Data Collection Skills

What is the first step in conducting an investigation?

• Define the problem or question

• Skip the planning phase

• Jump to conclusions

• Gather evidence immediately

Why is taking a sample important in data collection?

• Sampling is unnecessary and adds complexity to data collection.

• Data collection can be done effectively without considering a sample.

• Sampling helps in making data collection more efficient and cost-effective while still maintaining accuracy.

• Taking a sample leads to biased results and inaccurate conclusions.

Explain the difference between a random sample and a stratified sample.

• Random sample: individuals chosen based on their favorite color. Stratified sample: population divided into age groups, then random samples taken from each group.

• Random sample: individuals chosen purely by chance. Stratified sample: population divided into subgroups based on characteristics, then random samples taken from each subgroup.

• Random sample: individuals chosen based on their birth month. Stratified sample: population divided into educational levels, then random samples taken from each level.

• Random sample: individuals chosen based on their height. Stratified sample: population divided into income brackets, then random samples taken from each bracket.

What are some common methods used to collect data?

• Focus groups, Data mining, Sampling, Correlation analysis

• Online polls, Telephone calls, Literature review, Simulation

• Surveys, Interviews, Observations, Experiments, Existing data analysis

• Surveillance, Questionnaires, Fieldwork, Case studies

How can bias be introduced when taking a sample?

• Bias can be introduced when taking a sample by not ensuring the sample is representative of the entire population, using a non-random sampling method, having a small sample size, or having a biased sample selection process.

• Using a random sampling method

• Having a large sample size

• Ensuring the sample is representative of the entire population

What is the purpose of data validation in the data collection process?

• To introduce errors intentionally

• To increase data accuracy and reliability

• The purpose of data validation in the data collection process is to ensure data accuracy and reliability.

• To slow down the data collection process

Describe the process of data cleaning in data collection.

• Data cleaning is only necessary for small datasets.

• Data cleaning involves encrypting all data before analysis.

• The process of data cleaning in data collection involves identifying and correcting errors or inconsistencies in the data to improve its quality. This includes handling missing values, removing duplicates, standardizing formats, and resolving inconsistencies.

• Data cleaning is a one-time process and does not need to be repeated.

What is the significance of data accuracy in data collection?

• Data accuracy leads to biased decision-making

• Data accuracy is only important for small datasets

• Data accuracy ensures reliable, valid, and error-free information for better decision-making and analysis.

• Data accuracy has no impact on data collection

How can outliers affect the results of a data collection process?

• Outliers are always removed from the dataset automatically

• Outliers can distort the overall picture of the data and lead to incorrect analysis.

• Outliers always improve the accuracy of results

• Outliers have no impact on data analysis

Explain the concept of data integrity in the context of data collection.

• Data integrity ensures that data is always incorrect and unreliable.

• Data integrity in data collection allows for errors and corruption in the data.

• Data integrity means data consistency is not important in data collection.

• Data integrity in data collection ensures that the data is accurate, consistent, and reliable, without any errors or corruption.