Collecting Data

Appreciate the importance of data collection in analysing a situation or problem.
Distinguish between primary data and secondary data.
Identify primary data as data that you have gathered yourself, specifically for your research.
Recognise secondary data as data that was collected by someone else or for some other purpose, but can be utilised for current analysis.

Understand the various methods to collect data, including surveys, questionnaires, interviews, experiments and observations.
Recognise that the choice of data collection method should reflect the nature of the research problem, goals and resources available.
Acknowledge that for surveys, consideration must be given to the questions used, and the scale and stratification of the sample.
Identify stratified sampling as a method where the population is divided into multiple subgroups or ‘strata’, then random samples are taken from each stratum proportional to its size.

Appreciate the importance of fair data collection in producing unbiased results.
Understand how bias can be introduced in collecting data including through leading questions and non-random samples.
Recognise that a good questionnaire should contain clear, unbiased and relevant questions which allows a wide range of responses.
Acknowledge the importance of privacy - always ensure informed consent is obtained when collecting data and confidentiality should be maintained.

Understand the implications of errors in data collection which may lead to misleading results.
Identify errors that may occur during the data collection process, such as response bias, non-response, inaccurate responses etc.
Recognise response bias as a tendency of a person to answer questions on a survey untruthfully or misleadingly.
Understand non-systematic errors which may occur due to fluctuations and inconsistencies in measuring instruments or experimental conditions.
Be aware of misuses of data collection, e.g. intentionally misleading or distorting data to suit a specific viewpoint.

Recognise the importance of evaluating data based on sample size, adequacy and relevance.
Understand that evaluation involves critically analysing the obtained data to determine its validity, reliability and accuracy.
Appreciate that interpreting data involves converting raw data into meaningful information.
Understand that interpretation will be based on the statistical analysis results and involve turning abstract data into concrete and comprehensible information.