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How to Get an A* in an A-Level Geography NEA

In A-Level by Think Student Editor May 22, 2023 Leave a Comment

An NEA is worth 20% of your overall grade in A-Level Geography. It may not seem like much at first, but that’s 1/5 of your qualification – so you definitely want to make sure it boosts your overall grade! As well as this, there will be hundreds of other students writing A-Level Geography coursework, so yours needs to stand out amongst the others. But, if you were like me, this might be the first time you’ve ever had to complete coursework for an A-Level. So how do you do well?

In this article, I’ll be taking you through (as a former A-Level Geography student) all the dos and don’ts for your geography NEA, and the advice you need to get an A*!

Table of Contents

What is an A-Level Geography NEA?

The geography NEA is the mandatory coursework, that is a part of A-Level Geography. All UK exam boards require A-Level Geography students to produce an NEA. It is also referred to as an “Independent Investigation”.

For an NEA, you will choose a question related to physical or human geography, and then you will collect data to help you answer this question . Most NEAs are around 3,000- 4,000 words. They are essentially research papers!

For inspiration make sure you check out this Think Student article with 75+ NEA ideas!

How is an A-Level Geography NEA structured?

At the front of your geography NEA, you will have to attach a cover sheet provided by your exam board. This will usually have your name, candidate number, centre number, and your title question on it. It must be signed by you and your teachers.

An A-Level Geography NEA typically has around 7 sections :

• Introduction to the investigation
• Methodology/data collection
• Data presentation, analysis, interpretation and evaluation
• Evaluation of the investigation
• Bibliography

Exam boards are not too strict on formatting, however, so as long as you have all the major areas covered, you can format these how you like.

Your bibliography should contain all the references for any secondary material you used as part of your NEA. When you submit an NEA, you will be asked to declare that the work you have produced is 100% your own, and your NEA will be checked for plagiarism . Make sure to reference properly!

As for the rest of the NEA sections, I’ll be explaining them in more detail later in the article, so don’t worry!

How do you get an A* in an A-Level Geography NEA?

Getting an A* in your A-Level Geography NEA is not easy, but it’s definitely not impossible.

The UK exam boards will have their NEA criteria up on their website. NEA marking criteria is usually broken down into 4 “levels” (with level 1 being the least marks and level 4 being the most marks), for each section of the NEA.

To achieve an A* in an A-Level Geography NEA, you’ll need to meet the level 4 criteria in most if not all sections of your NEA.

As a former A-Level Geography student, below I’ll share with you my advice on how to achieve the top marks in your NEA.

The A-Level Geography NEA introduction

The introduction to your A-Level Geography NEA is one of the most important parts – it sets up the rest of your investigation and shows examiners why they should keep reading!

Your introduction will outline your argument and will vaguely demonstrate what you are going to say and why this is important. Remember, you don’t want to say too much, because you’ve got the rest of the NEA to write!

Your introduction should also link to your title question; how is your argument going to relate to and answer your question?

The key to a good geography NEA introduction is to be concise and keep it simple. You should ask yourself: ‘if someone who doesn’t do A-Level Geography read my introduction, would they know what I’m going to talk about?’

How to write an A* A-Level Geography NEA introduction

Your introduction should break down your purpose for the investigation. It isn’t like your typical essay introductions which are 100-200 words – your NEA introduction should be up to 500 words.

It could be helpful to break down your title question into three or four “key inquiry questions”, which you can answer throughout your NEA.

You should also explain your title question, why you chose it and how your research is useful in thinking about the future of the research your question tackles.

Your introduction is the opportunity to provide the examiner with details about your location; you could use maps (as these count as a form of data presentation), point out key geographical features, etc. You should give some local (relevant to your area) context and global context for the issue your question is answering.

To round off the introduction, include some basic geographical theory. For example, if your NEA investigation focuses on erosion, explain the different theories of erosion and how these apply to your investigation. This is an important demonstration of knowledge!

