Conducting Your Research Project

A Guide for Group Research in 2026

Introduction

Research is the systematic collection, analysis, and interpretation of data to answer a question. You were introduced in some detail to psychology research in Psy2015f, and this will continue in Psy3007s. In PSY3009F, you will conduct an original research project in groups of 5-6 students. This guide provides practical advice for every stage of your project, with attention to how modern AI tools can enhance (but not replace) your research skills.

ImportantThe Purpose of This Project

The research project is designed to help you:

• Apply theoretical knowledge from PSY2014S and Psy3009f to real-world questions
• Develop practical research skills in design, data collection, and analysis
• Learn to work effectively in research teams
• Master academic writing and APA style
• Navigate the modern research landscape responsibly with AI tools

What You Will Learn

By completing this project, you will gain:

• Critical Thinking: How to evaluate literature, identify research gaps, and formulate testable hypotheses
• Technical Literacy: Proficiency with literature search tools, reference managers, R, and perhaps Quarto
• AI Literacy: How to use AI tools ethically and effectively as research assistants
• Collaboration Skills: How to coordinate with team members, divide labour, and resolve conflicts
• Communication Skills: Scientific writing, data visualisation, and oral presentation

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Phase 0: Getting Organised as a Group

Before you begin the intellectual work, establish how your group will function. Many research projects fail not because of poor science, but because of poor teamwork.

First Meeting: Establishing Ground Rules

Within the first week of being assigned to your group, hold a meeting (in person or via Zoom/Teams) to establish:

Communication Channels

Decision to make: How will your group communicate?

• WhatsApp/Telegram: Good for quick updates and coordination
• Slack/Discord/Google Drive: Better for organising by topic (channels for lit review, data, writing)
• Email: Formal communications, file sharing with tutor
• Shared document (Google Docs): Meeting notes, task assignments

TipBest Practice

Create both a synchronous channel (e.g., WhatsApp for quick questions) and an asynchronous channel (e.g., Slack or Google Drive for documents and longer discussions). This prevents important information from being lost in chat scrollback.

Meeting Schedule

Agree on:

• Weekly meeting day and time (in person or online)
• Expected attendance policy (what happens if someone misses meetings?)
• Meeting length (recommend 30 - 45 minutes)
• Rotating roles: chairperson (keeps meeting on track), note-taker (records decisions). Note that if you make a recording of your meeting and transcribe it e.e., with an AI agent, you can upload the transcript to an AI agent and get good if rough minutes.

Division of Labour

Research is a team sport :). Discuss each member’s strengths and preferences:

• Who enjoys literature searching and reading?
• Who is comfortable with statistics, R, and other technical stuff?
• Who is a strong writer?
• Who is detail-oriented for APA formatting and references?

Important: Everyone must contribute to all phases, but you can divide subtasks according to strengths.

Conflict Resolution

Agree on a process for resolving disagreements:

1. Discuss issue openly in a group meeting
2. If unresolved, consult your tutor
3. If still unresolved, contact the course convener

WarningAddress Problems Early

If a group member is not contributing, do not wait until the project is due. Speak with them privately first, then involve your tutor if the problem persists. Document all communications.

Shared File Storage

Decision to make: Where will you store project files?

• Google Drive: Free, collaborative, version history
• OneDrive: Free with UCT account, integrates with Office
• Dropbox: Limited free storage but excellent syncing
• GitHub: Best for code and Quarto documents (requires learning git)

Essential folders to create:

PSY3009F_Project/
├── Literature/           # PDFs of papers
├── Notes/               # Literature summaries, meeting notes
├── Data/                # Raw and processed data (NEVER share publicly)
├── Analysis/            # R/Python scripts
├── Writing/             # Report drafts
└── Admin/               # Ethics forms, plagiarism declaration

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Phase 1: Choosing Your Research Topic

The most important decision you will make is choosing a feasible, interesting topic.

Feasibility Constraints

Before falling in love with a topic, evaluate:

Time

• Can you collect data in 3 weeks (maximum)?
• Will participants be easy to recruit (e.g., UCT students, online samples)?
• Does the study require multiple sessions, or can it be completed in one sitting?

