Criterion C: Method
This criterion assesses the extent to which the student has developed an appropriate and repeatable method to collect data that is relevant to the research question. The data could be primary or secondary, qualitative or quantitative
ESS investigations use a broad variety of techniques in collecting data, including but not limited to experimental work, fieldwork, secondary data mining, simulations, online calculators, mapping, surveys, questionnaires, interviews, observations, note-taking, audio and visual recordings, and collecting censuses.
The type of data collected should be determined by the research question posed and the environmental issue addressed. The methods and techniques of data collection and data processing need to be explored in relation to the type of data—quantitative and/or qualitative.
The individual investigation may consist of both appropriate quantitative and qualitative data.
Many ESS studies will involve ethical or safety considerations. You must address this, where necessary, paying attention to the IB animal experimentation policy (which includes guidelines on working with human subjects).
ESS investigations use a broad variety of techniques in collecting data, including but not limited to experimental work, fieldwork, secondary data mining, simulations, online calculators, mapping, surveys, questionnaires, interviews, observations, note-taking, audio and visual recordings, and collecting censuses.
The type of data collected should be determined by the research question posed and the environmental issue addressed. The methods and techniques of data collection and data processing need to be explored in relation to the type of data—quantitative and/or qualitative.
The individual investigation may consist of both appropriate quantitative and qualitative data.
Many ESS studies will involve ethical or safety considerations. You must address this, where necessary, paying attention to the IB animal experimentation policy (which includes guidelines on working with human subjects).
Designing Data Collection for an Individual Investigation
- Data Volume: Align data collection with the 10-hour requirement. ESS investigations vary, so no standard defines "sufficient data"—it depends on the investigation type and method.
- Methodology and Repeats: Choose an appropriate methodology, considering the nature of the investigation and time limits. Repeat measurements with a rationale to ensure data supports answering the research question.
- Qualitative Data: Use qualitative data as needed to add depth or when quantitative data isn’t feasible. This can show normal variations in observations.
- Example Variability:
- Germination experiments generally yield quick data; decomposition studies take longer.
- Secondary data from databases (e.g., air quality) is faster to obtain than direct fieldwork (e.g., surveys or ozone measurements).
- Minimum Data Check: Cross-check data needs for processing (e.g., correlation needs 30+ samples). Ensure data volume meets requirements for chosen statistical methods.
- Pilot Studies: Test data collection methods with a small trial to identify and address issues early:
- Seed Viability: Test seeds before a full germination experiment.
- Survey Testing: Trial questions with a small group to identify any misunderstandings.
- Sample Location: Test strategies for finding sample sites or probes.
- Adaptability: Be prepared to adjust your method if issues arise (e.g., expanding range, adding intervals). Include any adjustments in the report as part of your investigation process.
- Data Processing Requirements:
- Standard deviation: at least 5 samples.
- T-test: minimum of 10 replicates.
- Standard error of the mean: ideally 30 or more replicates, but variations can be shown with fewer replicates using ranges.
- Hybrid Data Collection: Combine primary and secondary data (e.g., a germination experiment plus database growth data) to streamline data gathering.
Guidelines for Designing Sufficient Data Collection
- Workload and Time: Data collection should align with the 10-hour investigation requirement, understanding that some methods generate data faster than others.
- Method Choice and Repeats: Choose a method appropriate for your topic and time available. Include enough repeats to draw reliable conclusions that address your research question.
- Use of Qualitative Data: Collect qualitative data when it adds depth, provides context, or highlights normal variation, especially when quantitative data is limited.
- Example Variability:
- Germination studies yield data faster than decomposition studies.
- Secondary data (e.g., air quality databases) is quicker than fieldwork.
- Data Requirements Check: Ensure the minimum data needed for analysis is met. For example, correlation studies typically require 30+ samples.
- Continuous Assessment: Process data as you collect it to spot issues early. This helps in adjusting range, intervals, or frequency if necessary.
