How to Write Expected Results in a Research Proposal: A Practical Guide

Writing the expected results section of a research proposal is a task that confuses many graduate students and early-career researchers. You must state what you anticipate finding before you have collected any data, and you must do so with enough specificity that reviewers can judge whether your study design is adequate. This guide explains how to write expected results that are specific, testable, and aligned with your research objectives and hypotheses. It includes templates, examples for different study designs, and practical advice for avoiding common mistakes.

The expected results section is not a promise of specific outcomes. It is a statement of what your study is designed to detect, how you will recognize meaningful findings, and what patterns of data would support or contradict your hypotheses. Reviewers use this section to assess whether your methods can actually answer your research question. A well-written expected results section demonstrates that you understand your study design, your variables, and the logic that connects your data to your conclusions.

What Are Expected Results in a Research Proposal

Expected results are the outcomes you anticipate observing if your hypotheses are correct. They are predictions derived from your theoretical framework, prior literature, and preliminary data. In a research proposal, expected results serve several functions. They show reviewers that you have thought through the logical implications of your hypotheses. They demonstrate that your study design has the statistical power and measurement precision to detect the effects you care about. They also provide a benchmark against which you can interpret your actual findings.

The concept of a prediction error is useful here. In learning theory, a prediction error is the difference between what you expected and what you actually experienced. Research proposals work on the same principle. Your expected results define what you predict, and your actual results will either confirm or disconfirm that prediction. The clearer your prediction, the more informative your actual results will be. This logic applies across all fields, from neuroscience to public health to agricultural science.

Expected results differ from expected outcomes in a subtle but important way. Expected outcomes are the broader impacts or deliverables of your research, such as a new intervention package, a policy recommendation, or a published dataset. Expected results are the specific empirical findings you anticipate, such as a mean difference between groups, a correlation coefficient, or a qualitative theme. Both belong in a proposal, but they belong in different places. Expected outcomes fit in the significance or impact section. Expected results fit in the methods or analysis section, where they connect your hypotheses to your statistical plan.

Why Reviewers Care About Expected Results

Reviewers read the expected results section to evaluate the internal logic of your proposal. They want to know whether your study design can actually test your hypotheses. A proposal that states a hypothesis but does not describe what pattern of data would support it is incomplete. The expected results section closes that gap.

Bias is a central concern in this evaluation. Bias is the lack of internal validity or the incorrect assessment of the association between an exposure and an effect in the target population. Biases can be classified by the research stage in which they occur or by the direction of change in an estimate. The most important biases are those produced in the definition and selection of the study population, data collection, and the association between different determinants of an effect in the population. When you write expected results, you are implicitly committing to a set of measurement and sampling procedures. Reviewers will check whether those procedures are vulnerable to bias and whether your expected results account for that vulnerability.

For example, if you propose a survey study, reviewers will ask whether your sampling frame covers the population you want to generalize to. If you propose a clinical trial, reviewers will ask whether blinding is adequate. If you propose a field experiment, reviewers will ask whether confounding variables are controlled. Your expected results section should acknowledge these threats and explain how your design addresses them.

Reviewers also care about replicability. Replication is a study for which any outcome would be considered diagnostic evidence about a claim from prior research. This definition reduces emphasis on operational characteristics of the study and increases emphasis on the interpretation of possible outcomes. The purpose of replication is to advance theory by confronting existing understanding with new evidence. When you write expected results, you are describing the evidence that would confront existing understanding. If your expected results are vague or unfalsifiable, your study cannot contribute to cumulative knowledge.

Core Principles for Writing Expected Results

Align Expected Results with Objectives and Hypotheses

Every expected result should trace back to a specific objective or hypothesis. If your proposal has three specific aims, you should have expected results for each aim. If you have a primary hypothesis and several secondary hypotheses, your expected results should distinguish between them. This alignment helps reviewers see the structure of your study and prevents the expected results section from becoming a list of vague aspirations.

