Research Skills & Analysis - LIB116 Lecture 1

Research Skills & Analysis - LIB116, Lecture One

• Lecture presented by Assoc. Prof. Emam Omar, Physics Department, New Mansoura University, 28/09/2024
• Course topic: Research Skills & analysis, Lecture One, LIB116

Data

• Data are individual facts, observations, statistics, characters, symbols, images, numbers, etc.
• Raw data, out of context, have no meaning.
• Raw data is difficult to understand.
• Raw Data Types:
  • Quantities
  • Numbers
  • Facts
  • Observations
  • Graphs
  • Measurements
• Example data: 5551237798 (phone number)

Types of Data

• Research data is classified as either quantitative or qualitative.
• Quantitative data: Data that can be counted or expressed numerically (e.g., number of students, age, grades, test scores).
• Qualitative data: Descriptive data with no numerical values (e.g., a person's attitude, perception, or feelings).

Types of Data (Based on Source)

• Primary data (first-hand information): Data originated by a researcher for a specific research problem. Collected directly from the subjects being studied.
• Secondary data: Existing data, collected by others, from readily available sources. Might be published or unpublished.
• Types of Secondary Data:
  • Files/records
  • Computer databases
  • Industry or government reports
  • Documents (budgets, organizational charts, policies, procedures, maps, monitoring reports)
  • Newspapers and television reports

Data Collection Methods

• Methods used to collect data for research include:
  • Forms and questionnaires
  • Interviews
  • Observation
  • Documents and records
  • Focus groups
  • Oral histories
  • Combination research
  • Online tracking
  • Online marketing analytics
  • Social Media Monitoring

Data Collection Methods (Continued)

• Data collection is crucial for any research as results are based on the data collected.
• The methods chosen depend on the research objectives and design.
• "Data" refers to any information collected about subjects, respondents, or participants of a study.

Methods of Data Collection

• Observation
• Questionnaire
• Interview
• Surveys
• Experimental devices 

1. Observation

• Observational research (field research) involves direct observation of phenomena in their natural setting.
• Types of Observation:
  • A. Non-controlled, participant observation: Researcher becomes a member of the group being studied.
  • B. Non-controlled, non-participant observation: Researcher observes from a distance without participating in the group's activities.
  • C. Systematic controlled observation: Researcher predetermines and controls variables, location, time, participants, and tools.

Disadvantages of Participant Observation Research

• Researchers lose their objectivity.
• It is challenging to remain neutral while participating in observation, forming opinions and changing behaviors.

2. Questionnaire

• A questionnaire is a set of written questions.
• Research subjects answer questions.
• Types of Questionnaires:
  • A. Structured (closed): Provides predetermined, clear, closed answers.
  • B. Unstructured (open): Uses open-ended questions, getting views, opinions, and attitudes.

3. Interview

• Interview is a conversation for a specific purpose.
• More than an oral exchange of information.
• Types of Interviews:
  • A. Non directive (unstructured): Interviewer avoids predetermined questions.
  • B. Directive (structured): Uses predetermined questions.
  • C. Repeated interview: Tracks changes in phenomenon over time
  • D. Focus interview: Focuses on relevant theory & evidence
  • E. Depth interview: Qualitative research for intensive interviews

4. Surveys

• Surveys are a research method in which subjects respond to statements or questions using questionnaires or interviews.
• Surveys target populations – the people being studied.
• Usually a sample (part of the population) is studied and represents the whole.
• Random sampling is used to ensure a representative sample.
• Questionnaires in surveys can be close ended or open ended.
• Surveys use statistical methods for data analysis.

5. Experimental devices

• Refers to laboratory equipment used in experiments, like graduated cylinders, beakers, microscopes, and more

Information

• Data is meaningless without interpretation.
• Information is processed data that is meaningful in a specific context.
• Information = Data + Context + Meaning

Data Processing

• Raw data is not inherently useful to an organization.
• Data processing involves collecting raw data and transforming it into usable information.
• Stages in Data Processing:
  • A. Data Collection: Methods like observation, questionnaires, interviews, surveys, experimental devices.
  • B. Data Preparation (Validation): Ensuring data accuracy and relevance; identifying and correcting errors.
  • C. Sorting: Organizing data in a sequence based on characteristics (descriptive or numerical).
  • D. Input: Converting processed/sorted data into a machine-readable format for processing unit.
  • E. Processing (Analysis): Manual (for small datasets) or Computer-aided.
  • F. Output/Interpretation: Presenting data in a usable format (e.g., graphs, tables, reports).
  • G. Storage: Storing data and metadata for future use.

Types of Data Processing

• Batch Processing: Data is collected and processed in batches. Large datasets.
• Real-Time Processing: Data is processed within seconds. Examples: Using an ATM.
• Online Processing: Data is processed immediately as it becomes available. Examples: Barcode scanning.
• Time-Sharing: Computer resources are allocated to multiple users.