- Pilot Studies: Conduct small trials to test and refine your method:
- Seed viability: Test seeds before a germination experiment.
- Survey pilot: Trial survey questions with a small group.
- Sample testing: Try probes or sampling strategies to confirm effectiveness.
- Reporting Challenges: Include any issues and adaptations in your report. If data is inconclusive, show adaptability in adjusting your approach.
- Data Processing Needs:
- Standard deviation: at least 5 samples.
- T-test: 10 or more replicates.
- Standard error of the mean: ideally 30+ replicates, though fewer can show variation as a range.
- Hybrid Data Collection: Combine primary data (e.g., experiments) with secondary data (e.g., databases) for a well-rounded dataset.
Guidelines for Defining Variables
- Purpose of Variables: While not always required, explicitly stating variables can clarify your investigation, especially in your research question and methodology.
- Types of Variables:
- Independent Variable: The factor you change or observe for different values. This variable is hypothesized to affect the dependent variable.
- Example: In a study on the effects of water pH on fish health, the independent variable is the pH level of the water.
- Dependent Variable: The outcome you measure or observe in response to changes in the independent variable.
- Example: In the water pH study, the dependent variable is the health of the fish, which could be measured by growth rate, activity level, or survival rate.
- Control Variables: Factors other than the independent variable that could influence the dependent variable. These should be kept constant to avoid confounding effects.
- Example: In the water pH study, control variables might include water temperature, fish species, tank size, and feeding schedule.
- Independent Variable: The factor you change or observe for different values. This variable is hypothesized to affect the dependent variable.
Guidelines for Choosing Primary vs. Secondary Data
- Primary Data: Collected directly by you through methods like experiments, surveys, or observations. This data offers firsthand insights and can be tailored specifically to your research question.
- Secondary Data: Sourced from existing materials, such as scientific studies, databases, or government reports. Useful when primary data is challenging to gather. Ensure proper citations for all secondary data sources.
Sampling Methods Overview
- Quadrat Sampling: Place quadrats (small square plots) randomly or systematically within the study area and record species found in each quadrat.
- Random Sampling: Collect data from randomly selected locations within the study area, ensuring each location has an equal chance of selection (e.g., using a random number generator).
- Systematic Sampling: Select sample points at regular intervals across the study area for even coverage.
- Transect Sampling: Walk along a set transect line, recording species within a certain distance on either side of the line.
- Stratified Sampling: Divide the study area into distinct groups or "strata" (e.g., by land use type) and sample randomly within each group.
Data Collection Ideas
- Values and Attitude Surveys and Questionnaires
- Interviews
- Fieldwork
- Ecosystem Modelling
- Models (physical, software, mathematical)
- Field Manipulation Experiments
- Lab Work
- EIAs
- Secondary Data (must use unique data)
- Qualitative and Quantitate Data
- A Combination of any of the above
Understanding Quantitative and Qualitative Data
- Quantitative Data
- This type of data is numerical and gathered through measurements. It can be analyzed using statistics and presented in tables, graphs, or maps.
- Qualitative Data
- Collected through observations and judgments, qualitative data is non-numerical. It might be processed through coding, quantified, or presented in images or text. Keep the word limit in mind if presenting as text.
- Combining Data Types
- Many students convert qualitative data into quantitative form to strengthen their analysis. For instance, qualitative observations, like the weather during data collection, can help provide context for the quantitative data.
- Data Collection Methods for Qualitative Data
- There are several ways to gather qualitative data:
- Observation: Can be direct or participant-based.
- Interviews: Structured, semi-structured, or open-ended.
- Questionnaires/Surveys
- Visual Media: Photography, film, and other multimedia resources.
