Recognise the opportunities, constraints and implications for subsequent mathematical analysis involved in obtaining appropriate data through careful design of primary data collection techniques or through the use of reference sources for secondary data to ensure unbiased research.
3.2.1 B1a
Basic foundation content | Additional foundation content | Higher content only |
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Know and apply terms used to describe different types of data that can be collected for statistical analysis: - raw data
- categorical
- ordinal
- discrete
- continuous
- ungrouped
- grouped.
| In addition to the terms in the basic foundation content, know and apply the following terms used to describe different types of data that can be collected for statistical analysis: - quantitative
- qualitative
- bivariate.
| In addition to the terms in the foundation content, know and apply the following term used to describe different types of data that can be collected for statistical analysis: multivariate. |
3.2.2 B1b
Basic foundation content | Additional foundation content | Higher content only |
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Know the advantages and implications of merging data into more general categories, and of grouping numerical data into class intervals. | | |
Notes: students may be required to make decisions about appropriate class intervals given a data set.
3.2.3 B1c
Basic foundation content | Additional foundation content | Higher content only |
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| | Know and apply the terms explanatory or independent variables and response or dependent variables. | |
3.2.4 B2a
Basic foundation content | Additional foundation content | Higher content only |
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Know the difference between primary and secondary data. | | |
Notes: students should be aware of limitations or implications of using secondary data.
3.2.5 B2b
Basic foundation content | Additional foundation content | Higher content only |
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Know that data can be collected from different sources:- experimental (laboratory, field and natural)
- simulation
- questionnaires
- observation
- reference
- census
- population
- sampling.
In addition,sources of secondary data should be acknowledged. | | |
Notes: students should know the specific or relative benefits and limitations of each of the collection methods and be able to discuss these in context.
3.2.6 B2c
Basic foundation content | Additional foundation content | Higher content only |
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| | Know the importance of reliability and validity with regards to collected data. | |
Notes: students should know that reliability is the extent to which something gives results that are consistent. Students should know that validity is the extent to which something measures what it is supposed to measure.
3.2.7 B2d
Basic foundation content | Additional foundation content | Higher content only |
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| | Determine factors that may lead to bias, including issues of sensitivity of the content matter, and know how to minimise data distortion. | In addition to the foundation content, students should know how to minimise data distortion including level of control. |
3.2.8 B3a
Basic foundation content | Additional foundation content | Higher content only |
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Know the difference between population, sample frame and sample. | | |
Notes: students should know that the population might not all be available for sampling.
3.2.9 B3b
Basic foundation content | Additional foundation content | Higher content only |
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Know that 'population' can have different meanings within a stated context. | | |
3.2.10 B3c
Basic foundation content | Additional foundation content | Higher content only |
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Know reasons for employing judgement or opportunity (convenience) sampling, and the associated risks of bias when these techniques are used. | | |
3.2.11 B3d
Basic foundation content | Additional foundation content | Higher content only |
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Use appropriate sampling techniques in the context of the problem to avoid bias: | Use appropriate sampling techniques in the context of the problem to avoid bias: quota. | |
3.2.12 B3e
Basic foundation content | Additional foundation content | Higher content only |
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Know the key features of a simple random sample. | Demonstrate understanding of how different techniques, both physical and electronic, are used to select random members from a population including, but not limited to:- dice
- cards
- random number lists
- calculator functions.
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Notes: students will not be expected to derive samples using these techniques in the exam.
3.2.13 B3f
Basic foundation content | Additional foundation content | Higher content only |
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| | Use stratification and know when this is appropriate before sampling takes place. | |
Notes: students should know that stratification is not a method of sampling but a method which may be used before sampling takes place.
3.2.14 B4
Basic foundation content | Additional foundation content | Higher content only |
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Know the key features to be considered when planning data collection:- leading questions
- avoiding biased sources
- time factors
- open/closed questions
- different types of interview technique.
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Notes: both in theory and in specific contexts.
3.2.15 B5a
Basic foundation content | Additional foundation content | Higher content only |
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Know and demonstrate understanding of techniques used to deal with problems that may arise with collected data for example:- missing data
- incorrect formats
- non-responses
- incomplete responses etc.
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Notes: both in theory and in context.
3.2.16 B5b
Basic foundation content | Additional foundation content | Higher content only |
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Know why data may need to be 'cleaned' before further processing, including issues that arise on spreadsheets and apply techniques to clean data in context. | | |
Notes: students may be asked to use techniques to identify issues in raw or summarised data. For example, identifying a wrongly recorded value by considering row or column totals or considering whether values are consistent with other values within the data. Awareness of the need to remove extraneous notation or symbols may be required.
3.2.17 B5c
Basic foundation content | Additional foundation content | Higher content only |
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| | Know the importance of identifying and controlling extraneous variables. | The use of control groups. |