A common framework for this alignment is the study protocol structure. A well-designed protocol states its objectives, describes its methods, and then specifies the expected outcomes. For example, a protocol for developing a prevention-focused intervention package might state that the study will lead to a modular intervention package with evidence of feasibility and acceptability. That statement is an expected outcome. The expected results would be the specific quantitative measures of feasibility and acceptability, such as recruitment rates, retention rates, and participant satisfaction scores.

Make Expected Results Specific and Testable

Vague expected results are a major weakness in research proposals. Statements like "we expect to find interesting results" or "this study will contribute to the literature" do not help reviewers evaluate your design. Instead, specify the direction and magnitude of the effects you expect to observe.

For quantitative studies, specify the variables you will measure, the direction of the expected difference or association, and the statistical significance threshold you will use. For qualitative studies, specify the themes or patterns you expect to emerge and how you will recognize them. For mixed-methods studies, specify how the quantitative and qualitative components will inform each other.

Specificity also helps you plan your analysis. If you expect a 20% reduction in a primary outcome, you can calculate the sample size needed to detect that effect. If you expect a correlation of 0.3 between two variables, you can determine whether your sample size provides adequate power. The Experimental Design Assistant from NC3Rs is a tool that can help you plan experiments and avoid common design flaws. While it is designed primarily for animal research, its principles apply to any study that involves hypothesis testing.

Distinguish Expected Results from Actual Results

The expected results section describes what you predict. The results section of your final paper describes what you actually found. These are different things, and you should not confuse them in your proposal. Some researchers worry that stating expected results will box them in or make them look foolish if their predictions are wrong. This concern is misplaced. Reviewers understand that hypotheses are often disconfirmed. A well-designed study that produces null results is still valuable, especially if the null result is informative about the theory being tested.

In fact, the value of replication and hypothesis testing is strongest when existing understanding is weakest. Unsuccessful replication indicates that the reliability of the finding may be more constrained than recognized previously. If your expected results are not confirmed, your study can still make a contribution by showing that the predicted effect does not occur under your conditions.

Use Conditional Language

Expected results should be written in conditional language. Use phrases like "we expect to observe," "if the hypothesis is correct," and "we anticipate that." This language signals that your predictions are hypotheses, not promises. It also helps you describe alternative scenarios. For example, you might write that if the intervention is effective, you expect to see a significant reduction in the primary outcome, but if the intervention is ineffective, you expect no significant difference between groups.

Conditional language is especially important in fields where the evidence base is weak or conflicting. When the literature does not support a clear prediction, you should say so and explain how your study will resolve the uncertainty. The GRADE approach for assessing certainty of evidence notes that serious concerns regarding limitations in study design, inconsistency, imprecision, indirectness, and publication bias can decrease the certainty of the evidence. If your field has these problems, your expected results should acknowledge the uncertainty and describe how your study will address it.

How to Structure the Expected Results Section

Position in the Proposal

The expected results section typically appears after the methods section and before the limitations or dissemination sections. Some funding agencies have specific formats that require expected results to be integrated into the methods or analysis plan. Check the guidelines for the specific funding mechanism you are applying to. For example, fellowship proposals modeled after National Institutes of Health mechanisms have specific requirements for the research plan, and the expected results may need to be integrated into the approach section.

Length and Format

The length of the expected results section depends on the complexity of your study and the requirements of the funding agency. For a single-study proposal, one to three paragraphs may be sufficient. For a multi-site study or a study with multiple aims, you may need a subsection for each aim. Use headings and subheadings to organize the section, especially if you have multiple hypotheses or study components.

Components to Include

A complete expected results section should include the following components:

A restatement of the hypothesis or objective that the expected results address. This helps reviewers connect the expected results to the rest of the proposal.

A description of the specific variables or outcomes you will measure. Name the primary outcome and any secondary outcomes. If you have multiple outcomes, explain which one is primary and why.