Equipment and Resources

• Do you need specialised equipment? If so, is it available and have you been trained?
• Can the study be conducted online, or must it be in person?
• Do you need to construct materials (e.g., stimuli)?

Ethics Approval

• Does your study involve deception, vulnerable populations, or sensitive topics? (These require more complex ethics applications)
• Will participants experience discomfort or risk?

TipSafe Bet Topics

Studies that are most feasible:

• Online surveys using validated questionnaires
• Simple cognitive experiments (e.g., Stroop, recognition memory, decision-making tasks)
• Studies using readily available stimuli (faces, words, images)
• Within-subjects designs with 2-3 conditions

Generating Ideas

Strategy 1: Start with Course Content

Look at topics covered in lectures:

• Face recognition: Can you test own-race bias or face-mask effects?
• Eyewitness memory: Can you test the weapon focus effect or misinformation?
• Decision making: Can you test framing effects or cognitive biases?
• Attention: Can you test change blindness or inattentional blindness?

Strategy 2: Use AI as a Brainstorming Partner

NoteAI Prompt for Topic Generation

Try this prompt with ChatGPT, Claude, or Perplexity:

“I am a third-year psychology student interested in [TOPIC, e.g., ‘social media and memory’]. I need to design a feasible empirical study that can be conducted online with UCT students in a single 30-minute session. Generate 5 testable research questions with 2-3 variables each. For each question, briefly describe a simple experimental or correlational design.”

Then: Critically evaluate the suggestions. Are they actually feasible? Have they been done before? Can you find supporting literature?

Strategy 3: Consult Recent Literature

Use AI-powered search tools (see Step 1: Initial Exploration) to find recent papers in your area of interest. Look for:

• Suggestions for future research in Discussion sections
• Failed replications that need follow-up
• Studies done in Western contexts that could be tested in South Africa

Formulating Your Research Question

A good research question is:

• Specific: Not “How does memory work?” but “Does self-referential encoding improve recall for emotional words?”
• Testable: Can be answered with empirical data
• Novel: Adds something new (even if small) to the literature
• Feasible: Can be completed within your constraints

From Research Question to Hypothesis

Once you have a research question, develop a specific, directional hypothesis:

• ❌ Weak: “There will be a difference between conditions.”
• ✅ Strong: “Participants will show higher recognition accuracy for self-owned objects (M > .40) compared to stranger-owned objects (M < .30), p < .05.”

Your hypothesis should be:

1. Derived from theory: Why do you predict this pattern?
2. Directional: Specify which condition will be higher/lower
3. Quantifiable: Specify the dependent variable and how it will be measured

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Phase 2: Conducting a Literature Review

A literature review is not just summarising papers – it is synthesising existing knowledge to identify what is known, what is contested, and what remains unknown.

Step 1: Initial Exploration

You are working in 2026, and the tools for discovering research have been revolutionised by AI. Here is a modern workflow:

Tool 1: Perplexity AI (https://www.perplexity.ai)

What it is: An AI search engine that combines large language models with real-time web search, providing answers with sources.

How to use it for research:

1. Go to https://www.perplexity.ai
2. Use academic-focused prompts:

NoteExample Perplexity Prompts

• “What are the main theories explaining the own-race bias in face recognition? Provide recent empirical evidence.”
• “Summarise the current debate about whether the self-reference effect in memory is due to deep processing or organisational factors. Cite key studies.”
• “What methods have been used to study weapon focus effects on eyewitness memory? What are their limitations?”

3. Verify every claim: Perplexity cites sources, but AI can misrepresent findings. Click through to the actual paper.

Strengths: Fast overview of a topic, finds recent papers, good for initial orientation Weaknesses: Can miss niche studies, sometimes cites papers incorrectly, should not be your only search tool

Tool 2: Google Scholar (https://scholar.google.com)

Still the workhorse of academic search.