Remember, you can format your NEA however you like (within reason), so you can put this information in whichever order you like. Just make sure you cover all the key areas of your investigation!

The A-Level Geography NEA methodology

Your NEA methodology is a breakdown of how you collected the data you use and present in your coursework.

Your methodology will be one of the most detailed parts of your NEA. This may be surprising, but it’s because your methodology is used to show that your data is legitimate and collected properly.

A methodology is included in the majority of research papers, and your A-Level Geography NEA is no exception! Make sure you put time and care into writing your methodology properly, or it could undermine your investigation.

How do you write an A* methodology for an A-Level Geography NEA?

The way you physically present your methodology is up to you, but it should cover all the qualitative data (non-measurable data), and quantitative data (measurable/numerical data).

For example, I presented my methodology as a big table across 2 pages of my NEA. Definitely don’t underestimate the size of your methodology – it’s what verifies that your data is legitimate!

In your methodology, you should include:

• The types of data you collected
• Where you collected this data (collection points)
• The equipment you used to collect your data
• A description of the method
• How often you collected data (intervals)
• The sampling technique (stratified, systematic, etc.)
• A justification for your method

As part of your methodology, you should also include what are called “ethical considerations” and a “risk assessment”.

Ethical considerations essentially means showing awareness of any ethical problems with your data collection methods. As an example, if you used a survey as a data collection, a problem with that may be that the participants’ privacy is not protected. Therefore, an ethical consideration would be to anonymise the survey.

A risk assessment is an awareness of the risks that are involved with data collection (such as getting lost, injured, weather events, etc.), and what you will do to prevent these risks. For example, having an emergency contact.

Data presentation, analysis, interpretation and evaluation in an A-Level Geography NEA

The data section of your NEA is the longest chunk and is worth the most marks. Now that you’ve set up your investigation, this is the section where you present all of your findings and interpret them, by explaining what they show and why.

Don’t panic if not all of the data you collected can be used – I certainly had a bunch of random data I didn’t need by the end! Try to use as much data as possible, and different types of data.

This section helps establish your argument; it’s essentially the evidence for your conclusion as well as just being the body of your NEA.

Since this is a long section, it’s helpful for you and your examiner to split it up into chunks using subheadings. It’s not a good idea to signpost, for example putting the subheading “Analysis”. Instead, you might divide up your data by the location, or the method you used to collect it.

How do you present data to get an A* in an A-Level Geography NEA?

Data presentation in a geography NEA is probably the most unique part of the process – you get to present your data however you want (in accordance with the exam board guidelines, of course)!

In the data presentation section, you need to display all the data you collected for your investigation. This can be in charts, graphs, tables, photos, and more.

The data needs to be readable, so your graphs should be labelled correctly, and your photos should have captions. If you’re using any data that isn’t yours, remember to reference it correctly.

It’s also a good chance to add a bit of colour, to make your A-Level Geography NEA look great!

How do you analyse and evaluate data to get an A* in an A-Level Geography NEA?

Your data analysis, interpretation, and evaluation section of your geography NEA is the most important section.

You should pick out key elements of the data and explain what they mean with regard to your NEA investigation question. How does the data you collected argue for/against your question?

Where applicable, it’s a good idea to calculate and explain medians, means, modes, and averages, to show that you aren’t just repeating what’s already in your presentation. You need to do something with the raw numbers, you definitely shouldn’t just relay your exact findings.

When you’re analysing, ask yourself the question: what does my data mean?

When you’re evaluating, ask yourself the question: how does my data answer my investigation?

By keeping these questions in mind when you’re interpreting your data, you can show the examiner that you can prove why your data is important and that you have a good understanding of analysis and evaluation.

Should you include statistical tests to get an A* in an A-Level Geography NEA?

The short answer to this question is: absolutely!

By now, you will have practiced a few statistical tests as part of the rest of A-Level Geography, such as Spearman’s Rank, the T-Test, Mann-Whitney U test, and standard deviation.