A statement of the expected direction and magnitude of the effect. For example, you might expect a 15% reduction in the primary outcome in the intervention group compared to the control group.

A description of the statistical or analytical methods you will use to test the hypothesis. This might include the specific statistical test, the significance threshold, and the confidence interval width.

A description of how you will interpret different possible outcomes. What pattern of results would support your hypothesis? What pattern would disconfirm it? What pattern would be ambiguous?

A statement of the limitations that could affect your ability to observe the expected results. This might include measurement error, missing data, or low statistical power.

Templates for Different Study Designs

Template for a Quantitative Hypothesis-Testing Study

For a study that tests a specific hypothesis using quantitative data, use the following template:

We hypothesize that [intervention or exposure] will [increase or decrease] [primary outcome] compared to [comparator]. We expect to observe a [direction and approximate magnitude] difference between groups. Specifically, we anticipate that the [intervention] group will have a [percentage or absolute] [higher or lower] [outcome] than the [comparator] group. This difference will be tested using [statistical test] at a significance level of [threshold]. We will also examine [secondary outcomes] to explore [secondary hypotheses]. If the hypothesis is correct, we expect to see [specific pattern of results]. If the hypothesis is incorrect, we expect to see [alternative pattern]. We will interpret the results in light of [potential confounders or limitations].

Template for a Qualitative Study

For a qualitative study, the expected results section describes the themes or patterns you expect to emerge from your data. Use the following template:

This study will explore [research question] using [qualitative method]. Based on [theoretical framework or prior literature], we expect to identify the following themes: [theme 1], [theme 2], and [theme 3]. We anticipate that [theme 1] will be the most prominent, reflecting [reason]. We will use [analytical technique] to code and categorize the data. We will assess the credibility of our findings through [validation strategy, such as member checking or triangulation]. If the data reveal themes that differ from our expectations, we will report these discrepancies and discuss their implications for [theory or practice].

Template for a Mixed-Methods Study

For a mixed-methods study, the expected results section should describe how the quantitative and qualitative components will inform each other. Use the following template:

This study uses a sequential mixed-methods design. In the quantitative phase, we expect to observe [specific quantitative result]. In the qualitative phase, we expect to identify [specific qualitative themes]. We anticipate that the qualitative findings will [explain, expand, or challenge] the quantitative results. For example, if the quantitative phase shows [result], the qualitative phase may reveal [explanation]. We will integrate the two data sources using [integration technique]. The expected results of the integrated analysis are [description of expected integrated findings].

Template for a Study Protocol

For a study protocol, the expected results section often appears under a heading called "Expected Outcomes" or "Anticipated Results." Use the following template:

The primary outcome of this study is [outcome]. We expect that [intervention or exposure] will [effect] compared to [comparator]. Secondary outcomes include [list of secondary outcomes]. We will assess these outcomes at [time points]. We anticipate that the results will [contribution to knowledge or practice]. The findings will inform [future research, policy, or practice]. We will disseminate the results through [dissemination channels].

Examples of Expected Results for Different Study Designs

Example 1: Randomized Controlled Trial

Consider a randomized controlled trial testing a new educational intervention for improving digital literacy in adolescents. The expected results section might read:

We hypothesize that students in the intervention group will show greater improvement in digital literacy scores compared to students in the control group. We expect to observe a mean difference of at least 10 points on the Digital Literacy Assessment Scale between the two groups at the post-intervention assessment. This difference will be tested using an independent samples t-test at a significance level of 0.05. We will also examine secondary outcomes, including self-reported confidence in using digital tools and frequency of digital technology use. If the intervention is effective, we expect to see significant improvements in both primary and secondary outcomes. If the intervention is ineffective, we expect no significant differences between groups. We will interpret the results in light of potential confounding variables, including baseline digital literacy and socioeconomic status.

