Research Skills & Analysis - LIB116 Lecture 1

Research Skills & Analysis - LIB116, Lecture One

• Lecture presented by Assoc. Prof. Emam Omar, Physics Department, New Mansoura University, on September 28, 2024.
• Course name: Research Skills & Analysis
• Course code: LIB116
• Lecture number: One

Data

• Data are individual facts, observations, statistics, characters, symbols, images, numbers.
• Raw data lacks context and meaning.
• Data types: Quantities, numbers, facts, observations, graphs, and measurements.

Types of Data

• Research data is often classified into quantitative and qualitative.
• Quantitative data can be counted or expressed numerically (e.g., number of students, age, grades, test scores).
• Qualitative data is descriptive and doesn't have numerical values (e.g., attitude, perception, feelings).

Types of Data (Based on Sources)

• Primary data (first-hand information): Data originated by a researcher for the specific purpose of addressing a research problem and collected directly from subjects.

• Secondary data: Existing data collected by others; it may be published or unpublished.

• Secondary data sources include: Files/records, computer databases, industry/government reports, documents (budgets, organizational charts, policies, procedures, maps, monitoring reports), newspapers and television reports.

Data Collection Methods

• Methods for collecting data: Forms and questionnaires, interviews, observations, documents and records, focus groups.
• Other methods include: Oral histories, combination research, online tracking, online marketing analytics, and social media monitoring.

Methods of Data Collection

• Observation: A research technique involving direct observation of phenomena in their natural settings.

  • Types of observation:
    • Non-controlled, participant observation (observer becomes part of the group).
    • Non-controlled, non-participant observation (observer observes the group from a distance)
    • Controlled, systematic observation (researcher pre-determines study variables and controls all elements).

• Questionnaire: A series of written questions given to subjects.

  • Types of questionnaires
    • Structured (closed-ended) questionnaires: Predetermined answers.
    • Unstructured (open-ended) questionnaires: Open responses.

• Interview: Conversation with a purpose, more than just an oral exchange of information.

  • Types of interviews:
    • Non-directive (unstructured): Flexible, open-ended questions.
    • Directive (structured): Predetermined questions, standardized technique.
    • Repeated interviews: Track changes in phenomenon over time.
    • Focused interviews: Limited by relevant theories and evidence.
    • Depth interviews: Qualitative research involving intensive individual interviews.

• Surveys: Research method where subjects respond to statements or questions in questionnaires or interviews.

  • Often involves closed-ended or open-ended questionnaires.
  • Techniques use statistical methods to analyze data.
  • A representative sample, a subset of the population, is studied.

• Experimental devices: Laboratory equipment used in research (e.g., glassware, microscopes, analytical instruments).

Information

• Data, in itself, lacks meaning but once processed, it becomes information.
• Information is a collection of processed data presented in a meaningful form within a given context.
• Information depends on data, context, and meaning. Processing turns data into information.
• Different figures present examples of data and how it transforms to information. A good example is the ice cream sales data (raw) and creating the chart and conclusion from it.

Data Processing

• Raw data is not directly useful for organizations.
• Data processing translates raw data into usable information through stages like collection, preparation, input, processing, output, and storage.
• Data processing operations: Includes data collection (observation, questionnaires, interviews, surveys, experimental devices), data preparation (ensuring data correctness), sorting (arranging data based on characteristics), input (converting sorted data into machine-readable format), processing (analyzing data), and output/interpretation (making data usable for the larger community).
• Types of data processing: Batch processing, real-time processing, online processing, and time-sharing.