IB Biology

Individual Investigation

NDIHS BIOLOGY II Handbook C2023

Different types of Individual Investaigations for IB Biology

IB_Biology_IA_Checklist

IB Biology and Animal Use

IB Biology Use of Animal.pdf

Avoiding Plagiarism Decision Tree

plagiarism_decision_tree.pdf

II student Template

II BIology Student template

Personal Engagement

This criterion assesses the extent to which the student engages with the investigation and makes it their own. Personal engagement may be recognized in different attributes and skills; these could include addressing personal interest or showing evidence of independent thinking, creativity, or initiate in the designing, implementation or presentation of the investigation.

For the personal engagement, criterion, you must provide clear evidence that you have contributed significant thinking, imitative, or insight to your investigation. Personal significance, interest, and curiosity are awarded. Unlike other criteria, in personal engagement there just has to be a point of evidence against an aspect, it does not have to comprehensively meet all mark points.

Independent Thinking:

Research question is novel and/or unusual.
Creativity in data collection methods or technique.
Arguments and discussion show independent thinking, considering data, published sources and observations together in a unique way.

Personal Significance:

Research question is based on authentic personal interest or curiosity, with explanation.
Research question is relevant to local issues, with explanation.

Initiative:

Novel or innovative approach to address the research question, with explanation.
Method uses known protocol, but adapts them for good reason, with explanation.
Novel or innovative approach to presentation of results, with explanation.

This is a holistic judgement, students can show personal engagement in;

the choice of topic or the reason why the student chose it, as mentioned above
the choice of sources in research for the background section of Exploration
originality in the design of the method, or the apparatus in Exploration
persistence (or resilience) in overcoming difficulties in the collection of the results
the quality of the observations made.
the methods used to analyze the data collected
the type of comments written in the evaluation about limitations, improvements or extensions to the experiment

EXPLORATION

This criterion assesses the extent to which the student establishes the scientific context for the work, states a clear and focused research question and uses concepts and techniques appropriate to the Diploma Programme level. Where appropriate, this criterion also assesses awareness of safety, environmental, and ethical considerations.

Research Question:

The research questions is clearly stated and precisely formulated.
Research question includes clear IV and DV.
Research question includes scientific name of organism, if relevant (Genus species).
The research question can be used to formulate a hypothesis predicting the relationship between the MV and IV.
Hypothesis explanation is scientifically accurate (with correctly cited sources).

There should be a single sentence which clearly and specifically states the objective of the investigation. Recognize the nature of the problem that has been set, the factors (variables) that will affect the outcome, and how they affect it (the hypothesis). The research question must clearly identify the manipulated and responding variables of the investigation

If you are using any living organisms, or products from living organisms, such as seeds from a certain plant, give the most precise name you can and give the scientific name if possible (e.g. Phaseolus vulgaris for kidney beans).

Although not required by the IB Organization, for many investigations it is appropriate for students to include a hypothesis. A hypothesis is like a prediction. It will often take the form of a proposed relationship between two or more variables that can be tested by experiment: “If X is done, then Y will occur.” Also provide an explanation for the hypothesis. This should be a brief discussion (paragraph form) about the theory or ‘why’ behind the hypothesis and prediction. Be sure the hypothesis is related directly to the research question and that the manipulated and responding variables for the experiment are clear. (Example: “The rate of transpiration will increase as wind speeds and temperatures rise”

Be sure your hypothesis is related directly to your research question and that the manipulated and responding variables for your experiment are clear.

Background:

The background sets the research question into context.
Appropriate and relevant background biology correctly described and explained.
Citations relevant to the research question are used.
Background information is used to form a hypothesis.
Null and alternative hypothesis given if a statistical test of significance is used.
If relevant, a predicted graph is used to illustrate the hypothesis.

Write a paragraph or two explaining why the experiment is appropriate and relevant to the study of biology. How does it relate to what is being learned in class? What process or phenomenon will the experiment support?

Safety, Ethics and Environmental Issues:

Safety issues fully considered (including human consent forms if needed).
Ethical issues fully considered (including animal experimentation policy if needed).
Environmental issues fully considered (such as reduction of waste and disposal of chemicals).
List any safety precautions that must be taken during the lab, including personal and environmental concerns.