Example 2: Observational Cohort Study

Consider an observational cohort study examining the association between air pollution exposure and respiratory symptoms in children. The expected results section might read:

We hypothesize that higher levels of air pollution exposure will be associated with increased respiratory symptoms in children. We expect to observe a positive correlation between ambient particulate matter concentration and the frequency of respiratory symptoms, with a correlation coefficient of approximately 0.3. This association will be tested using multivariable regression models adjusted for potential confounders, including age, sex, and socioeconomic status. We will also examine whether the association is stronger in children with pre-existing asthma. If the hypothesis is correct, we expect to see a significant positive association between pollution exposure and respiratory symptoms. If the hypothesis is incorrect, we expect no significant association after adjusting for confounders. We will interpret the results in light of the limitations of observational data, including the potential for residual confounding and exposure misclassification.

Example 3: Qualitative Interview Study

Consider a qualitative study exploring the experiences of farmers adopting conservation agriculture practices. The expected results section might read:

This study will explore the barriers and facilitators to adopting conservation agriculture practices among smallholder farmers. Based on the diffusion of innovations theory, we expect to identify the following themes: perceived economic benefits, perceived risks, social influence, and access to information. We anticipate that perceived economic benefits will be the most prominent theme, reflecting the importance of short-term profitability in adoption decisions. We will use thematic analysis to code and categorize the interview data. We will assess the credibility of our findings through member checking and peer debriefing. If the data reveal themes that differ from our expectations, we will report these discrepancies and discuss their implications for agricultural extension policy.

Example 4: Multi-Site Implementation Study

Consider a multi-site study testing a prevention-focused intervention for problematic digital technology use among youth. The expected results section might read:

This study will develop and validate a comprehensive package of prevention-focused interventions targeted at problematic use of digital technology among youth. The study will be conducted across six sites and will use a sequential mixed-methods design. We expect that the intervention package will demonstrate feasibility and acceptability, as measured by recruitment rates, retention rates, and participant satisfaction scores. We anticipate that participants will show improvements in knowledge, skills, confidence, and decision-making related to digital technology use. Qualitative data will be used to assess engagement, delivery quality, and contextual factors. The study will lead to a modular prevention-focused intervention package with evidence of feasibility and acceptability. Findings will inform future larger scale implementation and evaluations.

At a Glance

Practical Steps for Writing Expected Results

Step 1: Review Your Objectives and Hypotheses

Before you write the expected results section, review your research objectives and hypotheses. Each expected result should connect to a specific objective or hypothesis. If you cannot trace an expected result back to an objective, either the expected result is unnecessary or the objective is too vague.

Step 2: Identify Your Primary Outcome

Decide which outcome is most important for testing your hypothesis. This is your primary outcome. All other outcomes are secondary. Your expected results section should give the most detail to the primary outcome. If you have multiple primary outcomes, explain why and how you will handle the multiple testing problem.

Step 3: Specify the Expected Direction and Magnitude

State the direction of the expected effect. Will the intervention increase or decrease the outcome? Will the exposure be positively or negatively associated with the outcome? Then state the approximate magnitude. This can be a percentage change, a standardized effect size, or a correlation coefficient. If you do not have enough information to specify the magnitude, explain why and describe how you will determine the magnitude from your data.

Step 4: Describe Your Analytical Approach

Describe the statistical or analytical methods you will use to test your hypothesis. This might include the specific statistical test, the significance threshold, and the confidence interval width. If you are using qualitative methods, describe your coding and analysis approach. The level of detail should match the requirements of the funding agency.

Step 5: Describe Interpretation Criteria

Explain what pattern of results would support your hypothesis and what pattern would disconfirm it. This is the most important part of the expected results section. It shows reviewers that you have a clear plan for interpreting your data. It also protects you from the temptation to reinterpret ambiguous results after you see them.