- There are several ways to gather qualitative data:
Guidelines for Using Surveys and Questionnaires
Survey research involves the collection of information from a sample of individuals through their responses to questions.. It is an efficient method for systematically collecting data from a broad spectrum of individuals and educational settings
Survey research involves the collection of information from a sample of individuals through their responses to questions.. It is an efficient method for systematically collecting data from a broad spectrum of individuals and educational settings
- Data Volume: Aim for enough responses to achieve statistical validity. A 20–30% response rate is typical, so plan accordingly to meet minimum data needs.
- Question Design: Structure questions to align with your data analysis method. Start with general demographic questions, followed by questions related to the independent variable and potential confounding variables.
- Sample Size: For strong statistical outcomes, collect at least 30 responses per category of the independent variable (e.g., age cohorts).
- Statistical Processing:
- Descriptive: Use frequency, percentages, and measures of central tendency and dispersion.
- Inferential: Consider t-tests, chi-squared tests, ANOVA, correlation, and regression for deeper analysis.
Guidelines for Using Databases and Simulations
It is possible to use databases as the source for IA investigations, though this would need to be carefully managed. A challenge with using informatics/databases in IA work will be generating quality questions that can be explored effectively. However, this must be a unique analysis.
It is possible to use databases as the source for IA investigations, though this would need to be carefully managed. A challenge with using informatics/databases in IA work will be generating quality questions that can be explored effectively. However, this must be a unique analysis.
- Methodology and Purpose: Explain why you chose a specific database or simulation and how it aligns with your research question. Clearly outline the method used for data collection.
- Source Identification: Identify data sources, evaluate their reliability, and confirm their relevance to your research question.
- Documentation: Include screenshots of the database or simulation with URLs or program names to show data collection and manipulation steps.
- Multiple Sources: Use more than one database if possible for comprehensive data. If limited to one, explain why.
- Data Filtering and Selection: Describe how you controlled, extracted, or filtered data and justify your selection criteria. Screenshots can help illustrate these steps.
- Simulation Consistency: Avoid simulations that produce identical values each time. Use additional sources or simulations to gather varied data.
- Computational Analysis: Use tools that provide accurate, quantifiable data. For example, convert visual elements (like color saturation) into numerical values where possible.
- Field Data Databases: Databases with field data are acceptable; focus your method on how you used the database rather than on the original data collection process.
Data Visualization Techniques for Qualitative Data:
- Charts: Pie, bar, histogram, Gantt, area, scatter, bullet
- Maps: Heat map, choropleth map
- Graphs: Box and whisker plot, waterfall chart
- Diagrams: Network diagram, correlation matrix
- Other: Pictogram, timeline, highlight table, word cloud
Putting It All Together
Hypothesis:
Although not required by the IB Organization, for many investigations it is appropriate for you 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.”
No all investigations will have a hypothesis. However, they help you focus your ideas. Be sure that your hypothesis is related directly to your research question
Also justify your 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.
Methodology:
Make a list of materials needed.
State or discuss the method (procedure) that was used in the experiment.
“Control of variables” refers to the manipulation of the independent variable and the attempt to maintain the controlled variables at a constant value.
Justification of Sampling:
Safety, Ethics and Environmental Issues:
Risk Assessment Forms
Hypothesis:
Although not required by the IB Organization, for many investigations it is appropriate for you 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.”
No all investigations will have a hypothesis. However, they help you focus your ideas. Be sure that your hypothesis is related directly to your research question
Also justify your 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.
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.
- 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.”)
- 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)?
- 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.
“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.
Justification of Sampling:
- Justify your method
- Indicate why you choose to collect data the way you did
- Verify that the data is random and unbiased (specifically in survey data)
- Indicate how you made sure to collect sufficient and relevant data
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.
- Many ESS studies will involve ethical or safety considerations. You must address this, where necessary, paying attention to the IB animal experimentation policy (which includes guidelines on working with human subjects), and should write about their strategies for upholding safety and/or ethical standards in the report.
Risk Assessment Forms
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In line with the poster Ethical practice in the Diploma Programme, the following guidelines exist for all practical work undertaken as part of the Diploma Programme.
