Research Paper Examples for High School Students

Seeing examples of good research writing is one of the fastest ways to improve your own papers.

This guide provides annotated excerpts from each section of a research paper, explaining what makes them effective and how you can apply these techniques to your own writing.

Understanding the IMRaD Structure

Most scientific papers follow the IMRaD format:

• Introduction - Why did you do this?
• Methods - How did you do it?
• Results - What did you find?
• and
• Discussion - What does it mean?

Let's look at examples of each section.

Abstract Examples

The abstract summarizes your entire paper in 150-300 words.

Example 1: Biomedical Research

Background: Diabetic retinopathy affects over 100 million people worldwide and is a leading cause of preventable blindness. Early detection significantly improves outcomes, yet current screening methods require specialized equipment and trained ophthalmologists. Methods: This study developed a deep learning model to detect diabetic retinopathy from smartphone-captured retinal images. A MobileNetV3 neural network was trained on 500 images from three diabetes clinics, with ground truth labels from board-certified ophthalmologists. Results: The model achieved an area under the ROC curve of 0.94, with 91.2% sensitivity and 87.3% specificity for detecting any retinopathy. Performance was consistent across patient demographics. Conclusion: Smartphone-based screening could enable widespread diabetic retinopathy detection in resource-limited settings.

What makes it effective:

• ✅ Follows Background-Methods-Results-Conclusion structure
• ✅ Includes specific quantitative results
• ✅ States the significance clearly
• ✅ Appropriate length (~120 words)
• ✅ Can stand alone without reading the full paper

Example 2: Environmental Science

Urban heat islands contribute to increased energy consumption, heat-related illness, and reduced air quality in cities. This study analyzed the relationship between green space coverage and surface temperature across 50 locations in Phoenix, Arizona during summer 2024. Using Landsat 8 thermal imagery and NDVI vegetation indices, we found a strong negative correlation (r = -0.73, p < 0.001) between vegetation coverage and surface temperature. Locations with greater than 30% green coverage were on average 4.2°C cooler than locations with less than 10% coverage. These findings support expanding urban tree canopy as a strategy for heat mitigation, with implications for public health and urban planning.

What makes it effective:

• ✅ Clear problem statement
• ✅ Specific methodology mentioned
• ✅ Key statistical results included
• ✅ Practical implications stated
• ✅ Concise and complete

Introduction Examples

The introduction establishes context, identifies the gap, and states your research question.

Example: Psychology Research

[Hook - Significance] Social media use among teenagers has increased dramatically over the past decade, with the average American teen spending over 4 hours daily on social platforms (Pew Research, 2024). Simultaneously, rates of anxiety and depression among adolescents have risen to historic levels (CDC, 2023).

[Background - What's Known] Previous research has established correlations between social media use and mental health outcomes. A meta-analysis of 23 studies found a small but significant relationship between social media use and anxiety symptoms (r = 0.15, p < 0.001) (Huang, 2022). However, the nature of this relationship remains debated. Some researchers argue that social media causes anxiety through social comparison and fear of missing out (Przybylski & Weinstein, 2017), while others suggest that anxious individuals may simply use social media more as a coping mechanism (Vannucci et al., 2017).

[Gap - What's Missing] Notably, most studies have examined total screen time without distinguishing between different types of social media use. Active use (posting, commenting, messaging) and passive use (scrolling, viewing) may have distinct psychological effects, yet few studies have examined this distinction in adolescent populations.

[Research Question/Hypothesis] The present study investigates whether active versus passive social media use differentially predicts anxiety symptoms in high school students. We hypothesize that passive use will show a stronger association with anxiety than active use, as passive consumption facilitates social comparison without the benefits of social connection.

Breakdown:

What makes it effective:

• ✅ Starts with real-world significance
• ✅ Cites specific prior research
• ✅ Clearly identifies the gap
• ✅ States specific, testable hypothesis
• ✅ Logical flow from broad to specific

Methods Examples

The methods section describes exactly what you did, in enough detail for replication.