How to use it effectively:

1. Use precise keywords: “own-race bias face recognition” rather than “race and faces”
2. Use the “Cited by” feature: Find a seminal paper (e.g., Meissner & Brigham, 2001), then click “Cited by” to find recent work that cites it
3. Use the “Related articles” feature: Find similar work
4. Set date range: Click the sidebar to limit to recent years (e.g., 2020-2026)
5. Use advanced search: Click the ☰ menu for advanced options (e.g., author, publication)

TipGoogle Scholar Search Tips

• Use quotation marks for exact phrases: “weapon focus effect”
• Use Boolean operators: “face recognition” AND “own-race bias” NOT “AI”
• Use author search: author:“Meissner” face recognition
• Filter by date to find recent reviews: 2023-2026

Tool 3: Connected Papers (https://www.connectedpapers.com)

What it is: A visual tool that shows how papers are related to each other based on citation patterns.

How to use it:

1. Find a key paper in your area (via Google Scholar or Perplexity)
2. Go to https://www.connectedpapers.com
3. Paste the paper’s title or DOI
4. You will see a graph showing:
   • Papers that cite your seed paper (green)
   • Papers that are cited by your seed paper (yellow)
   • Papers with similar citations (connected by proximity)
5. Click on any node to see details, abstract, and links to the full text

Strengths: Helps you quickly identify the “core” papers in a field, discover papers you would have missed Weaknesses: Only as good as your seed paper; limited free searches per month

NoteConnected Papers Workflow

1. Start with a recent review article or highly cited empirical paper
2. Look at the “Prior Works” (yellow) to find foundational theories
3. Look at the “Derivative Works” (green) to find recent applications
4. Export the graph as an image to include in your notes

Tool 4: ResearchRabbit (https://www.researchrabbit.ai)

What it is: A free tool that helps you discover papers through “citation networks” and builds collections.

How to use it:

1. Create a free account at https://www.researchrabbit.ai
2. Create a “Collection” for your project
3. Add seed papers to the collection
4. Use the “Similar Work” and “Referenced By” tabs to discover related papers
5. Save promising papers to your collection
6. Export citations to Zotero (see Tool 1: Zotero (Recommended for This Course))

Strengths: Excellent for discovering papers you would not find via keyword search, integrates with Zotero Weaknesses: Requires building a collection (more work upfront than Perplexity)

Tool 5: Semantic Scholar (https://www.semanticscholar.org)

What it is: An AI-powered academic search engine that extracts key figures, methods, and claims from papers.

How to use it:

1. Search for your topic
2. Look at the “Influential Citations” tab (papers that are most cited by others in this area)
3. Use the TL;DR feature (AI-generated summaries) for quick overviews
4. Use the “Methods” filter to find papers using specific methods (e.g., eye-tracking)

Strengths: Faster than reading full papers; great for identifying methods Weaknesses: TL;DR summaries can be oversimplified; always read the original if you plan to cite it

Step 2: Reading Strategically

You cannot read every paper in depth. Use a triage system:

Level 1: Abstract Screening (2 minutes per paper)

Read the abstract and ask:

• Is this relevant to my research question?
• Is the method appropriate?
• Are the findings clear and reliable?

Decision: ❌ Discard, ⭐ Save for deeper reading, or 🔥 Critical (must read)

Level 2: Skim Reading (10 minutes per paper)

For papers marked ⭐ or 🔥, skim:

1. Introduction: What is the theoretical background? What is the research gap?
2. Method: Who were the participants? What was the design? What measures were used?
3. Results: What were the main findings? (Look at tables and figures)
4. Discussion: What do the authors conclude? What are the limitations?

Decision: ❌ Discard, or ✅ Include in literature review (read in full)

Level 3: Critical Reading (60+ minutes per paper)

For papers marked ✅, read in full and take detailed notes (see Step 3: Organising Your Reading).

TipUsing AI to Assist Reading

Upload PDFs to NotebookLM (see Method 3: NotebookLM (Recommended)) or use Claude.ai / ChatGPT with PDF plugins to:

• “Explain the interaction effect in Figure 3”
• “What was the difference between the encoding and retrieval conditions?”
• “Summarise the limitations discussed in the Discussion section”

BUT: Always verify the AI’s interpretation by reading the original section yourself. AI can misinterpret statistical results or figures.

Step 3: Organising Your Reading

Do not just save PDFs in a folder and hope to remember what they said. Use a systematic note-taking method.

