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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

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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

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Statistical analysis is a complex task requiring an in-depth knowledge of software packages including SPSS, Excel, eViews, Polystat, Stata, LaTeX, and Matlab. Without knowledge of these programs, it is difficult to complete any meaningful statistical analysis. Without statistical analysis, your dissertation may receive a lower grade than you were hoping for. Our dissertation statistical services can help you analyse the data for your dissertation, test your hypotheses and answer your questions using the most appropriate methods. We can help you with all of your analysis, whether you are working on a specific area of statistical research or analysing a large and complex dataset. Our service includes both quantitative and qualitative research.

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How To Write A Dissertation Or Thesis

8 straightforward steps to craft an a-grade dissertation.

By: Derek Jansen (MBA) Expert Reviewed By: Dr Eunice Rautenbach | June 2020

Writing a dissertation or thesis is not a simple task. It takes time, energy and a lot of will power to get you across the finish line. It’s not easy – but it doesn’t necessarily need to be a painful process. If you understand the big-picture process of how to write a dissertation or thesis, your research journey will be a lot smoother.  

In this post, I’m going to outline the big-picture process of how to write a high-quality dissertation or thesis, without losing your mind along the way. If you’re just starting your research, this post is perfect for you. Alternatively, if you’ve already submitted your proposal, this article which covers how to structure a dissertation might be more helpful.

How To Write A Dissertation: 8 Steps

• Clearly understand what a dissertation (or thesis) is
• Find a unique and valuable research topic
• Craft a convincing research proposal
• Write up a strong introduction chapter
• Review the existing literature and compile a literature review
• Design a rigorous research strategy and undertake your own research
• Present the findings of your research
• Draw a conclusion and discuss the implications

Step 1: Understand exactly what a dissertation is

This probably sounds like a no-brainer, but all too often, students come to us for help with their research and the underlying issue is that they don’t fully understand what a dissertation (or thesis) actually is.

So, what is a dissertation?

At its simplest, a dissertation or thesis is a formal piece of research , reflecting the standard research process . But what is the standard research process, you ask? The research process involves 4 key steps:

• Ask a very specific, well-articulated question (s) (your research topic)
• See what other researchers have said about it (if they’ve already answered it)
• If they haven’t answered it adequately, undertake your own data collection and analysis in a scientifically rigorous fashion
• Answer your original question(s), based on your analysis findings

In short, the research process is simply about asking and answering questions in a systematic fashion . This probably sounds pretty obvious, but people often think they’ve done “research”, when in fact what they have done is:

• Started with a vague, poorly articulated question
• Not taken the time to see what research has already been done regarding the question
• Collected data and opinions that support their gut and undertaken a flimsy analysis
• Drawn a shaky conclusion, based on that analysis

If you want to see the perfect example of this in action, look out for the next Facebook post where someone claims they’ve done “research”… All too often, people consider reading a few blog posts to constitute research. Its no surprise then that what they end up with is an opinion piece, not research. Okay, okay – I’ll climb off my soapbox now.

The key takeaway here is that a dissertation (or thesis) is a formal piece of research, reflecting the research process. It’s not an opinion piece , nor a place to push your agenda or try to convince someone of your position. Writing a good dissertation involves asking a question and taking a systematic, rigorous approach to answering it.

If you understand this and are comfortable leaving your opinions or preconceived ideas at the door, you’re already off to a good start!

Step 2: Find a unique, valuable research topic

As we saw, the first step of the research process is to ask a specific, well-articulated question. In other words, you need to find a research topic that asks a specific question or set of questions (these are called research questions ). Sounds easy enough, right? All you’ve got to do is identify a question or two and you’ve got a winning research topic. Well, not quite…

A good dissertation or thesis topic has a few important attributes. Specifically, a solid research topic should be:

Let’s take a closer look at these:

Attribute #1: Clear

Your research topic needs to be crystal clear about what you’re planning to research, what you want to know, and within what context. There shouldn’t be any ambiguity or vagueness about what you’ll research.

Here’s an example of a clearly articulated research topic:

An analysis of consumer-based factors influencing organisational trust in British low-cost online equity brokerage firms.

As you can see in the example, its crystal clear what will be analysed (factors impacting organisational trust), amongst who (consumers) and in what context (British low-cost equity brokerage firms, based online).

Need a helping hand?

Attribute #2:   Unique

Your research should be asking a question(s) that hasn’t been asked before, or that hasn’t been asked in a specific context (for example, in a specific country or industry).

For example, sticking organisational trust topic above, it’s quite likely that organisational trust factors in the UK have been investigated before, but the context (online low-cost equity brokerages) could make this research unique. Therefore, the context makes this research original.

One caveat when using context as the basis for originality – you need to have a good reason to suspect that your findings in this context might be different from the existing research – otherwise, there’s no reason to warrant researching it.

Attribute #3: Important

Simply asking a unique or original question is not enough – the question needs to create value. In other words, successfully answering your research questions should provide some value to the field of research or the industry. You can’t research something just to satisfy your curiosity. It needs to make some form of contribution either to research or industry.

For example, researching the factors influencing consumer trust would create value by enabling businesses to tailor their operations and marketing to leverage factors that promote trust. In other words, it would have a clear benefit to industry.

So, how do you go about finding a unique and valuable research topic? We explain that in detail in this video post – How To Find A Research Topic . Yeah, we’ve got you covered 😊

Step 3: Write a convincing research proposal

Once you’ve pinned down a high-quality research topic, the next step is to convince your university to let you research it. No matter how awesome you think your topic is, it still needs to get the rubber stamp before you can move forward with your research. The research proposal is the tool you’ll use for this job.