Step 6: Acknowledge Limitations

Describe the limitations that could affect your ability to observe the expected results. This might include measurement error, missing data, low statistical power, or confounding. Acknowledging limitations shows that you understand the threats to validity in your study. It also helps reviewers identify potential problems before you collect data.

Step 7: Get Feedback

Share your expected results section with mentors, colleagues, or writing groups. Ask them whether the expected results are specific enough, whether they align with the objectives and hypotheses, and whether they would be informative if the predictions are not confirmed. The National Research Mentoring Network has found that social support, peer accountability, and access to mentoring are key mechanisms for developing grant-writing skills. Use your network to improve your proposal.

Records and Measurements to Support Expected Results

The expected results section should be grounded in the measurements you plan to take. Reviewers will check whether your measurement plan can produce the data needed to test your hypotheses. This means you need to specify your measurement instruments, their validity and reliability, and the timing of measurements.

For quantitative studies, specify the measurement instruments and their psychometric properties. If you are using a validated scale, cite the validation study. If you are using a novel instrument, describe how you will validate it. The Research Data Framework from the National Institute of Standards and Technology provides guidance on managing research data throughout its lifecycle. Good data management practices support the credibility of your expected results.

For qualitative studies, specify your data collection methods, such as interviews, focus groups, or participant observation. Describe how you will ensure the trustworthiness of your data, including strategies for triangulation, member checking, and audit trails. The EQUATOR Network provides reporting guidelines for health research, including qualitative research. Following these guidelines strengthens the credibility of your expected results.

For studies that involve animal experiments, the Experimental Design Assistant from NC3Rs can help you plan your experiments and avoid common design flaws. This tool helps you think through the experimental design, including randomization, blinding, and sample size. Using such tools demonstrates that your expected results are grounded in sound methodology.

Common Failure Patterns in Expected Results Sections

Vague Predictions

The most common failure is writing expected results that are too vague to be tested. Statements like "we expect to gain new insights" or "this study will provide valuable information" do not help reviewers evaluate your design. Replace vague statements with specific predictions about the direction and magnitude of effects.

Misalignment with Objectives

Another common failure is writing expected results that do not connect to the stated objectives or hypotheses. This happens when researchers write the expected results section without referring back to their aims. Reviewers notice this misalignment and may conclude that the proposal lacks coherence.

Overpromising

Some researchers write expected results that promise more than the study can deliver. For example, a small pilot study cannot produce definitive evidence of effectiveness. A cross-sectional survey cannot establish causation. If your expected results overpromise, reviewers may question whether you understand the limitations of your design.

Confusing Expected Results with Expected Outcomes

As noted earlier, expected results are empirical findings, while expected outcomes are broader impacts. Confusing the two leads to a section that describes the significance of the research instead of the anticipated findings. Keep the two concepts separate.

Ignoring Alternative Outcomes

Some researchers write expected results that only describe the outcome they hope to see. They do not describe what they will conclude if the hypothesis is disconfirmed. This is a weakness because it suggests that the researcher has not thought through the interpretation of null or unexpected results. Describe the alternative outcomes and how you will interpret them.

Failing to Acknowledge Bias

Bias is a threat to the validity of any study. If your expected results section does not acknowledge the potential for bias, reviewers may conclude that you are not aware of the threats to your study. Describe the potential sources of bias in your design and how you will address them.

Limitations of Expected Results

Expected results are predictions, not guarantees. Several factors can cause your actual results to differ from your expectations. Understanding these limitations helps you write a more realistic expected results section and interpret your findings more accurately.

Measurement error is a common limitation. No measurement instrument is perfectly reliable. If your instrument has poor reliability, your expected results may not be observed even if the underlying effect exists. Describe the reliability of your instruments and how you will minimize measurement error.

Missing data is another common limitation. Participants may drop out of the study, fail to complete questionnaires, or provide unusable data. Missing data can bias your results and reduce your statistical power. Describe how you will handle missing data, such as using multiple imputation or sensitivity analysis.

