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Range of techniques that could be used

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1. Statistical tests

Spearman's Rank Correlation Test

Spearman's Rank Correlation is a statistical test to test whether there is a significant relationship between two sets of data.

The Spearman's Rank Correlation test can only be used if there are at least 10 (ideally at least 15-15) pairs of data.

There are 3 steps to take when using the Spearman's Rank Correlation Test

Step 1. State the null hypothesis

There is no significant relationship between _______ and _______

Step 2. Calculate the Spearman's Rank Correlation Coefficient

`r_s = 1-(6∑D^2) / (n(n^2-1))`

`r_s` = Spearman's Rank correlation coefficient

`D` = differences between ranks

`n` = number of pairs of measurements

Step 3. Test the significance of the result

Compare the value of `r_s` that you have calculated against the critical value for `r_s` at a confidence level of 95% / significance value of p = 0.05.

If `r_s` is equal to or above the critical value (p=0.05) the REJECT the null hypothesis. There is a SIGNIFICANT relationship between the 2 variables.

A positive sign for `r_s` indicates a significant positive relationship and a negative sign indicates a significant negative relationship.

If `r_s` (ignoring any sign) is less than the critical value, ACCEPT the null hypothesis. There is NO SIGNIFICANT relationship between the 2 variables.

Chi-squared test

Chi squared in a statistical test that is used either to test whether there is a significant difference, goodness of fit or an association between observed and expected values.

`chi^2 = ∑ (O-E)^2 / E`

The chi squared test can only be used if

the data are in the form of frequencies in a number of categories (i.e. nominal data).

there are more than 20 observations in total

the observations are independent: one observation does not affect another

There are 3 steps to take when using the chi squared test

Step 1. State the null hypothesis

There is no significant association between _______ and _______

Step 2. Calculate the chi squared statistic

`chi^2 = ∑ (O-E)^2 / E`

`chi^2` = chi squared statistic

`O` = Observed values

`E` = Expected values

Step 3. Test the significance of the result

Compare your calculated value of `chi^2` against the critical value for `chi^2` at a confidence level of 95% / significance value of P = 0.05, and appropriate degrees of freedom.

`"Degrees of freedom" = ("number of rows" - 1) xx ("number of columns" - 1)`

If Chi Squared is equal to or greater than the critical value REJECT the null hypothesis. There is a SIGNIFICANT difference between the observed and expected values.

If Chi Squared is less than the critical value, ACCEPT the null hypothesis. There is NO SIGNIFICANT difference between the observed and expected values.

Mann Whitney U test

Mann Whitney U is a statistical test that is used either to test whether there is a significant difference between the medians of two sets of data.

The Mann Whitney U test can only be used if there are at least 6 pairs of data. It does not require a normal distribution.

There are 3 steps to take when using the Mann Whitney U test

Step 1. State the null hypothesis

There is no significant difference between _______ and _______

Step 2. Calculate the Mann Whitney U statistic

`U_1= n_1 xx n_2 + 0.5 n_2 (n_2 + 1) - ∑ R_2`

`U_2 = n_1 xx n_2 + 0.5 n_1 (n_1 + 1) - ∑ R_1`

`n_1` is the number of values of `x_1`

`n_2` is the number of values of `x_2`

`R_1` is the ranks given to `x_1`

`R_2` is the ranks given to `x_2`

Step 3. Test the significance of the result

Compare the value of U against the critical value for U at a confidence level of 95% / significance value of P = 0.05.

If U is equal to or smaller than the critical value (p=0.05) the REJECT the null hypothesis. There is a SIGNIFICANT difference between the 2 data sets.

If U is greater than the critical value, then ACCEPT the null hypothesis. There is NOT a significant difference between the 2 data sets.

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Spearman's Rank Correlation is a statistical test to test whether there is a significant relationship between two sets of data. The Spearman's Rank Correlation test can only be used if there are at least 10 (ideally at least 15-15) pairs of data.

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Range of techniques that could be used

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Royal Geographical Society - Geography resources for teachers

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The Royal Geographical Society (with IBG) supports teaching in the classroom and field by providing award-winning geographical teaching and learning resources.

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Possible ideas for data presentation

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Data Presentation - FSC Geography Fieldwork

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Geographical enquiry. A level geography. Data presentation.

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Websites that can be used to create presentation methods

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Different forms of sampling

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Royal Geographical Society - Geography resources for teachers

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The Royal Geographical Society (with IBG) supports teaching in the classroom and field by providing award-winning geographical teaching and learning resources.