So, what’s in a research proposal?

The main “job” of a research proposal is to convince your university, advisor or committee that your research topic is worthy of approval. But convince them of what? Well, this varies from university to university, but generally, they want to see that:

• You have a clearly articulated, unique and important topic (this might sound familiar…)
• You’ve done some initial reading of the existing literature relevant to your topic (i.e. a literature review)
• You have a provisional plan in terms of how you will collect data and analyse it (i.e. a methodology)

At the proposal stage, it’s (generally) not expected that you’ve extensively reviewed the existing literature , but you will need to show that you’ve done enough reading to identify a clear gap for original (unique) research. Similarly, they generally don’t expect that you have a rock-solid research methodology mapped out, but you should have an idea of whether you’ll be undertaking qualitative or quantitative analysis , and how you’ll collect your data (we’ll discuss this in more detail later).

Long story short – don’t stress about having every detail of your research meticulously thought out at the proposal stage – this will develop as you progress through your research. However, you do need to show that you’ve “done your homework” and that your research is worthy of approval .

So, how do you go about crafting a high-quality, convincing proposal? We cover that in detail in this video post – How To Write A Top-Class Research Proposal . We’ve also got a video walkthrough of two proposal examples here .

Step 4: Craft a strong introduction chapter

Once your proposal’s been approved, its time to get writing your actual dissertation or thesis! The good news is that if you put the time into crafting a high-quality proposal, you’ve already got a head start on your first three chapters – introduction, literature review and methodology – as you can use your proposal as the basis for these.

Handy sidenote – our free dissertation & thesis template is a great way to speed up your dissertation writing journey.

What’s the introduction chapter all about?

The purpose of the introduction chapter is to set the scene for your research (dare I say, to introduce it…) so that the reader understands what you’ll be researching and why it’s important. In other words, it covers the same ground as the research proposal in that it justifies your research topic.

What goes into the introduction chapter?

This can vary slightly between universities and degrees, but generally, the introduction chapter will include the following:

• A brief background to the study, explaining the overall area of research
• A problem statement , explaining what the problem is with the current state of research (in other words, where the knowledge gap exists)
• Your research questions – in other words, the specific questions your study will seek to answer (based on the knowledge gap)
• The significance of your study – in other words, why it’s important and how its findings will be useful in the world

As you can see, this all about explaining the “what” and the “why” of your research (as opposed to the “how”). So, your introduction chapter is basically the salesman of your study, “selling” your research to the first-time reader and (hopefully) getting them interested to read more.

How do I write the introduction chapter, you ask? We cover that in detail in this post .

Step 5: Undertake an in-depth literature review

As I mentioned earlier, you’ll need to do some initial review of the literature in Steps 2 and 3 to find your research gap and craft a convincing research proposal – but that’s just scratching the surface. Once you reach the literature review stage of your dissertation or thesis, you need to dig a lot deeper into the existing research and write up a comprehensive literature review chapter.

What’s the literature review all about?

There are two main stages in the literature review process:

Literature Review Step 1: Reading up

The first stage is for you to deep dive into the existing literature (journal articles, textbook chapters, industry reports, etc) to gain an in-depth understanding of the current state of research regarding your topic. While you don’t need to read every single article, you do need to ensure that you cover all literature that is related to your core research questions, and create a comprehensive catalogue of that literature , which you’ll use in the next step.

Reading and digesting all the relevant literature is a time consuming and intellectually demanding process. Many students underestimate just how much work goes into this step, so make sure that you allocate a good amount of time for this when planning out your research. Thankfully, there are ways to fast track the process – be sure to check out this article covering how to read journal articles quickly .

Literature Review Step 2: Writing up

Once you’ve worked through the literature and digested it all, you’ll need to write up your literature review chapter. Many students make the mistake of thinking that the literature review chapter is simply a summary of what other researchers have said. While this is partly true, a literature review is much more than just a summary. To pull off a good literature review chapter, you’ll need to achieve at least 3 things:

• You need to synthesise the existing research , not just summarise it. In other words, you need to show how different pieces of theory fit together, what’s agreed on by researchers, what’s not.
• You need to highlight a research gap that your research is going to fill. In other words, you’ve got to outline the problem so that your research topic can provide a solution.
• You need to use the existing research to inform your methodology and approach to your own research design. For example, you might use questions or Likert scales from previous studies in your your own survey design .

As you can see, a good literature review is more than just a summary of the published research. It’s the foundation on which your own research is built, so it deserves a lot of love and attention. Take the time to craft a comprehensive literature review with a suitable structure .

But, how do I actually write the literature review chapter, you ask? We cover that in detail in this video post .

Step 6: Carry out your own research

Once you’ve completed your literature review and have a sound understanding of the existing research, its time to develop your own research (finally!). You’ll design this research specifically so that you can find the answers to your unique research question.

There are two steps here – designing your research strategy and executing on it:

1 – Design your research strategy

The first step is to design your research strategy and craft a methodology chapter . I won’t get into the technicalities of the methodology chapter here, but in simple terms, this chapter is about explaining the “how” of your research. If you recall, the introduction and literature review chapters discussed the “what” and the “why”, so it makes sense that the next point to cover is the “how” –that’s what the methodology chapter is all about.

