 16 divided by 4 is 4. If there isn't a good reason to use one of the other forms of central tendency, then you should use the mean to describe the central tendency.

The median is simply the middle value of a data set. In order to calculate the median, all values in the data set need to be ordered, from either highest to lowest, or vice versa. If there are an odd number of values in a data set, then the median is easy to calculate. If there is an even number of values in a data set, then the calculation becomes more difficult. Statisticians still debate how to properly calculate a median when there is an even number of values, but for most purposes, it is appropriate to simply take the mean of the two middle values. The median is useful when describing data sets that are skewed or have extreme values. Incomes of baseballs players, for example, are commonly reported using a median because a small minority of baseball players makes a lot of money, while most players make more modest amounts. The median is less influenced by extreme scores than the mean.

The mode is the most commonly occurring number in the data set. The mode is best used when you want to indicate the most common response or item in a data set. For example, if you wanted to predict the score of the next football game, you may want to know what the most common score is for the visiting team, but having an average score of 15.3 won't help you if it is impossible to score 15.3 points. Likewise, a median score may not be very informative either, if you are interested in what score is most likely.

Standard Deviation

The standard deviation is a measure of variability (it is not a measure of central tendency). Conceptually it is best viewed as the 'average distance that individual data points are from the mean.' Data sets that are highly clustered around the mean have lower standard deviations than data sets that are spread out.

For example, the first data set would have a higher standard deviation than the second data set:

Notice that both groups have the same mean (5) and median (also 5), but the two groups contain different numbers and are organized much differently. This organization of a data set is often referred to as a distribution. Because the two data sets above have the same mean and median, but different standard deviation, we know that they also have different distributions. Understanding the distribution of a data set helps us understand how the data behave.

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Descriptive Statistics | Definitions, Types, Examples

Published on July 9, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.

In quantitative research , after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics , which help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.

Table of contents

Types of descriptive statistics, frequency distribution, measures of central tendency, measures of variability, univariate descriptive statistics, bivariate descriptive statistics, other interesting articles, frequently asked questions about descriptive statistics.

There are 3 main types of descriptive statistics:

• The distribution concerns the frequency of each value.
• The central tendency concerns the averages of the values.
• The variability or dispersion concerns how spread out the values are.

You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.

• Go to a library
• Watch a movie at a theater
• Visit a national park

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A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarize the frequency of every possible value of a variable in numbers or percentages. This is called a frequency distribution .

• Simple frequency distribution table
• Grouped frequency distribution table

From this table, you can see that more women than men or people with another gender identity took part in the study. In a grouped frequency distribution, you can group numerical response values and add up the number of responses for each group. You can also convert each of these numbers to percentages.

Measures of central tendency estimate the center, or average, of a data set. The mean, median and mode are 3 ways of finding the average.

Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.

The mean , or M , is the most commonly used method for finding the average.

To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N .

The median is the value that’s exactly in the middle of a data set.

To find the median, order each response value from the smallest to the biggest. Then , the median is the number in the middle. If there are two numbers in the middle, find their mean.

The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.

To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.

Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.

The range gives you an idea of how far apart the most extreme response scores are. To find the range , simply subtract the lowest value from the highest value.

Standard deviation

The standard deviation ( s or SD ) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.

There are six steps for finding the standard deviation:

• List each score and find their mean.
• Subtract the mean from each score to get the deviation from the mean.
• Square each of these deviations.
• Add up all of the squared deviations.
• Divide the sum of the squared deviations by N – 1.
• Find the square root of the number you found.

Step 5: 421.5/5 = 84.3

Step 6: √84.3 = 9.18

The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.

To find the variance, simply square the standard deviation. The symbol for variance is s 2 .

Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.

If you were to only consider the mean as a measure of central tendency, your impression of the “middle” of the data set can be skewed by outliers, unlike the median or mode.

Likewise, while the range is sensitive to outliers , you should also consider the standard deviation and variance to get easily comparable measures of spread.

If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests .

Multivariate analysis is the same as bivariate analysis but with more than two variables.

Contingency table

In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read “across” the table to see how the independent and dependent variables relate to each other.

Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.

From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.

Scatter plots

A scatter plot is a chart that shows you the relationship between two or three variables . It’s a visual representation of the strength of a relationship.

In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.

From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Statistical power
• Pearson correlation
• Degrees of freedom
• Statistical significance

Methodology

• Cluster sampling
• Stratified sampling
• Focus group
• Systematic review
• Ethnography
• Double-Barreled Question

Research bias

• Implicit bias
• Publication bias
• Cognitive bias
• Placebo effect
• Pygmalion effect
• Hindsight bias
• Overconfidence bias

Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

• Distribution refers to the frequencies of different responses.
• Measures of central tendency give you the average for each response.
• Measures of variability show you the spread or dispersion of your dataset.
• Univariate statistics summarize only one variable  at a time.
• Bivariate statistics compare two variables .
• Multivariate statistics compare more than two variables .

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PhD Dissertations in Statistics

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Home > Statistics > Dissertations, Theses, and Student Work

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Department of statistics: dissertations, theses, and student work.

Measuring Jury Perception of Explainable Machine Learning and Demonstrative Evidence , Rachel Rogers

Examining the Effect of Word Embeddings and Preprocessing Methods on Fake News Detection , Jessica Hauschild

Exploring Experimental Design and Multivariate Analysis Techniques for Evaluating Community Structure of Bacteria in Microbiome Data , Kelsey Karnik

Human Perception of Exponentially Increasing Data Displayed on a Log Scale Evaluated Through Experimental Graphics Tasks , Emily Robinson

Factors Influencing Student Outcomes in a Large, Online Simulation-Based Introductory Statistics Course , Ella M. Burnham

Comparing Machine Learning Techniques with State-of-the-Art Parametric Prediction Models for Predicting Soybean Traits , Susweta Ray

Using Stability to Select a Shrinkage Method , Dean Dustin

Statistical Methodology to Establish a Benchmark for Evaluating Antimicrobial Resistance Genes through Real Time PCR assay , Enakshy Dutta

Group Testing Identification: Objective Functions, Implementation, and Multiplex Assays , Brianna D. Hitt

Community Impact on the Home Advantage within NCAA Men's Basketball , Erin O'Donnell

Optimal Design for a Causal Structure , Zaher Kmail

Role of Misclassification Estimates in Estimating Disease Prevalence and a Non-Linear Approach to Study Synchrony Using Heart Rate Variability in Chickens , Dola Pathak

A Characterization of a Value Added Model and a New Multi-Stage Model For Estimating Teacher Effects Within Small School Systems , Julie M. Garai

Methods to Account for Breed Composition in a Bayesian GWAS Method which Utilizes Haplotype Clusters , Danielle F. Wilson-Wells

Beta-Binomial Kriging: A New Approach to Modeling Spatially Correlated Proportions , Aimee Schwab

Simulations of a New Response-Adaptive Biased Coin Design , Aleksandra Stein

MODELING THE DYNAMIC PROCESSES OF CHALLENGE AND RECOVERY (STRESS AND STRAIN) OVER TIME , Fan Yang

A New Approach to Modeling Multivariate Time Series on Multiple Temporal Scales , Tucker Zeleny

A Reduced Bias Method of Estimating Variance Components in Generalized Linear Mixed Models , Elizabeth A. Claassen

NEW STATISTICAL METHODS FOR ANALYSIS OF HISTORICAL DATA FROM WILDLIFE POPULATIONS , Trevor Hefley

Informative Retesting for Hierarchical Group Testing , Michael S. Black

A Test for Detecting Changes in Closed Networks Based on the Number of Communications Between Nodes , Christopher S. Wichman

GROUP TESTING REGRESSION MODELS , Boan Zhang

A Comparison of Spatial Prediction Techniques Using Both Hard and Soft Data , Megan L. Liedtke Tesar