Example: Experimental Study

Participants

Participants were 127 students (68 female, 59 male, mean age = 16.2 years, SD = 0.9) recruited from three high schools in suburban Illinois during Fall 2024. Inclusion criteria required participants to be aged 14-18, own a smartphone, and use at least one social media platform daily. Students with diagnosed anxiety disorders were excluded to focus on subclinical symptom variation. All participants provided informed assent, and parental consent was obtained. The study was approved by the Springfield High School IRB (#2024-031).

Measures

Social Media Use. Participants installed the Screen Time Tracker app on their smartphones for two weeks. The app automatically recorded daily usage of each social media application and categorized behaviors as active (posting, commenting, messaging) or passive (scrolling feed, viewing stories, watching videos) based on in-app activity patterns.

Anxiety Symptoms. The Generalized Anxiety Disorder 7-item scale (GAD-7; Spitzer et al., 2006) was administered at baseline and after the two-week monitoring period. The GAD-7 is a validated self-report measure with scores ranging from 0-21 (α = 0.89 in the present sample).

Procedure

Participants completed baseline surveys during school hours. They then installed the monitoring app with researcher assistance and used their phones normally for 14 days. Daily automated prompts reminded participants to keep the app running. At the end of the monitoring period, participants completed the follow-up GAD-7.

Data Analysis

Hierarchical multiple regression examined whether active and passive social media use predicted anxiety symptoms, controlling for baseline anxiety, age, and gender. All analyses were conducted in R version 4.2.1 using the stats and car packages.

What makes it effective:

• ✅ Specific participant demographics
• ✅ Clear inclusion/exclusion criteria
• ✅ Ethical approvals mentioned
• ✅ Measures described with citations
• ✅ Reliability reported (α = 0.89)
• ✅ Procedure in chronological order
• ✅ Statistical approach stated

Results Examples

The results section presents findings objectively, without interpretation.

Example: Quantitative Results

Descriptive Statistics

Participants used social media an average of 4.3 hours per day (SD = 1.8), with 1.2 hours (SD = 0.7) classified as active use and 3.1 hours (SD = 1.4) as passive use. Mean baseline GAD-7 score was 6.8 (SD = 4.2), and mean follow-up score was 7.1 (SD = 4.5). Descriptive statistics are presented in Table 1.

Primary Analyses

Hierarchical regression results are summarized in Table 2. In Step 1, baseline anxiety, age, and gender accounted for 62% of variance in follow-up anxiety scores, F(3, 123) = 67.4, p < .001. In Step 2, adding total social media use did not significantly improve prediction, ΔR² = .01, p = .18.

In Step 3, when active and passive use were entered separately, the model significantly improved, ΔR² = .04, p = .008. Passive social media use was a significant positive predictor of follow-up anxiety (β = 0.19, p = .003), while active use was not significant (β = -0.05, p = .42).

Figure 1 displays the relationship between passive use and anxiety symptoms, with higher passive use associated with greater anxiety.

Secondary Analyses

Exploratory analyses examined potential gender differences. The interaction between gender and passive use was not significant (p = .31), suggesting the relationship between passive use and anxiety was similar for male and female participants.

What makes it effective:

• ✅ Descriptive statistics first
• ✅ References tables and figures
• ✅ Specific statistical values reported
• ✅ Effect sizes (β, R²) included
• ✅ Exact p-values given
• ✅ Organized logically (primary then secondary)
• ✅ No interpretation—just facts

How to Report Statistics

Discussion Examples

The discussion interprets results and places them in context.

Example: Full Discussion Section

[Summary of Findings] This study examined whether active and passive social media use differentially predict anxiety symptoms in high school students. Results supported our hypothesis: passive social media use significantly predicted higher anxiety, while active use did not. These findings held after controlling for baseline anxiety, suggesting that passive use may contribute to anxiety symptoms rather than simply reflecting pre-existing tendencies.

