statistics for dissertation

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/findings/analysis chapter is
• What you need to include in your results chapter
• How to structure your results chapter
• A few tips and tricks for writing top-notch chapter

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.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

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

Awesome content 👏🏾

this was great explaination

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Descriptive Statistics

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

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.

• Dissertation Coaching
• Qualitative Data Analysis
• Statistical Consulting
• Dissertation Editing
• On-Demand Courses
• AI-Powered Academic Coaching Tools

5 Steps to Interpreting Statistical Results for Your Dissertation: From Numbers to Insight

Interpreting results from statistical analysis can be daunting, especially if you are unfamiliar with the field of statistics. However, understanding statistical results is crucial when you’re conducting quantitative research for your dissertation. In this blog post, we will outline a step-by-step guide to help you get started with interpreting the results of statistical analysis for your dissertation.

🔍 Step 1: Review your Research Questions and Hypotheses

Before you start interpreting your statistical results, it is important to revisit your research questions and hypotheses. It is easy to be tempted to include as much information as possible, Doing so will ensure that you are interpreting your results in a way that answers your research questions. When initially confronted with the results of your statistical analyses, you may find it difficult to determine where to start. It is common to feel the temptation to include as much data as possible in your results chapter, fearing that excluding any information might compromise the integrity of the study. However, succumbing to this temptation can lead to a loss of direction and clarity in the presentation of results. Reviewing your research questions and hypotheses will help you to focus on the key findings that are relevant to your research objectives.

📊 Step 2: Examine the Descriptive Statistics

After reviewing your research questions and hypotheses (Step 1), the next crucial step in interpreting your statistical results is to examine your descriptive statistics. Descriptive statistics play a fundamental role in summarizing the basic characteristics of your data, providing valuable insights into its distribution, sample characteristics, frequencies, and potential outliers.

One aspect to consider when examining descriptive statistics is sample characteristics. These characteristics provide an overview of the participants or subjects included in your study. For example, in a survey-based study, you may examine demographic variables such as age, gender, educational background, or socioeconomic status. By analyzing these sample characteristics, you can understand the composition of your sample and evaluate its representativeness or any potential biases.

Additionally, descriptive statistics help you analyze the frequencies of categorical variables. Frequencies provide information about the distribution of responses or categories within a particular variable. This is particularly useful when examining survey questions with multiple response options or categorical variables such as occupation or political affiliation. By examining frequencies, you can identify dominant categories or patterns within your data, which may contribute to your overall understanding of the research topic.

Descriptive statistics allow you to explore additional measures beyond central tendency and dispersion. For example, measures such as skewness and kurtosis provide insights into the shape of your data distribution. Skewness indicates whether your data is skewed towards the left or right, while kurtosis measures the peakedness or flatness of the distribution. These measures help you assess the departure of your data from a normal distribution and determine if any transformation or adjustment is required for further analysis.

Analyzing descriptive statistics also involves considering any potential outliers in your data. Outliers are extreme values that significantly deviate from the majority of your data points. These data points can have a substantial impact on the overall analysis and conclusions. By identifying outliers, you can investigate their potential causes, assess their impact on your results, and make informed decisions about their inclusion or exclusion from further analysis.

Examining your descriptive statistics, including sample characteristics, frequencies, measures of distribution shape, and identification of outliers, provides a comprehensive understanding of your data. These insights not only facilitate a thorough description of your dataset but also serve as a foundation for subsequent analysis and interpretation.

✅ Step 3: Understand the Inferential Statistics and Statistical Significance

After reviewing your research questions and hypotheses (Step 1) and examining descriptive statistics (Step 2), you need to understand the inferential statistics and determine their statistical significance.

Inferential statistics are used to draw conclusions and make inferences about a larger population based on the data collected from a sample. These statistical tests help researchers determine if the observed patterns, relationships, or differences in the data are statistically significant or if they occurred by chance. Inferential statistics involve hypothesis testing, which involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha). The null hypothesis represents the absence of an effect or relationship, while the alternative hypothesis suggests the presence of a specific effect or relationship. By conducting hypothesis tests, you can assess the evidence in favor of or against the alternative hypothesis ( if you need a refresher on hypothesis testing – read more about it here ).

Statistical significance refers to the likelihood that the observed results are not due to random chance. It helps you determine if the findings in your study are meaningful and can be generalized to the larger population. Typically, a significance level (alpha) is predetermined (e.g., 0.05), and if the p-value (probability value) associated with the test statistic is less than the significance level, the results are deemed statistically significant.

By comprehending inferential statistics and assessing statistical significance, you can draw meaningful conclusions from your data and make generalizations about the larger population. However, it is crucial to interpret the results in conjunction with practical significance, considering the effect size, context, and relevance to your research questions and hypotheses.

💡 Step 4: Consider Effect Sizes

It is important to note that statistical significance does not imply practical or substantive significance. Effect size or practical significance refers to the meaningfulness or importance of the observed effect or relationship in real-world terms. While a statistically significant result indicates that the observed effect is unlikely due to chance, it is essential to consider the magnitude of the effect and its practical implications when interpreting the results. They help you assess the importance and meaningfulness of the findings beyond mere statistical significance.

There are various effect size measures depending on the type of analysis and research design employed in your study. For example, in experimental or intervention studies, you might consider measures such as Cohen’s d or standardized mean difference to quantify the difference in means between groups. Cohen’s d represents the effect size in terms of standard deviations, providing an estimate of the distance between the group means.

In correlation or regression analyses, you may examine effect size measures such as Pearson’s r or R-squared. Pearson’s r quantifies the strength and direction of the linear relationship between two variables, while R-squared indicates the proportion of variance in the dependent variable explained by the independent variables.

Effect sizes are important because they help you evaluate the practical significance of your findings. A small effect size may indicate that the observed effect, although statistically significant, has limited practical relevance. Conversely, a large effect size suggests a substantial and meaningful impact in the context of your research.

Additionally, considering effect sizes allows for meaningful comparisons across studies. By examining effect sizes, researchers can assess the consistency of findings in the literature and determine the generalizability and importance of their own results within the broader scientific context.

It is worth noting that effect sizes are influenced by various factors, including sample size, measurement scales, and research design. Therefore, it is crucial to interpret effect sizes within the specific context of your study and research questions.