STUDYING THE HANDLING OF HEAT STRESSED CATTLE USING THE ADDITIVE BI-LOGISTIC MODEL TO FIT BODY TEMPERATURE , Fan Yang

Estimating Teacher Effects Using Value-Added Models , Jennifer L. Green

SEQUENCE COMPARISON AND STOCHASTIC MODEL BASED ON MULTI-ORDER MARKOV MODELS , Xiang Fang

DETECTING DIFFERENTIALLY EXPRESSED GENES WHILE CONTROLLING THE FALSE DISCOVERY RATE FOR MICROARRAY DATA , SHUO JIAO

Spatial Clustering Using the Likelihood Function , April Kerby

FULLY EXPONENTIAL LAPLACE APPROXIMATION EM ALGORITHM FOR NONLINEAR MIXED EFFECTS MODELS , Meijian Zhou

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Statistics PhD theses

2015 onwards.

• How It Works

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Introduction

At SPSSanalysis.com , we empower PhD students, researchers, and academics by offering customised services in statistical data analysis and consulting . Our platform simplifies the complex process of statistical analysis, making it accessible for projects of any scale. From Data Processing to Reporting, our expertise spans across leading statistical software such as SPSS , R, STATA, and JASP. By connecting you with seasoned statisticians, we ensure your project is not just completed, but comprehensively understood and expertly presented.

Embarking on your statistical journey with us involves three straightforward steps, designed to integrate seamlessly into your research workflow. This simplicity, coupled with our deep commitment to accuracy and insight, makes SPSSanalysis.com the go-to destination for those seeking Thesis Statistics Help . Dive into a world where data becomes decisions, and analysis reveals insights, all tailored to propel your academic and research projects forward.

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1. Understanding Statistics Help

Thesis Statistics Help is a crucial support system for students grappling with the statistical elements of their research. This service streamlines the process of data analysis , from identifying the correct statistical tests to interpreting the results accurately. It’s not just about crunching numbers; it’s about leveraging statistical insight to bolster your research’s validity and reliability. With the right support, students can transform their data into compelling evidence that supports their thesis argument .

Navigating the complexities of statistical analysis can be daunting, especially for those without a strong background in statistics . This is where Thesis Statistics Help becomes invaluable, providing expert guidance to demystify the process. By tapping into this resource, students can ensure their thesis stands on a solid foundation of accurately analyzed data, enhancing their research’s overall quality and impact.

Navigate the complexities of your thesis with confidence by seeking our expert advice; simply submit your requirements for thesis statistics help on our Get a Free Quote page for personalized support.

  How to Get Thesis Support

Our process for thesis statistics help  is straightforward and efficient, encapsulated in  three simple steps :

• Get a Free Quote :  First, Fill out the form on our website detailing your project requirements. This step helps us understand your needs and provide a precise quote.
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2. Statistical Analysis Services 

Our  Thesis Statistics Help  services cater to a diverse clientele, ensuring that anyone in need of statistical assistance can benefit. This includes:

• PhD Students:  Enhancing their research with advanced statistical analysis.
• Academicians:  Supporting their scholarly work with precise data interpretation.
• Researchers:  Empowering their investigations with robust statistical tools.
• Master Students:  Assisting in the completion of their thesis with accurate data analysis.
• Individuals:  Offering personal projects the benefit of statistical expertise.
• Companies:  Improving business decisions through data-driven insights.

Regardless of your academic or professional background, our services are designed to meet your  statistical analysis  needs . We cater to projects of all sizes and complexities, ensuring that every client receives the support necessary to elevate their research. Thesis Statistics Help for Get a Free Quote Now!

3. PhD Data Analysis Help: For Master’s and PhD Candidates

PhD Thesis Statistics Help is an essential service tailored specifically for Master’s and PhD candidates who are embarking on the rigorous journey of thesis writing. This specialized support goes beyond basic statistical analysis, addressing the unique challenges and expectations faced at the doctoral level. Expert statisticians can provide guidance on advanced statistical methods, help in interpreting complex data sets, and offer advice on presenting findings clearly and compellingly. This level of support is invaluable for candidates looking to make a significant contribution to their field of study.

Elevate your PhD or Master’s thesis with our advanced statistical support. Visit our Get a Free Quote page, where our experts are ready to address your complex data analysis challenges.