Method 1: The Summary Table

Create a Google Sheet or Excel file with columns:

Authors (Year)	Research Question	Design	Sample	Measures	Key Findings	Limitations	Relevance to My Project
Meissner & Brigham (2001)	Does own-race bias occur?	Meta-analysis	39 studies	Recognition accuracy	Own-race faces recognised more accurately	Publication bias?	Justifies testing ORB in SA context

Method 2: The Concept Map

Use tools like Miro, Mural, or Obsidian to create a visual map showing how theories and findings relate.

Method 3: NotebookLM (Recommended)

What it is: Google’s AI-powered research assistant that lets you upload sources and query them.

How to use it:

1. Go to https://notebooklm.google.com
2. Create a new notebook for your project
3. Upload PDFs of key papers (up to 50 sources)
4. Ask questions like:
   • “What theories have been proposed to explain the own-race bias?”
   • “Which studies used South African samples?”
   • “Create a summary of the methods used across these papers”
5. NotebookLM will answer based only on your uploaded sources (it does not hallucinate outside sources)
6. Use the “Audio Overview” feature to generate a podcast-style discussion of your sources (great for group listening)

ImportantWhy NotebookLM is Excellent for Group Work

• Shared access: Invite group members to the same notebook
• Source grounding: All answers are linked to specific pages in your sources
• No hallucination: Unlike ChatGPT, it only uses your uploaded documents
• Export notes: Copy summaries into your shared Google Doc

Limitations:

• Only works with uploaded sources (does not search new papers)
• Cannot export citations in APA format (you still need Zotero for that)

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Phase 3: Managing References

You will collect dozens of papers during your literature review. Do not try to manage citations manually – you will make errors and waste time. Use a reference manager.

Why Use a Reference Manager?

• Automatic citation: Insert citations in your document with one click
• Automatic bibliography: Generates APA 7th edition references automatically
• Shared libraries: Group members can access the same references
• PDF storage: Store and annotate PDFs in one place
• Search and organise: Tag papers by topic, create folders

Tool 1: Zotero (Recommended for This Course)

What it is: A free, open-source reference manager.

Why Zotero:

• ✅ Free and open-source
• ✅ Integrates with Word, Google Docs, and Quarto
• ✅ Group libraries (perfect for team projects)
• ✅ Browser extension saves papers with one click
• ✅ PDF annotation and note-taking
• ✅ Works offline

Getting Started with Zotero

Step 1: Install Zotero

1. Go to https://www.zotero.org
2. Download Zotero (latest version)
3. Install the Zotero Connector browser extension for Chrome/Firefox/Edge

Step 2: Create a Group Library

1. Create a free Zotero account at https://www.zotero.org/user/register
2. In Zotero desktop app, go to File → New Library → New Group
3. Name the group (e.g., “PSY3009F_Group3_2026”)
4. Set it to Private (invite group members by email)
5. Invite all group members

TipZotero Storage

The free Zotero account includes 300 MB of cloud storage for PDFs. If you exceed this:

• Upgrade to paid plan (cheap), OR
• Use ZotFile plugin to store PDFs in Google Drive/OneDrive and link them in Zotero

Step 3: Add Papers to Your Library

From Google Scholar:

1. Search for a paper in Google Scholar
2. Click the Zotero Connector icon in your browser
3. The paper’s citation will be saved to Zotero (with PDF if available)

From a PDF on your computer:

1. Drag the PDF into Zotero
2. Right-click → Retrieve Metadata for PDF
3. Zotero will find the citation information automatically

Manually:

1. Click the green + icon in Zotero
2. Select item type (journal article, book, etc.)
3. Fill in fields manually

Step 4: Organise Your Library

• Create Collections (folders) for different topics (e.g., “Own-Race Bias”, “Methodology”, “Background”)
• Use Tags to mark papers (e.g., “relevant”, “review-article”, “south-africa”)
• Add Notes to items (your summary of the paper)

Step 5: Cite While You Write

In Microsoft Word:

1. Install the Zotero Word Plugin (happens automatically when you install Zotero)
2. In Word, go to the Zotero tab
3. Click Add/Edit Citation
4. Search for the paper, click OK
5. Zotero inserts the citation and updates the reference list

In Google Docs:

1. Install the Zotero Google Docs plugin
2. In Google Docs, go to Add-ons → Zotero → Add/edit citation

In Quarto/RMarkdown:

1. Export your Zotero library as a BibTeX file (File → Export Library)
2. Save it as references.bib in your project folder
3. In your Quarto document, cite using [@AuthorYear] syntax

TipZotero Pro Tip

Use the Better BibTeX plugin for Zotero to generate stable citation keys and auto-update your .bib file as you add papers.