🗣️ Step 5: Interpret your Results in the Context of your Research Questions

After reviewing your research questions and hypotheses (Step 1), examining descriptive statistics (Step 2), understanding inferential statistics and statistical significance (Step 3), and considering effect sizes (Step 4), the final step in interpreting your statistical results is to interpret them in the context of your research questions.

Interpreting your results involves drawing meaningful conclusions and providing explanations that align with your research objectives. Here are some key considerations for interpreting your results effectively:

• Relate the findings to your research questions: Begin by revisiting your research questions and hypotheses. Determine how your results contribute to answering these questions and whether they support or refute your initial expectations. Consider the implications of the findings in light of your research objectives.
• Analyze patterns and relationships: Look for patterns, trends, or relationships within your data. Are there consistent findings across different variables or subgroups? Are there unexpected findings that require further exploration or explanation? Identify any notable variations or discrepancies that might inform your understanding of the research topic.
• Provide context and theoretical explanations: Situate your results within existing theories, concepts, or prior research. Compare your findings with previous studies and discuss similarities, differences, or contradictions. Explain how your results contribute to advancing knowledge in the field and address gaps or limitations identified in previous research.
• Consider alternative explanations: Acknowledge and discuss alternative explanations for your results. Evaluate potential confounding factors or alternative interpretations that could account for the observed patterns or relationships. By addressing these alternative explanations, you strengthen the validity and reliability of your findings.
• Discuss limitations and future directions: Reflect on the limitations of your study and the potential impact on the interpretation of your results. Address any potential sources of bias, methodological constraints, or limitations in the generalizability of your findings. Suggest future research directions that could build upon or address these limitations to further enhance knowledge in the field.

Remember that interpreting your results is not a standalone process. It requires a holistic understanding of your research questions, data analysis techniques, and the broader context of your research field. Your interpretation should be logical, supported by evidence, and provide meaningful insights that contribute to the overall understanding of the research topic.

Tips for Interpreting Statistical Results

Here are some additional tips to help you interpret your statistical results effectively:

• 👀 Visualize your data: Graphs and charts can be a powerful tool for interpreting statistical results. They can help you to identify patterns and trends in your data that may not be immediately apparent from the numbers alone.
• 📋 Consult with a statistician : If you are struggling to interpret your statistical results, it can be helpful to consult with a statistician. They can provide guidance on statistical analysis and help you to interpret your results in a way that is appropriate for your research questions.
• ✍️ Be clear and concise: When interpreting your results, it is important to be clear and concise. Avoid using technical jargon or making assumptions about your readers’ knowledge of statistics.
• 🧐 Be objective: Approach your statistical results with an objective mindset. Avoid letting your personal biases or preconceptions affect the way you interpret your results.

Interpreting the results of statistical analysis is a crucial step in any quantitative research dissertation. By following the steps outlined in this guide, you can ensure that you are interpreting your results in a way that answers your research questions. Remember to be cautious, objective, and clear when interpreting your results, and don’t hesitate to seek guidance from a statistician if you are struggling. With a little bit of practice and patience, you can unlock the insights hidden within your data and make meaningful contributions to your field of study.

Author:  Kirstie Eastwood

Related posts.

Download our free guide on how to overcome the top 10 challenges common to doctoral candidates and graduate sooner.

Thank You 🙌

• Weblog home

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!!!

Related posts:

No related posts.

MSc Animal Science

View articles

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

Leave a reply Cancel reply

Your email address will not be published. Required fields are marked *

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

• Cookies & Privacy
• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

Dissertation & Thesis Statistics Coaching & Consulting

The Dissertation Coach statistical team is committed to excellence. We recognize that high caliber statistical consulting requires a firm knowledge of statistics, solid people skills, and an awareness of how to handle the challenges that arise as part of quantitative research. Our staff of highly trained and experienced experts will work closely with you to provide a positive service experience and ensure that your quantitative analysis needs are met.

All our statisticians have doctoral degrees and significant experience with quantitative research. They will provide you with step-by-step guidance to ensure you fully understand all aspects of the data analysis process and results. Our statisticians are here to demystify statistics and partner with you so that you will successfully finish your dissertation, thesis, or research project.

We will work with you on an hourly basis to give you more control over the cost of the services we provide. Once we have a chance to speak with you, we can provide a personalized estimate for the cost of assisting you based on your specific needs. We will be pleased to offer you a free consultation.

The Dissertation Coach statistical team is committed to excellence. We recognize that high caliber statistical consulting requires an advanced knowledge of statistics, solid people skills, and an awareness of how to handle the challenges that arise as part of quantitative research. Our staff of highly experienced experts will work closely with you to provide a personalized experience and ensure that your quantitative analysis needs are met.

Over the past 23 years we have successfully worked with thousands of clients seeking statistical assistance. We provide our assistance in an ethical, straightforward manner.

All our statisticians have doctoral degrees and world-class experience with quantitative research.They have served as professors, dissertation committee members, thesis advisors, outside experts, and industry analysts. We will provide you with step-by-step guidance to ensure you fully understand all aspects of the data analysis process and results. Our statisticians are here to demystify statistics and be a part of your team so that you will successfully finish your dissertation, thesis, or research project.

Our areas of expertise include:

• Education (K-12 and Higher Education)
• Social sciences (i.e., Psychology, Sociology, Geography, Anthropology, etc.)
• Business and economics
• Nursing, Medicine, and Health Sciences
• Public Policy, Government, and Political Science
• Biology and LIfe Sciences

We will work with you on an hourly basis to give you control over the cost of the services we provide. Once we have a chance to speak with you, we can provide a personalized estimate for the cost of assisting you based on your specific needs. We will be pleased to offer you a free, low-pressure consultation so you can get to know us better and learn about how we can help you.

Our Statisticians Offer the Following:

• Assistance with both quantitative and mixed-methods data analysis projects.
• Determine appropriate hypotheses, methods, and sample size justifications for your research project.
• Develop and plan the appropriate analytic design.
• Ensuring your research design and data collection process will produce the right information needed for analysis.
• Selection of the proper statistical analysis technique needed to answer your research questions and hypotheses.
• Assistance with survey development, sampling strategies, and the retrieval of data from popular survey platforms (i.e., SurveyMonkey, Qualtrics) and secondary data sources.
• Troubleshoot any programming and coding problems with your data.
• Design the best data management strategies in collaboration with you.
• Step-by-step guidance on how to conduct analyses in statistical software packages. We can run your data analysis for you, complete it with you over a shared screen, or teach you how to independently conduct your analysis. We can also review any statistics you have already run to make sure your analysis was completed correctly. 
• Demonstrate how to interpret and report each type of statistical procedure.
• Craft technical reports, tables, graphics, figures, and other product deliverables as needed.
• Teach and guide you through the process of writing up your findings*.
• Effectively help you address and respond wisely to reviewer feedback and questions about your data analysis and interpretation of findings.
• Provide developmental editing for your methods, results, discussion, and conclusion chapters.
• Provide effective coaching and consulting to help you prepare for your oral defense meeting and preparation to answer methods and statistical questions from your committee.