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Useful infographics for inspiration or secondary data

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Useful websites that are located data sets

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Possible ideas from a series of articles

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Linked breakdown of ideas for the NEA

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Download a copy of the guide below
Lawrlwythwch gopi o'r canllaw
i - Independent Investigation - Student Planning Form
Independent Investigation - Student Planning Form (Word document)

ii - A Guide to Writing A Research Plan

iii - A Guide to Effective Background Reading

iv - A Guide to Referencing

1a - A Guide to Hypotheses

2a - A Guide to Different Types of Data

2b - A Guide to Data Collection Techniques - Quantitative Measuring

2c - A Guide to Data Collection Techniques - Surveys

2d - A Guide to Data Collection Techniques - Interviews

2e - A Guide to Data Collection Techniques - Questionnaires

2f - A Guide to Data Collection Techniques - Observations, Photographs and Field Sketches

2g - A Guide to Sampling Techniques

2h - A Guide to Recording Data in the Field

2i - A Guide to Avoiding Biased Data

3a - A Guide to Column Charts and Histograms

3b - A Guide to Pictograms

3c - A Guide to Pie Charts

3d - A Guide to Scatter and Line Graphs

3e - A Guide to Box and Whisker Graphs

3f - A Guide to Kite Diagrams

3g - A Guide to Triangular graphs

3h - A Guide to Rose and Radial Graphs

3i - A Guide to Isoline Maps

3j - A Guide to Choropleth Maps

3k - A Guide to Pictorial Data Presentation

3l - A Guide to Presenting Qualitative Data

4a - A Guide to Measures of Central Tendency

4b - A Guide to Measuring Proportions

4c - A Guide to Measures of Dispersion

4d - A Guide to Cost-Benefit Analysis

4e - A Guide to Spearman's Rank

4f - A Guide to Chi-Squared Testing

4g - A Guide to Simpson's Diversity Index

4h - A Guide to Pearson's Product Moment

4i - A Guide to Nearest Neighbour Analysis

4j - A Guide to Mann-Whitney U Test

4k - A Guide to Qualitative Data Analysis

Section 5 - Conclusions

Section 6 - Evaluation

Section 7 - Final checks

Posters - A guide to reading your research landscape

A copy of FAQs from the awarding bodies

View a video from the Royal Met Soc - MetLink - Weather Fieldwork for your A Level Geography Independent Investigation

This project was funded by the Nuffield Foundation, but the views expressed are those of the authors and not necessarily those of the Foundation

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Royal Geographical Society - Resources for schools

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Download a copy of the guide below Lawrlwythwch gopi o'r canllaw i - Independent Investigation - Student Planning Form Independent Investigation - Student Planning Form (Word document) ii - A Guide to Writing A Research Plan iii - A Guide to Effective Background Reading iv - A Guide to Referencing

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spilt into secitons with some ideas of enquiry questions

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Human geography, including the changing socio-economic and demographic characteristics of local places, plus the cultural meanings and representations attached to different places

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Human geography, including the changing socio-economic and demographic characteristics of local places, plus the cultural meanings and representations attached to different places

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Possible ideas from a series of articles

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spilt into sections with some ideas of enquiry questions

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Water & Carbon. This section guides you through a fieldwork investigation into the water cycle and/or the carbon cycle, including in woodlands and peatlands.

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Split into sections with some ideas of enquiry questions

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Glaciation. This section guides you through a fieldwork investigation into the evidence for glaciation in the UK, both in upland and lowland areas.

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Linked breakdown of ideas for the NEA

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Geographical enquiry. This section guides you through the geographical enquiry process from planning to evaluation.

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Work out how many bits of data you need to collect

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How many people do you need to take your survey? Even if you're a statistician, determining survey sample size can be tough.

Want to know how to calculate it? Our sample size calculator makes it easy. Here's everything you need to know about getting the right number of responses for your survey.

What is sample size?

Sample size is the number of completed responses your survey receives. It's called a sample because it only represents part of the group of people (or target population) whose opinions or behavior you care about. For example, one way of sampling is to use a "random sample," where respondents are chosen entirely by chance from the population at large.

Understanding sample sizes

Here are two key terms you'll need to understand to calculate your sample size and give it context:

Population size: The total number of people in the group you are trying to study. If you were taking a random sample of people across the U.S., then your population size would be about 317 million. Similarly, if you are surveying your company, the size of the population is the total number of employees.

