You can find some helpful documents and some examples of IAs on squidinkbio.weebly.com

On the days of the trip Miss Shaughnessy can be contacted on the science department mobile:

07510840301

ES&S Internal Assessment

Flatford Mill 2nd -4th November 2016

25% of your mark

To do:

Consider a particular aspect of and ESS issue.

With that in mind- think up a focussed research question.

Develop appropriate methodologies that will generate sufficient data.

Analyse and evaluate your results

Apply your results to the larger ESS issue.

Propose a solution to the problem or issue. This could be related to your findings but is also a change for you to show creative thinking and novel solutions.

Keep the research question simple but collect lots of data! Make sure you are linking it with an issue.

Produce a report of between 1500 and 2250 words.

(This doesnt include tabulated data or the bibliography)

At Flatford you will be introduced to a variety of : ecological questions habitats and their biotic and abiotic factors sampling techniques and their limitations pieces of apparatus and their limitations.

After the first two days you will design and carry out your own investigation before writing it up.

Week commencing 21st November- deadline for the completed task.

(The IB allow for one draft to be done and improved upon- your teacher will make some comments on how your work could be improved and return it to you after your mock exams. For this to be useful feedback you must hand in fully completed task that has been done to the best of your ability. If not- your teachers feedback will be to merely advise you to finish sections.)

A later deadline will be set by your teacher for any final tweaks.

Program of events- subject to change

Wednesday

Arrival and welcome

Introduction to the centre and health and safety procedures.

Group 1

Group 2

Freshwater Pollution investigation

Introduction to investigation of the effects of pollution on freshwater ecosystems. Sampling at Riber Brook. Collation of invertebrate and abiotic data. Lunch at Riber Brook

Vegetation analysis. Introduction to sampling techniques problem finding exercise followed by a discussion of the problems and possible solutions.

Woodland Investigation

Introduction to the investigation, risk assessment.

To compare 2 habitats to see whether there is a difference in both abiotics and vegetation.

Identifying and measuring abiotic variables.

Lunch at

16.00

Afternoon break

16.30

Analysis of data including:

Simpsons reciprocal index of diversity. Spearmans rank correlation coefficient. Scattergraphs with range bars

Collate data and analyse soil samples.

Statistical analysis of data- Students t -test,

18.00

Evening Meal

18.45-20.00

Discussion of results, making links with abiotic variables and discussion of indicator species. Conclusions/evaluation of the investigation.

Follow up: discussion, conclusion and evaluation of results.

Thursday

Group 1

Group 2

08.00

Breakfast and make packed lunches

9.15

Vegetation analysis. Introduction to sampling techniques problem finding exercise followed by a discussion of the problems and possible solutions.

Freshwater Pollution investigation

Introduction to investigation of the effects of pollution on freshwater ecosystems. Sampling at Riber Brook. Collation of invertebrate and abiotic data. Lunch at Riber Brook

Woodland Investigation

Introduction to the investigation, risk assessment. Eat packed lunch at centre

Woodland investigation fieldwork to compare 2 habitats to see whether there is a difference in both abiotics and vegetation.

Identifying and measuring abiotic variables.

Lunch in woodland

Return to centre to collate data and analyse soil samples

16.00

Afternoon break

16.30

.

Statistical analysis of data- Students t -test, Discussion of results, making links with abiotic variables and discussion of indicator species. Conclusions/evaluation of the investigation.

Analysis of data including:

Simpsons reciprocal index of diversity. Spearmans rank correlation coefficient. Scattergraphs with range bars

Follow up: discussion, conclusion and evaluation of results.

18.00

Evening Meal

18.45-20.00

Internal Assessment

Students begin planning their investigation

Friday

08.00

Breakfast and make packed lunches

09.15

Continue planning individual investigations (in sections personal engagement, exploration, analysis, evaluation, communication)

10.00

Students collect equipment for their individual investigations and start collecting data. Lunch in the field.

14.30

Students bring data back to class and begin statistical analysis and data processing.

15.45

Pack up and reviews

16.00

Depart centre.

Write up. Include the following:

This guidance is most appropriate to ecological investigations- if in doubt- ask!

General title

Introduction and research question

What is the key ESS issue you will be discussing? What has led you to focus on this issue?

State your more focussed research question/aim What is the connection between your research question and an environmental issue.

E.g..

The aim of the investigation is to see if ______________ affects the abundance of ______________ (_________ ________) in (specific location) in November.