*Please note that The Dissertation Coach is not a dissertation or thesis writing service. We will not write a student’s dissertation or thesis on their behalf under any circumstances. We act as consultants, advisors, coaches and mentors to students but never as the authors of their doctoral dissertations or master’s theses.

Statistical Techniques We Offer

Our team of statisticians are ready to conduct a variety of basic and advanced analytic techniques to meet your needs, regardless of whether you are doing univariate, bivariate, or multivariate analyses. Our staff is knowledgeable and skilled in using SPSS, Stata, SAS, R, and Minitab. Our statisticians are experts in the following statistical techniques:

• Power Analysis & Sample Size Calculations
• T-tests (single-sample, independent-samples, paired)
• Factorial designs (ANOVA, ANCOVA, MANOVA, MANCOVA)
• Categorical models (Proportions test, chi-squares, contingency tables, loglinear models)
• Correlations (Pearson, Spearman, partial)
• Repeated measures tests
• Regression models (linear, non-linear, hierarchical, logistic, ordinal, poisson, cox)
• Mediation and moderation models
• General and generalized linear models (GLM)
• Path analysis
• Principal Components Analysis & Factor Analysis (Exploratory & Confirmatory)
• Structural Equation Modeling (SEM)
• Cluster Analysis
• Propensity score analysis
• Missing Data Imputation
• Meta-analysis
• Econometrics (forecasting, time series, ARIMA, panel, cointegration)
• Psychometric (reliability, validity, IRT) 
• Longitudinal models (latent growth, mixed effects)
• Nonparametric tests (Mann-Whitney, Kruskal-Wallis, Wilcoxon Signed-Ranks, McNemar’s, Friedman’s)
• Simulations and bootstrapping
• Data visualizations 
• Market basket analysis, neural networks, and decision trees
• Text mining
• Sentiment analysis

Our statisticians will help you understand all data analyses and make sure you are fully prepared to explain and defend your analyses. They will provide ample phone and email support to you and will work with you until you have successfully completed your dissertation or thesis. No matter your needs, our statistical team can help you get to the finish line!

To read what clients are saying about our Statistical Analysis Services, visit our Testimonials Page

Doctoral Program

Program summary.

Students are required to

• master the material in the prerequisite courses ;
• pass the first-year core program;
• attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year);
• satisfy the depth and breadth requirements (2nd/3rd/4th year);
• successfully complete the thesis proposal meeting (winter quarter of the 3rd year);
• present a draft of their dissertation and pass the university oral examination (4th/5th year).

The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.

All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in our PhD handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).

Statistics Department PhD Handbook

All students are expected to abide by the Honor Code and the Fundamental Standard .

Doctoral and Research Advisors

During the first two years of the program, students' academic progress is monitored by the department's Graduate Director. Each student should meet at least once a quarter with the Graduate Director to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.

Qualifying Examinations

Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for Ph.D. Candidacy, a university milestone, by the end of spring quarter of their second year.

While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.

Thesis Proposal Meeting and Dissertation Reading Committee 

The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.

The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member. 

The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.

 For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.

University Oral Examinations

The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.

The Dissertation Reading Committee must also read and approve the thesis.

For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .

Dissertation

The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's dissertation reading committee.

• How It Works

Dissertation Statistics Help

Seeking a Statistician for Dissertation Statistics Help ?

We specialize in Statistical Data Analysis , providing comprehensive support to Master’s, Ph.D. students, and researchers. Our team delivers high-quality analysis for your research, regardless of the topic or tight deadlines . We offer tailored solutions for Data Analysis using SPSS , R-Studio, and STATA.

1. Submit Your Data Task

Start by clicking on GET a FREE QUOTE button. Indicate the instructions, the requirements, and the deadline, and upload supporting files for your Data Analysis Task .

2. Make the Payment

Our experts will review and update the quote for your quantitative data analysis task. Once you agree, make a secure payment via PayPal, which secures a safe transaction.

3. Get the Results

Once your analysis report is ready, we’ll email you the original solution attachment. You will receive a high-quality result that is 100% plagiarism-free within the promised deadline.

What is a Dissertation?

A dissertation is a substantial piece of academic work that Ph.D. students and researchers undertake to demonstrate their mastery of a particular subject or field. It’s a structured and in-depth document that explores a specific research question, presents a unique contribution to the existing body of knowledge, and is often a requirement for earning a doctoral degree.

What is Dissertation Statistics Help?

Dissertation Statistics Help is a specialized service aimed at supporting Ph.D. students, academicians, and individuals in conducting statistical analysis for their dissertations. It plays a crucial role in ensuring that the data collected for the dissertation is processed accurately and that the results are interpreted meaningfully. Dissertation Statistics Help services like those offered at SPSSanalysis.com can guide you through the intricacies of data analysis, helping you obtain reliable, insightful results .

Get Help with Dissertation Statistics in three Easy Steps

SPSSanalysis.com is your trusted partner for obtaining comprehensive Dissertation Statistics Help. Our user-friendly platform offers an easy three-step process to get a quote for your data analysis task. By filling out our order form with your project details, you’ll receive a free quote promptly. Once your project is approved, our team of experts initiates the task and ensures that you receive the results within the agreed-upon timeframe. Here are easy three steps.

• STEP 1 : Submit your Dissertation Statistics Task – Get a Free Quote !
• STEP 2 : Make the Payment
• STEP 3 : Get the Results Chapter Done!

We are dedicated to making the process of obtaining Dissertation Statistics Help efficient and effective for Ph.D. students, academicians, and individuals working on research projects.

Why Do You Need to Get Help with Dissertation Statistics?