Low statistical power is a critical limitation. If your sample size is too small, you may fail to detect a true effect. This is a particular concern in studies with small effect sizes or high variability. Describe how you determined your sample size and what effect size you are powered to detect.

Confounding is a limitation in observational studies. If you cannot control for all relevant confounders, your expected results may be biased. Describe the potential confounders in your study and how you will address them through design or analysis.

Generalizability is a limitation in all studies. Your results may not generalize to populations or settings that differ from your study sample. Describe the population your results will apply to and the limitations on generalizability.

Safety and Regulatory Context

The expected results section should be consistent with the safety and regulatory requirements of your study. If your study involves human participants, you must describe the ethical considerations and how you will protect participant safety. If your study involves animals, you must describe the welfare considerations and how you will minimize harm. If your study involves regulated substances or procedures, you must describe the regulatory approvals you will obtain.

The challenges of conducting research in regulated areas are well documented. For example, research on psychedelic medicine faces challenges related to blinding, expectancy, the use of therapy, and sources of bias. These challenges affect the interpretation of expected results. If your study involves a regulated substance or procedure, acknowledge the regulatory context and describe how it affects your expected results.

Similarly, research on HIV prevention faces challenges related to the design of clinical trials and the interpretation of results. The limited progress with microbicides and vaccines for HIV prevention reinforces the need for a concentrated exploration of the utility of antiretrovirals. If your study involves a novel intervention, acknowledge the challenges of the field and describe how your design addresses them.

Professional Escalation Criteria

Knowing when to seek additional help is an important skill in research proposal writing. If you are struggling with the expected results section, consider seeking help from a mentor, a statistician, or a writing consultant. The following situations warrant professional escalation:

If you cannot specify the direction or magnitude of your expected effect, you may need help from a statistician to determine what effect size is plausible and what sample size is needed.

If you are unsure whether your study design can test your hypothesis, you may need help from a methodologist or a colleague with expertise in your study design.

If you are working in a regulated area and are unsure about the regulatory requirements, you may need help from your institutional review board or animal care committee.

If you are a graduate student, your thesis advisor or committee members can provide guidance on the expected results section. The process of turning a qualifying exam into a fellowship-style research proposal is a useful model for developing your proposal writing skills.

If you are an early-career researcher, consider participating in grant-writing coaching programs. These programs provide social support, peer accountability, knowledge of grant-writing strategies, technical grant-writing knowledge, and access to mentoring. These mechanisms have been shown to support early-stage investigators in developing grant-writing skills.

Frequently Asked Questions

What is the difference between expected results and expected outcomes in a research proposal?

Expected results are the specific empirical findings you anticipate, such as a mean difference between groups, a correlation coefficient, or a qualitative theme. Expected outcomes are the broader impacts or deliverables of your research, such as a new intervention package, a policy recommendation, or a published dataset. Expected results belong in the methods or analysis section, where they connect your hypotheses to your statistical plan. Expected outcomes belong in the significance or impact section.

How specific should expected results be?

Expected results should be specific enough that a reviewer can determine whether your study design can detect the predicted effect. For quantitative studies, specify the direction and approximate magnitude of the expected effect, the variables you will measure, and the statistical test you will use. For qualitative studies, specify the themes or patterns you expect to emerge and how you will recognize them. If you cannot specify the magnitude, explain why and describe how you will determine it from your data.

What if my expected results are not confirmed?

A well-designed study that produces null results is still valuable, especially if the null result is informative about the theory being tested. Reviewers understand that hypotheses are often disconfirmed. Your expected results section should describe what you will conclude if your predictions are not confirmed. This shows that you have thought through the interpretation of alternative outcomes.

Should I include expected results for every hypothesis?

Yes. Every hypothesis should have a corresponding expected result. If you have multiple hypotheses, organize the expected results section by hypothesis or by specific aim. This helps reviewers see the structure of your study and prevents the expected results section from becoming a list of vague aspirations.

