Podcast
Questions and Answers
What type of data is described as non-numerical and describes characteristics or attributes?
What type of data is described as non-numerical and describes characteristics or attributes?
What is the term for data collected directly from the source?
What is the term for data collected directly from the source?
What is the term for data that is thorough and includes all necessary information?
What is the term for data that is thorough and includes all necessary information?
What type of data has a natural order or ranking?
What type of data has a natural order or ranking?
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What is the process of identifying and correcting errors in data?
What is the process of identifying and correcting errors in data?
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What is a central repository that stores data from various sources in a single location?
What is a central repository that stores data from various sources in a single location?
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What is the process of discovering patterns and relationships in large datasets?
What is the process of discovering patterns and relationships in large datasets?
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What is the process of accessing and extracting specific data from a database or storage system?
What is the process of accessing and extracting specific data from a database or storage system?
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Study Notes
Types of Data
- Qualitative Data: Non-numerical data that describes characteristics or attributes, such as text, images, and videos.
- Quantitative Data: Numerical data that can be measured and analyzed, such as numbers, percentages, and statistics.
Data Sources
- Primary Data: Collected directly from the source, such as through surveys, experiments, or observations.
- Secondary Data: Collected from existing sources, such as books, articles, or online databases.
Data Characteristics
- Accuracy: The degree to which data is correct and free from errors.
- Completeness: The degree to which data is thorough and includes all necessary information.
- Consistency: The degree to which data is uniform and follows a standard format.
- Relevance: The degree to which data is applicable and useful for a particular purpose.
Data Levels of Measurement
- Nominal Data: Categorical data with no inherent order or scale, such as names or categories.
- Ordinal Data: Categorical data with a natural order or ranking, such as grades or rankings.
- Interval Data: Numerical data with a fixed unit of measurement, such as temperatures or dates.
- Ratio Data: Numerical data with a fixed zero point and unit of measurement, such as heights or weights.
Data Processing
- Data Cleaning: The process of identifying and correcting errors, inconsistencies, and missing values.
- Data Transformation: The process of converting data into a more suitable format for analysis.
- Data Reduction: The process of simplifying data to reduce its complexity and improve analysis.
Data Storage and Retrieval
- Data Warehouse: A central repository that stores data from various sources in a single location.
- Data Mining: The process of discovering patterns and relationships in large datasets.
- Data Retrieval: The process of accessing and extracting specific data from a database or storage system.
Data Classification
- Qualitative Data: Describes characteristics or attributes, such as text, images, and videos, which are non-numerical.
- Quantitative Data: Consists of numerical data that can be measured and analyzed, such as numbers, percentages, and statistics.
Data Sources
- Primary Data: Collected directly from the source through methods like surveys, experiments, or observations.
- Secondary Data: Collected from existing sources, such as books, articles, or online databases.
Data Quality
- Accuracy: The degree to which data is correct and free from errors.
- Completeness: The degree to which data is thorough and includes all necessary information.
- Consistency: The degree to which data is uniform and follows a standard format.
- Relevance: The degree to which data is applicable and useful for a particular purpose.
Data Levels of Measurement
- Nominal Data: Categorical data with no inherent order or scale, such as names or categories.
- Ordinal Data: Categorical data with a natural order or ranking, such as grades or rankings.
- Interval Data: Numerical data with a fixed unit of measurement, such as temperatures or dates.
- Ratio Data: Numerical data with a fixed zero point and unit of measurement, such as heights or weights.
Data Manipulation
- Data Cleaning: Identifying and correcting errors, inconsistencies, and missing values in data.
- Data Transformation: Converting data into a more suitable format for analysis.
- Data Reduction: Simplifying data to reduce its complexity and improve analysis.
Data Management
- Data Warehouse: A central repository that stores data from various sources in a single location.
- Data Mining: Discovering patterns and relationships in large datasets.
- Data Retrieval: Accessing and extracting specific data from a database or storage system.
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Description
Learn about the differences between qualitative and quantitative data, and primary and secondary data sources in research.