Getting help with dissertation statistics is essential for several reasons. Firstly, statistical analysis can be complex and challenging , especially for individuals who are new to it. Professional guidance ensures that your data analysis is accurate and that you obtain meaningful results. Additionally, obtaining help with dissertation statistics saves time and reduces the stress associated with complex data analysis tasks. At SPSSanalysis.com , our experts are ready to provide the support you need to ensure the success of your dissertation.

What is Dissertation Statistics Service?

Dissertation Statistics Service encompasses various specialized forms of assistance to ensure the success of your dissertation. These services include quantitative dissertation statistics help, dissertation statistics consulting, SPSS dissertation support, dissertation statistics tutoring, and more. Whether you require assistance with data analysis , result interpretation , or guidance in using statistical software , our Dissertation Statistics Service at SPSSanalysis.com is designed to meet your specific needs. The following section shows our expert’s areas.

1. Quantitative Dissertation Statistics Help

For those conducting quantitative research, our service includes dedicated quantitative dissertation statistics help. This form of assistance is ideal for researchers dealing with numeric data, surveys, and experiments. Our experts can guide you through the entire data analysis process, from data entry to conducting statistical tests. Get a Free Quote for your Dissertation Now!

2. Dissertation Statistics Consultant

If you require personalized support and guidance throughout your dissertation journey, our dissertation statistics consultants are here to assist. These experts work closely with you to ensure that the statistical analysis aligns with your research goals and that the results are interpreted accurately. Get a Free Quote for your Dissertation Now!

3. SPSS Dissertation

Many researchers opt to use SPSS for their data analysis. Our SPSS dissertation assistance is tailored to those who prefer this software. We provide guidance on using SPSS, ensuring that your data is analysed effectively and in line with the best practices. Get a Free Quote for your Dissertation Now!

4. Dissertation Statistics Tutor

For those who want to develop their statistical skills, our dissertation statistics tutors can offer one-on-one guidance. This is particularly helpful for Ph.D. students who wish to enhance their understanding of statistical analysis and be better equipped to tackle data analysis independently. Get a Free Quote for your Dissertation Now!

5. Statistics Help for Students

We offer statistics help tailored for students. This service is designed to assist students in understanding and applying statistical techniques, making it a valuable resource for those who are new to data analysis. Get a Free Quote for your Dissertation Now!

6. Ph.D. Statistics Help

Ph.D. students often face unique challenges in their research, and our Ph.D. statistics help service address those challenges. We provide expert support in designing experiments, conducting complex analyses, and interpreting results. Get a Free Quote for your Dissertation Now!

7. Statistical Analysis Help

If you find statistical analysis daunting, our statistical analysis help service can simplify the process for you. We can help you choose the appropriate statistical tests, ensure data integrity, and provide in-depth explanations of your results. Get a Free Quote for your Dissertation Now!

8. Statistics Help Online

For added convenience, our statistics help is available online, making it accessible from anywhere at any time. You can reach out to our experts, ask questions, and receive guidance without leaving your workspace. Get a Free Quote for your Dissertation Now!

What are the five steps in Dissertation Statistics?

Quantitative analysis typically involves the following five steps:

• Define the Research Question : Clearly articulate the research question or hypothesis that you aim to investigate through quantitative analysis.
• Data Collection : Gather relevant data using surveys, experiments, or other data collection methods.
• Data Cleaning and Preparation : Clean, organize, and format the data for analysis, including dealing with missing values and outliers.
• Data Analysis : Employ statistical techniques to analyze the data and test your hypothesis or research question.
• Interpret and Report Results : Interpret the findings and report the results, often using tables, graphs, and statistical tests to support your conclusions.

Quantitative data analysis can be a complex process, but  SPSSanalysis.com  is here to simplify it for you. We assist you at every step of the way, from defining your research question to reporting your results. Our team of experienced professionals ensures that your data is handled with precision and accuracy, helping you navigate the intricacies of  statistical analysis . With our expertise, you can trust that your quantitative research is in capable hands, making your academic journey smoother and more successful.  Get a Free Quote Now!

  How Statistics are Used in a Dissertation?

Statistics play a vital role in a dissertation by providing the tools to analyse and interpret data effectively. In a dissertation, statistics are used to process data, test hypotheses, identify trends, and draw meaningful conclusions. Whether you’re conducting surveys , experiments , or analysing existing datasets, statistical methods help you gain valuable insights from your research.

How Do You Write a Statistical Analysis for a Dissertation?

Writing a statistical analysis for a dissertation involves a structured approach. Firstly, you need to describe the data you collected and the statistical methods used. Then, present the results of your analysis, including tables, figures, and graphs. Finally, interpret the results and discuss their implications in the context of your research question.

How to Write a Dissertation Results/Finding Chapter?

The dissertation results or findings chapter is where you present the results of your research. It should include clear descriptions of the data, the statistical tests applied, and the results obtained. Use visual aids like tables and charts to enhance clarity and make sure to interpret the findings in the context of your research questions.

Why is Statistics Important in a Dissertation?

Statistics are crucial in a dissertation as they provide the means to analyze data systematically and draw valid conclusions. Statistics help in testing hypotheses, determining relationships between variables, and ensuring the reliability of research findings. They add rigor and credibility to your research

How Much Does it Cost to Hire a Statistician for Your Dissertation?

The cost of hiring a statistician for your dissertation can vary based on factors like the complexity of your project and the level of assistance needed. Dissertation statistics services typically provide transparent pricing structures and offer different packages to suit your budget and requirements. Our dissertation statistics services price starts from £ 279.99, please visit the pricing page and Get a FREE Quote for Your Dissertation Statistics .

Can I Get Help with My Dissertation?

Yes, you can get help with your dissertation, and it’s a common and practical approach. Dissertation assistance services like SPSSanalysis.com offer support in various aspects of the dissertation process, including data analysis , writing , and editing . Whether you need guidance on statistical analysis, result interpretation, or writing the results chapter, professional help can enhance the quality of your dissertation. Get Help from Us Now!

1. Can You Hire a Statistician for Dissertation?

Yes, you can hire a statistician for your dissertation . Many researchers choose to collaborate with professional statisticians to ensure that their data analysis is conducted accurately and efficiently. Statisticians bring expertise in statistical methods and software tools, making them valuable partners in achieving high-quality results.