Measurement Precision:

Unless there is a digital display, always measure to one digit beyond the smallest unit of CERTAIN measurement of the tool. For example, if you use a ruler that can accurately measure to the tenth of a centimeter, your measurement would be to the hundredth of a centimeter. The number of significant digits should reflect the precision of the measurement.

There should be no variation in the precision of raw data. The same number of digits past the decimal place should be used. For data derived from processing raw data (i.e., means), the level of precision should be consistent with that of the raw data.

Methodology:

IV correctly identified with units and levels, including how the levels were chosen.
Minimum of five levels of IV over a suitable range (unless comparing populations or correlating variables without manipulation).
DV (as directly recorded and/or calculated) correctly identified with units.
Important CV identified, with the potential impact of each discussed. Validity measures and/or control group are not misunderstood as CV.
List or photo of apparatus and materials including size, graduation and uncertainty.
Reference to preliminary trials, if completed.
Method to change and measure IV fully detailed (including tools, units and uncertainty).
Method for measuring DV fully detailed (including tools, units and uncertainty).
Sufficient repeats of DV measurement to ensure reliability and allow for statistics (5 for SD, 10 for T-test, 20+for correlation).
Collection of data from other students or sources is explained and referenced.
If sampling only a portion of a population, include the method for ensuring the sample was randomly selected.
Method for maintaining and measuring CV is detailed (including tools, units and uncertainty).
Method includes validity measures to ensure experimental measurements are valid and consistent.
Method is clear, specific and easily replicated as described.
Full citation of a published protocol (or elements of), if used.

Make a list of materials needed. Be as specific as possible (example: “50 mL beaker instead of ‘beaker’). A well labeled diagram or photograph of how the experiment is set up may be appropriate. Be sure the diagram includes a title and any necessary labels.

State or discuss the method (procedure) that was used in the experiment. This should be in the form of a step-by-step direction. Provide enough detail so that another person could repeat your work by reading the report! You don’t have to go into detail about standard, well-understood actions. If a standard technique is used, it should be referenced.

If something is done in the procedure to minimize an anticipated error, mention this as well. (Example: “Carefully cutting plant stem under water to reduce affect of air on transpiration rate.”)

In the method, clearly state how to collect data. What measuring device was used, what data was recorded and when? Or what qualitative observations were looked for (such as color change)?

The procedure must allow collection of sufficient relevant data. As a rule, the lower limit is five measurements, or a sample size of five. Very small samples run from 5 to 20, small samples run from 20 to 30, and big samples run from 30 upwards. Obviously, this will vary within the limits of the time available for an investigation.

The data range and amount of data in that range are also important. For example, when trying to determine the optimum pH of an enzyme, using a range of pH values between 6 and 8 would be insufficient. Using a range of values between 3 and 10 would be better, but would also be insufficient if only three different pH values were tested in that range.

“Control of variables” refers to the manipulation of the independent variable and the attempt to maintain the controlled variables at a constant value.

describe how the control of variables is achieved. If the control of variables is not practically possible, some effort should be made to monitor the variable(s).
state an explicit procedure or method for how each variable will be controlled and monitored.
if using a known experimental protocol, you must explain how you modified the standard method to make it your own.

Individual investigation student template

ANALYSIS

This criterion assesses the extent to which your report provides evidence that you has selected, recorded, processed and interpreted the data in ways that are relevant to the research question and can support a conclusion.

Raw Data:

Data is collected for a minimum of 5 levels over a suitable range of the IV.
Data is collected for a minimum of 5 repeats (for Standard Deviation, more for correlations).
Data is collected to show consistency of CV.
Insightful and thorough qualitative data (observations and/or photos).
All data are recorded correctly and honestly.

Raw data is the data you collect during the investigation to help answer the research question. Raw data can be quantitative (numbers) and/or qualitative (descriptions). The best way to record data is by using data tables. Give a clear title to each data table. Number tables consecutively through the report.