Find out more in Animal Experimentation
- No experiments involving other people will be undertaken without their written consent and their understanding of the nature of the experiment.
- No experiment will be undertaken that inflicts pain on, or causes distress to humans or live animals.
- No experiment or fieldwork will be undertaken that damages the environment.
Find out more in Animal Experimentation
Guidelines for the use of animals in IB World Schools from IBO
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.
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.
You may need to estimate the degree of precision sometimes especially with stop watches. Digital stop watches are said to be accurate to 0.01s but human reaction time is only +/-0.1s.
For electronic probes you may have to go to the manufacturer's specifications (on their web site or in the instructions manual).
Uncertainty
All measurements have uncertainties and are only as accurate as the tool being used. For general purposes, the accuracy of a measurement device is one half of the smallest measurement possible with the device. To determine uncertainty:
So, for example, the rulers in class measure to the millimeter (0.10 cm). Therefore, the ruler’s measurement uncertainty is +/- 0.05 cm.
The numerical value of a ± uncertainty value tells you the range of the result. For example a result reported as 1.23 ± 0.05 means that the experimenter has some degree of confidence that the true value falls in between 1.18 and 1.28.
Examples:
Experimental uncertainties should be rounded UP to one significant figure. Uncertainties are almost always quoted to one significant digit and we round up because it’s better to suggest higher uncertainty than to imply there is less uncertainty.
The measurement should have the same number of digits (decimal places) as the uncertainty. It would be confusing to suggest that you knew the digit in the hundredths (or thousandths) place when you admit that you unsure of the tenths place.
Just as for units, in a column of data students can show the uncertainty in the column heading and don’t have to keep re-writing if for every measurement in the table.
Units
The system of units used in science is called the International System of Units (SI units). In the table below are some of the more common SI units
For electronic probes you may have to go to the manufacturer's specifications (on their web site or in the instructions manual).
Uncertainty
All measurements have uncertainties and are only as accurate as the tool being used. For general purposes, the accuracy of a measurement device is one half of the smallest measurement possible with the device. To determine uncertainty:
- Find the smallest increment of measurement on your measurement device
- Divide it by two
- Round to the first non-zero number
So, for example, the rulers in class measure to the millimeter (0.10 cm). Therefore, the ruler’s measurement uncertainty is +/- 0.05 cm.
The numerical value of a ± uncertainty value tells you the range of the result. For example a result reported as 1.23 ± 0.05 means that the experimenter has some degree of confidence that the true value falls in between 1.18 and 1.28.
Examples:
- Mass of a penny on a centigram balance: 3.12g (+/- 0.05g)
- Temperature using a typical lab thermometer: 25.5°C (+/- 0.5°C)
Experimental uncertainties should be rounded UP to one significant figure. Uncertainties are almost always quoted to one significant digit and we round up because it’s better to suggest higher uncertainty than to imply there is less uncertainty.
- Wrong: ± 12.5 mL
- Correct: ± 20 mL
The measurement should have the same number of digits (decimal places) as the uncertainty. It would be confusing to suggest that you knew the digit in the hundredths (or thousandths) place when you admit that you unsure of the tenths place.
- Wrong: 1.237 s ± 0.1 s
- Correct: 1.2 s ± 0.1 s
Just as for units, in a column of data students can show the uncertainty in the column heading and don’t have to keep re-writing if for every measurement in the table.
Units
The system of units used in science is called the International System of Units (SI units). In the table below are some of the more common SI units
The following example shows different ways to express the same unit.
- Oxygen consumption (millilters per gram per hour)
- Oxygen consumption (ml/g/h)
- Oxygen consumption (ml g-1 h-1)
Planning Rubric
The fourth in a series of videos from Science Sauce focusing on where to get your Data
The sixth in a series of videos from Science Sauce focusing on your Planning