2. Can I Pay for Someone to Write My Dissertation Results?

Yes, you can pay for someone to write your dissertation results . While it’s common to seek assistance with the statistical analysis of your dissertation , the ethical considerations surrounding the actual writing of the dissertation results may vary. Some students choose to work with professional editors or writers to enhance the clarity and coherence of their results chapters, but it’s essential to maintain academic integrity and contribute your original research.

3. Is It Ethical to Get Help with Dissertation Statistics?

Getting help with dissertation statistics is generally considered ethical, as it ensures the accuracy and validity of your research. However, it’s essential to clearly attribute the assistance received in your dissertation and follow the ethical guidelines set by your academic institution.

4. Hire a Statistician for Dissertation

Hiring a statistician for your dissertation is a prudent choice, especially if your research involves complex data analysis. Statisticians can guide you through the entire process, from data cleaning and preprocessing to conducting advanced statistical tests. Their expertise adds a layer of quality assurance to your work.

At SPSSanalysis.com , we offer a range of services that cater to different aspects of your dissertation, whether it’s statistical analysis, result interpretation, or writing assistance. We aim to provide the support you need to ensure your dissertation is of the highest quality and meets the standards of academic integrity. Get a FREE Quote Now!

What Statistical Software is Used for Dissertations?

Various statistical software tools are used for dissertations, including SPSS, R, STATA, and more. Here is some most common statistical software in survey data analysis .

• MAXQDA, and more.

The best software for you depends on your specific research needs and familiarity with the tools. Get a Free Quote Now!

Help With Dissertation Data Analysis Software

Obtaining help with dissertation data analysis software can be invaluable for ensuring that your data analysis is conducted accurately and efficiently. Dissertation data analysis often involves using software tools like SPSS, and professional guidance can enhance your proficiency in these tools.

SPSS   is known for its user-friendly interface and comprehensive analytical capabilities, making it suitable for a wide range of research projects. R-Studio is a powerful open-source tool with extensive statistical packages, favored by statisticians and data analysts. AMOS is ideal for structural equation modeling (SEM) in survey research. STATA  excels in data manipulation and  statistical analysis . Excel is user-friendly and versatile, making it a great choice for simple analyses. NVivo and  MAXQDA  are preferred for qualitative data analysis.

What Statistical Analysis Should I Use for Dissertation?

The choice of statistical analysis for your dissertation depends on your research questions and the type of data you have collected. Common statistical analyses used in dissertations include t-tests, ANOVA , regression analysis, factor analysis, and more. The selection should be based on the specific goals of your research.

How to Choose the Right Statistical Test

Selecting the right statistical test is crucial to obtaining accurate results in your dissertation. To choose the appropriate test, you need to match it to your research objectives and the nature of your data. Consulting with experts at SPSSanalysis.com can ensure that you make the right choice for your specific research needs. Get a Free Quote for your Data Analysis Now!

Elevate Your Research with Custom Quantitative Data Analysis Service for Academic Excellence

Why Choose Us ?

On-time delivery, free revisions, experienced statisticians.

We’ve established a platform that directly connects students to our in-house team of certified statisticians, offering expert support for Statistical Data Analysis in the UK. Our top-tier statistical reports not only enhance your academic performance but also leave a lasting impression on your supervisor, all while ensuring punctual submissions.

Dr. Su Statistics | Statistical Consulting

Statistical consulting services | dr. su statistics.

Do you need a statistician who can provide PhD-level statistical consult ing services and data analysis help for your dissertation, thesis, or research project ? My name is Dr. Yuhua Su and I have a doctoral degree in Statistics. I am the owner and the sole statistic al consultant of Dr. Su Statistics. 

Statistical Consulting Services I Offer

I have extensive experience in statistical consultation , data analysis , and research design for both quantitative and qualitative projects. I offer statistic s help for

PhD dissertations and Master's theses , 

DNP capstone projects ,

Business projects (e.g., marketing research, business intelligence, predictive analytics, etc.), 

Biostatistics consulting (e.g., design and analysis of c linical trials, observational studies, and surveys, analysis of public databases, sample size and power calculations , etc.),

Analysis of survey data, 

Any kind of academic projects (e.g., research proposals, manuscripts, IRB/grant applications, undergraduate projects, etc.), 

Testimony by declaration, and 

Statistics assignments/homework tutoring . 

Regardless of your study field , research questions, hypotheses, methods, or analysis plans, I can help you analyze your data. Take a look at the statistical consulting services I offer to academic researchers and students and to businesses and professionals .

Statistical Consulting Services You Will Receive

I will tackle your projects with full attention and my advanced knowledge of statistics, and treat your research just like my own personal research. I can offer you a more personalized service than you would find at a large statistical consulting firm. You and I will work together to develop a personalized approach to your research questions. The statistics and methods used will match your interests and questions. You will receive a clear explanation of the statistical methods and results so you are in charge of your research and projects. To maintain the quality of my work, I do not subcontract out any of my projects. I do all the consulting myself.

Hire a Statistician for Your Research Study 

Working as a freelance statistician and statistical consultant since 2011 , I have worked with hundreds of students (both undergraduate and graduate) , researchers, professionals, and business organizations, across multiple disciplines in over 25 countries . Let me help you, too! Contact Dr. Su ( [email protected] ) for a free quote and hire a statistician now to receive PhD-level statistics and data analysis help for your project.

Dr. Su Statistics | Statistical C onsulting

PhD statistician for business solutions and academic success

Email: [email protected]

Phone: 808-4941545

Dr. Su Statistics is a statistical consulting firm that specializes in statistical data analysis for quantitative and qualitative research projects, dissertation/thesis statistics consultation, statistical assistance for business decisions, and statistics tutoring for assignments and homework. Services are offered for clients across the USA and other continents.  

Contact Dr. Su

Dissertation Statistician | Dr. Yuhua Su

"Good afternoon- I wanted to share with you that I successfully defended my dissertation last week. I cannot thank you enough for the excellent and thorough job you did on my statistics. I am so glad that I found you and I will definitely recommend you in the future!" -- United States

Need a statistician/statistical consultant for your research project? Contact Dr. Su via email ( [email protected] ) for a free quote.  

Frequently Asked Questions  

Q1: Is it ethical to hire a statistician for my dissertation, thesis, or research project?