Tool 2: EndNote

What it is: A commercial reference manager (free for UCT students via site license).

How to get it:

1. Go to UCT ICTS website
2. Download EndNote (UCT provides free licenses)
3. Install and activate with UCT credentials

Pros:

• Powerful search and organisation
• Excellent Word integration
• Good PDF annotation

Cons:

• ❌ Steeper learning curve than Zotero
• ❌ Group libraries require paid subscription (not included in UCT license)
• ❌ Less flexible for Quarto/RMarkdown workflows

Recommendation: Use Zotero for this course due to its free group libraries and easier collaboration.

Tool 3: Mendeley

What it is: A free reference manager owned by Elsevier.

Pros:

• Free
• Social features (follow other researchers)
• Good PDF annotation

Cons:

• ❌ Poor APA formatting (often requires manual fixes)
• ❌ Limited storage (2 GB free)
• ❌ Privacy concerns (owned by publisher Elsevier)

Recommendation: Use Zotero instead.

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Phase 4: Ethics and Participant Recruitment

You cannot begin data collection until you have received ethics approval and planned your recruitment strategy.

Ethics Application

Your tutor will guide you through the ethics application process. Key points:

What You Need to Submit

1. Ethics application form (available on Amathuba)
2. Participant information sheet: Explains the study in plain language
3. Informed consent form: Participants sign this before participating
4. Debrief sheet: Explains the purpose after participation
5. Measures/materials: Copies of questionnaires, stimuli, interview protocols
6. Data management plan: How will you store and protect data?

Ethical Principles

• Informed consent: Participants must understand what they are agreeing to
• Voluntary participation: Participants can withdraw at any time without penalty
• Confidentiality: Do not collect names unless absolutely necessary; use participant IDs
• No harm: Participants should not experience distress, discomfort, or risk
• Deception: If you use deception (e.g., cover story), you must debrief participants afterwards

POPIA Compliance (South African Law)

The Protection of Personal Information Act (POPIA) requires:

• Collect only data that is necessary for the research
• Store data securely (password-protected folders, encrypted drives)
• Do not share data with third parties (including public AI tools – see AI Ethics in Research)
• Destroy data after 5 years (UCT policy)

AI Ethics in Research

ImportantZero-Data-Leakage Policy

NEVER paste raw participant data into ChatGPT, Claude, Gemini, or any public AI tool. This is a violation of POPIA and research ethics.

Forbidden:

• Uploading participant responses to ChatGPT for “analysis”
• Pasting interview transcripts into Perplexity
• Asking AI to “clean” your dataset by uploading the CSV

Allowed:

• Using AI to generate example stimuli (e.g., “Create 20 neutral words for a memory task”)
• Using AI to explain statistical concepts (e.g., “Explain what a mixed ANOVA is”)
• Using AI to check your code for errors (paste the code, not the data)

Recruiting Participants

Online Studies

Strategies:

1. PSY3009F Participation Pool: Your course has a system for recruiting from other PSY3009f students
2. Recruiting from outside PSY3009F: We do not recommend this, as special levels of ethics approval are needed.

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Phase 5: Data Collection and Management

Once ethics approval is granted, you can begin collecting data.

Pre-Registration (Optional but Recommended)

Consider pre-registering your study on the Open Science Framework (https://osf.io):

1. Create a free OSF account
2. Create a new project for your research
3. Upload a pre-registration document specifying:
   • Your hypothesis
   • Your design (IV, DV, covariates)
   • Your planned sample size
   • Your planned analysis
4. Make the pre-registration public after data collection

Why pre-register?