Margin of error: A percentage that tells you how much you can expect your survey results to reflect the views of the overall population. The smaller the margin of error, the closer you are to having the exact answer at a given confidence level.

If you want to calculate your margin of error, check out our margin of error calculator.

How to calculate sample size

Wondering how to calculate sample size? If you'd like to do the calculation by hand, use the following formula:
Population Size = N | Margin of error = e | z-score = z e is percentage, put into decimal form (for example, 3% = 0.03).
The z-score is the number of standard deviations a given proportion is away from the mean. To find the right z-score to use, refer to the table below:

Things to watch for when calculating sample size

If you want a smaller margin of error, you must have a larger sample size given the same population.

The higher the confidence level you want to have, the larger your sample size will need to be.

Does having a statistically significant sample size matter?

Generally, the rule of thumb is that the larger the sample size, the more statistically significant it is-meaning there's less of a chance that your results happened by coincidence.

But you might be wondering whether or not a statistically significant sample size matters. The truth is, it's a case-by-case situation. Survey sampling can still give you valuable answers without having a sample size that represents the general population. Customer feedback is one of the surveys that does so, regardless of whether or not you have a statistically significant sample size. Listening to customer thoughts will give you valuable perspectives on how you can improve your business.

On the other hand, political pollsters have to be extremely careful about surveying the right sample size-they need to make sure it's balanced to reflect the overall population. Here are some specific use cases to help you figure out whether a statistically significant sample size makes a difference.

The Effect Survey Values have on the Accuracy of its Results

Value Increased

Value Decreased

Population Size

Accuracy Decreases

Accuracy Increases

Sample Size

Accuracy Increases

Accuracy Decreases

Confidence Level

Accuracy Increases

Accuracy Decreases

Margin of Error

Accuracy Decreases

Accuracy Increases

Employee and Human Resources Surveys:
Working on an employee satisfaction survey? All HR surveys provide important feedback on how employees feel about the work environment or your company. Having a statistically significant sample size can give you a more holistic view on employees in general. However, even if your sample size isn't statistically significant, it's important to send the survey anyway. HR-related surveys can give you important feedback on how you should improve the workplace.

Customer Satisfaction Surveys:
Like we said earlier, customer satisfaction surveys don't necessarily have to rely on having a statistically significant sample size. While it's important that your responses are accurate and represent how customers feel, you really should be taking a closer look on each answer in a customer satisfaction survey. Any feedback, positive or negative, is important.

Market Research:
When conducting a market research survey, having a statistically significant sample size can make a big difference. Market research surveys help you discover more information about your customers and your target market. That means a statistically significant sample size can easily help you discover insights on your overall target market. It also assures you're getting the most accurate information.

Education Surveys:
For education surveys, we recommend getting a statistically significant sample size that represents the population. If you're planning on making changes in your school based on feedback from students about the institution, instructors, teachers, etc., a statistically significant sample size will help you get results to lead your school to success. If you're planning on just receiving feedback from students for the sake of seeing what they think-and not necessarily making a change in the system-a statistically significant sample size might not be as important.

Healthcare Surveys:
When conducting healthcare surveys, a statistically significant sample size can help you find out what health issues are a greater concern for your patients over others. It can also help you come to conclusions in medical research. However, if you're using Healthcare Surveys for Patient Satisfaction reasons or questioning patients about their regular care, a statistically significant sample size might not be as important. Without it, you're still able to get valuable information from individual patients about their needs and experience.

Casual surveys:
On a day-to-day basis, you might want to send surveys to friends, colleagues, family, etc. In this case, it really depends on what you're looking for from your survey. If you'd like your results to be used as evidence, a statistically significant sample size is important. If not, and you're just using SurveyMonkey for fun, sending your survey to just a few people won't hurt.

Do you need more responses?

Don't just take a guess at how many people should take your survey and don't get bogged down in probability sampling or probability distribution models-use our sample size calculator. Get familiar with sample bias, sample size, statistically significant sample sizes, and how to get more responses. Soon you'll have everything you'll need to get better data for your survey.
If the sample size calculator says you need more respondents, we can help. Tell us about your population, and we'll find the right people to take your surveys. With millions of qualified respondents, SurveyMonkey Audience makes it easy to get survey responses from people around the world instantly, from almost anyone.

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Sample Size Calculator: Understanding Sample Sizes | SurveyMonkey

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How many people do you need to take your survey? Even if you're a statistician, determining survey sample size can be tough. Want to know how to calculate it? Our sample size calculator makes it easy. Here's everything you need to know about getting the right number of responses for your survey.