Research Methodology Study Notes

Research Methodology Study Notes

Research Design

• Definition: Framework for collecting and analyzing data.
• Types:
  1. Descriptive: Observes and describes behavior without manipulating variables.
  2. Correlational: Examines relationships between variables; does not imply causation.
  3. Experimental: Involves manipulation of one or more variables to determine effects.
  4. Quasi-Experimental: Similar to experimental but lacks random assignment.
• Considerations: Objectives, resources, and ethical implications.

Data Analysis Techniques

• Descriptive Statistics: Summarizes data (mean, median, mode, range).
• Inferential Statistics: Makes predictions/inferences about a population based on sample data.
• Qualitative Analysis: Thematic analysis, content analysis, grounded theory.
• Software Tools: SPSS, R, NVivo, Python.

Qualitative Methods

• Purpose: Understand human behavior and social phenomena.
• Data Collection:
  • Interviews (structured, semi-structured, unstructured)
  • Focus Groups
  • Observations
  • Document Analysis
• Analysis Techniques: Coding, thematic analysis, narrative analysis.
• Strengths: Depth of understanding, context-rich data.
• Limitations: Subjectivity, smaller sample sizes.

Mixed Methods

• Definition: Combines qualitative and quantitative approaches.
• Purpose: Provides comprehensive understanding by corroborating findings.
• Design Types:
  1. Convergent: Data collected simultaneously and compared.
  2. Explanatory Sequential: Quantitative data collected first, followed by qualitative.
  3. Exploratory Sequential: Qualitative data collected first, leading to quantitative.
• Strengths: Broad perspective, validates results through triangulation.
• Challenges: Complexity in design and analysis, requires proficiency in both methods.

Quantitative Methods

• Purpose: Test hypotheses, measure variables, and analyze relationships.
• Data Collection:
  • Surveys and Questionnaires
  • Experiments
  • Observational studies with numerical data
• Statistical Analysis:
  • T-tests, ANOVA, regression analysis.
  • Use of large sample sizes for generalizability.
• Strengths: Objectivity, replicability, ability to analyze large data sets.
• Limitations: May overlook contextual factors, can be inflexible in response options.

Research Design

• Framework for systematically collecting and analyzing data.
• Types include:
  • Descriptive: Observes behavior, no variable manipulation.
  • Correlational: Studies relationships, does not establish causation.
  • Experimental: Manipulates variables to observe effects.
  • Quasi-Experimental: Similar to experimental without random assignment.
• Key considerations encompass research objectives, available resources, and ethical implications.

Data Analysis Techniques

• Descriptive Statistics: Summarizes data points using metrics such as mean, median, mode, and range.
• Inferential Statistics: Allows predictions/inferences about a larger population based on sample data.
• Qualitative Analysis: Involves methods like thematic analysis, content analysis, and grounded theory.
• Common software tools for analysis include SPSS, R, NVivo, and Python.

Qualitative Methods

• Aim to deepen understanding of human behavior and social contexts.
• Data collection methods:
  • Interviews: Ranges from structured to unstructured formats.
  • Focus Groups: Engaging discussions to gather insights.
  • Observations: Watching participants in their natural environment.
  • Document Analysis: Examining existing documents for information.
• Analysis techniques involve coding, thematic analysis, and narrative analysis.
• Strengths include rich, context-driven data; limitations involve subjectivity and typically smaller sample sizes.

Mixed Methods

• Integrates qualitative and quantitative strategies for a holistic view.
• Purpose is to enhance understanding by cross-validating findings.
• Types of designs:
  • Convergent: Concurrent data collection with comparison.
  • Explanatory Sequential: Collect quantitative data first, followed by qualitative insights.
  • Exploratory Sequential: Begin with qualitative data to inform quantitative collection.
• Strengths include a broader perspective and validation through triangulation; challenges entail design complexity and the need for expertise in both qualitative and quantitative methods.

Quantitative Methods

• Focus on hypothesis testing, variable measurement, and relationship analysis.
• Data collection methods include:
  • Surveys and Questionnaires: Gather numerical data.
  • Experiments: Controlled studies for hypothesis testing.
  • Observational Studies: Numeric data collection from behavior observation.
• Common statistical analysis techniques are t-tests, ANOVA, and regression analysis.
• Emphasizes large sample sizes to ensure generalizability.
• Strengths include objectivity and replicability; limitations are potential oversight of contextual factors and rigidity in response options.

Explore the key concepts of research methodology in this quiz. Learn about various research designs, data analysis techniques, and qualitative methods to enhance your understanding of research processes. Perfect for students and researchers looking to deepen their knowledge.