The aim of the investigation was to see if there are more____________________ (___________ __________) in/on_________ or ______________ in (specific location) in November.

Describe the context location (and its relevance eg popular tourist destination? Site of special scientific interest?) , species (and how to identify it), or any other biological /ess concept if relevant. Use diagrams, maps, photographs etc.

Hypothesis with biological justification. You may believe that the location of an organism could be linked to its physiology, its trophic level, its pollution tolerance etc.

Eg

As the __________ increases the abundance of ______ will ___________because.

You must include good quality theory which helps to explain what you are researching and your hypothesis. This should be IB standard and link the 2 variables together.

You could include a reference to a secondary source if possible, one which you can later compare your results/conclusions with. Give the reference as a footnote if a secondary source is used.

Variables (these could be in a table)

Independent state clearly what your independent variable is and the range of values you will test.

Dependent state clearly what your dependent variable is. (Exactly what you will be measuring)

Controlled state each of the variables that you can and will control; state how you will control each one and why it needs to be controlled (i.e. what would happen to the expected result If this factor was higher/lower and why?). Consider your sampling method. Also can you avoid the effect of other variables- e.g. always sampling at a certain distance from the river bank, or never sampling directly under trees as this will reduce light intensity.

Uncontrolled state any variables that are important but cannot be controlled. Think can they at least be monitored to see if they vary? (E.g. if water depth is your independent variable ideally you would want everything else to be controlled. However the water velocity will be different at different depths and you cant change that. All you can do is monitor it and comment at the end that it may have been the velocity and not the depth that made the impact in your investigation)

When writing about variables- the word amount is banned! Refer to volumes of, mass of, number of sample sites etc.

Apparatus

List each piece of apparatus and where possible state sizes. A labelled diagram showing how apparatus is set up may be helpful

Use SI units of measurement and be precise i.e. state uncertainties of measurements e.g. +/- 0.5 cm3

Method- this should give you sufficient appropriate results and be reproducible

Write your method as a logical sequence of numbered steps/bullet points.

Avoid writing things like I will measure out 5cm3 water into a test tube instead, say Measure out 5cm3 water into a test tube

Describe method so that it is reproducible. Dont forget to be very specific about the location you use. Eg dont just say I will lay a transect and sample every 10 metres- where did the transect line start from? You could describe it in relation to features such as a path or river bank.

What strategy for selecting sites did you choose and why? (Random/stratified/systematic)

What sampling technique was used and why (quadrats/transects/sweepnetting etc)? What was done to increase the accuracy and reliably (i.e. how did you try to get a representative sample and what did you ensure you did the same way each time? How were inaccuracies reduced- eg turning the ruler sideways to avoid water building up in front and overestimating the depth, holding anemometer into wind. Doing light intensity measurements as close together time wise as possible so weather has less of an impact)

How the organism was identified- what features were you looking for?

What other factors will you be controlling/monitoring- describe how these factors will be measured. Again giving detail of how apparatus is used to ensure accuracy and the fact that this is done in the same way each time to ensure accuracy

How many samples sites will be used? How many repeats? There is a focus on collecting sufficient data

Enough to be representative? If in doubt do more!

Enough for statistics?

5 repeats at a site/for a particular value of the independent variable where appropriate/possible.

(eg if youre looking at the correlation between water depth and the number of a particular species I would expect 10-15 different depths. If you were comparing something in only 2 different areas I would expect lots of samples to be taken in both areas). More is better!

If 20% of samples lack the organism youre investigating you need more samples.

Say how you will process your data

Risk Assessment

State any important risks/hazards and how you will minimise them.

Ethical considerations

Are there any ethical implications to your investigation? How will you minimise harm/impacts on the environment?

Qualitative Results

Record any qualitative observations made during the investigation. Eg, change in appearance of vegetation across an area, comparison of land use at sampling sites, observation about species behaviour etc. This could be included as part of the results table or separately.

Results table

Record all raw data in a table, as you collect it. The table must have a title/heading.

To do:

Consider a particular aspect of and ESS issue.

With that in mind- think up a focussed research question.

Develop appropriate methodologies that will generate sufficient data.

Analyse and evaluate your results

Apply your results to the larger ESS issue.

Propose a solution to the problem or issue. This could be related to your findings but is also a change for you to show creative thinking and novel solutions.

The independent variable should be in the left-hand column, with the dependent results to the right.