In this section, you’ll need to make firm decisions about your research design. This includes things like:

• Your research philosophy (e.g. positivism or interpretivism )
• Your overall methodology (e.g. qualitative , quantitative or mixed methods)
• Your data collection strategy (e.g. interviews , focus groups, surveys)
• Your data analysis strategy (e.g. content analysis , correlation analysis, regression)

If these words have got your head spinning, don’t worry! We’ll explain these in plain language in other posts. It’s not essential that you understand the intricacies of research design (yet!). The key takeaway here is that you’ll need to make decisions about how you’ll design your own research, and you’ll need to describe (and justify) your decisions in your methodology chapter.

2 – Execute: Collect and analyse your data

Once you’ve worked out your research design, you’ll put it into action and start collecting your data. This might mean undertaking interviews, hosting an online survey or any other data collection method. Data collection can take quite a bit of time (especially if you host in-person interviews), so be sure to factor sufficient time into your project plan for this. Oftentimes, things don’t go 100% to plan (for example, you don’t get as many survey responses as you hoped for), so bake a little extra time into your budget here.

Once you’ve collected your data, you’ll need to do some data preparation before you can sink your teeth into the analysis. For example:

• If you carry out interviews or focus groups, you’ll need to transcribe your audio data to text (i.e. a Word document).
• If you collect quantitative survey data, you’ll need to clean up your data and get it into the right format for whichever analysis software you use (for example, SPSS, R or STATA).

Once you’ve completed your data prep, you’ll undertake your analysis, using the techniques that you described in your methodology. Depending on what you find in your analysis, you might also do some additional forms of analysis that you hadn’t planned for. For example, you might see something in the data that raises new questions or that requires clarification with further analysis.

The type(s) of analysis that you’ll use depend entirely on the nature of your research and your research questions. For example:

• If your research if exploratory in nature, you’ll often use qualitative analysis techniques .
• If your research is confirmatory in nature, you’ll often use quantitative analysis techniques
• If your research involves a mix of both, you might use a mixed methods approach

Again, if these words have got your head spinning, don’t worry! We’ll explain these concepts and techniques in other posts. The key takeaway is simply that there’s no “one size fits all” for research design and methodology – it all depends on your topic, your research questions and your data. So, don’t be surprised if your study colleagues take a completely different approach to yours.

Step 7: Present your findings

Once you’ve completed your analysis, it’s time to present your findings (finally!). In a dissertation or thesis, you’ll typically present your findings in two chapters – the results chapter and the discussion chapter .

What’s the difference between the results chapter and the discussion chapter?

While these two chapters are similar, the results chapter generally just presents the processed data neatly and clearly without interpretation, while the discussion chapter explains the story the data are telling  – in other words, it provides your interpretation of the results.

For example, if you were researching the factors that influence consumer trust, you might have used a quantitative approach to identify the relationship between potential factors (e.g. perceived integrity and competence of the organisation) and consumer trust. In this case:

• Your results chapter would just present the results of the statistical tests. For example, correlation results or differences between groups. In other words, the processed numbers.
• Your discussion chapter would explain what the numbers mean in relation to your research question(s). For example, Factor 1 has a weak relationship with consumer trust, while Factor 2 has a strong relationship.

Depending on the university and degree, these two chapters (results and discussion) are sometimes merged into one , so be sure to check with your institution what their preference is. Regardless of the chapter structure, this section is about presenting the findings of your research in a clear, easy to understand fashion.

Importantly, your discussion here needs to link back to your research questions (which you outlined in the introduction or literature review chapter). In other words, it needs to answer the key questions you asked (or at least attempt to answer them).

For example, if we look at the sample research topic:

In this case, the discussion section would clearly outline which factors seem to have a noteworthy influence on organisational trust. By doing so, they are answering the overarching question and fulfilling the purpose of the research .

For more information about the results chapter , check out this post for qualitative studies and this post for quantitative studies .

Step 8: The Final Step Draw a conclusion and discuss the implications

Last but not least, you’ll need to wrap up your research with the conclusion chapter . In this chapter, you’ll bring your research full circle by highlighting the key findings of your study and explaining what the implications of these findings are.

What exactly are key findings? The key findings are those findings which directly relate to your original research questions and overall research objectives (which you discussed in your introduction chapter). The implications, on the other hand, explain what your findings mean for industry, or for research in your area.

Sticking with the consumer trust topic example, the conclusion might look something like this:

Key findings

This study set out to identify which factors influence consumer-based trust in British low-cost online equity brokerage firms. The results suggest that the following factors have a large impact on consumer trust:

While the following factors have a very limited impact on consumer trust:

Notably, within the 25-30 age groups, Factors E had a noticeably larger impact, which may be explained by…

Implications

The findings having noteworthy implications for British low-cost online equity brokers. Specifically:

The large impact of Factors X and Y implies that brokers need to consider….

The limited impact of Factor E implies that brokers need to…

As you can see, the conclusion chapter is basically explaining the “what” (what your study found) and the “so what?” (what the findings mean for the industry or research). This brings the study full circle and closes off the document.

Let’s recap – how to write a dissertation or thesis

You’re still with me? Impressive! I know that this post was a long one, but hopefully you’ve learnt a thing or two about how to write a dissertation or thesis, and are now better equipped to start your own research.

To recap, the 8 steps to writing a quality dissertation (or thesis) are as follows:

• Understand what a dissertation (or thesis) is – a research project that follows the research process.
• Find a unique (original) and important research topic
• Craft a convincing dissertation or thesis research proposal
• Write a clear, compelling introduction chapter
• Undertake a thorough review of the existing research and write up a literature review
• Undertake your own research
• Present and interpret your findings

Once you’ve wrapped up the core chapters, all that’s typically left is the abstract , reference list and appendices. As always, be sure to check with your university if they have any additional requirements in terms of structure or content.  