Absolutely yes. Obtaining statistics help from a qualified and experienced statistician/statistics consultant can help ensure that your research study is conducted and analyzed correctly, leading to reliable and valid results. I have helped hundreds of graduate students and professionals , and some of them have acknowledged me in their dissertation/thesis or publications . In fact, m any of my clients were referred to me by their committees. N onetheless, a t Dr. Su Statistics, I maintain confidentiality for all of my clients.  

Q2: What are the usual turnaround times like?

The turnaround time varies depending on my workload. I typically quotes 2 -4 weeks for new clients. For small projects, quotes of one week or less may be provided. For rush jobs, please send me an email ( [email protected] ) to inquire my availability.

Q3:  How much does the statistical consulting service cost?

My hourly rate is $150 USD and I can give you a quote for the help you are looking for.

Q4: Do you have experience with…?

I utilize a variety of statistical methods in my statistical consulting work, including some common data analysis methods and more advanced statistical analysis methods . If you do not see your analysis methods listed, please email me ( [email protected] ) and ask about the possibility of utilizing the methods. I have never had a problem bringing my statistical expertise into an area that I have not previously had experience with.

Q5: Do you keep the data and research details secure ?

Yes, I confirm that all your data and details are confidential with me and will not be shared with third parties for any commercial or non-commercial under any circumstances.

Q6: Do you have an example of your work? 

Click here to see a list of my published works. 

• USF Research
• USF Libraries

Digital Commons @ USF > College of Arts and Sciences > Mathematics and Statistics > Theses and Dissertations

Mathematics and Statistics Theses and Dissertations

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

Recursive Methods in Number Theory, Combinatorial Graph Theory, and Probability , Jonathan Burns

On the Classification of Groups Generated by Automata with 4 States over a 2-Letter Alphabet , Louis Caponi

Statistical Analysis, Modeling, and Algorithms for Pharmaceutical and Cancer Systems , Bong-Jin Choi

Topological Data Analysis of Properties of Four-Regular Rigid Vertex Graphs , Grant Mcneil Conine

Trend Analysis and Modeling of Health and Environmental Data: Joinpoint and Functional Approach , Ram C. Kafle

Advanced Search

• Email Notifications and RSS
• All Collections
• USF Faculty Publications
• Open Access Journals
• Conferences and Events
• Theses and Dissertations
• Textbooks Collection

Useful Links

• Mathematics and Statistics Department
• Rights Information
• SelectedWorks
• Submit Research

Home | About | Help | My Account | Accessibility Statement | Language and Diversity Statements

Privacy Copyright

CALL Dr. Fisher : +1-402-953-5167

Statistics  Consulting

Affordable statistics consulting service, statistics consulting, data analysis, and results help at affordable prices. professional, client-oriented, and prompt statistics consulting service for doctoral candidates with dissertations, master and undergraduate students with a thesis or a final project, postdoctoral, researchers requiring help with manuscripts or articles, private businesses, government and ngos requiring help with data analysis or research projects..

Success! Your Message Has Been Received.

Dr. Fisher will get back to you!

Having issues

With your  data, a nalysis, results,, or statistics, do you h ave statistics, or data analysis for your ph.d. dissertation, master thesis project, proposal, journal paper, or any statistics question have you run out of time, or are not sure how to do the work right and on time do you need to pass your final statistics or research methods class and graduate on time.

Dr. Fisher is h ere to help you with:

Doctoral D issertations, Journal Papers & Articles, Doctoral Proposals, Conference Papers & Abstracts, Master Thesis Projects, DNP projects, Quantitative Data Analysis, Qualitative Analysis, Statistics Assignments Projects, Statistics Exams, Dissertation Prospectus, Concept Papers & SMR, and Conference Presentations. 

Whether you have lar ge or small data or a single statistics problem, Dr. Fisher can help you: Send Dr. Fisher an email to  Ron @FisherStat.com  for prompt help! I always monitor my email and reply promptly to emails.

Contact me for Free Review and Quote

[email protected], 100% guarantee.

Upon our agreement, I will do the work and send you the report on the agreed date/time. The standard data analysis and results report will be fully comprehensive to ensure that you not only get the complete work, but you learn and understand everything behind it so you defend or discuss it with confidence. The standard data analysis and results report will include detailed and complete information about the performed statistical analysis procedures, and detailed results presentation and statistical interpretations. I always provide complete and comprehensive statistical interpretations; not just brief annotations of raw outputs as most other statisticians do.

I also provide free and unlimited customer support. I will answer all questions and address any concerns that you might have about the analysis, results or write up. In addition, I will be happy to address any comments or feedback you may receive from your advisor, chair, or committee members about the statistical analysis and results.

Senior Statistician

My name is Dr. Ron Fisher. I have Ph.D. in Statistics and over 20 years of experience as a university professor, academic researcher, and now private statistics consultant.

Dr. Fisher offers Statistical Consulting service to help with both quantitative and qualitative projects requiring data analysis or statistics help including PhD dissertations, thesis projects (both graduate and undergraduate), journal and conference articles, manuscripts, and presentations, research proposals, concept papers, DNP projects, statistics assignments projects and statistics homework/assigment and exams projects. 

No project is too big, too small or too complex. Dr. Fisher can assist  with any task requiring statistics consulting or quantitative or qualitative data analysis or results presentatation and interpretations.

Contact Dr. Fisher with your request for FREE initial consultation and quote:  Email:  [email protected]    Phone: (402) 953-5167

get help from

I quote all work on a per-project basis so you know the exact amount you will pay for the whole project. No matter how many hours or days I actually spend working to do the work or how many questions you may ask, you will not pay any extra. I also offer a free initial consultation to determine the exact requirements and best route forward before initiating the contract. In order for me to provide you with an upfront quote of the total cost, please send me all relevant material and detailed instructions about the help you need.

1) For data analysis and results help , please send me:

a.The data and research instruments (i.e. questionnaire or survey tools)

b. The study proposal or Introduction and Methodology.

If you do not have a written proposal, please send me a list of your research questions /hypotheses or objectives and brief background information about your research and methodology.

2) For research proposals or prospectus, concept papers, and IRB applications , please send me the most up-to-date draft, and highlight/mention the areas you need help with. If you have received comments from your committee, chair, or methods advisor, please send me the comments you need help addressing.

Without this information, I wouldn't be able to provide you with an upfront quote for the cost. I need to review the material that you need help with because each research or project is very different. Please make sure to let me know exactly what it is that you need help with. I also need to know if you have a deadline before I can give a quote.

