Peas In the Pod’s Post

Not all research is valued the same → Here is how your research design dictates the weight of your findings. (The Research Pyramid) 1. Expert Opinion → Based on respected professionals’ insights. → Valuable for forming hypotheses, but highly subjective and prone to bias. 2. Case Report/Case Series → Describes rare or unique cases, providing in-depth insight into unusual conditions. → Useful for sparking further research but lacks generalizability. 3. Cross-Sectional Study → Captures a snapshot of a population at a single point in time. → Helps understand prevalence but does not establish causality. 4. Case-Control Study → Retrospective design comparing individuals with and without a particular outcome. → Efficient for rare diseases but vulnerable to recall bias. 5. Cohort Study → Follows groups over time to identify risk factors and outcomes. → Provides stronger evidence but may be affected by confounding variables. 6. Non-Randomized Control Trial (nRCT) → Offers insight when randomization isn’t feasible. → Prone to bias but still valuable for testing interventions. 7. Randomized Controlled Trial (RCT) → The gold standard for experimental research. → Randomization reduces bias, providing highly accurate, causal evidence. 8. Systematic Review and Meta-Analysis → Aggregates data from multiple studies, offering the highest level of evidence. → Comprehensive and rigorous, it minimizes bias and strengthens conclusions. —————— ♻️♻️♻️Repost for others #ResearchMethodology #EvidenceHierarchy #DataDrivenDecisions #HealthcareResearch

In the world of research you should always have in mind your destination thanks Ignatius N Esene MD, MSc, PhD, MPH for sharing such a summary.

Online surveys are a staple in market research, but they're not the only tool in the box. To truly understand your customers, you need to step outside the digital world and into theirs. Observational research is a goldmine of insights. Instead of asking people about their behavior, you watch them in action. It's like being a detective, piecing together clues to uncover hidden patterns. My team recently used observation to understand how people interact with our product in real-life settings. We shadowed customers, observed their habits, and identified pain points we never would have uncovered through surveys alone. Remember, understanding your customers is key to success. So, don't just ask them what they think – watch what they do! #marketresearch #customerinsights #observation #researchmethods What other unconventional research methods have you found effective? https://lnkd.in/eTi_85Gc

Understanding root causes is critical for successful product development, quality improvement, clinical reasoning, and making good everyday decisions. In "The Book of Why," Judea Pearl, in collaboration with Dana Mackenzie, guides us through the complex and often misunderstood realm of causality. 1. Understanding Causation: Pearl begins by untangling the frequent confusion between correlation and causation. He introduces a conceptual tool known as the "Ladder of Causation" to clarify these differences. This tool helps us see that just because two things occur together, it does not mean one causes the other—a principle crucial for both scientific research and everyday reasoning. 2. Significance of Counterfactuals: The authors advocate for "counterfactual thinking," a method of asking "what if" questions to explore alternative scenarios. Counterfactual thinking is a method if asking "what if" questions to explore alternative scenarios and their possible outcomes. This approach is vital for understanding the potential outcomes of different causes and is particularly useful in fields ranging from epidemiology to economic policy. 3. Causal Inference in Real-world Settings: Not all situations permit randomized controlled trials. Pearl and Mackenzie introduce non-experimental tools like the "do" operator and structural causal models. These tools allow us to infer causal relationships from observational data, broadening our ability to apply scientific reasoning in everyday situations where controlled experiments are impractical. 4. Role of Causal Models: Constructing causal models is akin to drawing maps of complex systems, showing how various elements interact to produce certain outcomes. These models are crucial for predicting the effects of interventions and are widely applicable in areas such as public health, economics, and environmental science. 5. Limits of Big Data: While large datasets can reveal patterns, Pearl and Mackenzie caution against overreliance on big data without understanding the causal mechanisms behind these patterns. They stress the importance of integrating causal thinking with data analysis to avoid misleading conclusions that could result from mere correlations. 6. Empowerment Through Causal Understanding: A deeper understanding of causality empowers us to make better decisions in our personal lives and to engage more effectively with societal issues. By fostering a causal understanding, people can navigate complex information and make informed choices that reflect a better understanding of potential consequences. Their work serves as a call to integrate causal thinking into our daily lives, enhancing our understanding of the world and improving our capacity to act within it effectively. https://lnkd.in/g76t_8Ks #datascience #systemsthinking #qualityimprovement

What if you knew which message was most persuasive to each audience? BEESY has a new whitepaper that gets into the power of persuasion and a method that the team has developed to get at just that. And it's had a huge impact for teams that use it for message testing. #marketresearch #BEESY #messagetesting

Pharma messaging demands a rigorous approach. Beyond just identifying which messages work, our persuasion equation analyzes them through key psychological drivers to pinpoint - how and why - they work. The remarkable part: this message scoring is highly predictive of persuasive power in market research results. Read how BEESY’s behavioral science is helping pharma companies evolve more impactful #messaging for their brand. #DataDrivenMarketing #marketresearch #behavioralscience #BEESY 

What we're reading: How Diverse Perspectives Benefit Data Science by Marie A. Bernard, National Institutes of Health Chief Officer for Scientific Workforce Diversity "Data science has emerged as a powerful tool for addressing biomedical and behavioral research challenges. However, to fully realize its potential, we must address a critical gap: the need for diverse perspectives and inclusive environments within the field. Research demonstrates that diverse representation produces more accurate, fair, and impactful data science." Continue Reading: https://ow.ly/4NRE50TCMLT

Gain a comprehensive understanding of patient and staff behaviour through our Behavioural Insights service. Our professional team, led by experts with clinical experience and academic research, employs scientific processes to provide you with valuable insights. We can help you with our innovative digital and social listening tools to make data-driven decisions and create impactful campaigns. https://lnkd.in/e34xNQSR #DataDrivenInsights #BehaviouralUnderstanding

Understand the biggest pain points in market research today and the potential to get better insights with speed, convenience, and impact.

Gain a comprehensive understanding of patient and staff behaviour through our Behavioural Insights service. Our professional team, led by experts with clinical experience and academic research, employs scientific processes to provide you with valuable insights. We can help you with our innovative digital and social listening tools to make data-driven decisions and create impactful campaigns. https://lnkd.in/e34xNQSR #DataDrivenInsights #BehaviouralUnderstanding