4. Exploring Quantitative Theses

Quantitative theses are characterized by their use of statistical methods to analyze numerical data, offering a clear, objective lens through which research questions can be explored. This approach is integral to many fields, particularly the sciences and social sciences, where quantifiable evidence is paramount. Students embarking on quantitative dissertations must develop a strong understanding of statistical principles to apply the correct methodologies and interpret their data effectively.

The backbone of a quantitative thesis is its reliance on empirical evidence derived from statistical analysis . This requires a meticulous approach to data collection, from designing surveys to conducting experiments. Each step must be carefully planned to ensure the integrity of the data, which in turn, supports the validity of the thesis’s conclusions. Mastery of statistical tools and techniques is essential, turning raw data into meaningful insights that drive research forward.

Utilizing Statistics in Quantitative Theses

In quantitative theses, statistics are not just tools but critical components that shape the research narrative. The proper application of statistical methods enables researchers to test hypotheses, identify patterns, and draw conclusions with confidence. This process begins with selecting the right statistical tests, which must align with the research design and objectives. It’s a step that demands not only technical skill but also a strategic understanding of the thesis’s goals.

For precise and insightful quantitative analysis, visit our Get a Free Quote page. Tell us about your data, and we’ll craft a statistical approach that illuminates your research.

5. The Nature of Qualitative Theses

Qualitative theses explore the depth of human experiences, beliefs, and interactions, offering a nuanced understanding of research questions. This approach values the complexity of social phenomena, seeking to uncover the meanings and motivations behind human behavior. Through interviews, observations, and textual analysis, the qualitative dissertation provides a rich tapestry of insights that numerical data alone cannot capture. It challenges researchers to look beyond the surface, engaging with the subjective experiences of their subjects.

The strength of a qualitative thesis lies in its ability to provide detailed, context-rich insights that illuminate the intricacies of its subject matter. This requires a delicate balance between data collection and interpretation, where the researcher’s skill in analyzing and presenting data becomes paramount. Qualitative research demands not just technical proficiency but also empathy and an open mind, allowing for a deep connection with the research topic and participants. It’s a journey into the heart of the subject matter, where statistics complement narratives to build a compelling argument.

Applying Statistics in Qualitative Theses

While qualitative theses primarily focus on narrative and thematic analysis, integrating statistical elements can significantly enhance their depth and validity. Statistics in qualitative research are not about reducing experiences to numbers but about supporting and validating the emerging themes with quantifiable evidence. This complementary approach can illuminate patterns and trends within the data, providing a firmer ground for conclusions and recommendations.

Enhance your qualitative research with thesis statistics help. Outline your project needs on our Get a Free Quote page to explore how our expertise can elevate your thesis.

7. Key Thesis Sections Concerning Statistics: Methodology, Methods, Results

The methodology , methods, and results sections of a thesis are crucial for showcasing the statistical underpinnings of the research. The methodology outlines the overarching approach, detailing how the research was conducted and why certain statistical methods were chosen. This section sets the stage, explaining the framework within which the data was analyzed and interpreted. It’s here that the researcher justifies their methodological choices, highlighting the rigor and reliability of their approach.

Following the methodology, the methods section dives deeper into the specifics of data collection and analysis. It describes the statistical tests used, the rationale behind their selection, and how they were applied to the research data. This section is key for demonstrating the technical competence of the researcher and the validity of the research design. Finally, the results section presents the findings in a clear, logical manner, supported by statistical evidence. This triad of sections forms the backbone of the thesis, underpinning the research with solid statistical foundations.

Subject Areas 

SPSSanalysis.com offers expert  statistical support  across a wide range of subject areas for your dissertation, including but not limited to:

• Psychology :  We assist with statistical analysis for studies in behaviours, cognition, and emotion, among other topics.
• Medical Research :  Our services cover clinical trials, epidemiology, and other health-related research.
• Nursing :  We provide a wide range of  PhD-level statistical consultation  and data analysis services for DNP students.
• Education:  We support research in teaching methods, learning outcomes, and educational policy analysis.
• Sociology :  Our team helps with analyses of social behaviour, community studies, and demographic research.
• Business and Marketing:  We provide statistical insights for market research, consumer behaviour, and business strategy studies.
• Economics:  Our expertise extends to economic models, financial analysis, and policy impact assessments.
• Sports : Statistical support for research on athletic performance, sports psychology, and physical education.
• Nutrition : Analysis for dietary studies, nutritional epidemiology, and health outcome research related to nutrition

This list represents the core areas where we offer specialized statistical support, ensuring that your dissertation benefits from precise and insightful analysis tailored to your specific field of study.