You should aim to use one or two stats tests when presenting the data, you collected for your geography NEA. There is no ‘right’ or ‘wrong’ stats test, so choose whichever is applicable for your data.

Statistical tests are a good demonstration of your analytical, interpretative and evaluative skills . By including a couple, you are showing the examiner that you have a clear knowledge of what the tests mean and why they’re useful!

If you struggle with the calculations, don’t be afraid to ask for help. Obviously other people can’t do it for you (remember that as part of submitting your geography NEA, you will have to testify that your work is entirely your own), but you can always ask to be shown how to do them!

Evaluating your A-Level Geography NEA investigation

Your A-Level Geography NEA investigation evaluation is slightly different to the evaluation of your data. In this section of your NEA, you should evaluate the success of the overall investigation.

You should discuss your locations and the methods you used to collect your data (both primary AND secondary data!). What was good about them? What wasn’t so good? If you had been somewhere else and used different methods, how might the outcome of your investigation have changed?

It’s also important to acknowledge the validity of your conclusions.

For example, you may not have had time or access to the correct resources to collect some really important data, that would’ve affected your outcome and potentially changed it. Showing an awareness of this helps build a more sophisticated and mature argument.

It’s important to note than an evaluation is not the same as a conclusion! You shouldn’t be summarising your research. Instead, explain the positives and negatives of your research choices.

The A-Level Geography NEA conclusion

Your conclusion is crucial because it ties together your methods, research, and analysis. Remember those “key inquiry questions” I mentioned earlier? Well now is the time to answer them!

Your NEA conclusion will answer your title question and provide the examiner with a neat, rounded summary of your investigation. By reading the conclusion, someone should be able to know the key parts of your argument and why they are important.

A conclusion is also a place to propose solutions – what can we do in future that we aren’t doing now? How might future events like climate change impact your research?

If there are relevant questions that could impact the outcome of your investigation, but you don’t have time to consider them in detail, put them in your conclusion. This shows the examiner that you have an awareness of micro- and macro-scale issues!

How do you write an A* A-Level Geography NEA conclusion?

Like most essay conclusions, your geography NEA conclusion will summarise your main arguments, what you found, and what your data means. It can also be a good place to ask any of the questions you still don’t have answers to.

You could start by going through your inquiry questions and writing “sub-conclusions” in response to them. Then, you should move on to the big conclusion: answering your title question.

In your conclusion, you should highlight the key things you found as a result of your research, broadly and specifically. Showing consideration to the “big” and “small” issues is good for showing your critical thinking skills!

Your conclusion should be about the same length as your introduction, give or take. If you start running out of things to say, don’t add things unnecessarily to fill the word count – your conclusion should be the most clear and concise part of your NEA.

Examples of A-Level Geography NEAs

Most, if not all, exam boards will have an “exemplar” coursework on their website. For example, I’ve linked the OCR exemplar coursework for you here , so check your exam board website for their exemplar!

The exemplar coursework is written and submitted by a real student, but it’s important to follow the mark scheme, not just copying someone else’s coursework. Your NEA will be checked for plagiarism!

Similarly, most schools keep exemplar coursework from each year, so if you need some inspiration, ask your teachers for the coursework they have.

If you’re struggling for ideas of what to write on for your Geography NEA, check out this Think Student article with 75+ ideas!

*To learn more about the A-Level Geography NEA, check out the specifications from the main exam boards, AQA , Pearson Edexcel and OCR by clicking on their respective links.

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Edexcel Syllabus A Geography Coursework Guidance: Analysis and Conclusions

Assessment Criterion 4 - Analysis and conclusions (15 marks)

This section should:

a describe what the data shows

b include analytical comments that relate the data to the original aim(s)

c identify, where appropriate, any links or relationships between different data sets

d where relevant, consider the values and attitudes of people involved

e return to the original aim(s), and consider to what extent the question has been answered, the problem solved or the hypothesis proved

f show an appreciation of the limitations of the study and suggest how it could be improved or taken further.