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 ...

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20 Comments

thankfull >>>this is very useful

Thank you, it was really helpful

unquestionably, this amazing simplified way of teaching. Really , I couldn’t find in the literature words that fully explicit my great thanks to you. However, I could only say thanks a-lot.

Great to hear that – thanks for the feedback. Good luck writing your dissertation/thesis.

This is the most comprehensive explanation of how to write a dissertation. Many thanks for sharing it free of charge.

Very rich presentation. Thank you

Thanks Derek Jansen|GRADCOACH, I find it very useful guide to arrange my activities and proceed to research!

Thank you so much for such a marvelous teaching .I am so convinced that am going to write a comprehensive and a distinct masters dissertation

It is an amazing comprehensive explanation

This was straightforward. Thank you!

I can say that your explanations are simple and enlightening – understanding what you have done here is easy for me. Could you write more about the different types of research methods specific to the three methodologies: quan, qual and MM. I look forward to interacting with this website more in the future.

Thanks for the feedback and suggestions 🙂

Hello, your write ups is quite educative. However, l have challenges in going about my research questions which is below; *Building the enablers of organisational growth through effective governance and purposeful leadership.*

Very educating.

Just listening to the name of the dissertation makes the student nervous. As writing a top-quality dissertation is a difficult task as it is a lengthy topic, requires a lot of research and understanding and is usually around 10,000 to 15000 words. Sometimes due to studies, unbalanced workload or lack of research and writing skill students look for dissertation submission from professional writers.

Thank you 💕😊 very much. I was confused but your comprehensive explanation has cleared my doubts of ever presenting a good thesis. Thank you.

thank you so much, that was so useful

Hi. Where is the excel spread sheet ark?

could you please help me look at your thesis paper to enable me to do the portion that has to do with the specification

my topic is “the impact of domestic revenue mobilization.

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Enjoy a completely custom, expertly-written dissertation. Choose from hundreds of writers, all of whom are career specialists in your subject.

Dissertation Statistics Help From Experts

One of the most fundamental parts of a dissertation is statistics. Unfortunately, many students struggle with writing a statistics dissertation mainly because statistics could be complex. Plus, it involves numerical portions. This is why most students need statistics dissertation help at one point or the other.

But more than that, statistics is a difficult subject. It is quite tasking and involves a lot of research and analysis. This is why students find it so burdensome. But, on the other hand, dissertation statistics help equip students with analytical and problem-solving skills. The truth is that instructors don’t care how you get the assignments done; they only want to see valid results.

Besides, writing a statistical dissertation needs time as a question might take about 30 minutes to an hour to solve. Dealing with this every day can make you tired and unable to complete your other tasks. That’s why we’re here with our team of expert dissertation consultants who have lots of experience writing statistical dissertations.

Statistics Help For Dissertation

Statistics is one of the ways that instructors judge the validity of your research. A researcher also learns data, observes, and draws inferences from it. Yet, this learning process is like a never-ending maze.  Meanwhile, the right statistics are the only proof of your discoveries. Without them, your ideas would not be credible.

For most students, expressing their results in statistics is the most difficult part. And that’s why we are offering statistics help for the dissertation. Our dissertation statisticians will solve all the riddles of statistics and guide you on every step of the statistical process for the dissertation!

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Our dissertation statistics help services include summarizing the collected data, analyzing the data, presenting the results, and conclusion. Our experienced statisticians will also help you understand all the aspects of your statistical analysis.

In our team, you’ll find experts in several statistical methods. Do you know why? It’s because we do not just employ anyone into our team, but till after they clear a series of tests that shows their competencies.

• Our statisticians can help you with the following:
• Deducing the sample size for data collection
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• Helping you to analyze and understand all the statistical tables and graphs alongside the procedure adopted to conduct the statistics.  

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Our services are highly flexible since we understand the need for unique statistical requirements of research. We have a knack for quantitative and qualitative research and different types of analysis such as exploratory or descriptive analysis. We also use structured equation modeling with software like MATLAB, SPSS, AMOS, EViews, and Stata.

We deliver prompt responses. Usually, we reply to emails in a few minutes to hours or latest, the next day. We also offer statistics consulting to support scholars by organizing various events relating to statistics and its usage. Intellectuals from different fields did as engineering, management, sociology, medical sciences, etc., are free to join the events.

We employ only experts who apply and monitor their quality in each order. When you reach out to us, you can rest assured that we’ll write your dissertation statistics from scratch, using your specifications. Our support team works in shift schedules to provide both days and nights best dissertation statistics help services.

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We always give the best to you in data analysis, along with the plagiarism report. In addition, we analyze each dissertation using plagiarism-free software so that your content can turn out original and credible. We have secure and flexible payment methods. Our clients say we offer the best security methods for payment. For instance, we use PayPal, Debit Card, Credit Card, etc.

We respect your privacy. And apart from that, our team protects each student’s details to avoid privacy issues. Therefore you can feel free while sharing information about yourself.

Different Aspects of Statistics That We Offer

Descriptive Statistics: This involves concluding the collected information, usually in graphical or numerical form. Descriptive statistics are in two parts: the measures of variability and the measure of central tendency.

