Understanding Data Types and Collection Methods

Data

• Data are individual facts, observations, statistics, characters, symbols, images, numbers
• Data without context lack meaning and are difficult to understand. They are often called "raw data".
• Research data can be classified as quantitative or qualitative.

Quantitative Data

• Quantitative data can be counted or expressed numerically.

Qualitative Data

• Qualitative data are descriptive and lack numerical values.

Types of Data

• Primary data are generated by a researcher for a specific purpose and collected directly from subjects. (First-hand information)
• Secondary data are already available and collected from other sources.

Data Collection Methods

• Data collection is essential to research, as conclusions are based on the data.
• Common methods include:
  • Observation
  • Questionnaire
  • Interview
  • Surveys
  • Experimental devices

1- Observation

• Observational research (field research) involves directly observing phenomena in natural settings.
• Types include:
  • Non-controlled, participant observation: Researcher becomes a member of the group under study. (Disadvantage: Researcher may lose objectivity)
  • Non-controlled, non-participant observation: Researcher observes the group from a distance.
  • Systematic, controlled observation: Researcher pre-determines variables (location, time, participants, tools), and controls the study.

2- Questionnaire

• A questionnaire is a series of written questions given to subjects.
• Structured (closed) questionnaire: Closed questions have predetermined, rigid and clear answer options.
• Unstructured (open) questionnaire: Open questions elicit view points and opinions.

3- Interview

• Interviews are conversations with a purpose, more than just exchanging information.
• Types include:
  • Non-directive
  • Directive
  • Repeated
  • Focused
  • Depth

4- Surveys

• Surveys involve subjects responding to statements or questions in questionnaires or interviews, often directed at populations.
• Typically a sample representing the whole is studied. Random sampling ensured representativeness.

Information

• Information is processed data, structured, and presented to make it meaningful and useful.
• Steps in data processing:
  • Collection
  • Preparation
  • Input
  • Processing
  • Output
  • Storage

Data Processing Operations

• Data in raw form is not useful.
• Data processing transforms raw data into usable information.
• Operations include:
  • Data collection: Methodology of gathering data
  • Data preparation (validation): Checking collected data for errors and correcting them.
  • Sorting: Ordering data (descriptive or numerical).
  • Input: Converting sorted data into a machine-readable format.
  • Processing: Analyzing data.
    • Manual data analysis: Suitable for small datasets, but time-consuming.
    • Computer-based data analysis: Requires knowledge of appropriate software and statistics.
  • Output/Interpretation: Presenting data in a usable format for non-data scientists.
  • Storage: Saving data and metadata for future use

Types of Data Processing

• Batch processing: Collecting and processing data in batches, used for large amounts of data.
• Real-time processing: Processing data within seconds when input is given (e.g., ATM).
• Online processing: Data fed to the CPU as soon as available (e.g., barcode scanning).
• Time-sharing: Allocating computer resources to multiple users simultaneously.

Statistical Analysis and Software

• Statistical analysis is used to collect, analyze large amounts of data to identify patterns and trends.
• Descriptive analysis: Summarizes data using measures like mean, median, mode, range, standard deviation etc.
• Inferential statistics: Drawing conclusions about a population from samples; often involves hypothesis testing and regression analysis.
• Associative/relative statistics: Identifying meaningful interrelationships between data, (e.g. relationship between salt intake and blood pressure).
• Major statistical analysis software include SPSS, Microsoft Excel, Matlab, Minitab, OriginPro, GraphPad Prism.

Database

• A database is a collection of organized information for easy access, management, and updation.
• Types of databases:
  • Relational
  • Document-oriented
  • Graph
  • Hypertext
  • Operational
  • Distributed
  • Flat file

Relational Database

• A relational database is a collection of tables containing data items.
• Tables contain rows (tuples) and columns (attributes).

Document-Oriented Database

• A type of nonrelational database designed to store data in JSON-like documents.

Graph Database

• Uses graph structures (nodes, edges, properties).

Hypertext Database

• Database system linking objects (text, images, programs)

Operational Database

• Designed for real-time data definition, modification, retrieval and management in organizations.

Distributed Database

• Portions of the database are stored on multiple computers in a network.

Flat File Database

• Stores data in a single table.
• Typically in plain text format, with fields separated by commas or tabs. Its ideal for small datasets.

Data & Information & Knowledge

• Knowledge combines information, experience, and insight.
• Information answers "who, when, where"; knowledge answers "why, how."

Basis for Information & Knowledge Comparison

• Meaning: Information is systematically presented data; knowledge is gained through experience.
• Combination: Data and context; Information, experience, and intuition.
• Processing: Improves representation; Increases awareness.
• Transfer: Easily transferable, Requires learning.
• Prediction: Information alone is insufficient for prediction; prediction possible with knowledge.
• One in other: All knowledge is information, but not all information is knowledge.

Knowledge Acquiring Sources

• Unscientific:
  • Empiricism: Knowledge gained through senses.
  • Trial and error: Knowledge from practical experience.
  • Tradition: Knowledge passed down through generations.
  • Tenacity: Believing something true simply because it's heard repeatedly.
  • Rationalism: Knowledge gained through logical reasoning.
  • Authority: Knowledge from respected figures.
• Scientific: Knowledge based on empirical evidence.
  • Objectivity
  • Verifiability/Measurability
  • Reliability

Meaning of Research

• Research is an investigation to find solutions to a problem or discover new knowledge and information.
• "RE" means "again, anew, or over again," and "SEARCH" means "to examine closely, carefully, to test or to probe."

Types of Research

• Application:
  • Pure research: Systematic study for a better understanding of phenomena without immediate application.
  • Applied research: Applying Pure Research outcomes (theories) to real-world situations.
• Objectives:
  • Exploratory research: Studying areas not well understood.
  • Descriptive research: Describing characteristics of a problem, phenomenon, or group.
  • Correlational research: Identifying relationships between variables.
  • Explanatory research: Explaining why something occurs.
• Inquiry mode:
  • Qualitative research: Unstructured approach using soft data (impressions, words, symbols).
  • Quantitative research: Structured approach measuring hard data.