• Prevents HARKing (Hypothesising After Results are Known)
• Increases transparency and credibility
• Good practice for future research

Data Management

File Naming Conventions

Use a consistent system, e.g.:

PSY3009F_Group3_Data_Raw_2026-04-15.csv
PSY3009F_Group3_Data_Cleaned_2026-04-20.csv
PSY3009F_Group3_Analysis_Script_v1.R

Data Security

• Store data in a password-protected folder on UCT OneDrive or Google Drive (set to private)
• Never share raw data publicly or with unauthorised people
• Use participant IDs (e.g., P001, P002) rather than names

Data Cleaning

Before analysis, clean your data:

1. Check for missing data: Why is it missing? Can it be recovered?
2. Check for outliers: Are there implausible values? (e.g., reaction time = 0 ms)
3. Check for duplicate responses: Did anyone complete the survey twice?
4. Code variables: Convert text responses to numerical codes if needed
5. Create a codebook: Document what each variable means

TipUsing AI for Data Cleaning Code

You can ask AI to tutor you on how to write data cleaning scripts i.e., write your script in R and use AI to check and remedy errors.

Unsafe (do not do this):

❌ “Here is my dataset with participant IDs and responses. Clean it for me.”

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Phase 6: Data Analysis

Your analysis depends on your research design and hypothesis. This section provides general guidance.

Descriptive Statistics

Always start with descriptives:

• Measures of central tendency: Mean, median, mode
• Measures of variability: Standard deviation, range, interquartile range
• Visualisations: Histograms, boxplots, scatterplots

Visualisations are especially helpful when they show the entire distribution.

Using R for Descriptive Statistics

Refer back to your notes from Psy2015f for how to do this. Read relevant sections in https://r4ds.hadley.nz/ if you want to learn more.

Inferential Statistics

Choose the appropriate test based on your design, but keep to what you were taught in Psy2015f, or elsewhere:

Design	Test
Two independent groups	Independent samples t-test
Two related groups (within-subjects)	Paired samples t-test
Three+ independent groups	One-way ANOVA
Three+ related groups	Repeated measures ANOVA
Two+ IVs	Factorial ANOVA
Continuous predictor → continuous outcome	Regression
Categorical predictor → categorical outcome	Chi-square

Using AI to Understand Statistics

TipGood Use of AI for Statistics

• “Explain the assumptions of a repeated measures ANOVA”
• “How do I interpret an interaction effect in a 2x2 ANOVA?”
• “What is the difference between a Type I and Type II error?”
• “Comment on my R code as pasted here, which is intended to analyse a 2 group experiment - why am I getting errors?”

Reporting Statistics (APA Style)

• Report exact p-values (e.g., p = .032) unless p < .001
• Always report effect sizes (Cohen’s d, \(\eta^2\), \(\omega^2\))
• Report confidence intervals where appropriate
• Example: t(58) = 2.34, p = .023, d = 0.62, 95% CI [0.10, 1.14]

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Phase 7: Writing the Report

The final report should follow APA 7th edition style. See the companion guide “Writing the Research Report” for detailed section-by-section guidance.

Time Management for Writing

Do not wait until the week before the deadline to start writing. Use this timeline:

Week	Task
Week 1-2	Topic selection, literature search
Week 3-4	Literature review, write draft Introduction
Week 5	Ethics application
Week 6-8	Data collection
Week 9	Data cleaning and analysis
Week 10	Write Results section
Week 11	Write Discussion section
Week 12	Write Abstract, format references, proof-read

Division of Writing Labour

Strategy 1: Each person writes one section (Introduction, Method, Results, Discussion), then one person integrates and edits for consistency.

Strategy 2: Everyone writes together in a shared Google Doc during group meetings (slower but ensures everyone is involved).

Strategy 3: Divide the Introduction by sub-topics (e.g., one person writes about face recognition theory, another writes about the own-race bias).

WarningAvoid Patchwork Writing

If each person writes independently, the report will read like a patchwork quilt. Assign one person as the lead editor to ensure consistent voice, flow, and style.

