Volume 1 course home and chapter map

You've Got This! Quantitative Methods

Welcome to Volume 1 of You've Got This! Quantitative Methods. Begin with the purpose behind the work, read the author's letter, preview the complete group-comparison journey, and use the chapter map to see how each statistical method answers a different kind of question.

Course objective

  • Use the course map to locate and open the chapter that matches the group-comparison question you need to answer.

A letter from the author

I Am Doing This Because

Research is a way to turn meaningful questions and lived experience into evidence that can improve the world.

Headshot of the course author
The author of You've Got This! Quantitative Methods

I believe research is one of the most powerful tools we have to understand the world and improve it — but too often, it feels distant, intimidating, or reserved for a select few.

Through my years of teaching, mentoring, and coaching students through the dissertation process, I’ve seen the same story repeat itself: people care deeply about solving problems, but they doubt their ability to do so through research. They get stuck, not because they aren’t capable, but because they haven’t been given the vocabulary, structure, or confidence to translate their ideas into something measurable and defensible. I know that feeling well — and I also know that it’s possible to overcome it.

That’s why I created this resource and why I continue this work. My goal is to make research more accessible to everyone, especially those who have been told — directly or indirectly — that this kind of work isn’t for them. I want to break down the walls around research and show that it isn’t about jargon or perfection; it’s about curiosity, purpose, and impact. And quantitative methods, in particular, offer a unique and powerful way to do that. They allow us to ask questions that go beyond “what we think” into “what we can show.” They help us identify patterns, test relationships, and evaluate interventions in ways that directly inform practice, shape policy, and improve outcomes.

But this work is about more than numbers. It’s about people. It’s about giving individuals and communities the tools to turn their lived experiences and urgent questions into evidence that can drive change. I’ve seen firsthand how empowering it is when someone learns to speak the language of research — when they realize they can design a study, interpret data, and contribute knowledge that matters. That shift doesn’t just change their dissertation; it changes how they see themselves and their power to influence the world.

This is why I am passionate about helping others find their way into this process. I want to share what I’ve learned so that more people can participate in creating knowledge — not just consuming it. I want them to see that quantitative research isn’t cold or distant; it’s a way of telling stories with evidence, of translating complex realities into insights that can guide real-world decisions. When we understand how to measure what matters, we give ourselves the power to improve systems, address inequities, and design solutions that last.

Ultimately, I’m doing this because I believe in the ripple effect of research. Every person who gains the skills and vocabulary to conduct meaningful inquiry becomes a force for change — in classrooms, communities, organizations, and beyond. My hope is that this work will inspire others to step into that role, to use research not just as an academic exercise but as a pathway to justice, equity, and transformation. That is the heart of this journey: to share my passion for research in a way that helps others build theirs, and to make sure that passion is put to work improving the world we all share.


Welcome to the journey. You've got this.

Author purpose

Research becomes powerful when more people can use it

The barrier is often not ability. It is access to the language, structure, and confidence needed to make an important question measurable and defensible.

Quantitative research is not reserved for a select few. It begins with curiosity about a real problem and grows through a structure that helps you measure, test, interpret, and communicate what the evidence shows.

From barriers to evidence that can drive change

What can hold people back

  • Research feels distantJargon and unfamiliar procedures can make meaningful inquiry seem inaccessible.
  • Ideas lack structureImportant questions need a clear path from concern to measurable evidence.
  • Confidence is still developingPeople may doubt their ability before they have had a chance to practice the process.

What changes

Curiosity becomes a defensible research question

Vocabulary, structure, and practice make it possible to move from what we think to what we can show.

What becomes possible

  • Patterns become visibleData can reveal change, difference, relationship, and impact.
  • Decisions become strongerEvidence can inform practice, policy, programs, and resource choices.
  • Knowledge becomes participatoryMore people and communities can create evidence, not only consume it.

Course map

Explore the complete group-comparison course

Use this chapter menu to see the full route through the workbook. The method you choose depends on who is measured, how many groups or factors are involved, and whether another variable must be accounted for.

The group comparison toolkit

Chapter 1 · Paired Samples t Test

Did the same people change across two related measurements?

  • Starts on workbook page 1
  • Same participants
  • Two related scores
  • Change within people
Begin Chapter 1

Chapter 2 · Independent Samples t Test

Are two separate groups different on a continuous outcome?

  • Starts on workbook page 94
  • Two independent groups
  • One continuous outcome
  • Difference between groups
Open Chapter 2

Chapter 3 · One Way ANOVA

Are three or more independent groups different?

  • Starts on workbook page 187
  • One grouping factor
  • Three or more levels
  • Overall and pairwise differences
Open Chapter 3

Chapter 4 · Two Way ANOVA

Do two factors matter, and does the effect of one depend on the other?

  • Starts on workbook page 332
  • Two factors
  • Two main effects
  • One interaction
Open Chapter 4

Chapter 5 · ANCOVA

Do groups differ after accounting for a continuous covariate?

  • Starts on workbook page 558
  • Adjusted comparison
  • Continuous covariate
  • Estimated marginal means
Open Chapter 5

Chapter 6 · Assumption Checks

Is the comparison fair enough to interpret confidently?

  • Starts on workbook page 597
  • Design conditions
  • Distribution and variance checks
  • Bounded conclusions
Open Chapter 6

Closing Toolkit · Group Comparison Toolkit

How do you choose, interpret, and report the right analysis?

  • Workbook pages 671–672
  • Select the test
  • Integrate the evidence
  • Communicate responsibly
Open the Toolkit

The reasoning pathway you will use in every chapter

  1. Ask

    Identify the comparison or change the research question is asking about.

  2. Match

    Choose the statistical method that fits the participants, groups, factors, and variables.

  3. Check

    Evaluate the assumptions that make the comparison interpretable.

  4. Interpret

    Read statistical significance, direction, magnitude, precision, and follow-up evidence.

  5. Communicate

    Report what the evidence supports without claiming more than the design can show.

Statistical-test overview

Find the question behind the test

Use the design cues in a study question to identify the analysis that is most likely to fit. This overview is a reference, not an activity.

Do not start with the name of a statistical test. Start with the structure of the question: the same people or different groups, one factor or two, and whether the comparison needs an adjustment variable.

The question patterns behind each method

Same people, twice

Paired Samples t Test

A researcher measures the same employees' confidence before and after a training program.

Two separate groups

Independent Samples t Test

A researcher compares final performance scores for employees in an online training group and a face-to-face training group.

One factor, 3+ groups

One Way ANOVA

A researcher compares satisfaction scores across three different service programs.

Two factors

Two Way ANOVA

A researcher examines whether teaching format and grade level affect achievement, including whether the effect of format changes by grade level.

Adjust for a covariate

ANCOVA

A researcher compares program outcomes across three groups while accounting for participants' baseline knowledge scores.

Next step

Start Chapter 1: Change within the same people

Begin with the paired samples t test, a method for examining whether the same participants changed across two related measurements.

Your first question is simple and powerful: Did the same people really change, or does it only look that way?

Begin Chapter 1