Each column must have a descriptive heading with SI units and uncertainties (These depend on the apparatus being used. For subjective assessments e.g. % cover an estimate for uncertainty should be made. Counts still have uncertainties! See the uncertainties section). Units must only go in the heading

Your data must be recorded to an appropriate number of decimal places based on the measuring apparatus used, and within each column the use of decimal places must be consistent. They must also be consistent with the uncertainty (so you may have to add .0 to your results)

Never split a table across 2 pages.

Identify anomalous results e.g. by highlighting them and including a key.

Table 1: A table to show how water temperature affects the time taken for a sugar cube to dissolve.

Temperature of water (oC)

0.5 oC

Time taken for a sugar cube to dissolve. (s 1s)

Repeat 1

Repeat 2

Repeat 3

20.0

300

310

305

30.0

290

300

275

Processing data and statistics

If appropriate calculate the mean. Processed data should be calculated to the same number of decimal places as the raw data (or 1 more place)

If appropriate you could calculate standard deviation (Standard deviation is technically statistically insignificant if less than 25 repeats have been carried out! But, it is suggested that it is still used but that the student points out that the results are insignificant. You dont have to show working- you could just do it on your calculator.)

Youre investigation may require you to do another calculation such as the Lincoln Index or Simpsons Biodiversity Index.

Further stats- You may be able to look at the significance of a difference or correlation using more complex statistics:

Say which statistical test you have chosen to do and why.

Null hypothesis

Processing with clear layout

Must include summary statement (use the field centres examples. Eg what was your calculated t value? At how many degrees of freedom? Is it higher or lower than the critical value at the 0.05 significance level? Do you accept or reject your null hypothesis?).

Quick guide:

Is there a correlation between.? = Spearmans rank

Is there a significant difference between 2 means (eg mean surface area of leaves in 2 areas) = t test

Is there a significant difference in the number of mayfly nymphs in 2 areas?= Mann Whitney U

Graphs

If being done on a computer, you must still follow all of the conventions below! You must choose the option to show grid lines so that values can be read to a high level of accuracy. If in doubt draw it and scan it (but check that all the lines, plots and labels are very clear).

Plot graphs of processed data (i.e. means) where possible. (Line graph for continuous data, Bar chart for discrete data.) The type of chart or graph will depend on your investigation

Add a title to your graph.

Ensure the scale of the graph is correct and allows the data points to be plotted accurately.

Always include range /error bars either to show the range or +/- 1 standard deviation. Include a key to say what type of bars they are. (These can be tricky on excel- you could use a drawing tool if in doubt. The bars mustnt extend beyond the axis of the graph)

Adjacent data points should be joined by a straight line and the line should start with the first data point and end with the last one, as there should be no extrapolation beyond these points

Add a line of best fit where relevant and label it as such. Make it different to the previous line.

The IV should be on the X axis and the DV on the Y axis.

Add descriptive labels to axis and include units and uncertainties. If youre plotting a mean say so on the axis (otherwise it wont be clear its processed).

Conclusion

Make a clear, precise statement based on your data. Describe any trends and quote data. (This should include reference to statistics- eg does a t- test show a significant difference?)

Justify your conclusion using your data.

Explain your conclusion with concepts from ESS. Does this match the known theory (refer back to your background information) reference to a secondary source is essential here. Refer to cited published data if available. You can reference using footnotes. If your results dont match your expectations or published data refer back to what you would have expected and why. Do not worry about repeating what you have written earlier in the report.

Evaluation of your research

Consider your data:

Evaluate your results.

Where your results accurate? This can be judged by considering if they matched what your research said should happen. Also- how close were your points to the line of best fit. Closer= more accurate.

Where your results reliable? Reliable results mean that your repeats were similar and so your range bars were small. Dont just be general- refer to specific range bars. Point out the most/least precise. Remember- more reliability means less uncertainty in your data and more confidence in your results.

What is the impact of these uncertainties in your data. Does it reduce the confidence in your conclusion?

Consider your method (you could use a table like the one below to set this bit out):

Aim for 5 weaknesses/limitations/sources of error in your method and indicate the magnitude of impact on data (small, medium, large or rank order the limitations which is the most important?). See the common limitations sheet for some ideas. Eg Was your method repeatable? What did you control well? What made it difficult to do the method in exactly the same way each time? What were the things you couldnt control?

