Quasi-Experimental Designs vs Correlational Research
Developers should learn quasi-experimental designs when working on data science, analytics, or research projects that require evaluating the impact of interventions, policies, or features without the ability to conduct randomized controlled trials meets developers should learn correlational research when working in data science, analytics, or user experience (ux) roles to analyze relationships in datasets, such as between user behavior and app performance metrics. Here's our take.
Quasi-Experimental Designs
Developers should learn quasi-experimental designs when working on data science, analytics, or research projects that require evaluating the impact of interventions, policies, or features without the ability to conduct randomized controlled trials
Quasi-Experimental Designs
Nice PickDevelopers should learn quasi-experimental designs when working on data science, analytics, or research projects that require evaluating the impact of interventions, policies, or features without the ability to conduct randomized controlled trials
Pros
- +For example, in A/B testing where random assignment is limited, or in observational studies analyzing user behavior changes after a software update
- +Related to: experimental-design, causal-inference
Cons
- -Specific tradeoffs depend on your use case
Correlational Research
Developers should learn correlational research when working in data science, analytics, or user experience (UX) roles to analyze relationships in datasets, such as between user behavior and app performance metrics
Pros
- +It is useful for identifying trends, informing feature development, and making data-driven decisions in product design or A/B testing scenarios
- +Related to: statistical-analysis, data-science
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Quasi-Experimental Designs if: You want for example, in a/b testing where random assignment is limited, or in observational studies analyzing user behavior changes after a software update and can live with specific tradeoffs depend on your use case.
Use Correlational Research if: You prioritize it is useful for identifying trends, informing feature development, and making data-driven decisions in product design or a/b testing scenarios over what Quasi-Experimental Designs offers.
Developers should learn quasi-experimental designs when working on data science, analytics, or research projects that require evaluating the impact of interventions, policies, or features without the ability to conduct randomized controlled trials
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