Agree & Join LinkedIn
By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.
Create your free account or sign in to continue your search
or
New to LinkedIn? Join now
Learn how to use software tools and techniques such as factorial designs, response surface designs, optimization techniques, and validation experiments to…
Learn how to choose a model, check assumptions, perform analysis, interpret results, present results, and communicate implications of a factorial block design.
Learn what parallel and crossover designs are, how they differ, and what are their pros and cons for your experiment.
Learn how to use multiple regression to test and present the results of interaction and moderation analysis in experimental design and data analysis.
Learn how to plan and conduct a scientific investigation in six steps and avoid common pitfalls that can compromise your data quality and interpretation.
Learn about single-subject design, a type of experimental design that measures and changes the behavior of one or a few individuals over time.
Learn how to design and analyze factorial experiments and what are the benefits of using them for testing multiple factors and interactions.
Learn how to incorporate blocking, randomization, and replication in fractional factorial designs to test multiple factors and their interactions efficiently.
Learn about the benefits and drawbacks of using replication in experimental design, and how to choose the right type and level of replication for your experiment.
Learn what a central composite design is, why it is useful for response surface methodology, how to create and analyze it, and what are its pros and cons.
Learn how to optimize resources and costs in a split-plot design, a type of experimental design that tests multiple factors with different constraints.
Learn how to craft titles that communicate your experimental design and results effectively using keywords, conciseness, specificity, and creativity.
Learn how to design and analyze a split-plot experiment that minimizes confounding and bias. Follow four steps to test the effects of two or more factors.
Learn how to design and implement a data quality assurance plan for your experiment, following six steps to ensure the validity and reliability of your data.
Learn about the challenges and rewards of working as an experimental designer in academia, and the skills, roles, career paths, and tips to succeed.
Learn how to adjust your data collection and management methods to different contexts and settings in experimental design.
Learn about the career opportunities for experimental designers in the private sector, and the skills and qualifications that they need to succeed.
Learn how to use graphical and numerical methods to understand the characteristics, patterns, and relationships in your data in experimental design.
Learn about the main types of randomization methods for experimental design, their pros and cons, and how to choose the best one for your experiment.
Learn how to identify, handle, and visualize missing or noisy data in your experimental design. Create clear, accurate, and informative plots with interaction…
Learn some best practices for designing experiments that produce valid and generalizable results across different settings and populations.
Learn about the challenges and opportunities of using experimental design in online and digital settings, and how to overcome or leverage them for your research…
Learn how to design, conduct, analyze, and communicate a valid and rigorous experiment that can solve real-world problems and scenarios.
Learn about the best practices and tools for validating and optimizing the response surface models obtained from central composite designs (CCDs).
Learn about four common types of plots for showing interaction effects in experimental design and how to choose the best one for your data and question.
Learn what split-plot designs are, how they differ from other experimental designs, and when they can be useful in healthcare research.
Learn how to identify, test, interpret, and report interaction terms in R for your experimental design. Find out how to capture complex effects and relationships in…
Learn how to apply agile and lean principles to your software experimental design process. Discover tips to design and run fast, flexible, and customer-focused…
Learn what split-plot designs are, how they work, and what are their advantages and disadvantages for healthcare research.
Learn how to design, conduct, and analyze online experiments and surveys with this guide. Discover the principles, methods, tools, and tips for online research.
Learn some best practices and tools for covariate adjustment, a technique that can reduce confounding effects and increase validity in experimental design.
Learn how to report and communicate the results of crossover designs in six sections: design and methods, results and analysis, discussion and interpretation…
Learn how to compare balanced incomplete block designs (BIBDs) based on their efficiency and information content, using various criteria and methods.
Learn how to pre-register and report your experimental design and analysis for causal inference methods, and why it matters for your research quality and…
Learn how to plan and conduct high-quality experiments with these tips and best practices for experimental design.
Learn how to present your split-plot design results clearly and convincingly to your stakeholders or clients, using tips from experimental design experts.
Learn what Latin square design is, how it works, and what are its advantages and limitations in reducing confounding effects in experiments.
Learn how to design balanced incomplete block experiments using software tools or online platforms that can generate, assign, and analyze BIB data.
Learn what balanced incomplete block designs (BIBDs) are, how to find, randomize, analyze, generalize, and address ethical issues.
Learn how to design and implement effective pretest and posttest instruments for measuring learning outcomes in education. Find out how to analyze and interpret…
Learn the difference between confounding and collinearity in experimental design, how to detect and deal with them, and how to select variables when collinearity is…
Learn what blocking is, why it is useful, how to block your experiment using different methods, and what are the pros and cons of each method.
Learn what confounding and collinearity are, how to detect them, and how to avoid or minimize them in your experimental design.