8. Conducting Statistical Analysis for Your Thesis

Conducting statistical analysis for your thesis requires a careful blend of technical skill and critical thinking. It begins with a thorough understanding of your research questions and a strategic approach to data collection. This foundation ensures that the data you gather is both relevant and robust, suitable for the statistical tests you plan to apply. The next step involves selecting the appropriate statistical methods, a decision that hinges on the nature of your data and the objectives of your research. This process is critical for generating valid, reliable results that can support your thesis claims.

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9. Support for Interpreting Thesis Results

Interpreting the results of your thesis requires a keen understanding of both your research context and the statistical methods employed. It’s a critical phase where data speaks to your hypotheses, guiding the conclusions you draw. Expert support in this stage can illuminate the nuances of your findings, helping you to articulate the implications of your research with clarity and precision. This support is particularly crucial for complex analyses, where the interpretation of results can determine the impact of your research.

At SPSSanalysis.com , our statisticians are not just experts in numbers; they are skilled in translating statistical outcomes into meaningful insights. This support extends beyond mere interpretation, offering guidance on how to present your findings in a way that is accessible to your audience. Whether it’s discussing the significance of your results in the broader context of your field or identifying areas for future research, expert interpretation can elevate the quality of your thesis, making your contributions stand out.

10. Assistance with Writing the Methodology Section

Writing the methodology section of your thesis is about more than just listing the steps you took in your research; it’s about justifying your choices and demonstrating the rigor of your approach. This section is foundational, setting the stage for the credibility of your entire thesis. Expert assistance in crafting this section can ensure that your methodology is clearly articulated, from the selection of your sample to the choice of statistical tests. This clarity is essential for readers and reviewers, who must understand and trust your research process.

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11. Statistical Analyses Employed in Theses

The range of statistical analyses employed in theses is vast and varied, tailored to suit the specific demands of each research question. From basic descriptive statistics that summarize data to complex inferential tests that explore relationships and causality, the choice of analysis is critical. This decision should be informed by the research design, the nature of the data, and the hypotheses under investigation. Each statistical method offers unique insights, and selecting the most appropriate one is key to unlocking the full potential of your data.

Understanding the various statistical analyses available and their applications is essential for any researcher. Whether it’s a simple t-test to compare two groups or a multivariate regression analysis to explore multiple predictors of an outcome , the right statistical tools can illuminate the path to significant, meaningful findings. Familiarity with these methods not only enhances the robustness of your thesis but also equips you with the skills to contribute valuable knowledge to your field.

Whether it’s regression analysis or ANOVA, get the statistical guidance your thesis deserves by visiting our Get a Free Quote page and sharing your analytical requirements.

Statistical Tests for Dissertation Statistics

Choosing the right statistical tests is pivotal for analyzing dissertation data effectively. The main tests include:

• Descriptive Statistics : This involves summarizing and organizing data to understand its central tendencies and variability.
• Comparative Statistics : Utilizes tests such as T-tests,  ANOVA  (Analysis of Variance), and Mann-Whitney tests to evaluate differences between groups.
• Inferential Statistics : Employs statistical methods to infer properties about a population based on a sample.
• Correlation Analysis : Measures the degree and direction of association between two variables. For example  Pearson Correlation ,  Spearman’s Rho rank order ,  Kendall’s Tau ,  Partial Correlation ,  and  Canonical Correlation .
• Regression Analysis :  This analysis is key for predicting outcomes and understanding the strength and character of the relationship between variables. For Example  Simple Linear Regression ,  Binary Logistic Regression , and Hierarchical Regression . Probit Regression
• Univariate Analysis : Focuses on analyzing a single variable to describe its characteristics and distribution. This includes measures of central tendency, dispersion, and skewness, providing insights into the pattern of data for that variable.
• Multivariate Analysis : Involves examining multiple variables simultaneously to understand relationships and influences among them.