Data Processing:

Calculations to determine DV, if necessary (i.e. rate)
Mean and standard deviations included, where appropriate,
Calculations and/or significance tests appropriate to investigation
Justification of the data processing methods.
Statistical tests include full details including null and alternative hypothesis, DF, critical values and probability levels.
Formula, Excel formula, worked example or screen shot of calculations given.
Appropriate choice of graph with variables on the appropriate axis

This is where raw data is transformed into results that answer the research question. You will show the calculations that give a numerical result. Statistics are useful mathematical tools which are used to analyze data.

For help you can go to the Biology For Life

Data processing involves combining and manipulating raw data to determine the value of a physical quantity (such as adding, subtracting, squaring, dividing), and taking the average of several measurements and transforming data into a form suitable for graphical representation. It might be that the data is already in a form suitable for graphical presentation, for example, distance traveled by woodlice against temperature. If the raw data is represented in this way and a best-fit line graph is drawn, the raw data has been processed. Plotting raw data (without a graph line) does not constitute processing data.

You should present your work for processed data so that all the stages to the final result can be followed.

Show at least one example of the working required for each data processing calculation.
Inclusion of metric units are expected for final derived quantities, which should be expressed to the correct number of significant figures.
Show the units of measurements in all calculations. Pay attention to significant digits! Don’t lose accuracy by carelessly rounding off.

Impact of Uncertainty:

Correct uncertainty reported for raw measurements.
Uncertainties justified and/or explained.
Correct and consistent number of digits throughout.
Discussion of the size of uncertainties compared to the data collected.
SD error bars included and labeled on graphs

In addition to reporting the correct measurement uncertainty, you must explain the impact (or not) of the measurement uncertainty on the results and/or conclusion.

Interpretation of Processed Data:

Patterns in the data related to the RQ stated, with specific numerical reference to graphs/tables.
Data pointed joined to illustrate the trend (unless comparing qualitative IV).
Patterns and trends in data described with reference to graphs.
Variation (i.e. SD) within the data discussed.
Correct conclusion of significance is drawn.

Data Tables

Title the table; make sure the title relates to the data you will put in your table. The data table title is NOT a repeat of the research question; the title SHOULD be descriptive of the data contained in the table.
Figure out how many columns and rows are needed. Rows are a series of horizontal cells and columns are a series of vertical cells. Although not required, in most cases the manipulated variable (that which is purposefully changed) is in the left column, the raw data for the responding variable (that which you measure) with the different trials is in the next columns, and the processed data (often average and standard deviation) is in the far right column. Be sure to include a row for the heading of each column.
Draw the table with a program like Microsoft Excel or Google Sheets. Show lines around all rows and columns. Be sure the table does not break across multiple pages.
Label the columns, including units and measurement uncertainty of the raw data.
Record the data from the experiment or research in the appropriate columns. The information in the table must be clear and obvious When you're finished there should be a number in every space. All numerical values must have the consistent and correct number of digits. There should be no variation in the precision of the data; the same number of decimal places (significant digits) should be used.
Check your table. Look over the work to make sure everything is correct and clear

Graphing:

For Graphing help click on Biology For Life

EVALUATION

This criterion assesses the extent to which the student’s report provides evidence of evaluation of the investigation and the results with regard to the research question and the accepted scientific context.

Conclusion:

The conclusion given is correct and clearly supported by the interpretation of the data.
Key data from the analysis is given and trends in the data are discussed.
The extent to which the hypothesis is supported by the data is explained (avoiding “proves”).
The level of support (strong, weak, none or inconclusive) for the hypothesis/ conclusion is identified, correct and justified.

The conclusion starts with one (or more) paragraphs in which you draw conclusions from results, and state whether or not the conclusions support the hypothesis . The conclusion should be clearly related to the research question and the purpose of the experiment. Use the expressions ‘confirmed by the data’ or ‘refuted by the data’ rather than ‘right,’ ‘wrong,’ or 'proven.'

You must also provide a brief explanation as to how you came to the conclusion from the results. In other words, sum up the evidence and explain observations, trends or patterns revealed by the data.