Using AI to Improve Writing

TipEthical Use of AI for Writing

Do:

• Ask AI to check grammar and clarity: “Improve the clarity of this paragraph while keeping the meaning unchanged”
• Ask AI to convert passive voice to active voice
• Ask AI to suggest a better sentence structure

Do NOT:

• Ask AI to “write the Introduction for me”
• Copy-paste AI-generated text without significant revision
• Use AI to paraphrase sources without understanding them (this is still plagiarism)

Always disclose in your Method section if you used AI tools (see APA 7th citation guidelines for AI).

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Phase 8: Final Checks Before Submission

The Pre-Submission Checklist

Print this checklist and tick each item:

Content

• Title page includes: title, author names, student numbers, word count
• Abstract is <250 words and includes problem, method, results, conclusion
• Introduction includes: background, theoretical framework, research gap, hypothesis
• Method includes: participants (N, demographics), materials, procedure, analysis plan
• Results includes: descriptive statistics, inferential statistics, figures/tables
• Discussion includes: summary of findings, interpretation, limitations, implications
• References are in APA 7th edition format
• All in-text citations appear in reference list, and vice versa

Formatting

• Font: 12pt Times New Roman or Arial
• Line spacing: Double-spaced
• Margins: 1 inch (2.54 cm) on all sides
• Page numbers: Top right corner
• Headings: APA-style (Level 1, Level 2)
• Figures and tables: Numbered, titled, cited in text

Ethics and Integrity

• Signed plagiarism declaration included as first page
• All AI tool use disclosed in Method section
• No participant data shared with public AI tools
• All sources cited (no uncited quotes or paraphrases)

Technical

• File name: PSY3009F_Group#_Surname1_Surname2_Report.pdf
• File format: PDF (not .docx)
• File size: <10 MB
• Uploaded to Amathuba before 23:59 on due date

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Common Pitfalls and How to Avoid Them

Pitfall 1: Poor Time Management

Problem: Starting too late, panicking at the deadline.

Solution: Use the timeline in Phase 7: Writing the Report. Hold weekly group meetings. Set internal deadlines (e.g., “Introduction draft due Week 5”).

Pitfall 2: Unequal Contribution

Problem: One person does all the work, or one person does nothing.

Solution: Document contributions (who did what) in meeting notes. Address problems early with your tutor.

Pitfall 3: Over-Reliance on AI

Problem: Using AI to generate text without understanding it.

Solution: Use AI as a co-pilot, not a pilot. Always critically evaluate AI outputs. Never copy-paste without revision.

Pitfall 4: Poor Literature Synthesis

Problem: The literature review is a list of summaries (“Smith (2020) found X. Jones (2021) found Y.”) rather than a synthesis.

Solution: Organise by themes, not by paper. Compare and contrast findings. Identify patterns and gaps.

Pitfall 5: Ignoring APA Style

Problem: Inconsistent formatting, incorrect citations, missing reference list entries.

Solution: Use a reference manager (Zotero). Use the APA Style Guide (https://apastyle.apa.org). Have one person be the “APA police” to check formatting.

Pitfall 6: Plagiarism (Accidental or Intentional)

Problem: Copying text without quotation marks, paraphrasing without citing, using AI-generated text without disclosure.

Solution:

• Always cite ideas that are not your own
• Use quotation marks for exact phrases
• Paraphrase in your own words, then cite
• Disclose AI use in Method section
• Use Turnitin to check similarity score before submission

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Conclusion: Research is Formalised Curiosity

“Research is formalised curiosity. It is poking and prying with a purpose.” — Zora Neale Hurston

The research project is challenging, but it is also an opportunity to contribute new knowledge to psychology. Use the tools and strategies in this guide to make the process more manageable and effective. Remember:

• Start early: Procrastination is the enemy of good research
• Communicate: Talk to your group, your tutor, and the course convener when problems arise
• Use tools: AI, reference managers, and collaborative platforms are there to help you
• Stay ethical: Protect participant data, cite sources, and disclose AI use
• Learn: This project is preparation for Honours, postgraduate research, and careers in science

Good luck!

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Additional Resources

APA Style

• APA Style Guide: https://apastyle.apa.org
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