Data Collection (DP IB Applications & Interpretation (AI)): Revision Note

Keep the number of variables you investigate to a minimum

• Too many variables at once can be overwhelming

• It can be time-consuming to process unnecessary data

You should choose variables that are linked to what you are investigating

• If you are investigating the ability of adults to solve puzzles you might use the time it takes them as a variable

• Consider which variables are likely to have an effect on what you are investigating

  • An adult’s reading speed will affect their time to solve a puzzle

  • An adult’s height is unlikely to affect their time to solve a puzzle

A survey is a method of collecting data

Consider whether the survey needs to be in-person

• A person might be less likely to answer questions truthfully in person

• You can quickly survey more people remotely or electronically

  • Such as postal surveys, phone surveys, internet surveys

Consider whether the interviewer could unintentionally influence participants’ responses

• If a headteacher is asking students whether they enjoy school then they are more likely to say yes as they think that is what the headteacher wants to hear

• This will introduce bias

A questionnaire is a list of questions

The questions should be unbiased

• Questions should not be leading

  • For example: “You enjoy school, don’t you?”

• If options are given for the participant to choose from then they should cover all possible responses

The questions should not be personal

• This means you should not ask for unnecessary personal information

  • Such as date of birth, address, etc

• The questions should not reflect your personal opinions

  • For example: “Do you enjoy watching the boring news on TV?”

• People can find it difficult to rate personal feeling/qualities

  • For example: “How smart do you think you are?”

Questions can be structured or unstructured

• Structured questions usually ask the participants to choose from options, give a rating or rank options

  • These can be quick to analyse

  • The answer choices should be consistent where appropriate

• Unstructured questions let the participants to express their views in their own words

  • These tend to be more open-ended questions

  • These can take longer to analyse but can give more in-depth views

Questions should be precise and unambiguous

• They should be phrased in a way in which the participants understand exactly what you mean

• For example: “Do you study French or Spanish at school” is not precise

  • Some people might reply with “Yes” or “No”

  • Some people might reply with “French” or “Spanish”

Reliability measures how consistent a process is at measuring a variable

• A process is reliable if you would get the same results by repeating the process with the same sample using the same conditions

Validity measures how accurate a process is at measuring a variable

• A process is valid if it is accurately measuring the variable you want it to measure

If your process is found not to be reliable or valid then:

• Adjust the data collection process

• Change the sampling technique

• Use a larger sample

Test-retest

• This is where you use a data collection process with a sample and then repeat the same process with the same sample at a later time

  • The results should show positive correlation if the process is reliable

  • The results might not perfectly match due to external factors during the gap between the data collection

  • Once the sample has been through the process once they will be familiar so this could lead to different results from the second process

Parallel forms

• This is where you give the same sample a second set of questions (or second set of experiments) which are similar to the first set

  • The results should show positive correlation if the process is reliable

  • It can be difficult to make the two processes similar to each other

Content-related validity checks

• This is where you check how well the process measures all aspects of the variable

  • If the process is valid then it should cover all aspects of the variable

  • These checks require knowledge of the variable so experts are often used

  • An example of a process that is valid:
    A teacher wants to assess how well students understand calculus so they set questions covering differentiation, integration and applications

  • An example of a process that is not valid:
    A restaurant manager wants to assess how good a chef is at cooking steaks so asks the chef to make 10 medium steaks

Criterion-related validity checks

• This is where you check how well one variable predicts the outcome for another variable (called the criterion variable)

  • If the process is valid then the variable should be a good predictor

  • An example of a process that is valid:
    Results from a mock exam being used to predict the results in the actual exam

  • An example of a process that is not valid:
    Results from measuring the heights of meerkats being used to predict the heights of squirrels