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IB Chemistry: Guide to a Successful Internal Assessment (IA)

Be sure to read pages 177-178 in “Baby Blue” for IA hints.

The Internal Assessment write-up should be between 6 and 12 pages long. Investigations exceeding this

length will be penalized in the communication criterion as lacking in conciseness.

The following outline is suggested for your IA, however, you may adjust in any way you choose.

You do not need a title page.

Diploma Candidate Number – Please put this at the top of each page of your investigation. Do not

place your name anywhere on this document.

Investigation Title – Identifies the topic of your investigation

Research Question – Clearly and concisely state your research question.

Introduction – Set the context for your investigation by discussing background information you

have found, through your research, regarding what is already known about the topic of your

investigation. You may describe alternate methods of gathering data that you discovered during

your research and explain why you have chosen the method you will use.

Prediction – Predict what you think the outcome of your investigation will be.

Method - This is your procedure. This must be written with great detail. See below.

Analysis – See below.

Evaluation – See below.

Citation – Bibliography- Use APA Format

The following information provides detailed guidance for your IA. The bold categories represent the criteria

that are being evaluated and the rubrics incorporated into this document are the grading rubrics used by IB

to evaluate your IA. Before submitting your document, you should read all of the included rubrics and

evaluate your IA against the rubric.

Criterion: Personal Engagement

This criterion assesses the extent to which the student engages with the exploration and makes it their

own. Personal engagement may be recognized in different attributes and skills. These could include

addressing personal interests or showing evidence of independent thinking, creativity or initiative in the

designing, implementation or presentation of the investigation.

If your exploration involves a method that can easily be found with an internet search, your work is too

simplistic. You might start with a method found through a search, but you must change the investigation to

study a variable that is unique and your interest in this variable is described with mild enthusiasm.

Criterion: 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

Program level. Where appropriate, this criterion also assesses awareness of safety, environmental, and

ethical considerations.

Methodology (Procedure):

In great detail, outline the steps of your procedure in chronological order.

Your methods of controlling variables should be very apparent in your procedural steps. If the control of certain

variables is not practically possible, some effort should be made to monitor or control the variable(s) in a limited way.

It will be important to discuss your inability to control a variable in your evaluation.

You must have 5 manipulations (variations) of your independent variable and you should run 3 trials for each

manipulation.

If you will be graphing your data, you must have at least 5 data points. If you are determining a specific value such as

density you should have an initial trial and then as many repeated trials as necessary until consistent results are

obtained (usually 5 or more trials).

Include safety precautions and clean-up/disposal procedures. Research all chemicals and indicate all safety and

disposal precautions. If there is danger of burning skin, indicate how to avoid this such as stating you should use

beaker tongs to remove a hot beaker. At a minimum, indicate that goggles and an apron must be worn.

Be very specific about the equipment used. Always name the piece of equipment to be used and indicate what size

should be used as well. For example: use a 100.0mL graduated cylinder to measure 75.0 mL of water.

Once you’re done, read through the lab and make sure you can visualize each step as you read it.

Do not use the first person “I”, “we”, etc. when writing the steps of your procedure.

Criterion: Analysis

In this section you will record all qualitative as well as quantitative data you collected during your experiment.

Qualitative data could include things such as a description of an odor if present, changes in color or solubility, gas

production, heat released or absorbed, and so on. While conducting the lab you should record all of your

observations, measurements, or any other data you collect. For any measurements, be sure to include

uncertainties and units. Data should be organized in tables whenever possible. The following recommendations

should be considered when creating data tables:

Recording Raw Data

Data is collected independently.

Data is primarily quantitative (numerical)

Data must include qualitative observations. (This may provide inspiration in the conclusion and

especially the evaluation later.)

Raw data should be recorded in suitable format(s) as described below.

Table organization

Must have a title

Column headings should include the name of the variable, its associated metric unit and

measurement uncertainty if it is the same for all measures in the column or row. The estimated

digit in recorded measurements should match the decimal position of the measuring tool’s

uncertainty

Column & row headers identical to graph axes labels (if table is source of graph data)

Uses specific terms (ie. NaCl instead of salt; volume instead of amount; length instead of size)

Do not split tables between pages

Cells contain only one value

Tables show grid lines

Table numbers

Uncertainty in column headings after units. Absolute uncertainties expressed to 1 sig fig.