Once I receive your research material and instruction, I will review them and will get back to you with my FREE up-front exact quote along with a turnaround to complete the work and a proposed plan of action for your review and approval.

I prefer to communicate via email. I must review and study the project material. You will receive an accurate quote by sending me the data, research proposal or relevant information (research questions and a brief background about your project). It is not possible to provide quotes over the phone.

Send me your email with attachments to  [email protected]  or, for short messages, you may use the simple online form.  If you require a non-disclosure agreement before you sent any material, please let me know and I will forward one to you.

I have helped hundreds of Ph.D. candidates, graduate, and undergraduate students, and researchers in different areas including, but not limited to:

*  Psychology  * Education Research * Nursing research * Genetics *  Clinical Trials *  Biostatistics * Agriculture Science * Business Management 

*  Client Feedback Data *  Criminal Justice

*  Econometrics *  Engineering Management

*  Environmental Engineering *  Field Data

*  Finance *  Marketing Statistics

*  MBA dissertation Statistic *  Medicine

*  Pharmaceutical Statistical Analysis

*  Political Science *  Public Health Data Retrieval &      Analysis *  Psychometrics * Social Sciences

*  Sports Data Analysis *  Statistics for Nursing

*  Survey construction, testing, and validation and many other areas.

Statistics Software:

I can assist you with your statistics or data analysis project using any statistics software of your choice. The majority of my clients ask for SPSS, SAS, STATA, SmartPLS, R, NVivo, MPlus, MINITAB, EViews, MATLAB, S-Plus, LISREL, or AMOS. I have all major statistics and data analysis software packages but I can also help with other statistical software packages, so please let me know if you are required to use a specific software package.​​​​​​​

Do You Know! Hiring Professional Statistics Consultant Saves You

Time and Money!

How much would it cost if you do not graduate in time? How about lost work time and stress? It costs much less to contract out a professional statistics consulting service.  I can do all your statistical analysis and results write-up within one or two weeks, or faster if needed. Ultimately,   I guarantee your project will be completed with the highest accuracy and on time. I will work continuously to help answer any questions you may have from your research methodologist or supervisory committee.​

The best reward I get is your success and satisfaction!

Please see below for some testimonials​

"Thank you! Thank you! Thank you!

I am crying tears of joy at this moment. This process is truly nearing completion thanks to you! I thank God for you and your expertise! Thank you so very much for the complimentary extras. You have been such a blessing to me. You have provided more support, guidance, expertise, and encouragement than my entire experience with the university.

God bless you!"  

Alisa Jones

"Hello Dr. Fisher,

I am so impressed with the quality of work. I wish you could have shared in my excitement when I opened and reviewed the work. I leapt quite high and I'm no small chick. :) I have referred several friends to you and hopefully you will be hearing from them soon. I will read the entire paper and get in touch if I have any questions. Thank you so very much."  

See Feedback and Testimonial for the more feedback or to leave feedback

Why choose my service when there are many other "experts" offering statistical consulting services?

                                                                      frankly speaking, people should search for,                                                                        consultants who can get the job done right and in,                                                                        time. i have personally heard many terrible stories,                                                                        from students failing or having to rework their,                                                                        statistical analysis because they contracted the,                                                                         wrong statistician or consultant.,                                                                       i completely understand why choosing a cheaper,                                                                    service may sound tempting; however, sacrificing professional statistical analysis proves to be more costly. this also challenges your every belief that the highest-priced service is the best. after all, experienced consultants are genuine and worth the money .

I have worked for over 20 years at the university and as private statistics consultant with graduate students and researchers. As a private statistics consultant, I know what it takes to do statistical analysis right from the first time. My commitment to excellence demonstrates experience, knowledge, dedication, and hard work.

I deal with your research project under strict confidentiality while dedicating all my time and effort to do it right while, in most cases exceeding your expectations. The vast majority of my clients; Ph.D. candidates and researchers; need help with statistical analysis of their dissertation, thesis, or publish a peer-reviewed journal article. I also receive and provide assistance at the early proposal, prospectus, or concept paper stages to determine the most appropriate statistical tests, power analysis, and sample size calculations and by drafting the data analysis plan (statistical methodology section).

For Ph.D. Candidates: I can do all your statistical analyses, regardless of the complexity or length. I present the results in a "Results Chapter" following APA or any other style of your choice, along with writing detailed statistical methodology, which you can use as part of your "Methodology Chapter” if required. I have extensive and up-to-date expertise about specific requirements of all major universities, including online and distance learning ones such as Capella, Walden, Nova (NSU), Argosy, University of Phoenix (UoP), and NCU. I have extensive experience with the Concept Paper phase at NCU. I’m an expert with APA 6 ed style, which is required by many universities, including UoP. I have also worked with many students from Capella on their SMRs, and I am familiar with the typical committee and mentor requirements at different universities.

I will outline and describe the tests and procedures in great detail so that you understand everything. The report will be properly referenced, 100% original, and guaranteed to be free from plagiarism. It will be written specifically for you based on your exact requirements and using the data you send for analysis.

I help clients from all over the world, and most of my clients come from the United States, Canada, Australia, the United Kingdom, Germany, New Zealand, France, Sweden, Spain, Italy, Brazil, Mexico, Argentina, Saudi Arabia, Qatar, and UAE.

                                Your Confidentiality and Rights 

                               (In large font and plain English)

.                                       All communication, information, and data are dealt  with as "

                                         Strictly Confidential," and no information will be released to any

                                        other person or third party. You retain all the property rights of your data and any results coming out of the statistical analysis. I will not ask for or require any personal, financial or affiliation information from you. You do not have to acknowledge my contribution to your publications. I have a great deal of experience with IRBs, data privacy , and ethics. I understand the importance of keeping data confidential and secure. All of your data is kept on an encrypted hard drive in a 24-hour secure office.

There will be NO hidden fees or surprises along the way!

My consulting fees are set to be affordable by  most students and academic or private researchers. I offer the lowest possible prices. I believe no other company or statistics consultant provides a similar service for less. I guarantee to match any reasonable quote you get for the same service I provide (complete write-up with FREE and unlimited after-service support).

I normally require a 50% down payment to do the work. Only rushed or small projects require a full upfront payment.

Fee Payment:

All payments are processed online through PayPal.com, a highly secure system. All major credit cards, debit cards, and bank transfers are accepted. You will not be required to sign up for a PayPal account, as you can make the payment without having a  PayPal account.

