Practical examples of ANOVA in educational research

Real examples of ANOVA in educational research

Let’s skip the definitions and go straight to concrete situations. The best examples of ANOVA in educational research usually share three features:

• There are three or more groups being compared.
• The outcome is quantitative (test scores, GPA, time-on-task, etc.).
• The researcher wants to know whether group means differ more than we’d expect by chance.

Below are several real examples of ANOVA examples in educational research that mirror how current studies are designed in schools, districts, and universities.

Example 1: Comparing three reading interventions in elementary schools

Imagine a district rolling out three different reading programs for 3rd graders:

• Program A: Phonics-heavy curriculum
• Program B: Balanced literacy
• Program C: Tech-based adaptive reading app

After a semester, each student takes the same standardized reading test. The research question is straightforward: Do average reading scores differ across the three programs?

This is a textbook one-way ANOVA situation:

• Independent variable (factor): Reading program (A, B, C)
• Dependent variable: Standardized reading score

If the ANOVA F-test is statistically significant, the district concludes that at least one program’s mean score differs from the others. Post-hoc tests (Tukey, Bonferroni, etc.) then show which pairs differ.

This is a classic example of ANOVA in educational research because:

• It involves a real decision (which program to scale up).
• It compares three or more instructional approaches.
• It treats test scores as the outcome.

Districts and researchers routinely publish similar work; for example, the Institute of Education Sciences (IES) in the U.S. regularly funds and reports on reading intervention trials that use ANOVA or related models to compare outcomes across conditions.

Example 2: Evaluating online vs hybrid vs in-person college courses

Post‑COVID, institutions are still trying to figure out how online and hybrid formats stack up against traditional classrooms. Suppose a university offers an introductory statistics course in three formats:

• Fully in-person
• Fully online (asynchronous)
• Hybrid (one in-person meeting per week plus online modules)

At the end of the term, all students take the same final exam. The researcher asks: Are final exam scores different across the three delivery modes?

Again, this is a one-way ANOVA:

• Factor: Course format (in-person, online, hybrid)
• Outcome: Final exam score

If the ANOVA shows a significant effect of format, it suggests that mode of delivery matters, at least for this course and population. This kind of design appears frequently in higher-ed research, often combined with additional variables like prior GPA or demographics in more advanced models.

In 2024–2025, as institutions refine their online offerings, this type of design is one of the best examples of ANOVA examples in educational research for understanding how instructional modality affects achievement.

Example 3: Repeated-measures ANOVA for progress across the school year

Sometimes, the same students are measured multiple times. Imagine a middle school implementing a new math curriculum and giving a benchmark test three times:

• Fall baseline
• Winter mid-year
• Spring end-of-year

The question: Do average math scores change significantly across the three testing points?

This calls for a repeated-measures ANOVA:

• Within-subjects factor: Time (fall, winter, spring)
• Outcome: Math benchmark score

Here, each student serves as their own control. The ANOVA tests whether mean scores at the three time points differ more than we’d expect from random fluctuation.

This is a powerful example of ANOVA in educational research when schools want to know whether a curriculum or intervention leads to growth over time, not just a single end-of-year difference.

Example 4: Two-way ANOVA for teaching method by gender

Real classrooms are messy. Researchers often want to know whether a teaching method works differently for different groups of students.

Suppose a high school tries two approaches to teaching algebra:

• Method 1: Traditional lecture
• Method 2: Problem-based learning (PBL)

Students are also categorized by gender (for simplicity, assume two categories in the dataset: male and female). The outcomes are end-of-unit algebra test scores. The research questions:

• Are there overall differences between the two teaching methods?
• Are there overall differences between gender groups?
• Is there an interaction: does one method work better for one gender group?

This is a two-way ANOVA:

• Factor 1: Teaching method (lecture, PBL)
• Factor 2: Gender
• Outcome: Algebra test score

If the interaction is significant, the effect of teaching method depends on gender. This is the kind of nuance that informs equity-focused decisions: a method might look effective on average but mask subgroup differences.

This setup is a strong example of ANOVA examples in educational research because it shows how ANOVA can handle more realistic, multi-factor questions instead of a single, simple comparison.

Example 5: Comparing tutoring models across multiple schools

Tutoring is a major policy topic in 2024–2025, especially for addressing learning loss. Imagine a district running three tutoring models for struggling 8th-grade students:

• Model A: One-on-one in-person tutoring
• Model B: Small-group in-person tutoring
• Model C: Online tutoring platform with live support

Students across several schools participate. After a semester, they take a standardized math test. The research question: Which tutoring model leads to higher math scores?

A one-way ANOVA compares the three models on the outcome scores. To keep the design clean, schools might randomly assign students to models, or at least control for school effects.

This case is one of the best examples of ANOVA in educational research tied directly to policy and funding decisions. Districts and state agencies often use this kind of analysis when deciding where to allocate limited intervention dollars.

For context on tutoring and intervention research, the U.S. Department of Education’s What Works Clearinghouse (WWC) provides summaries of studies using ANOVA and related methods: https://ies.ed.gov/ncee/wwc.

Example 6: ANOVA for student engagement across learning platforms

Beyond test scores, many 2024–2025 studies focus on engagement with digital tools. Suppose a district adopts three different learning management systems (LMS) across schools:

• LMS X
• LMS Y
• LMS Z

Researchers measure student engagement using a validated survey scale (for instance, a 1–5 Likert-type scale averaged across several items). The question is: Do average engagement scores differ across the three LMS platforms?