Evaluate the impact of the limitations. Does it cause you to overestimate a value? Or does it reduce accuracy or reliability? Why?

Suggest realistic improvements which would address each of the limitations you have identified. These must be achievable in a school environment.

Limitation

How/why it affects data

Ranking

Suggested improvement

Difficult to maintain a stable temperature in a water bath made using a beaker of water and a Bunsen burner

The temperature fluctuated by +/- 8 C. If it got higher than it should be, the rate of the reaction would increase.

1

Use a thermostatically controlled water bath, and monitor it constantly with a thermometer

Only three repeats were collected due to time constraints

The raw data collected was unreliable as it had a large range. Therefore I cannot be confident that my mean is accurate. More repeats would have helped me to identify anomalies and omit them if necessary.

2

Collect at least 5 repeat readings at each temperature.

Now that you have evaluated do your results really allow you to answer your research question. How confidant are you.

Discussion

What are the implications of your research in regards to the environmental issue?

Evaluate the conclusion in the context of the environmental issue. Was your question relevant to the issue. Can the findings from your study be related to the real world issue are the scale/land use/level of economic development/ attitudes of locals etc etc comparible?

How could your investigation be extended? Have your results raised any other questions that could be investigated?

Applications

Justify one potential application of the outcomes of your investigation or (if your investigation didnt give such data) suggest a different solution to the environmental issue being discussed.

Evaluate the strengths and weaknesses of this solution. (Consider social, economic and environmental implications).

Reference list

There are different ways of referencing secondary sources in the text of your work. The IB does not have a particular preference but it must be consistent in your work. Whichever way you choose to reference in the text it must be accompanied by a full reference list at the end of your work. These must be written alphabetically in the format below.

If its a book:

Barber, J. Tribes of Kenya. Frankin Watts. 1998

Douglas, A. Symbiotic Interactions. Oxford University Press. 1994

If its a journal:

Knight J. Gene therapy. Biological Sciences Review. Vol 6, No.1, pp22-24.

If its a website:

Emma Brenard. Tardigrades: Water bears in space. Viewed online on 20/08/14 at http://www.bbc.co.uk/nature/12855775 (Theres not always an author- if not replace it with the name of the general website. You must still include the full URL.)

Throughout the text, the easiest option is to use numbered footnotes. So if the first reference was the Tardigrade one, next to the relevant info in your paragraph do a superscript 1. Then put the 1 and the full reference at the bottom of the page. It will then be repeated again in this reference list. Your numbers should continue throughout the whole piece of work- not sto p and start again for different sections.

Making sure your work is suitable for electronic submission:

All sections of the internal assessment should be on one document.

Dont use your name, session number or the name or number of the school anywhere in your work. Include your name in the file name only.

Use Arial as your font (size 11 or 12)

Single (or greater) line spacing

Numbered pages

Portrait orientation (rather than landscape). If necessary it is acceptable to have a page or two landscape- eg if a graph or table would only fit in this orientation.

You should keep files to the smallest possible size that does not negatively affect the quality of the work submitted. Maximum file size 50MB

Acceptable file types .doc .docx .pdf (non editable) .rtf

Do not include hyperlinks in your coursework. They can not be viewed by examiners or moderators and look unprofessional if left in a footnote or reference list.

Check how the document will be presented to an examiner for marking. You want to make sure that tall the contents appear as expected and that all the content is readable. You could:

1) View the document in a print preview mode

2) Print the document (but remember you wont be handing in a paper copy)

3) Export the document to a pdf file.

Marking Criteria

Identifying the context

Planning

Results, Analysis and Conclusion

Discussion and evaluation

Applications

Communication

Information to help

Common limitations to investigations.

(These will be more relevant to science style investigations rather than social studies style ones)

Limitations

A limitation is any factor that has not been controlled or taken into account in the design of an experiment can be referred to as a limitation. A limitation can be described as a design fault. Limitations will reduce the confidence you can have in the conclusions you draw from your investigation.

(Errors are different- if you make a mistake this doesnt count as a limitation.)

The limitations will obviously depend on the investigation you carried out but here are some common ones. Ensure that you discuss them in the context of your investigation.

Were a limited number of values for the independent variable investigated?

Imagine you wanted to investigate the effect of pH on something. You investigate pH 4,5,6,7 and 8. This is a limited range and so you could not conclude what the impact of lower or higher pHs would be. Also intermediate values should be investigated to more accurately determine where the optimum pH is. Intermediate values are good for confirming any trend, giving a more complete picture of how one thing affects another, and if a change occurs (eg an increase, decrease, plateau) you can more accurately say when this occurs.