Inferential Statistics: Here, we take random model data from the desired population. Then, we use it to represent and make assumptions about the population used in an experiment. Of course, it can only benefit someone analyzing each person that belongs to the group, but that might not be possible.

How Can Students Write Dissertation Statistics Effectively?

Students should, first of all, improve their knowledge of statistics. There is nothing they cannot learn if they put their minds to it. For students planning to stay in academia, this can be a long-term investment.

Then, they need to understand the different statistics concepts and the methodology to use them. They can achieve this by reading books and other people’s dissertations.

Also, many resources are available to help you understand and learn about statistics; many of them are accessible at no cost. For example, various institutions provide training sessions, seminars, and workshops that help you improve your statistical skills. In addition, they can attempt to connect with the dissertation statisticians and academic communities that are associated with their colleges and universities.

They could also explore other options by searching for online resources such as informative sites, tutorial videos, and question-answering sites. Don’t forget to check the credibility of those sites too.

To complete a statistics dissertation, a student must have critical thinking ability alongside the proper knowledge of statistics subject and how to implement the statistical formula.

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Dissertation Statistics and Thesis Statistics

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

What are dissertation statistics and thesis statistics?

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

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

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

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

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

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

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

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

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Benefits of using our Dissertation Statistics Service

• Customizable: Our Dissertation Statistics Services are highly flexible as we understand the need of distinct statistical requirements of research.
• Comprehensive Approach: The ambit of our expertise extends to qualitative and quantitative research, exploratory or descriptive analysis, and structured equation modeling using software like SPSS, EViews, AMOS, Stata, and MATLAB.
• Prompt Response: We usually reply to emails within four business hours or at the max the next business day.

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Dissertations - from the initial research design to writing up your statistical results - create anxiety for many students.  Graduate courses in research methods and statistics are challenging.  

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Dissertation Statistics Help

Looking for Dissertation Statistics Help for your Quantitative Research? We help students with their methodology, research questions/hypotheses, analysis plan, data analysis, and interpretation of the results.

Get Top-Quality Authentic Dissertation Statistics Help, Provided by the Subject Matter Statistician Only.

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Start by clicking on the GET INSTANT QUOTE button to Get a Free Quote! Enter the required details of the dissertation, and upload supporting files to submit through our easy-to-navigate order form.

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Our experts will review and update the quote for your Dissertation Statistics. Make the payment to start the processing, we have PayPal integration which is quick and secure.

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What is Statistical Analysis in a Dissertation?

When you prepare a dissertation study, you might want to back your hypothesis with quantitative data. Or even you might even want to test some of your hypotheses in your thesis study. So, you will need reliable data analysis to assess the results. Therefore, without data analysis, it is not possible to understand the data you have. Accordingly, you might need Dissertation Statistics Help for:

• Statistical Tests
• Sample size selection
• G-Power Analysis
• Descriptive Statistics
• Central Tendency Analysis
• Comparison tests
• Regression analysis
• Correlation tests
• Nonparametric tests

can be conducted for your data set. Since each data set is unique, it is important to choose the right statistical analysis method. So, an incorrect method selection might lead to biased or inaccurate analysis results. Therefore, it is important to get professional statistical analysis help for your studies.

What is Dissertation Statistics Help?

If you are preparing a dissertation, you are probably working with data. When it comes to data analysis, you will face a completely different world. Most students have no idea about how to work on data. Also, these students often do not know how to prepare statistics for their dissertations. At this point, we built the dissertation statistics to help students with their statistical processes.

We offer our statistical data analysis services, based on a thorough review of your documents, such as your methodology, research questions/hypotheses, analysis plan, and your data. Once you submit your documents, our academic consultant will carefully assess each document and provide an offer of services, including a quote and turnaround time that is directly related to the amount of work required. We take pride in offering our services with a quote that was based on thoughtful reflection of the work your individual project requires.

How Do We Help a Qualitative Analysis for a Dissertation?

We bring together statistical expertise, technology, and a teaching attitude to complete your quantitative results chapter for your dissertation that meets your school’s rubric. Our statisticians will help you understand all data analyses and make sure you are fully prepared to explain and defend your analyses. Here’s a detailed list that we can help you with;

• Defining Research Questions
• Determining Statistical Tests for Research Questions
• Determining Sample Size
• Determining Dependent and Independent Variables
• Designing Questionnaire/Surveys
• Writing Methodology Section
• Data Management
• Analyzing Collected Data
• Writing up Result Section

Which software is best for Statistical Analysis in a Dissertation?

The Statistical Package for the Social Sciences ( SPSS ), R-Studio, and STATA are the most popular software used for statistical analysis. We can help with any statistical analysis software in the list below;

• Graph Prism

Why Do You Need Dissertation Results Section Help?

Every student working on a dissertation, regardless of its type, will need to formulate and test hypotheses. Some may opt to use surveys as part of their research process. In any case, these students will require dissertation statistics help , which can take various forms.

Research Question

Firstly, students can seek assistance in formulating their research questions. Crafting appropriate research questions for a dissertation is crucial as it helps address gaps in the related field. Moreover, the dissertation topic must align with the potential data collection process, as an ill-suited topic can complicate both data collection and analysis.

Statistical Methods

Secondly, students can request assistance with the statistical analysis of their data. In this scenario, our expert statisticians will work with the data collected by the students. These experts will select the appropriate statistical methods and tools to analyze the data.

Write-Up and Data Visualization

Finally, students can seek assistance with visualizing their statistical data, a crucial element in any dissertation. Effective graphics and tables are essential to give meaning to statistical results. Consequently, this is one of the most common areas where students request help for their dissertations. At OnlineSPSS, we excel in providing the best data analysis reports in APA or other academic writing formats.