If someone else is making the payment on your behalf, or the account is in a different name, please insert your name as a reference in the "Instructions to the seller" box. To make a payment, click the appropriate PayPal currency button. Or you can make the payment to my PayPal account [email protected] . Once the payment is completed successfully, PayPal sends an immediate notification to you and me. I will also confirm back with you once the payment is received.

If you have a problem making the payment, please get in touch with your bank or PayPal customer service; they are usually very prompt and helpful. 

Pay in GBP £

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

Sponsor: Tian Zheng

Nabarun Deb

Blessing of Dependence and Distribution-Freeness in Statistical Hypothesis Testing

Sponsor: Bodhisattva Sen / Co-Sponsor: Sumit Mukherjee

Elliot Gordon Rodriguez

Advances in Machine Learning for Compositional Data

Sponsor: John Cunningham

Charles Christopher Margossian

Modernizing Markov Chains Monte Carlo for Scientific and Bayesian Modeling

Sponsor: Andrew Gelman

Alejandra Quintos Lima

Dissertation TBA

Sponsor: Philip Protter

Bridgette Lynn Ratcliffe

Statistical approach to tagging stellar birth groups in the Milky Way

Sponsor: Bodhisattva Sen

Chengliang Tang

Latent Variable Models for Events on Social Networks

On Recovering the Best Rank-? Approximation from Few Entries

Sponsor: Ming Yuan

Sponsor: Sumit Mukherjee

2021 Ph.D. Dissertations

On the Construction of Minimax Optimal Nonparametric Tests with Kernel Embedding Methods

Sponsor: Liam Paninski

Advances in Statistical Machine Learning Methods for Neural Data Science

Milad Bakhshizadeh

Phase retrieval in the high-dimensional regime

Chi Wing Chu

Semiparametric Inference of Censored Data with Time-dependent Covariates

Miguel Angel Garrido Garcia

Characterization of the Fluctuations in a Symmetric Ensemble of Rank-Based Interacting Particles

Sponsor: Ioannis Karatzas

Rishabh Dudeja

High-dimensional Asymptotics for Phase Retrieval with Structured Sensing Matrices

Sponsor: Arian Maleki

Statistical Learning for Process Data

Sponsor: Jingchen Liu

Toward a scalable Bayesian workflow

2020 Ph.D. Dissertations

Jonathan Auerbach

Some Statistical Models for Prediction

Sponsor: Shaw-Hwa Lo

Adji Bousso Dieng

Deep Probabilistic Graphical Modeling

Sponsor: David Blei

Guanhua Fang

Latent Variable Models in Measurement: Theory and Application

Sponsor: Zhiliang Ying

Promit Ghosal

Time Evolution of the Kardar-Parisi-Zhang Equation

Sponsor: Ivan Corwin

Partition-based Model Representation Learning

Sihan Huang

Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates

Peter JinHyung Lee

Spike Sorting for Large-scale Multi-electrode Array Recordings in Primate Retina

Statistical Analysis of Complex Data in Survival and Event History Analysis

Multiple Causal Inference with Bayesian Factor Models

New perspectives in cross-validation

Quick Links

• Undergraduate Programs
• M.A. Statistics Programs
• M.A. in Mathematical Finance
• M.S. in Actuarial Science
• M.A. in Quantitative Methods in the Social Sciences
• M.S. in Data Science
• PhD Program
• BA/MA Program
• Department Directory
• Faculty Positions
• Founder’s Postdoctoral Fellowship Positions
• Staff Hiring
• Joint Postdoc with Data Science Institute
• Department News
• Department Calendar
• Research Computing

Upcoming Events

DigitalCommons@University of Nebraska - Lincoln

Home > Statistics > Dissertations, Theses, and Student Work

Statistics, Department of

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

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

Advanced Search

Search Help

• Notify me via email or RSS
• Administrator Resources
• How to Cite Items From This Repository
• Copyright Information
• Collections
• Disciplines

Author Corner

• Guide to Submitting
• Submit your paper or article
• Statistics Website

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

• Entertainment

Applying the mathematical principles of Pareto to Mario Kart 8

In this thesis, i....

By Emilia David , a reporter who covers AI. Prior to joining The Verge, she covered the intersection between technology, finance, and the economy.

Share this story

If you’re the kind of Mario Kart 8 player who cares about winning and not just playing their favorite characters (Daisy and Peach supremacy), choosing the best combination of driver, vehicle, and wheels gets tricky. 

Luckily, thanks to data science and 19th-century Italian economist Vilfredo Pareto, there’s a way to figure that out. Data scientist Antoine Mayerowitz applies one of Pareto’s principles, the Pareto front, to plot the best combination among the 703,560 possible decisions players must make in Mario Kart. Eurogamer helpfully explains that a Pareto front finds the best possible solution to a problem with different objectives. 

In a cool piece of data visualization you should definitely check out, Mayerowitz narrows down the possible choices to 25,704. From here, Mayerowitz plots the potential builds on a chart. Starting with the speed factor, the fastest characters are technically Bowser and Wario, but the story is different after adding acceleration to the mix. Some characters are dominated by just speed or just acceleration, but those that aren’t form a curve that’s called the Pareto front, the drivers most optimal if you want to prioritize those two factors. Cat Peach is in the middle of the Pareto front for speed and acceleration.

Next, consider the vehicle build. There are around 585 combinations of karts, wheels, and gliders. Mayerowitz applies the Pareto front concept to this factor and ends up with 14 choices. The best choice from these 14 options depends on which factor the player wants to prioritize. 

However, if, like Mayerowitz, you prioritize speed and acceleration, then the best build is Peach on the Teddy Buggy with roller tires and the Cloud Glider.

See, Princess Peach supremacy. Huh, I guess my method of just playing my favorite character works. 

The invisible seafaring industry that keeps the internet afloat

Meta’s battle with chatgpt begins now, tesla recalls all 3,878 cybertrucks over faulty accelerator pedal, us air force confirms first successful ai dogfight, google is combining its android and hardware teams — and it’s all about ai.

More from Gaming

The Hades 2 technical test is a trial in self-control

AirPlay turns the Delta emulator into a full-on retro console

Razer’s Kishi Ultra gaming controller brings haptics to your USB-C phone, PC, or tablet

The Meta Quest 2 gets a permanent price cut to $199