10 free data analytics courses you can take online

Data analytics is the science of taking raw data, cleaning it, and analyzing it to inform conclusions and support decision making. From business to health care to social media, data analytics is changing the way organizations operate.

“It’s not hyperbole to say that data analytics has really taken over the world,” says Brian Caffo, professor of biostatistics at Johns Hopkins University’s Bloomberg School of Public Health and director of academic programs for the university’s Data Science and AI Institute. “Every domain has become increasingly quantitative to inform decision making.”

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And this space isn’t slowing down anytime soon: The U.S. Bureau of Labor Statistics projects that employment for data scientists will grow 35% from 2022 to 2032, with 17,700 new job openings projected each year on average during that decade. 

Interested in becoming a data analyst? Below, we’ve compiled ten free data analytics courses to help give you a firmer grasp of this rapidly growing field.

A/B Testing  

About: This course covers the design and analysis of A/B tests, which are online experiments that compare two versions of content to see which one appeals to viewers more. A/B tests are used throughout the tech industry by companies like Amazon and Google. This course is offered through Udacity. 

Course length: Six self-paced modules

Who this course is for: Beginners

What you’ll learn: In this course you’ll learn about A/B testing, experiment ethics, how to choose metrics, design an experiment, and analyze results.

Prerequisites: None  

Data Analytics Short Course  

About: In this quick, five-tutorial course you’ll get a broad overview of data analytics. You’ll learn about the different types of roles in data analytics, a summary of the tools and skills you’ll need to develop, and a hands-on introduction to the field. This course is offered by CareerFoundry.

Course length: 75 minutes, divided into five 15-minute lessons

What you’ll learn: In this course you’ll get an introduction to data analytics. You’ll also analyze a real dataset to solve a business problem through data cleaning, visualizations, and garnering final insights.

Prerequisites: None 

Data Science: R Basics  

About: This program gives you a foundational knowledge of programming language R. Offered by HarvardX through the EdX platform, this course is offered for free; the paid version includes a credential. It’s the first of ten courses HarvardX offers as part of its Professional Certificate in Data Science.

Course length: Eight weeks, 1–2 hours per week

What you’ll learn: In this course you’ll learn basic R syntax and foundational R programming concepts, including data types, vectors arithmetic, and indexing. You’ll also perform operations that include sorting, data wrangling using dplyr, and making plots. 

“It’s the basics of how to wrangle, analyze, and visualize data in R,” says Dustin Tingley, Harvard University’s deputy vice provost for advances in learning and a professor of government in the school’s government department. “That gets you writing a little bit of code, but you’re not doing anything that heavy.”

Prerequisites: HarvardX recommends having an up-to-date browser to enable programming directly in a browser-based interface 

Fundamentals of Qualitative Research Methods  

About: This course will teach you the fundamentals of qualitative research methods. Qualitative research provides deeper insights into real-world problems that might not always be immediately evident. This course is offered through Yale University on YouTube.

Course length: 90 minutes spread out over six modules

What you’ll learn: In this course you’ll learn how qualitative research is a way to systematically collect, organize, and interpret information that is difficult to measure quantitatively. This includes developing qualitative research questions, gathering data through interviews and focus groups, and analyzing this data. 

“Qualitative research is the systematic, rigorous application of narratives and tools to better understand a complex phenomenon,” says Leslie Curry, a professor of public health and management at the Yale School of Public Health and a professor of management at the Yale School of Management. She adds that this approach can help understand flaws in large data sets. “It can be used as an adjunct to a lot of the really important work that’s happening in large data analysis.”

Getting and Cleaning Data  

About: This course covers the basic ways that data can be obtained and how that data can be cleaned to make it “tidy.” It will also teach you the components of a complete data set, such as raw data, codebooks, processing instructions, and processed data. This course is offered by Johns Hopkins University through Coursera, and is part of a 10-course Data Science Specialization series.

