Chapter Guide: Analysing Findings and Drawing Conclusions (Language Investigation NEA)
Welcome! In this guide, we are tackling the heart of your Language Investigation (Component 7702/C): how to make sense of your collected data, analyse your findings with linguistic precision, and draw compelling, evaluative conclusions. Don't worry if this seems a bit daunting at first—once you master the recipe for balancing numbers with close linguistic analysis, you will find it rewarding and straightforward!
Quick Specification Fact: The Non-Exam Assessment (NEA) represents \(20\%\) of your overall AQA A-Level English Language grade (worth \(100\) marks). The Language Investigation itself has a recommended word count of \(2,000\) words (excluding data and appendices), alongside your Original Writing and Commentary task (\(1,500\) words total: \(750\) words writing + \(750\) words commentary).
1. Understanding the Target Assessment Objectives
When you analyse your data and write your conclusions, examiners mark your work using three core Assessment Objectives:
AO1 (Linguistic Methods & Terminology): Apply precise linguistic frameworks, terms, and clear academic structure. You must identify specific word classes, grammatical structures, phonological features, or discourse patterns rather than just describing the topic or content.
AO2 (Concepts & Research): Connect your findings back to established linguistic theories, models, and previous studies. Does your data align with or challenge published research?
AO3 (Contextual Factors): Explain why patterns occur by looking at context—such as audience, purpose, genre, socio-cultural background, mode, or the era in which the language was produced.
Key Takeaway: Think of AO1 as what linguistic features exist, AO3 as why they are used in this specific context, and AO2 as how this connects to wider linguistic theories.
2. The Golden Rule: Balancing Quantitative and Qualitative Analysis
A top-grade analysis strikes a careful balance between numbers and in-depth word-level exploration. Think of your analysis like an investigative crime drama:
1. Quantitative Analysis (The Clues / The Big Picture):
This is the numerical data. It includes counts, frequencies, proportions, or percentages of specific linguistic tokens across your data sets. For example, counting how many modal verbs, interruptions, or dialect features appear per \(1,000\) words across two different groups.
2. Qualitative Analysis (The Detective Work / Micro-Analysis):
This is where you zoom in on specific quotes or transcript extracts to explain how meaning is created. You unpack the pragmatic effects, subtle connotations, or power dynamics behind the numbers.
Analogy to Remember: Quantitative data is your map (showing you where to look), while Qualitative data is your magnifying glass (inspecting the fine linguistic detail). Quantitative figures should always act as the entry point for qualitative analysis, never just left to stand on their own.
Key Takeaway: Never just dump a table of numbers and move on. Always follow up a numerical pattern with close linguistic analysis of real examples from your corpus.
3. Structuring Your Analysis: Linguistic Levels vs. Text-by-Text
One of the most frequent traps students fall into is analysing their data chronologically or text-by-text (e.g., writing all about Text A in section 1, and then all about Text B in section 2). This prevents you from making direct comparisons!
The Best-Practice Structure: By Linguistic Level or Sub-Question
Organise your analysis thematically around linguistic frameworks or your research sub-questions. This allows for constant, fluent comparison:
Framework 1: Lexical and Semantic Patterns
Examine vocabulary choices, semantic fields, evaluative adjectives, jargon, or colloquialisms. Compare how both data sets use lexis to construct representations or identities.
Framework 2: Grammatical and Syntactic Choices
Investigate sentence types, clause structures, active vs. passive voice, transitivity, or modal auxiliary verbs.
Framework 3: Pragmatics and Interactional Strategies (or Discourse / Graphology / Phonology)
Explore implicature, politeness strategies, turn-taking, overlapping, pauses, prosodic features, or visual layout choices depending on your data type.
Conventions for Presenting Data
Keep these presentation rules in mind:
• Summary Charts & Tables: Clearly label and embed small summary tables or graphs directly into the body of your analysis to illustrate comparative trends.
• Short Citations: Embed concise quotes or line-numbered transcript extracts directly within your analytical paragraphs.
• Appendices: Keep your full, unannotated corpus, complete transcripts, or clean source texts in the appendices at the very end of your project.
Key Takeaway: Structure your analysis by linguistic focus (e.g., Grammar, Lexis, Pragmatics) rather than splitting your project text-by-text. This guarantees natural comparison.
4. Drawing Meaningful Conclusions
Your conclusion is not just a summary of what you wrote—it is where you synthesize your discoveries, answer your main question, and evaluate the investigation.
Step 1: Directly Answer the Research Question
Revisit your original hypothesis or research question(s). State clearly what your data has revealed in relation to your initial inquiry.
Step 2: Synthesize with Linguistic Theory (AO2 & AO3 Integration)
Discuss how your findings interact with existing linguistic research. For example, did your spoken language data support, challenge, or add nuance to theories by researchers such as Lakoff, Zimmerman & West, Cameron, Trudgill, Labov, or Fairclough? Explain why your findings might differ due to modern context or specific audience variables.
Step 3: Critical Evaluation and Methodological Reflection
Demonstrate mature linguistic thinking by evaluating the limitations of your study:
• The Observer's Paradox: Did participants alter their speech because they knew they were being recorded?
• Corpus Size & Scope: Acknowledge that a small-scale sample cannot represent an entire global population.
• Uncontrolled Variables: Were there differences in age, social class, or familiarity among participants that influenced the data?
• Future Research: Suggest how another researcher could expand on your investigation.
Key Takeaway: A great conclusion ties the data directly back to the original hypothesis, compares the findings with published theory, and reflects honestly on methodological limitations.
5. Common Pitfalls to Avoid
Examiners frequently highlight several avoidable errors when marking the analysis and conclusion sections:
1. Descriptive "Storytelling" & Data Dumping: Retelling the story or topic of the texts without applying linguistic methods, word-class labels, or grammatical terms (failing AO1).
2. Sweeping, Universal Generalisations: Writing statements like "This proves that all men interrupt more than women" based on a \(500\)-word transcript. Always use cautious, academic phrasing (hedging), such as: "The data suggests a tendency within this specific interaction for..."
3. Disconnection from the Hypothesis: Forgetting your original research question and writing a vague, disconnected conclusion that ignores the theories mentioned in your introduction (failing AO2).
4. Isolated Tables: Pasting large, complex graphs into your analysis without unpacking any specific linguistic examples qualitatively.
6. Chapter Quick Review
Memory Trick: The "Q-Q-C" Method
Whenever you present a finding, remember Q-Q-C:
• Quantitative: State the pattern or frequency count.
• Qualitative: Zoom in on a specific linguistic example with precise terminology (AO1).
• Context/Concept: Explain why it happens in this context and connect it to theory (AO2/AO3).
Summary Checklist for Your NEA Investigation:
• Are my findings structured by linguistic frameworks or sub-questions rather than text-by-text?
• Have I combined quantitative counts with qualitative micro-analysis?
• Does my conclusion directly answer the research question set in the introduction?
• Have I evaluated my methodology (e.g., sample size, Observer's Paradox) with tentative, academic phrasing?