How long should the expected results section be?

The length depends on the complexity of your study and the requirements of the funding agency. For a single-study proposal, one to three paragraphs may be sufficient. For a multi-site study or a study with multiple aims, you may need a subsection for each aim. Check the guidelines for the specific funding mechanism you are applying to.

Can I use conditional language in the expected results section?

Yes. Conditional language is appropriate and expected. Use phrases like "we expect to observe," "if the hypothesis is correct," and "we anticipate that." This language signals that your predictions are hypotheses, not promises. It also helps you describe alternative scenarios.

How do I align expected results with my research objectives?

Every expected result should trace back to a specific objective or hypothesis. Before you write the expected results section, review your research objectives and hypotheses. Each expected result should connect to a specific objective or hypothesis. If you cannot trace an expected result back to an objective, either the expected result is unnecessary or the objective is too vague.

What are the most common mistakes in writing expected results?

The most common mistakes are writing vague predictions, misaligning expected results with objectives, overpromising, confusing expected results with expected outcomes, ignoring alternative outcomes, and failing to acknowledge bias. Avoiding these mistakes will strengthen your expected results section and improve your overall proposal.

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References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
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• How to choose a good scientific problem.. Molecular cell, 2009.
• What is replication?. PLoS biology, 2020.
• Assessing the certainty of the evidence in systematic reviews: importance, process, and use.. American journal of epidemiology, 2025.
• Kenya.. Population policy compendium, 1985.
• Pharmacologic opportunities for HIV prevention.. Clinical pharmacology and therapeutics, 2010.
• The challenges ahead for psychedelic 'medicine'.. The Australian and New Zealand journal of psychiatry, 2022.
• An experiment in Beer-tasting.. International journal of epidemiology, 2014.
• Reforming competitive global public health funding: Bridging equity and practice gaps.. 2026.
• Ten simple rules for turning your qualifying exam into an NIH-style fellowship proposal: A guide for graduate students.. 2026.
• Development and validation of a comprehensive prevention-focused intervention package for problematic digital technology use among youth: a multi-site study protocol.. 2026.
• Building Capacity for Rigorous Health Research Through Grant Writing Coaching.. 2026.
• A brief history of dopamine prediction errors.. 2026.
• Behavioral Drivers of Digital Participation: Security Trust, Outcome Efficacy, and Procedural Cues in South Korea. 2026.
• The EXACERBANTES Study (&lt,i&gt,Exacerbation of Chronic Obstructive Pulmonary Disease According to the ANTES Proposal&lt,/i&gt,): Rationale, Goals and Design.. 2026.
• Incidence and Outcomes of in-Hospital Cardiac Arrest and Cardiopulmonary Resuscitation in the Kidney Replacement Therapy Population: Protocol for a Linked National Disease Registry Study.. 2026.
• Empowering EFL Teachers for Scientific Writing in PPG Workshop: Writing Classroom Action Research Proposal. Indonesian Journal of EFL and Linguistics, 2021.
• Self-management of learning in students of pharmaceutical services: results and proposal to promote academic autonomy. Open Access Research Journal of Multidisciplinary Studies, 2024.
• Discussion on the existing methodology of entropy-weights in water quality indexing and proposal for a modification of the expected conflicts. Environmental science and pollution research international, 2021.
• Methodology of Cyclical Research of the Tourist Image of Podkarpackie Province (Poland)-A Proposal of a Solution and Selected Research Results. Sustainability, 2022.
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• Exploring the potential of novel research proposals as signals of successful national R&D: A case study on energy and resource sector in South Korea. Journal of Informetrics, 2025.
• PBSI Students' Competency Using Letters and Punctuation in Writing Research Proposal about Language and Literature Learning based Environment. Bio Web of Conferences, 2023.
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• Learning from the Ethics of AI - A Research Proposal on Soft Law and Ethics of AI. Tilburg Law Review, 2022.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.