12. Selecting the Appropriate Statistical Test for Your Thesis

Selecting the appropriate statistical test is a pivotal step in the research process, one that requires careful consideration of your data and research questions. This choice is guided by several factors, including the type of data you have collected, the distribution of that data, and the specific hypotheses you aim to test. The correct test will provide the most accurate and relevant insights, helping you to draw meaningful conclusions from your research.

Make informed decisions on statistical tests by consulting our experts. Submit your project details on our Get a Free Quote page for a strategy that strengthens your thesis.

13. The Importance of Seeking Help with Thesis Statistics

Engaging with Thesis Statistics Help is not merely a convenience; it’s a strategic decision that can significantly elevate the quality of your thesis. Statistical analysis, with its inherent complexity, can be a formidable challenge for many students. Expert guidance can simplify these complexities, providing clarity and confidence in your statistical choices. This support is invaluable for ensuring your research methodologies are sound and your interpretations of data are accurate, lending credibility and authority to your findings.

Moreover, seeking help with thesis statistics can save invaluable time and resources, allowing you to focus more on the substantive aspects of your research. Expert statisticians bring a level of proficiency and insight that can transform your data analysis from a daunting task into a clear, manageable process. This collaboration not only enriches your research experience but also enhances the overall integrity and impact of your thesis. By investing in professional statistical support, you’re ensuring your thesis stands as a testament to high-quality, rigorous research.

14. The Cost of Hiring a Statistician for Your Dissertation

The cost of hiring a statistician for your thesis starts from £250, however, Investing in a statistician for your dissertation represents a significant step towards ensuring the quality and integrity of your research. The cost of such services can vary, reflecting the complexity of the statistical analysis required and the level of expertise of the statistician. At SPSSanalysis.com , we understand the financial constraints faced by students and researchers, which is why we strive to offer competitive rates without compromising on the quality of our services. Our pricing structure is transparent and tailored to meet the needs of a diverse client base, ensuring you receive value for your investment.

Understanding the cost and value of expert statistical analysis is just a step away. Share your thesis statneeds on our Get a Free Quote page, and we’ll outline how our services can fit your budget.

15. What Statistical Software is Used in Theses?

Statistical software plays a pivotal role in theses, offering the tools needed to conduct sophisticated analyses with efficiency and accuracy. The choice of software often depends on the specific needs of the research, including the complexity of the data and the preferred statistical methods. Commonly used software includes:

• SPSS: Renowned for its user-friendly interface, SPSS is widely used across social sciences for a variety of statistical tests.
• R: A powerful and flexible open-source software, R is favored for its extensive range of packages and capabilities, suitable for advanced statistical modeling.
• STATA: Popular in economics and health sciences, STATA offers robust data management and statistical analysis features.
• JASP: An open-source alternative known for its ease of use, JASP is gaining popularity for standard statistical tests and Bayesian analyses.

Selecting the right software is a crucial decision that can influence the efficiency and effectiveness of your statistical analysis. Each program has its strengths, and the best choice for your thesis will depend on your specific research needs and familiarity with the software. Engaging with statistical experts can provide valuable insights into the most appropriate software for your project, ensuring your analysis is conducted with the utmost precision.

16. SPSS Data Analysis Help for Academic Research

Our  SPSS data analysis help  extends across various fields, assisting students to excel in their respective domains:

• Medical : Applying statistical analysis to medical research for groundbreaking findings.
• Nursing :  Enhancing nursing studies with accurate data interpretation.
• Healthcare :  Supporting healthcare research with comprehensive statistical insights.
• Education :  Analyzing educational data to improve teaching and learning outcomes.
• Sociology :  Examining social phenomena through detailed statistical analysis.
• Psychology :  Interpreting psychological data to understand human behavior better.
• Marketing :  Interpreting marketing data to understand human behavior better.