Course length: Four weeks, totaling approximately 19 hours

What you’ll learn: Through this course you’ll learn about common data storage systems, how to use R for text and date manipulation, how to use data cleaning basics to make data “tidy,” and how to obtain useable data from the web, application programming interfaces (APIs), and databases. 

“It’s the starting point” when it comes to data analysis, Caffo says. “Without a good data set that is cleaned and appropriate for use, you have nothing. You can talk all you want about doing models or whatnot—underlying that has to be the data to support it.”

Prerequisites: None

Introduction to Data Science with Python  

About: This course teaches you concepts and techniques to give you a foundational understanding of data science and machine learning. Offered by HarvardX through the EdX platform, this course can be taken for free. The paid version offers a credential.

Course length: Eight weeks, 3–4 hours a week

Who this course is for: Intermediate

What you’ll learn: This course will give you hands-on experience using Python to solve real data science challenges. You’ll use Python programming and coding for modeling, statistics, and storytelling. 

“It gets you up and running with the main workhorse tools of data analytics,” says Tingley. “It helps to set people up to take more advanced courses in things like machine learning and artificial intelligence.”

Prerequisites: None, but Tingley says having a basic background in high school-level algebra and basic probability is helpful. Some programming experience—particularly in Python—is recommended 

Introduction to Databases and SQL Querying  

About: In this course you’ll learn how to query a database, create tables and databases, and be proficient in basic SQL querying. This free course is offered through Udemy.

Course length: Two hours and 17 minutes

What you’ll learn: This course will acquaint you with the basic concepts of databases and queries. This course will walk you through setting up your environment, creating your first table, and writing your first query. By the course’s conclusion, you should be able to write simple queries related to dates, string manipulation, and aggregation.

Introduction to Data Analytics  

About: This course offers an introduction to data analysis, the role of a data analyst, and the various tools used for data analytics. This course is offered by IBM through Coursera.

Course length: Five modules totaling roughly 10 hours 

What you’ll learn: This course will teach you about data analytics and the different types of data structures, file formats, and sources of data. You’ll learn about the data analysis process, including collecting, wrangling, mining, and visualizing data. And you’ll learn about the different roles within the field of data analysis.

Learn to Code for Data Analysis  

About: This course will teach you how to write your own computer programs, access open data, clean and analyze data, and produce visualizations. You’ll code in Python, write analyses and do coding exercises using the Jupyter Notebooks platform. This course is offered through the United Kingdom’s Open University on its OpenLearn platform.

Course length: Eight weeks, totaling 24 hours

What you’ll learn: In this course you’ll learn basic programming and data analysis concepts, recognize open data sources, use a programming environment to develop programs, and write simple programs to analyze large datasets and produce results.

Prerequisites: A background in coding—especially Python—is helpful  

The Data Scientist’s Toolbox  

About: This course will give you an introduction to the main tools and concepts of data science. You will learn the ideas behind turning data into actionable knowledge and get an introduction to tools like version control, markdown, git, GitHub, R, and RStudio. This course is offered by Johns Hopkins University through Coursera, and is part of a 10-course Data Science Specialization series.

Course length: 18 hours

What you’ll learn: This course will teach you how to set up R, RStudio, GitHub, and other tools. You will learn essential study design concepts, as well as how to understand the data, problems, and tools that data analysts use. 

“That course is a very accessible introduction for anyone who wants to get started in this,” Caffo says. “It’s an overview that covers the full pipeline, from things like collecting and arranging data to asking good questions, all the way to creating a data deliverable.”

The takeaway  

From businesses estimating demand for their products to political campaigns figuring out where they should run advertisements to health care professionals running clinical trials to judge a drug’s efficacy, data analytics has a wide variety of applications. Getting a better understanding of the field on your own time can be done easily and freely. And the field is only growing.

“Just about every field is having a revolution in data analytics,” Caffo says. “In fields like medicine that have always been data driven, it’s become more data-driven.”

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