The IMRAD (Introduction, Methods, Results, and Discussion) structure is a common organizational pattern used to write manuscripts for scientific papers and research articles. IMRAD is recommended by scientific journals and style manuals as a framework that readers expect and it allows authors to effectively communicate their research. Though some variations exist between different disciplines, IMRAD provides a helpful template for scientists to systematically present their work.
This article provides a sample IMRAD research paper based on a fictitious study. Each section of the paper is fleshed out with example content to demonstrate how IMRAD is applied in practice. Accompanying annotations are included to explain key aspects of the writing. It is hoped this sample IMRAD paper can serve as a template for students and researchers new to scientific writing. By understanding the flow and content expected in each section, it can help develop skills in structuring manuscripts clearly and effectively communicating research.
METHODS
The Methods section describes how the study was conducted including details about participant selection, variables, equipment, procedures, and data analysis. This allows readers to evaluate the appropriateness of the methodology and replicate the study if desired.
Participants
Twenty healthy volunteers between the ages of 18-35 years were recruited from the local university. An advertisement was placed on the school’s online jobs board. Respondents were screened for inclusion/exclusion criteria over the phone and those eligible provided written informed consent prior to participation.
Procedure
A within-subjects repeated measures design was used. Participants attended one session in the psychophysiology lab. Electroencephalography (EEG) was collected using a 64-channel ActiCap connected to a BrainVision recording system. Scalp sites were prepared with abrasive gel and impedances were kept below 50kΩ. Continuous EEG data was recorded with a 500Hz sampling rate during three 5-minute conditions: 1) eyes closed resting state, 2) eyes open viewing a blank screen, 3) performing a Go/NoGo task presented on a computer.
Data Analysis
EEG data preprocessing and analysis was conducted using BrainVision Analyzer software. Data was re-referenced to averaged mastoids and bandpass filtered from 1-30Hz. Independent components analysis was used for ocular correction. Fast Fourier transforms generated power spectra that were averaged for theta (4-7Hz), alpha (8-12Hz), and beta (13-30Hz) bands. Condition comparisons were made using repeated measures ANOVA with Greenhouse-Geisser corrected p-values.
RESULTS
The Results section objectively presents the analysis outcomes without interpretation. Tables and figures are included to clearly display significant findings.
EEG Power Spectra
Mauchly’s test indicated that the assumption of sphericity had been violated for theta χ2(2) = 15.48, p < .001, alpha χ2(2) = 8.23, p < .05, and beta χ2(2) = 12.36, p < .01 power. Degrees of freedom were corrected using Greenhouse-Geisser estimates of sphericity (ɛ = 0.65, 0.74, 0.69, respectively).
For theta power, condition comparisons revealed a significant difference between conditions, F(1.30, 24.74) = 5.23, p < .05, ƞp2 = 0.22. Post-hoc tests with Bonferonni adjustment showed theta was higher in eyes closed than eyes open (p < .05) and Go/NoGo conditions (p < .05), see Figure 1.
For alpha power, the main effect of condition was also significant, F(1.48, 28.09) = 15.72, p < .001, ƞp2 = 0.45. Alpha was higher in eyes closed than eyes open and Go/NoGo conditions (both p < .001).
For beta power, condition differences were not significant, F(1.38, 26.26) = 2.07, p = .16, ƞp2 = 0.10. No post-hoc tests were examined.
DISCUSSION
The Discussion synthesizes findings, relates results to previous literature, acknowledges study limitations, and suggests future research directions. Well-supported conclusions are drawn while avoiding overgeneralization.
The main finding was that alpha and theta EEG power differed across resting conditions, supporting the notion that these rhythms vary depending on eyes open vs. closed states. Alpha was reliably higher during eyes closed rest, consistent with its characterization as an “idling rhythm” of the visual system (Banghart & Gollo, 2020). Interestingly, while theta also elevated during eyes closed rest, it did not differ from the active Go/NoGo condition. This tentatively suggests theta may be less task-specific than alpha and related more to internal mental processes during both relaxation and cognitive engagement (Knyazev, 2007).
Notably, beta power was similar across conditions, contradicting its proposed role in attention and cognitive control (Yilmaz et al., 2019). The Go/NoGo task may not have elicited sufficient beta modulation. Future studies could employ more attentionally demanding paradigms. Additionally, only healthy young participants were recruited so these EEG fingerprints may differ in clinical populations.
Limitations include the cross-sectional design, small sample size, and lack of condition counterbalancing. The single session prevented examination of test-retest reliability. Additional conditions such as eyes open with a visual task could further elucidate effects of attention and arousal on oscillatory dynamics.
This work makes progress toward characterizing EEG rhythms during standard resting state protocols. With replication, it could guide development of norms for experimental and clinical use. Continued research is warranted to refine understanding of oscillatory mechanisms across conditions and populations.
CONCLUSION
This study found condition-dependent changes in alpha and theta EEG power during different resting state protocols but not beta power. Namely, alpha and theta increased with eyes closed rest compared to eyes open or an active task. These findings align with proposed roles for these rhythms in visual attention and internal cognitive processes. Future work is needed with larger samples, additional conditions, and clinical groups to better delineate electrophysiological correlates of brain state. Overall, this study demonstrated the utility of employing multiple resting state recordings to evaluate oscillatory dynamics.