Limitations:

The variation in results is reported, showing the strength of the conclusion.
The appropriateness of the apparatus in obtaining relevant data is commented on.
Weaknesses in the methodology are discussed.
The reliability of the data is commented on.
The quantity of the data is commented on (both MV and RV).
The precision, accuracy and uncertainty in the data is commented on.
Outlier data or irregularities in the data are addressed.

The design and method of the investigation must be commented upon as well as the quality of the data. You should consider how large your errors or uncertainties are in the results. How confident are you in the results? Are you fairly conclusive, or are other interpretations/results possible?

Identify and discuss significant errors and limitations that could have affected the outcome of the experiment. Were there important variables that were not controlled? Were there flaws in the procedure which could affect the results? Are measurements and observations reliable? Was there a lack of replication?

Emphasis in this section should be on systematic errors, not the random errors that always occur in reading instruments and taking measurements. Identify the source of error and if possible, tie it to how it likely affected the results.

Scientific Context:

Scientific explanation for the results is described.
Comparison is made with published data and theoretical texts (with citations).

The results of the experiment should be explained using accurate and relevant biology. You should compare your results of your investigation with what would be expected; reference published data or theoretical texts. Compare the conclusions with published research or with the general scientific consensus among biologists about the research question. Do your conclusions conform to the consensus or are they unexpected? It is not necessary to find an exact same investigation with the exact same results, it is possible to compare findings with another investigation that is different but with results that either confirm or refute those of the student's investigation.

You must not only list the weaknesses but must also appreciate how significant the weaknesses are. Comments about the precision and accuracy of the measurements are relevant. When evaluating the procedure used, the specifically look at the processes, use of equipment and management of time.

Suggestions:

Where limitations are determined to be significant, specific improvements are proposed.
Improvements effectively and specifically address the limitations.
Improvements are given which are possible within the context of a school laboratory.
An addition research question is stated with clear IV and DV.
The research questions are an extension from the conclusion and evaluation.
A short explanation for the question is given to establish its importance and relevance.

Suggestions for improvements should be based on the weaknesses and limitations identified. Modifications to the experimental techniques and the data range can be addressed here. The modifications proposed should be realistic and clearly specified. Suggestions should focus on specific pieces of equipment or techniques used. It is not sufficient to state generally that more precise equipment should be used. Vague comments such as “I should have worked more carefully” are not acceptable.

Communication

This criterion assesses whether the investigation is presented and reported in a way that supports effective communication of the focus, process and outcomes.

Presentation:

A consistent linguistic style is maintained throughout the writing.
No spelling or grammar errors are present.
Written in past tense with good paragraph structure.
Legible font style and size are used.
Use of color in images or graphs is appropriate.
Citations given for all material taken from sources.

Structure:

There are clear headings for each section, with consistent formatting.
Graphs, tables, images sequentially titled (i.e. “Figure 1…”).
Graphs, tables and images included as close as possible to its first reference.
Tables and graphs do not break across pages.
Parenthetical in-text references/citations are given in consistent format.
A Works Cited List with consistent formatting is given at the end of the report.
Sources are written in alphabetical order by author’s last name.
Paper is 6-12 pages in length.

Focus:

All data, graphs and images are relevant to the RQ.
All citations are relevant to the RQ.
All analysis and discussion are relevant to the RQ.

Terminology & Conventions:

Tables are well organized, with specific and clear title, headings and units.
Table column headers are present and correct (IV in first column).
Graphs are well organized, with specific and clear titles, labeled axis (with unit) and appropriately scaled axis.
Images annotated with captions to add information of value to the investigation.
Avoid excessive use of jargon.
Non-standard technical terms are explained and used in the correct context.

Advice on the Individual investigation for Biology

The below websites are known to contain information ideas for research questions about methods that produce good results:

Science And Plants for Schools has lots of botanical investigations and ideas
Practical Biology brings together lots of different biology practicals for all ages of student
Senior Biology has a list of investigation ideas for extended essays that are also suitable for individual investigations
There are many examples also on the Pasco Biology pages.
Roger Frost’s DataLoggerama is a wealth of advice, too.