Evidence-based policy making relies on various types and sources of data to inform decisions. Quantitative evidence uses numbers and stats, while qualitative evidence draws on observations and interviews. Both can be combined for a fuller picture of complex issues.

Scientific research and expert opinions are key sources of evidence. Case studies offer detailed looks at specific examples. Statistical and big data, systematic reviews, and different study types all play roles in building a solid evidence base for policy choices.

Types of Evidence

Quantitative and Qualitative Evidence

• Quantitative evidence uses numerical data and statistical analysis to measure and evaluate phenomena (surveys, experiments, economic indicators)
• Qualitative evidence relies on non-numerical data, such as observations, interviews, and focus groups, to gain insights into complex issues and experiences (ethnographic studies, participant observation)
• Both types of evidence can be used in combination to provide a more comprehensive understanding of policy issues and inform decision-making processes
• The choice between quantitative and qualitative evidence depends on the research question, available resources, and the nature of the problem being addressed

Scientific Research and Expert Opinion

• Scientific research involves systematic investigation using established methods to generate reliable and valid evidence (randomized controlled trials, systematic reviews, meta-analyses)
• Expert opinion refers to the knowledge and insights provided by individuals with specialized expertise in a particular field (policy advisors, academics, industry professionals)
• Scientific research is often considered more robust and objective than expert opinion, as it is based on rigorous methodologies and peer review
• However, expert opinion can be valuable in interpreting scientific findings, identifying knowledge gaps, and providing context-specific insights

Case Studies

• Case studies are in-depth investigations of specific instances or examples of a phenomenon (policy interventions, social programs, organizational practices)
• They provide detailed, context-rich information about how policies or programs are implemented and their outcomes in real-world settings
• Case studies can be used to identify best practices, challenges, and unintended consequences of policy interventions
• While case studies may have limited generalizability, they can offer valuable insights and generate hypotheses for further research

Data and Analysis Methods

Statistical Data and Big Data

• Statistical data refers to numerical information collected through surveys, administrative records, or other sources (census data, economic indicators, health statistics)
• Big data involves large, complex datasets that are often generated through digital technologies (social media, mobile devices, sensors)
• Statistical data is commonly used to describe populations, identify trends, and test hypotheses using established statistical techniques (regression analysis, hypothesis testing)
• Big data analytics leverages advanced computational methods (machine learning, data mining) to uncover patterns and insights that may not be apparent through traditional statistical approaches

Systematic Reviews and Policy Evaluations

• Systematic reviews are comprehensive, structured assessments of existing research on a specific topic or question (meta-analyses, evidence syntheses)
• They involve a rigorous process of identifying, appraising, and synthesizing relevant studies to draw conclusions about the effectiveness of interventions or the state of knowledge in a field
• Policy evaluations assess the design, implementation, and outcomes of policies or programs to determine their effectiveness, efficiency, and impact
• Evaluations can be formative (conducted during implementation to inform improvements) or summative (conducted after completion to assess overall effectiveness)

Randomized Controlled Trials and Observational Studies

• Randomized controlled trials (RCTs) are experimental studies in which participants are randomly assigned to treatment and control groups to assess the effectiveness of an intervention
• RCTs are considered the gold standard for establishing causal relationships between interventions and outcomes, as they minimize bias and confounding factors
• Observational studies, such as cohort studies and case-control studies, involve collecting data on naturally occurring variations in exposures and outcomes
• While observational studies cannot definitively establish causality, they can provide valuable insights into associations between variables and generate hypotheses for further research