Align decimals

All values in a column must end at the same decimal place

Mean/average contains one more digit than significant figures in values

Table units

Units in column headings, not in cells

No parentheses

Use SI units - according to IB

Variable that is measured or recorded is clearly stated (e.g. in the column heading in a table).

Units for every variable.

Uncertainty of measurements – based on significant digits –in the column headings.

The same level of precision (number of decimal places) is used for all the items of a variable.

You will also carry out all processing of your data necessary to draw a conclusion to your research question. The

work for calculations must be shown. Include one example for ALL calculations and ALL results in a clear and

concise manner using headings to describe your calculations. Brief explanations should be used to create a flow in

calculations. Be meticulous and label EVERYTHING! You must show the propagation of uncertainties here. Be sure

to calculate a percent uncertainty and an absolute uncertainty. Also you must calculate a % error if there is an

accepted value with which you may compare your results. If it is appropriate, display data in the form of a graph.

A second data table with a title should be created to show ALL calculated results.

Criterion: 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. The

evaluation criterion is allocated six marks and focuses on describing and justifying a conclusion, identifying

weaknesses in the procedure and suggesting improvements to the investigation.

Describing and Justifying Your Conclusion

A common error is for students to want to get their investigation 'over and done with' at this point and not

spend enough time and effort on this section. Although you are nearly at the finishing line, it is important to keep up

with your hard work in order to archive the best possible grade for your investigation.

To be awarded a high mark in this section, you should aim to write a conclusion that is fully justified (explains

how the data in the analysis section supports your conclusion). Trends in the data that you identified in the

analysis section should now be explained using your scientific knowledge. This should involve referring back to

your research question and background research in the exploration section of the investigation. Does your

data answer the research question? You must draw a conclusion that clearly relates to your research question.

Indicate if your conclusion supports your original thinking on the topic. If it does not, a consideration of why it

does not will lead into an evaluation of the limitations of the method and suggestions as to how the method

and approach could be adjusted to generate data that could help draw a firmer conclusion. For example, data

collected might have such great variability that no reasonable conclusion can be drawn.

You must justify your conclusion by comparing your result to an accepted scientific context or value. If a

percentage error was calculated, you should comment on that percentage error. Discuss the precision and

accuracy of your measurements in terms of their limitations on your data and the role they played as a source

of error. Commenting on your percent error and comparing your percent error to your percent uncertainty is

required and will help support your discussion of precision and accuracy. Compare your percent error to your

percent uncertainty (random error). Percent uncertainty indicates how far your experimental values are

allowed to be from the accepted value due to the limitations of your measuring tools. If your percent error is

greater than your percent uncertainty, this indicates that there are flaws in your methods (systematic error)

that are causing your experimental density to be further away from the accepted value.

?Further justification of your conclusion is required through the discussion of whether systematic errors or

random errors were encountered. The direction of systematic errors and their impact on your conclusion must

be discussed. For example, let’s say you are finding a density. If you have a graduated cylinder with a glass bubble

occupying a portion of the measured volume, this would cause the measure of volume to always be greater than

it should be. You would need to also discuss the impact this systematically higher volume has on density. Since

the volume measure is higher than it should be, when mass is divided by volume to find density, this would result

in a density that is lower than it actually is.

Systematic errors arise from a problem in the experimental set-up that results in the measured values always

deviating from the accepted value in the same direction-that is, always higher or always lower. An example

would be a miscalibrated thermometer that always measures temperature as 0.30 degrees higher that the true

temperature. Another example would be a poorly insulated device that allows heat that should be absorbed

by water in a container to escape to the surroundings. The temperature of the water would always be

measured as lower than it should be due to the loss of that heat.

Random errors arise from the imprecision of measurements due to the limitations of measuring tools. These

errors can lead to readings being above or below the accepted value. Random errors can be reduced with the

use of more precise measuring equipment or their effect can be minimized through repeating measurements

so that the random errors cancel out.