Here, ANOVA is again appropriate:

• Factor: LMS platform (X, Y, Z)
• Outcome: Engagement score

If the ANOVA shows significant differences, it suggests that platform design may influence how engaged students feel, even when course content is similar. This kind of example of ANOVA in educational research shows that the method is not limited to test scores; it also works for survey-based outcomes, as long as the data are treated as approximately continuous and meet basic assumptions.

Example 7: Comparing professional development formats for teachers

Educational research is not just about students. Consider a district offering three professional development (PD) formats on formative assessment:

• Format 1: One-day in-person workshop
• Format 2: Four weekly after-school sessions
• Format 3: Fully online self-paced module

Teacher outcomes might include a test of assessment literacy or classroom observation scores on how well they use formative assessment strategies.

The research question: Which PD format leads to higher teacher assessment literacy scores?

Again, a one-way ANOVA compares the three PD formats. This is another real example of ANOVA examples in educational research, often seen in teacher education and instructional coaching studies.

For more on teacher professional learning research methods, you can explore resources from the Harvard Graduate School of Education: https://www.gse.harvard.edu.

Example 8: Multi-level context with ANOVA-style questions

In 2024–2025, many studies use multilevel or mixed-effects models to account for students nested in classes and schools. But the core questions often still look like ANOVA questions.

For instance, a researcher might ask:

• Do average reading scores differ across school types (public, charter, private)?
• Do average science scores differ across three curriculum versions?

At a conceptual level, these are still examples of ANOVA in educational research. The difference is that modern software (R, SAS, SPSS, Stata) often fits them using linear mixed models to handle clustering. The logic of comparing group means remains the same.

Organizations like the National Center for Education Statistics (NCES) routinely publish analyses comparing mean scores across groups in large datasets such as NAEP. While they may use complex survey-weighted models, the underlying questions mirror ANOVA: https://nces.ed.gov.

Why these are strong examples of ANOVA in educational research

All of these cases share a few patterns that make them good examples of ANOVA examples in educational research:

• Three or more groups or time points. T-tests handle two groups; ANOVA shines when there are multiple instructional methods, formats, or time points.
• Clear factor–outcome structure. Whether it’s teaching method, course format, or PD type, there is a categorical factor and a quantitative outcome.
• Actionable decisions. The results inform real choices: which curriculum to adopt, which tutoring model to fund, which PD format to expand.
• Alignment with current trends. Online vs in-person learning, tutoring, adaptive platforms, and teacher PD are all hot topics in 2024–2025.

When you’re designing your own study, a quick way to see if ANOVA fits is to ask: Am I comparing mean outcomes across three or more groups or time points? If yes, your project is probably another solid example of ANOVA in educational research.

Practical tips for using ANOVA in your own education study

If you want your project to look like the best examples of ANOVA examples in educational research, a few practical moves go a long way:

Be explicit about your groups

Spell out your factor levels clearly:

• Instead of “different teaching methods,” specify “lecture, flipped classroom, and project-based learning.”
• Instead of “three schools,” specify “urban magnet, suburban comprehensive, and rural high school.”

Clear labeling makes your ANOVA results interpretable for non-statisticians.

Check assumptions without getting lost in the weeds

Classical ANOVA assumes:

• Approximately normal residuals
• Homogeneity of variances (similar spread across groups)
• Independence of observations

Education data are rarely perfect, but mild violations are often tolerated, especially with larger samples. Use residual plots and tests as guides, not as absolute gatekeepers.

Report effect sizes, not just p-values

In education research, effect sizes help stakeholders judge whether differences are meaningful. Alongside the F-statistic and p-value, report measures like:

• η² (eta squared) or partial η² for the proportion of variance explained
• Group means and standard deviations

This brings your work in line with reporting standards promoted by organizations like the American Educational Research Association (AERA).

Tie the stats back to real decisions

The strongest examples of ANOVA in educational research always circle back to practice:

• If one reading program outperforms others, what does that imply for scaling or training?
• If hybrid courses show higher performance, how might that affect scheduling or technology investments?
• If PD formats differ in impact, how should districts redesign their professional learning plans?

Numbers matter, but they matter most when they drive smarter policy and classroom decisions.

FAQ: ANOVA examples in educational research

Q1. What is a simple example of ANOVA in educational research?
A straightforward example of ANOVA in educational research is comparing average test scores of students taught using three different teaching methods in the same grade. If you have, say, lecture, flipped classroom, and project-based learning groups, and you want to know whether their mean scores differ, a one-way ANOVA is appropriate.

Q2. Can ANOVA be used for survey data in education?
Yes, many examples of ANOVA examples in educational research use survey-based outcomes, such as student engagement or satisfaction scores on 1–5 scales. As long as the scale is treated as approximately continuous and the assumptions are reasonably met, ANOVA is commonly used.

Q3. How is ANOVA different from t-tests in school research?
T-tests compare two groups (for example, boys vs girls). ANOVA compares three or more groups or time points (for example, three reading programs or fall vs winter vs spring tests). Many real examples in education involve more than two groups, which is why ANOVA appears so often.

Q4. Are repeated-measures ANOVA and mixed models used in modern education studies?
Very much so. When the same students are measured multiple times (like fall, winter, spring), repeated-measures ANOVA or mixed-effects models are used. They are common in progress monitoring, curriculum evaluations, and intervention studies.

Q5. Where can I see published examples of ANOVA in educational research?
You can find real examples of ANOVA in educational research in:

• Reports from the Institute of Education Sciences and What Works Clearinghouse: https://ies.ed.gov
• National Center for Education Statistics reports: https://nces.ed.gov
• University education research centers, such as Harvard Graduate School of Education: https://www.gse.harvard.edu

These sources often describe their analytic methods, including ANOVA, in technical appendices or methods sections.