Our services are designed to meet the unique needs of each field, providing tailored support that enhances your research. With our expert guidance, you can harness the power of SPSS to uncover insights that make a difference.  Get a FREE Quote Now!

Stay connected with SPSSanalysis.com on  LinkedIn for the latest updates and insights!

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Statistics and Actuarial Science

Graduate theses.

• Statistics Workshop
• Actuarial Science
• Data Science
• Course Information
• Getting Involved
• Accreditation
• EAL and Other Resources
• Actuarial Science Info Session
• Statistics Admission
• Actuarial Science Admission
• Data Science Admission
• Moving to SFU
• Program Information
• Teaching Assistant Positions
• Intranet Grad Students
• Statistics M.Sc.
• Statistics Ph.D.
• Actuarial Science M.Sc.
• Tuition and Financial Support
• Academic Resources
• M.Sc. and Ph.D. Defences
• Statistical Consulting
• Graduate Students
• News and Events

Below is a list of the theses produced by graduate students in the Department of Statistics and Actuarial Science.

Projects and Theses From Previous Years

2015 - 2019 2010 - 2014 2005 - 2009 2000 - 2004 1990's 1980's and prior

2024-25 Bulletin

Statistics major, program requirements.

• 10 core requirement courses (30 credits)
• Four elective courses (12 credits)

Required Courses

Each of these core courses must be passed with a grade of C- or better.

AP credit can be applied for  Math 131 Calculus I  ,  Math 132 Calculus II , and  Math 233 Calculus III . Students who have completed  Math 203 Honors Mathematics I  and  Math 204 Honors Mathematics II  will have this requirement waived.

CSE 131 Introduction to Computer Science  may be waived after consultation with the director of undergraduate studies of the Department of Computer Science and Engineering. 

Elective Courses

Students complete four additional elective courses (12 units). At least two probability or statistics courses must be taken at the 400 level or above; the other courses may be chosen from an approved list of electives. For more information, visit the Department of Statistics and Data Science website .

Courses in Probability and Statistics

The major and minor in statistics require electives in probability and statistics. Below is the list of these allowed courses:

SDS 3200 and SDS 3211 cannot both be counted toward a major or minor.

Notes to All Majors in Statistics and Data Science

• Upper-level courses have course numbers that begin with a "3" or higher (e.g., SDS 3200 Elementary to Intermediate Statistics and Data Analysis ). Lower-level courses do not count toward upper-level data science requirements, even if they are cross-listed as an upper-level course in another department or program. For example, if SDS 2200 Elementary Probability and Statistics was cross-listed by another department as 3XXX, registering for that 3XXX course would not satisfy an upper-level data science requirement.
• Certain approved substitutions are found on the Department of Statistics and Data Science website .

Course Substitutions

At most, one approved substitution can be made using a course not based in the major's home departments. Please note the policy that, at most, one course from a different department at Washington University can count toward a major or minor.

• ESE 326  can be taken in place  SDS 3200 .  ESE 326 Probability and Statistics for Engineering  and  SDS 3200 Elementary to Intermediate Statistics and Data Analysis  cannot both count toward a major or minor.
• Any course from another department that is cross-listed as a Statistics and Data Science L87 course can count as an upper-level elective. Such L87 courses always end with a "C."
• Econ 4151  (this course can count as a statistics elective)
• ESE 319 ,  ESE 403

Distinctions in Statistics

Distinction.

• Complete at least 33 units of courses from the approved list of electives (besides Math 131, Math 132, Math 233, and CSE 131).
• The GPA for these 33 upper-level units must be at least 3.7. If more than 33 units are taken for a letter grade, the courses with the lowest grades can be omitted when computing the GPA for this purpose.
• Complete at least five courses, each with a B or better, at level 400+.
• All of these courses must be classroom courses (not independent study or study for honors), and they must all be taken for a letter grade.

High Distinction

• Complete all requirements for Distinction.
• Complete an honors thesis.