[Interpretation] The differential effects of active versus passive use align with theoretical models emphasizing social comparison as a mechanism linking social media to mental health. Passive scrolling exposes users to curated, idealized content without the social connection benefits of interaction (Verduyn et al., 2017). In contrast, active use—messaging friends, posting content, commenting on others' posts—may fulfill belonging needs that buffer against anxiety.

[Comparison to Prior Research] Our findings extend previous work showing overall correlations between social media use and anxiety (Huang, 2022) by identifying passive use as the key driver. This may explain mixed findings in prior studies that did not distinguish use types. Our effect size (β = 0.19) is comparable to Hunt et al.'s (2018) experimental study that found reduced passive use improved well-being.

[Implications] These results have practical implications for adolescents, parents, and educators. Rather than limiting total screen time, interventions might more effectively target passive consumption specifically. Features that interrupt extended scrolling or encourage active engagement could be beneficial.

[Limitations] Several limitations should be noted. First, despite controlling for baseline anxiety, our correlational design cannot establish causation. Experimental manipulation of use patterns is needed. Second, our sample was predominantly suburban and may not generalize to urban or rural populations. Third, the two-week monitoring period may not capture longer-term patterns.

[Future Directions] Future research should experimentally manipulate passive versus active use to establish causality. Longitudinal studies could examine whether early passive use predicts anxiety development over years. Additionally, examining specific platforms and content types may reveal more nuanced patterns.

[Conclusion] In summary, this study provides evidence that not all social media use is equivalent for mental health. Passive consumption, but not active engagement, predicted anxiety symptoms in high school students. These findings point toward targeted interventions focusing on reducing passive scrolling rather than blanket screen time restrictions.

What makes it effective:

• ✅ Opens with clear summary of findings
• ✅ Interprets results with reference to theory
• ✅ Compares to prior research specifically
• ✅ Discusses practical implications
• ✅ Acknowledges limitations honestly
• ✅ Suggests specific future directions
• ✅ Ends with strong conclusion

Putting It All Together

Section Length Guide

Quality Checklist

Introduction:

• Starts with significance/hook
• Provides relevant background
• Identifies specific gap
• States clear research question

Methods:

• Describes participants/samples
• Explains measures/materials
• Details procedure chronologically
• States analysis approach

Results:

• Reports descriptive statistics
• Presents main findings
• Includes effect sizes
• References all tables/figures

Discussion:

• Summarizes key findings
• Interprets with theory
• Compares to literature
• Acknowledges limitations
• Suggests future work

Where to Find More Examples

Published Student Research

• Journal of Emerging Investigators - Peer-reviewed high school research
• Journal of Student Research - Undergraduate and high school
• Science fair databases (search past ISEF abstracts)

Reading Published Papers

How to learn from papers:

1. Find papers in your topic area
2. Read the abstract first
3. Note the structure of each section
4. Pay attention to transitions
5. See how they discuss limitations
6. Observe citation patterns

Getting Expert Feedback

The best way to improve your writing is expert feedback. The YRI Fellowship provides:

• 1:1 PhD Mentorship - Experts review your writing
• Multiple Revision Rounds - Improve with each draft
• Publication Guidance - Write at journal standards
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Frequently Asked Questions

Can I use these examples as templates? Use the structure and style as guides, but write your own content. Never copy text—that's plagiarism. Understand why each section works, then apply those principles to your own research.

How long should each section be? It varies by paper length and field. For a typical 10-15 page high school paper: Introduction 1.5-2 pages, Methods 2-3 pages, Results 2-3 pages, Discussion 2-3 pages.

Do I need to include all these elements? Yes, for a complete research paper. Science fair papers may be shorter but should still have all sections. Check specific competition requirements.

What's the biggest mistake students make? The most common issue is vague writing. Instead of "there was a significant difference," write "Group A scored 23% higher than Group B (t(48) = 3.45, p = .001)."

How do I know if my writing is good enough? Get feedback. Ask teachers, mentors, or peers to read your paper. Programs like YRI provide multiple rounds of expert review.

Related Guides

• How to Write a Research Paper
• Research Paper Outline Template
• How to Write a Literature Review
• Research Paper Format Guide