Identifying Weaknesses and Suggesting Improvements

In this section, strengths and weaknesses or limitations in the procedure should be identified and explained. In

addition, improvements to your investigation should be suggested. If you wish to score highly in this section, a

simple list of possible procedural improvements will not suffice. Reflect upon how you could adapt the method

to deal with significant factors such as range, sample size, or alternative reaction system so that your conclusion

is more valid. This should include a discussion of the uncertainties that you calculated in the analysis section

and how they might have affected the results of your investigation. In addition, experimental errors should be

classified as random or systematic. The direction of error may be determined by comparing the % error with %

uncertainty (an example is shown below).

When suggesting improvements to your procedure, you should refer back to the random or systematic errors

identified in the conclusion and explain how they can be minimized or prevented. The precision of the

apparatus used in your investigation should also be considered. For example, a volumetric pipette has a higher

precision than a graduated cylinder and can help reduce random errors. Make suggestions as to how the effects

of random uncertainties may be reduced and systematic errors eliminated. You should be aware that random

errors (not systematic errors), are reduced by taking repeated measurements. Suggested improvements to

your investigation should be related to the weaknesses or limitations in the procedure and the types of errors

identified. You should avoid suggesting improvements that are superficial or unrealistic or non-feasible in the

environment of a school context or course. Errors due to careless manipulation of apparatus or events of which

there is no evidence should not be included. Don’t just say use better measuring tools. If a better tool should

be used, suggest a specific tool and give justification. Don’t just say find a different method, research and with

detail suggest an improvement to the current method. If more trials would improve the lab, indicate how many

more and why that would be an improvement.

Finally, possible extensions to your investigation should be discussed with reference to your research question

and methodology. Here, you should discuss realistic extensions to your investigation that would further help

answer the research question. For extension, discuss a new variable or factor that could be investigated that is

tied to the topic of your current investigation.

Example Evaluation:

Following on from the example in the analysis section where the enthalpy change of neutralization was

calculated, we will now calculate the percentage error and classify the types of errors in the

investigation.

The ΔH for the reaction was calculated as - 44 ± 5 kJ mol-1.

The literature value for the enthalpy change of neutralization is - 57 kJ mol-1. The percentage error can be

calculated using the following equation:

Percentage error = (experimental value – theoretical value) ÷ theoretical value × 100

Percentage error = (-44 - -57) ÷ -57 × 100 = - 23% (the negative sign means that the experimental value

was lower than the literature value).

Comparing this with the percentage uncertainty, which was 12%, it can be seen that the percentage error

is greater, meaning that the major types of error in the investigation were systematic errors. These will be

discussed in more detail in the evaluation section.

In the conclusion, the main types of errors in the investigation were identified as systematic errors. These

are caused by heat being lost to the surroundings as the reaction took place. As soon as the

reactants were mixed, the temperature of the mixture started to increase, which was expected as

neutralization is an exothermic process. However, some of the heat was lost to the surroundings as the

polystyrene cup is not a perfect insulator. This would cause the increase in temperature to be lower,

which would result in the calculated ΔH value for the reaction being less than the literature value. An

improvement to the investigation would be to use a material for the cup that is a more effective insulator

than polystyrene or perhaps using two cups together to reduce heat loss. In addition, a lid could also be

added to the cups to reduce the heat loss from the top. There were also assumptions made when

calculating the ΔH, mainly that the density and specific heat capacity of the solution were the same as that

of water. Looking at the balanced equation for the reaction, the products are salt (NaCl) and water, not

pure water. Therefore, the specific heat capacity and density of salt water could be used to get a more

accurate result.

This investigation could be extended by conducting the experiment at varying ambient temperatures.

Does the initial temperature of the surroundings have an impact on the change in enthalpy for the

reaction? The reaction could be carried out by heating and cooling the room to different temperatures

prior to the start of the reaction.

Criterion: Communication

This criterion assesses whether the investigation is presented and reported in a way that supports effective

communication of the focus, process and outcomes.