Research Methods

Quantitative research collects and analyzes numerical data to identify patterns, test hypotheses, and establish relationships between variables. This approach emphasizes measurement, statistical analysis, and objective findings that can be generalized to larger populations.

Core principles of quantitative research:

• Measurement: Variables are operationally defined and measured using standardized instruments
• Sampling: Large, representative samples enable generalization to broader populations
• Standardization: Consistent procedures ensure reliability and replicability
• Statistical analysis: Mathematical techniques identify patterns and test significance
• Objectivity: Researchers minimize bias through controlled procedures and statistical inference

Key quantitative research designs:

• Survey research:
  • Uses questionnaires or structured interviews to collect data from large samples
  • Effective for measuring attitudes, behaviors, demographics, and relationships
  • Can be cross-sectional (one time point) or longitudinal (multiple time points)
  • Examples: Public opinion polls, customer satisfaction surveys, academic performance studies
• Experimental research:
  • Manipulates independent variables to measure effects on dependent variables
  • Includes control groups and random assignment to establish causation
  • Can be conducted in laboratory or field settings
  • Examples: Clinical trials, educational interventions, marketing experiments
• Correlational research:
  • Examines relationships between variables without manipulation
  • Uses statistical techniques to measure strength and direction of associations
  • Cannot establish causation but identifies predictive relationships
  • Examples: Relationship between income and health outcomes, academic performance predictors
• Quasi-experimental research:
  • Tests interventions when true randomization is not possible
  • Uses comparison groups and statistical controls to approximate experimental conditions
  • Common in educational and policy research
  • Examples: Program evaluations, natural experiments, interrupted time series

When to use quantitative research:

• You want to test specific hypotheses or theories
• You need results that can be generalized to larger populations
• You're measuring relationships between clearly defined variables
• You want to compare groups or track changes over time
• You need to make predictions or forecasts
• Your field values statistical evidence and replication
• You have access to large samples and standardized measures

Sample size considerations:

• Descriptive studies: Minimum 30 participants for basic statistics
• Correlational studies: Generally 30+ participants per variable analyzed
• Experimental studies: Power analysis determines adequate sample size
• Survey research: Sample size depends on population size and desired precision
• Complex analyses: Larger samples needed for multivariate statistics

Data collection methods:

• Online surveys: Cost-effective for large samples but may have selection bias
• Telephone interviews: Good response rates but increasingly difficult to conduct
• Face-to-face interviews: High quality data but expensive and time-consuming
• Mail surveys: Reaches diverse populations but low response rates
• Administrative data: Large samples but limited to available variables

Advantages:

• Results can be generalized to larger populations through statistical inference
• Findings are replicable when methods are clearly documented
• Statistical significance testing provides confidence in results
• Efficient data collection from large samples using standardized instruments
• Can identify relationships and patterns across many variables simultaneously
• Enables prediction and forecasting based on statistical models
• Widely accepted in many academic and professional fields

Limitations:

• May oversimplify complex social and psychological phenomena
• Limited ability to capture context, meaning, and subjective experiences
• Requires large samples to achieve statistical power and generalizability
• Less flexible once data collection begins
• Risk of measurement error if instruments are not well-designed
• May miss unexpected findings that don't fit predetermined categories
• Can be expensive for large-scale studies requiring representative samples

Qualitative research collects and analyzes non-numerical data to understand experiences, meanings, processes, and social phenomena. This approach emphasizes interpretation, context, and in-depth understanding of how people make sense of their world.

Core principles of qualitative research:

• Naturalistic inquiry: Studies phenomena in natural settings rather than controlled environments
• Inductive analysis: Develops theories and concepts from patterns observed in data
• Holistic perspective: Considers context, relationships, and multiple influences
• Subjective understanding: Values participants' perspectives and lived experiences
• Flexible design: Adapts methods and questions as understanding develops

Major qualitative research approaches:

• Phenomenological research:
  • Explores the lived experience of a particular phenomenon
  • Focuses on how individuals perceive and make meaning of experiences
  • Uses in-depth interviews and detailed descriptions
  • Examples: Experience of chronic illness, first-generation college students, grief processes
• Ethnographic research:
  • Studies cultures and social groups through immersive observation
  • Researcher spends extended time in the field
  • Combines observation, interviews, and document analysis
  • Examples: Workplace cultures, online communities, educational settings
• Grounded theory:
  • Develops theoretical explanations from systematic data analysis
  • Uses constant comparative method to build conceptual frameworks
  • Iterative process of data collection and analysis
  • Examples: Social processes, behavior change, decision-making patterns
• Case study research:
  • Intensive examination of single cases or small number of cases
  • Uses multiple data sources for comprehensive understanding
  • Can be exploratory, descriptive, or explanatory
  • Examples: Organizational change, policy implementation, individual life histories
• Narrative research:
  • Focuses on stories people tell about their experiences
  • Analyzes structure, content, and meaning of narratives
  • Considers how stories shape identity and understanding
  • Examples: Career transitions, recovery stories, cultural narratives

Key data collection methods:

• In-depth interviews:
  • Semi-structured or unstructured conversations lasting 1-3 hours
  • Allow participants to tell their stories in their own words
  • Use open-ended questions and follow-up probes
  • Generate rich, detailed accounts of experiences and perspectives
• Focus groups:
  • Group discussions with 6-12 participants facilitated by researcher
  • Explore shared experiences and group dynamics
  • Useful for understanding social influences and collective meanings
  • Generate interaction and debate among participants
• Participant observation:
  • Researcher observes and participates in activities being studied
  • Documents behaviors, interactions, and environmental factors
  • Provides insider perspective on social processes
  • Requires careful attention to researcher role and influence
• Document analysis:
  • Systematic examination of written materials, images, or artifacts
  • Includes personal documents, official records, media representations
  • Provides context and historical perspective
  • Complements other qualitative data sources

When to use qualitative research:

• You're exploring new, complex, or poorly understood phenomena
• You want to understand personal experiences, meanings, and interpretations
• You need to explain unexpected quantitative findings
• You're developing new theories or conceptual frameworks
• Your research question asks "how" or "why" rather than "how many"
• You want to capture context, process, and change over time
• You need to understand cultural or social influences

Sampling in qualitative research:

• Purposeful sampling: Select participants who can provide rich information
• Theoretical sampling: Choose participants to develop emerging theories
• Snowball sampling: Use referrals to reach hard-to-find populations
• Maximum variation: Include diverse perspectives and experiences
• Sample sizes: Typically 5-50 participants, depending on approach and saturation

Data analysis approaches:

• Thematic analysis: Identify patterns and themes across data
• Content analysis: Systematic categorization of data
• Discourse analysis: Examine language use and social construction
• Narrative analysis: Focus on story structure and meaning
• Constant comparative: Continuously compare data to develop concepts

Advantages:

• Provides rich, detailed understanding of complex phenomena
• Captures context, meaning, and subjective experiences
• Flexible and adaptable throughout the research process
• Can uncover unexpected insights and generate new theories
• Gives voice to participants and marginalized perspectives
• Useful for exploring sensitive topics requiring trust and rapport
• Can inform policy and practice through deep understanding

Limitations:

• Findings may not be generalizable to other populations or contexts
• Time-intensive data collection, transcription, and analysis
• Potential for researcher bias in data collection and interpretation
• Difficult to replicate exactly due to contextual factors
• May lack statistical power for making broad claims
• Requires significant interpretive skills and theoretical knowledge
• Can be challenging to establish reliability and validity using traditional criteria