How Can You Get Dissertation Statistics Help?

All you need to do is send your dissertation documents to us by clicking the ‘GET INSTANT QUOTE’ button and specifying the necessary details. Once you submit your documents, our academic consultant will carefully assess each one and provide you with an offer of services, including a quote and turnaround time that is directly related to the amount of work required. Finally, our statisticians will begin working immediately to complete your 100% authentic  Dissertation Results Section,  and we will deliver it to your email address on time.

Here’s the step-by-step statistical analysis help process. You can see three easy-to-follow steps.

• Submit and Get a FREE Quote 
• Get Solutions

Dissertation data analysis help will give you all the right tools you will need to obtain reliable and accurate results from your data set. Our experts will work closely with you in this process. Thus, we will choose the most suitable statistical data for your dissertation.

Customer Satisfaction

The OnlineSPSS.com company concentrates on providing complete customer satisfaction. Every one of our clients will get as many free revisions as necessary if his or her requirements are not met by our writers. Firstly, We employ experienced customer support representatives who are ready to provide high-quality R-Studio assistance 24/7 and answer all your questions. Secondly, our staff is friendly and highly dedicated to every customer. Finally, We value every individual who turns to Online R-Studio Programming Help for assistance and promise to do our very best and provide every person with top-quality statistical analysis.

Who We Are?

We are a unique team of statistics experts, with members located in the USA, UK, and around the world. Our certified online statisticians bring significant expertise and years of experience in their respective fields, along with strong communication skills. Each team member has a minimum of 9 years of experience in statistics, and many hold postgraduate degrees, including some Ph.D. experts. Our main office is located in the UK, and if you happen to be nearby, feel free to drop in for a coffee on us. Furthermore, our customer support team is always ready to assist you, no matter where you are in the world.

We Are Expert

How does the dissertation statistics help offered by your platform work?

The fastest way to better results.

To place an order for dissertation statistics help with us, you can easily navigate to the Submit Form located on our home page or click the following link: https://www.onlinespss.com/submit-your-project

Firstly, fill up the order form with essential details of your dissertation data analysis.

Secondly, Get a Quote and proceed with the payment for your order.

Finally, sit back and relax.

Who will handle my dissertation statistics if I place an order with OnlineSPSS.com?

We maintain a substantial team of statisticians, with each member meticulously chosen through a rigorous selection process. All our tutors hold master’s or Ph.D. degrees, coupled with extensive writing experience in the field of statistics. Our in-house team takes meticulous care in selecting the ideal expert for your Dissertation Statistics Analysis.

What will be the cost of my dissertation analysis help service?

We offer the most competitive prices in the entire industry. The cost of our service is determined by the specifications and requirements of your dissertation analysis help. This is influenced by several factors, including content Type, deadline, available discounts and bonuses, as well as any additional services you might require.

How much time is required to complete my dissertation statistics analysis help?

We accommodate both urgent and standard orders.

You can select from the following time frame options:

• 6 hours, 12 hours, 24 hours, 48 hours, 72 hours, 5 days, 7 days, and more.

You can submit your request within a reasonable timeframe that grants you control over your review process.

However, it’s important to note that the delivery time hinges on the task’s type and complexity. For example, managing a dissertation with intricate datasets and content within a short timeframe presents considerable challenges. We want to ensure you are informed about the potential that we may not be able to accept such requests.

Ensuring timely delivery for all orders to our valued customers remains our foremost concern. As such, you will receive a specific delivery date, day, and time for your order. Before proceeding with payment, we will provide you with a clear delivery date pertaining to your work. Do keep in mind that the urgency level has an impact on the overall cost.

What guarantees does OnlineSPSS.com provide?

OnlineSPSS.com offers the following guarantees:

• On-time delivery
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Please take note: Our free revision rights remain applicable only if the initial project guidelines are not adhered to. Any supplementary requirements following project completion will not be covered under the free service. For instance, additional test requests, alterations to the dataset used for testing, and different software requests will incur additional charges.

How will I be informed when my order is ready?

We always keep our customers updated on the progress of their orders. When the expert complete the project, we send an e-mail with the document attached to the address you provided. Further, you may also track the progress via email or our live chat page.

Is my personal information shared with third parties?

No, your personal details are not shared with any third party, nor are they provided to your assigned tutor.

Our service ensures 100% confidentiality. To ensure the privacy of your details, we remove any personal data from attachments you send us for reference purposes. Additionally, OnlineSPSS.com does not require you to register on our platform.

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Data Analysis Help For Dissertation

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Embarking on your dissertation’s data analysis journey? Look no further than SPSSanalysis.com . Our platform offers comprehensive help with data analysis for dissertations . Whether you’re dissecting quantitative data with complex statistical tests or unraveling qualitative insights through thematic analysis, our team delivers sharp, insightful analysis.

What is Statistical Help for Dissertation?

