12.2 Data collection and analysis methods

Data collection and analysis methods are crucial for monitoring and evaluating nonprofit initiatives. These techniques range from quantitative surveys to qualitative interviews, providing organizations with valuable insights into their impact and effectiveness.

Nonprofits use various approaches to gather and interpret data, including surveys, interviews, and observational methods. By employing both quantitative and qualitative techniques, organizations can gain a comprehensive understanding of their programs' outcomes and make informed decisions for improvement.

Data Collection Methods

Quantitative and Qualitative Approaches

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• Quantitative methods gather numerical data through structured techniques
  • Involves collecting measurable information (height, weight, income)
  • Utilizes statistical analysis to draw conclusions
  • Produces generalizable results across larger populations
• Qualitative methods explore in-depth experiences and perspectives
  • Gathers non-numerical data through open-ended questions
  • Focuses on understanding complex social phenomena
  • Provides rich, detailed descriptions of participants' views
• Mixed methods combine quantitative and qualitative approaches
  • Integrates strengths of both methodologies
  • Enhances validity through cross-verification of findings
  • Addresses complex research questions from multiple angles

Survey and Interview Techniques

• Surveys collect standardized information from large groups
  • Can be administered online, by mail, or in person
  • Use structured questions with predefined response options
  • Allow for efficient data collection from geographically dispersed populations
• Interviews involve one-on-one conversations with participants
  • Structured interviews follow a predetermined set of questions
  • Semi-structured interviews use a flexible guide to explore topics
  • Unstructured interviews allow for free-flowing conversation
• Focus groups facilitate group discussions on specific topics
  • Typically involve 6-10 participants led by a moderator
  • Encourage interaction and debate among group members
  • Generate insights through collective brainstorming and shared experiences

Observational Methods

• Observation involves systematically watching and recording behavior
  • Participant observation immerses researcher in the studied environment
  • Non-participant observation maintains distance between observer and subjects
  • Structured observation uses predefined categories to record specific behaviors
• Ethnographic methods study cultures or groups in their natural settings
  • Requires extended fieldwork and immersion in the community
  • Combines observation with interviews and artifact analysis
  • Produces detailed descriptions of cultural practices and social interactions

Data Analysis Techniques

Secondary Data Analysis and Triangulation

• Secondary data analysis examines existing datasets
  • Utilizes previously collected information for new research purposes
  • Saves time and resources compared to primary data collection
  • Requires careful evaluation of data quality and relevance
• Data triangulation compares multiple data sources or methods
  • Enhances validity by cross-verifying findings from different perspectives
  • Identifies convergence or divergence in results
  • Types include method triangulation, investigator triangulation, and theory triangulation

Sampling and Statistical Techniques

• Sampling techniques select a subset of a population for study
  • Probability sampling gives all members an equal chance of selection (simple random, stratified, cluster)
  • Non-probability sampling uses non-random methods (convenience, purposive, snowball)
  • Sample size determination balances precision, confidence, and resource constraints
• Descriptive statistics summarize and describe data characteristics
  • Measures of central tendency (mean, median, mode)
  • Measures of variability (range, standard deviation, variance)
  • Graphical representations (histograms, box plots, scatter plots)
• Inferential statistics draw conclusions about populations from sample data
  • Hypothesis testing assesses the likelihood of observed results occurring by chance
  • Regression analysis examines relationships between variables
  • Analysis of variance (ANOVA) compares means across multiple groups