Were any important factors not controlled? Consider all the factors that affect something (productivity /enzyme action/ diffusion/ respiration / photosynthesis/ transpiration etc ) They should all be controlled unless used as the independent variable.

Does any part of the investigation involve making judgments about colour, clarity, % cover? These are subjective judgements (based on opinion) and so could vary each time you make an observation. Could a comparison chart or electronic devise be used instead? This would increase the accuracy of measurements.

Did the precision of your apparatus lead to uncertainties you consider to be unacceptable? If uncertainties are large it is more likely that data for each of the independent variable values could overlap. If they do then you cant confidently say that there was a real difference between them. An improvement could be to use more precise apparatus.

A small number of repeats (or none at all) is problematic as it reduces your ability to judge reliability. More repeats help you identify anomalies and omit them if necessary. More reliable data means that you can have more confidence that mean data is accurate.

If sampling- was your sample size large enough to be representative of the population or area? A small sample could mean that you are not getting a true impression of a characteristic or area.

If sampling- were there temporal, special, or safety constraints that stopped you from sampling in the ideal place or time?

Uncertainties

When you take measurements you cannot be sure that the data is completely accurate. The measurement apparatus you use will have uncertainties.

(Consider this- I could measure out 10 mls of liquid but the real value could actually be 10.05 mls. I wouldnt notice this if my measuring cylinder only had whole mls on the scale. The uncertainty tells you how much your measurement could be out by.)

Quote these uncertainties:

On your apparatus list (This allows people to copy your method using apparatus of the same precision)

In the headings of results tables

On the axis of graphs.

They can also be discussed in your evaluation. If the uncertainties are large your conclusions could be in doubt.

The uncertainties depend on what you are using to collect the data.

Rulers that measure mm :

Rulers are a special case. The uncertainty is always 1mm

The bar is 1.4cm or 14mm

The uncertainty in the table heading would be 0.1cm or 1mm

Measuring apparatus (eg measuring cylinders, thermometers) but not rulers.

The uncertainty is half of the place value (ie half of that decimal place) of the last measured value.

Eg if a syringe has markings that go up in 0.1mls its uncertainty would be 0.05mls

Eg. A measuring cylinder has markings that go up in 10mls. Its uncertainty would be 5mls.

NB It would be sensible for you to measure the amount of liquid here as 25mls. This doesnt affect your uncertainty value as they only relate to measured values (as opposed to estimated ones).

Only estimate halves of measurements.

40

30

20

10

Electronic instruments (eg a balance)

The minimum uncertainty is 1 unit of the last decimal place. (Note that this is different to the non-electronic things above.)

Eg. A balance gives measurements in grams to 2 decimal places. Its uncertainty would be 0.01g

NB. You uncertainty is also linked to how you use the apparatus. If a timer measures to 0.01 of a second but you ignore the decimal places and just record whole seconds the uncertainty is 1s and not 0.01s.

Estimated uncertainties

When you are counting something (particularly observations on living things) you will have an uncertainty. You need to make a sensible estimate of uncertainty.

Eg abdominal breathing movements of a locust. 1 or 2

Eg. Heart beats of Daphnia 10 (The uncertainty is higher because the movement is faster so youre likely to be out by more)

Eg % cover of moss in a quadrat. 4%

Uncertainties and your raw data.

Uncertainty and raw data should have the same number of decimal places. This may mean adding a zero onto your raw data.

Here are some examples showing just 1 column from data tables. Refer back to the previous information to justify to yourself why these uncertainty values have been given ( ie what type of apparatus is being used). Note how many decimal places the raw data has been recorded to.

A) A gas syringe has a scale which goes up in 10ml increments

Volume of gas (ml 5ml)

30

20

This gas syringe was half way between 20 and 30. This was an estimate (but it doesnt affect the uncertainty).

25

10

B) A thermometer has a scale which goes up in 1oC increments.

Temperature of water. (oC 0.5oC)

4.0

54.0

78.0

11.0

C) A student was measuring his own pulse rate.

Pulse rate (beats min-1 2)

55

56

61

55

D) A balance measures to a precision of 0.1g

Mass of glucose (g 0.1g)

10.0

15.5

20.2

21.0