Statistical help for dissertation is a comprehensive service tailored to meet the specific needs of your academic research, ensuring that you receive expert support in the following key areas:

• Methodology Writing : We develop a detailed plan for your research approach, outlining the statistical methods to be used in your study. This foundational step ensures that your research is built on a robust methodological framework.
• Data Management : Our services include importing your data into the preferred statistical software, recoding variables to suit analysis requirements, and data cleaning to ensure accuracy and reliability. This process prepares your data for meaningful analysis, setting the stage for insightful findings.
• Data Analysis & Hypothesis Testing: Whether your research calls for quantitative data analysis or qualitative data analysis , our experts are equipped to handle it. We apply appropriate statistical tests for your hypothesis and techniques to analyse your data, uncovering the patterns and insights that support your research objectives.
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Embarking on a dissertation requires a robust statistical foundation, and our custom service offers just that. From managing and cleaning your data to delivering insightful analysis and well-articulated results , SPSSanalysis.com is your partner in academic achievement. Our expert statisticians are adept at transforming complex data into comprehensible insights, making your dissertation stand out in the realm of academic research. Finally, if you need help with data analysis for dissertation, Get a Free Quote Now.

Why Choose Us Choosing SPSSanalysis.com for your dissertation statistical analysis comes with a set of clear promises and benefits, designed to ensure your absolute satisfaction and confidence in our services

Experienced statisticians.

Our team comprises PhD-holding statisticians with a minimum of 7+ years of experience in a variety of fields

Privacy Guarantee

We pledge never to share your details or data with any third party, maintaining the anonymity of your project.

Plagiarism-Free Work

We provide work that is completely free from plagiarism, adhering to the highest standards of academic integrity.

On-Time Delivery

We commit to meeting your deadlines, ensuring that your project is completed within the agreed timeframe.

Free Unlimited Revisions

Your satisfaction is our priority. We offer unlimited revisions until your expectations are fully met.

24/7 Support Service

Our support team is available around the clock to answer your questions and provide assistance whenever you need it.

How Data Analysis Service Works

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  Dissertation Statistics Task .

2. Make the Payment

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

3. Get the Results

Once your dissertation statistics results 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.

Dissertation Statistical Analysis

In today’s data-driven research environment, acquiring proficient statistical help for dissertations has become a cornerstone for achieving academic excellence. SPSSanalysis.com stands at the forefront, offering unparalleled assistance to researchers, students, and academics embarking on their statistical journey. Our bespoke services streamline the complex process of data analysis , ensuring your project not only meets but exceeds the rigorous standards of academic research. With a focus on delivering expert guidance at every step, we empower you to navigate the intricacies of statistical analysis with confidence and precision.

Our platform simplifies the engagement with seasoned statisticians, transforming the daunting task of data analysis into a seamless and manageable process. From the initial free quote to the meticulous delivery of your statistical task, SPSSanalysis.com guarantees a personalized and efficient service tailored to your research needs . Embrace the opportunity to elevate your dissertation with our comprehensive support, designed to guide you towards academic success.

How Data Analysis for Dissertation Works

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

• Get a Free Quote : First, Fill out the form on our website detailing your project requirements. This step helps us understand your needs and provide a precise quote.
• Make a Payment : If you’re satisfied with the quote, proceed with payment through our secure PayPal system to initiate your project.
• Receive Your Results: Our experts will then conduct the statistical analysis, ensuring high-quality results directly to your email by the set deadline.

Who Can Get Dissertation Data Analysis Help?

Dissertation data analysis help from SPSSanalysis.com is an essential resource for master students, PhD candidates, researchers, and academicians across a spectrum of disciplines . Whether you are at the inception of your research or in the throes of data analysis, our services are designed to offer substantial support. Our expertise is not limited by the scope of your study; instead, we provide a scaffolding to elevate your research with statistical precision and academic rigour.

  Subject Areas 

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

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

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

Which Statistical Software Can We Help With?

At SPSSanalysis.com, we are proficient in a wide range of statistical software, offering support for the following tools to accommodate your dissertation’s specific analysis needs:

• SPSS : Ideal for managing and analyzing social science data.
• R : A powerful tool for statistical computing and graphics.
• STATA : Versatile software for data management, statistical analysis, and graphics.
• SAS: Comprehensive software for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.
• JAMOVI: A user-friendly tool for basic to advanced statistical analysis, built on R.
• JMP: Specializes in dynamic data visualization and exploratory data analysis.
• MINITAB: Easy-to-use software for quality improvement and statistics education.
• MATLAB: A high-level language and interactive environment for numerical computation, visualization, and programming.
• EXCEL: Widely used for basic statistical analysis and data management.
• Nvivo & MAXQDA: Qualitative data analysis software for complex data sets.
• JASP: A fresh approach to statistical analysis, emphasizing simplicity and ease of use.
• G-power: For power analysis and sample size determination.
• Graphpad-Prism: Focused on scientific graphing, curve fitting, and biostatistics.

Whether your project demands the advanced statistical analysis capabilities of R or the user-friendly interface of SPSS, our team is on hand to ensure your data is analysed with precision and insight. Choose SPSSanalysis.com for support that transcends the limitations of software, focusing instead on elevating the quality of your research.

Pricing: Help with Data Analysis for Dissertation

Understanding the cost involved in securing expert statistical assistance is crucial, and at SPSSanalysis.com, we believe in transparency and custom solutions. Our pricing structure is dependent on the complexity of your dissertation, the deadline, and the requested services, such as data management, data analysis, writing results, and methodology. To ensure you receive a service that is tailored precisely to your needs, we encourage obtaining a free quote , allowing us to provide you with a custom price that reflects the value and expertise our service brings to your project.

Our minimum order value starts at £500 , a testament to the quality and depth of service you can expect from our team of PhD-holding statisticians, each with a minimum of 7+ years of experience. This investment in your dissertation is an investment in its success, ensuring that your statistical analysis is conducted with the utmost precision and professionalism .

