Research Methods Quiz: Data Types & Ethics

Data derived from analysis of existing personal information or other sources, including observations, experimental results, and information from human biospecimens.

Techniques used to gather information, such as surveys, interviews, observations, and sensor data.

Surveys are quick ways to collect large amounts of data, offering closed or open-ended questions.

Used to gather people's opinions and beliefs, with varying levels of structure, from highly structured to completely unstructured.

Often used for describing, predicting, improving processes, and data cleansing.

Useful for providing rich descriptions of a sample group and conducting in-depth participant studies.

The Australian Bureau of Statistics conducts a census every five years, collecting information on the population, housing, and other demographics. It informs policy decisions and urban planning.

AI systems generate both quantitative and qualitative data. Quantitative data includes things like sensor data, predictive analytics, and user interaction metrics. Qualitative data encompasses natural language processing, computer vision, and audio analysis.

Accepted moral standards guiding behavior, distinct from laws.

Essential for ethical data management, involves specifying data collection purpose, storage, access, and ownership.

Can lead to bias and unfair outcomes in decision-making processes.

Crucial for managing and communicating sensitive data, protecting individuals from harm.

Seeking permission from individuals before collecting their data, ensuring informed consent.

Using personal data beyond its intended purpose or sharing without permission.

Individuals or organizations have a duty to ensure accurate and complete data.

Individuals have the right to know how their data is used and to access, use, and benefit from it.

The accuracy of data refers to its correctness, ensuring that information is reliable.

Bias in data occurs when prejudice or an unreasoned judgment skews results, leading to unreliable information.

Data integrity refers to the quality of data, ensuring it's accurate, complete, and consistent for reliable analysis and decision-making.

Relevant data directly supports the objectives of analysis or decision-making, ensuring information is valuable.

Accessible data is available to the right people at the right time, ensuring its usability and effectiveness.

Clear data is well-defined and easy to understand, making it analyzable and preventing errors in interpretation.

Context provides background information about the data, including source, collection methods, and relevant metadata.

Data visualization techniques use visual representations like charts and graphs to effectively communicate data patterns and insights.

A type of data that can only be either true or false.

To remove identifying information from data, protecting individuals' privacy.

The process of making encrypted data readable by translating it back into plain text.

A backup method where only altered files since the last full backup are copied. You need the full backup to restore.

Distinct facts, ideas, or information that can include numbers, text, images, and more.

Different categories for variables and data, determining what kind of information they can hold and how they can be handled.

A prejudicial or biased judgment that may influence or distort data interpretation.

A generative pre-trained transformer (GPT). ChatGPT is an example of an AI chat interface using a large language model.

A key word, phrase, or symbol that represents a value that may change within a computer program.

Data that is collected at a specific time can be misleading if it was not relevant to the purpose.

The information you input into an AI program will influence the response.

An individual, group, or organization having a vested interest may lead to biased data.

The accuracy of the data is essential for generating a useful output. Errors can happen during data entry due to misreading or human error.

Data should be processed while it is current to maintain relevance for decision-making.

To ensure reliable findings, a large sample size generally leads to greater precision in data analysis.

This principle emphasizes the importance of protecting personal information from misuse, unauthorized access, modification, and disclosure. Organizations must implement reasonable measures to ensure data security and potentially destroy or de-identify personal information in specific situations.

The Privacy Act 1988 was amended in 2012 to implement the Australian Privacy Principles (APPs), which replaced the National Privacy Principles and Information Privacy Principles. These principles are applicable to Australian government agencies.

This principle outlines that personal information should be used solely for the primary purpose it was collected for. Secondary use requires consent from individuals, emphasizing data use transparency and respecting personal privacy.

This principle acknowledges the individual's right to interact with organizations using a chosen name or pseudonym related to a specific matter. This provides a layer of privacy and anonymity for individuals.

Username and password systems are used for authenticating user access to networks. This helps ensure only authorized individuals access sensitive information and resources.

Cloud computing companies, like Google Drive or Dropbox, offer remote storage, processing, and computing resources. They often store data in real-time in a remote database.

Protecting individual privacy is crucial and involves striking a balance between research interests and participant rights. Privacy laws aim to prevent inappropriate intrusions into individuals' lives.

Researchers have a responsibility to store data securely in a durable and accessible form, regardless of the format. This can involve physical security measures like backing up data and software-based controls for protection.