Highest Distinction

• Complete all requirements for High Distinction.
• Complete at least five courses, each with a grade of B+ or better, at the 400 level or higher. These courses can be the same five courses used for the Distinction requirement, but the grades must be B+ or better.
• Graduate qualifier courses* in Statistics and Data Science are two-semester sequences that start in the fall. The two-semester sequence has a qualifier exam only at the end of the sequence in spring.
• Students must complete and pass one full-year qualifier course sequence and its corresponding exam in statistics. 
• Complete at least 42 units of upper-level statistics and data science courses (besides Math 131, Math 132, Math 233, and CSE 131). The GPA for these 42 upper-level units must be at least 3.7. If more than 42 units are taken for a letter grade, the courses with the lowest grades can be omitted when computing the GPA for this purpose.
• Complete at least nine total courses at the 400 level or above, all with a B+ or better. These courses can include the five courses taken for distinction. All of these courses must be classroom courses (not independent study or study for honors), and they must all be taken for a letter grade.

These qualifier courses can count toward the additional course requirements for Distinction.

Additional Information

Additional requirements.

• All Statistics and Data Science majors must take  Math 131 Calculus I ,  Math 132 Calculus II , and Math 233 Calculus III . There are other ways to fulfill this requirement, including AP credit and  Math 203 Honors Mathematics I  or  Math 204 Honors Mathematics II . Some students may obtain a waiver if they took similar courses before coming to Washington University.
• All required courses (both lower- and upper-level courses) must be completed with a letter grade of C- or better.
• Courses transferred from a two-year college (e.g., a community college) cannot be used to satisfy upper-level requirements.
• At least half of the upper-level units required in a major or minor program must be fulfilled by courses taken in the major's home departments or by approved courses taken in Washington University-approved overseas study programs.
• Courses from the School of Continuing & Professional Studies cannot be used to fulfill major requirements.
• No  upper-level  course used to satisfy a major requirement can be counted toward the requirements of any other major or minor (i.e., no double-counting of courses).
• At most, 3 units of independent study or research work can count toward the major requirements.
• A student cannot declare more than one major or minor in the department. 

Latin Honors

At the time of graduation, the Department of Statistics and Data Science will recommend that a candidate receive Latin Honors (cum laude, magna cum laude, or summa cum laude) if that student has completed the department's requirements for High Distinction or Highest Distinction in Mathematics, including an Honors Thesis. The actual award of Latin Honors is managed by the College of Arts & Sciences.

The Honors Thesis

Arts & Sciences majors who want to be candidates for Latin Honors, High Distinction, or Highest Distinction must complete an honors thesis. Writing an honors thesis involves a considerable amount of independent work, reading, creating mathematics, writing a paper that meets acceptable professional standards, and making an oral presentation of the results.

Types of Projects

An honors thesis can take three forms: 

• A thesis that presents significant work by the student on one or more nontrivial statistics or probability problems.
• A project in applied statistics that involves an in-depth analysis of a large data set. To do an honors thesis involving data analysis, it is usually necessary to have completed SDS 3200 ,  SDS 493 , and  SDS 494   (or SDS 3211 Statistics for Data Science I and SDS 4211 Statistics for Data Science II )  by the end of the junior year and to have the ability to work with statistical software such as SAS, R, or Python. 
• A substantial expository paper that follows independent study on an advanced topic under the guidance of a department faculty member. Such a report would involve the careful presentation of ideas and the synthesis of materials from several sources.

Process and Suggested Timeline

Junior Year, Spring Semester:

• Talk with a faculty advisor about possible projects.
• Complete the Honors Proposal Form and submit it to Dr. Figueroa-López.

Senior Year: 

• By the end of January, provide the advisor with a draft abstract and outline of the paper.
• By the end of February, submit a rough draft, including an abstract, to the advisor.
• The student and the advisor should agree on a date that the writing will be complete and on a date and time for the oral presentation in mid-March (the deadline is March 31).

Departmental Prizes

Each year, the department considers graduating majors for several departmental prizes and also awards a prize to a junior. Recipients are recognized at an annual awards ceremony in April where graduating majors each receive a certificate and a set of honors cords to be worn as part of the academic dress at Commencement. Awards are noted on the student's permanent university record. 

Visit the Statistics and Data Science page for additional information about this program.

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