Why Choose SPSSanalysis.com and What are Our Guarantees?

Choosing SPSSanalysis.com for your dissertation statistical analysis comes with a set of clear promises and benefits, designed to ensure your absolute satisfaction and confidence in our services:

• Experienced Statisticians : Our team comprises PhD-holding statisticians with a minimum of 7+ years of experience in a variety of fields.
• Free Unlimited Revisions: Your satisfaction is our priority. We offer unlimited revisions until your expectations are fully met.
• Privacy Guarantee: We pledge never to share your details or data with any third party, maintaining the anonymity of your project.
• Non-Disclosure Agreements: Our statisticians are bound by NDAs, ensuring all data is handled with the utmost confidentiality.
• On-Time Delivery: We commit to meeting your deadlines, ensuring that your project is completed within the agreed timeframe.
• No AI Writing : We guarantee that all work is original and crafted by our experienced statisticians, ensuring authenticity and personal touch.
• Plagiarism-Free Work : We provide work that is completely free from plagiarism, adhering to the highest standards of academic integrity.
• 24/7 Support Service : Our support team is available around the clock to answer your questions and provide assistance whenever you need it.
• Secure Payment: We offer a secure payment process through PayPal, protecting your financial information and providing peace of mind.

These guarantees underscore our commitment to delivering quality, reliability, and support, ensuring that SPSSanalysis.com is the premier choice for your dissertation statistical analysis needs. Finally, if you need help with a methodology, you can get Statistical help for your Dissertation. Get a Free Quote Now.

What is statistical analysis in a dissertation?

Statistical analysis in a dissertation is a critical component that involves applying mathematical and statistical techniques to the collected data to test hypotheses, analyze patterns, and draw conclusions. It serves as the backbone of the research, providing a quantitative foundation for validating the research questions and supporting the research findings with empirical evidence.

What is a dissertation in research methodology?

A dissertation in research methodology is an extensive academic document that represents the culmination of a researcher’s work in a specific field of study. It encompasses a detailed exploration of a particular research question, including the design, execution, and analysis of the research. The methodology section specifically outlines the procedures and techniques used to collect, analyze, and interpret data, serving as a blueprint for the research process. Finally, You can get Help with Data Analysis for Dissertation,   Get a Free Quote Now.

How to do hypothesis testing in a research paper?

Hypothesis testing in a research paper involves several key steps:

• Formulating a null and alternative hypothesis,
• Selecting an appropriate statistical test,
• Setting a significance level (commonly 0.05),
• Calculating the test statistic from the data,
• Comparing it to a critical value to determine whether to reject the null hypothesis.

This process allows researchers to make informed decisions about the validity of their hypotheses based on statistical evidence.

Why is statistics important in a dissertation?

Statistics are vital in a dissertation because they provide a systematic method for analyzing data, ensuring that conclusions are based on objective, empirical evidence rather than subjective interpretation. In addition, the use of statistics enhances the credibility of the research findings , allows for the generalization of results to larger populations, and enables the researcher to address complex research questions with clarity and precision.

What is the meaning of results in a dissertation?

The results section of a dissertation presents the outcomes of the statistical analysis, including the data in its raw or processed form, often showcased through tables, graphs, and charts. Finally, you can get Help with Data Analysis for Dissertation. Get a Free Quote Now!

Which statistical tools are good for a thesis?

Statistical tools such as SPSS, R, STATA, SAS, and others are highly regarded for thesis research due to their robust analytical capabilities. The choice of tool often depends on the specific requirements of the research, including the nature of the data and the complexity of the analyses.

Can you hire a statistician for a dissertation?

Hiring a statistician for a dissertation is increasingly common and beneficial, especially for researchers who may lack the statistical expertise required to analyse their data effectively. A statistician can provide invaluable assistance in designing the study, selecting appropriate statistical tests, analysing data, and interpreting results, thereby enhancing the quality and integrity of the research.

Can I pay someone to do my dissertation statistics?

Yes, paying for professional assistance with dissertation statistics is permissible and can be a wise investment in the quality of your dissertation. In addition, Professional statisticians bring a level of expertise and precision to the analysis that can significantly improve the clarity and impact of your research findings.

How much does it cost to hire a statistician for your thesis?

The cost of hiring a statistician for your thesis can vary widely based on the complexity of the analysis, the amount of data, and the required turnaround time. Prices might start from around £500 for basic analyses but can increase significantly for more complex projects. It’s essential to get a detailed quote upfront that outlines the scope of work and associated costs.

Are you allowed to get help with a dissertation?

Yes, seeking help with aspects of your dissertation , such as statistical analysis, editing, or proofreading, is allowed and often encouraged within academic institutions. Therefore, this support can help ensure that your research is presented clearly and professionally, adhering to the highest academic standards.

Is it ethical to get help with statistical analysis for a thesis?

Yes, it is ethical to seek help with statistical analysis for a thesis. Many academic institutions allow, and even recommend, consulting with statisticians to ensure that research methodologies are sound and that analyses are correctly performed and interpreted.

What happens if you pay someone to do your dissertation?

Paying someone to do your dissertation or significant parts of it, raises ethical concerns and can undermine the academic integrity of your work. While it is acceptable to seek help with certain aspects of your dissertation, such as statistical analysis or editing, the core ideas, research, and writing should be your own. In addition, Misrepresenting someone else’s work as your own can have serious academic consequences. However, ethical collaboration, where the assistance is correctly acknowledged, and the student’s contribution remains primary, is widely accepted.

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