You're at odds with your team on statistical methods. How do you ensure the project's success?

Stats is maths. Disagreement is often because someone overlooked an assumption or was unaware of a theorem. If so, reason it out. If not, because there is a genuine trade-off -- say between Nickell bias and incidental parameter bias -- then show results for both methods. If that blows up the paper, write two papers. Really, statistical methods are not something to fight about.

Buscando consensos e mantendo a mente aberta. Pode ser que minha equipe esteja certa, e eu não!!! Mas o mais importante é saber como o consenso obtido impactará com o planejamento, e em última instância, com o negócio em que estamos envolvidos.

How can you be "at odds with statistical methods"? I can understand that you may have different interpretations of what statistical analysis is saying or you have different opinions in the team on which methods should be applied for a particular project. But I'd say that if you are "at odds with methods" you are in trouble...

To ensure project success despite disagreements on statistical methods: Understand Differences: Identify and respect each perspective. Facilitate Communication: Hold an open discussion meeting. Review Objectives: Align on project goals. Consult Literature: Refer to relevant sources. Seek Expert Opinions: Consult an unbiased external expert. Conduct Comparisons: Test methods on sample data. Consider Hybrid Approaches: Combine method strengths. Prioritize Flexibility: Allow for adjustments. Document Decisions: Record discussions and choices. Establish a Contingency Plan: Prepare alternatives. Promote Cohesion: Foster a collaborative team environment. This structured approach ensures focus on the best project outcomes.

Being at odds is perhaps beyond the point of constructive diversity of thought. If it truly has got beyond that, reset and relook at the vision and objectives, ensure that everyone is clear on the scope and the problem statement or research question. Then set clear guardrails for methods discussion, supported by transparent decision frameworks ensuring buy in by everyone. If there remains dissent then consider seeking third party or independent advice, again taking the stakeholders alongside those conversations and approaches.

Consider organizing a methodology mediation session where team members can openly discuss their preferred statistical methods while focusing on shared goals. Facilitate a roundtable discussion to explore how different approaches can complement each other and address the project’s objectives. This collaborative effort fosters mutual respect and helps identify a balanced approach that leverages the strengths of diverse methodologies while maintaining quality.

Buscar terreno común es fundamental cuando hay desacuerdo en métodos estadísticos. Identificar objetivos y preocupaciones compartidos ayuda a unir al equipo. Según Fisher y Ury (1991), enfocarse en intereses comunes más que en posiciones opuestas facilita soluciones colaborativas. Este enfoque permite cerrar la brecha entre diferentes preferencias metodológicas sin comprometer el rigor o la calidad. Al centrarse en los resultados consensuados, se puede encontrar un método equilibrado que satisfaga las necesidades del proyecto y aproveche la experiencia del equipo, asegurando así el éxito del proyecto.

Make sure to find out and understand on which level of statistics each member is and why they might choose different approaches. It is not helpful to argue about Bayes vs Frequentists approach if the team is not familiar with these terms. Education goals should be in reach for everyone in order to have the whole team improve.

Consider hosting a statistical method swap where team members take turns teaching each other their preferred techniques in a simplified, jargon-free manner. By embracing this two-way educational approach, you can uncover new insights and enhance the overall success of your projects through shared knowledge.

Educar y aprender en un entorno colaborativo es crucial cuando hay desacuerdos sobre métodos estadísticos. Según Argyris y Schön (1978), el aprendizaje organizacional mejora la capacidad del equipo para adaptarse y resolver problemas complejos. Al educar a tu equipo sobre los méritos de diferentes métodos y estar abierto a sus perspectivas, se fomenta un intercambio de conocimientos que puede desmitificar técnicas desconocidas y ofrecer nuevas soluciones. Explicar conceptos con claridad y evitar jerga técnica asegura que todos comprendan y participen, facilitando un enfoque más informado y cohesivo para el éxito del proyecto.

For quantitative social science, one thing I promote in my research lab is having a documentation document, so you have a record of all the decisions that have been/are being made and why. I start out with the research question/topic of interest, the data that are being used, a list of variables of interest, and univariate/bivariate descriptive statistics. Then add to that with decisions on recoding variables, creating scales, etc. Since the modeling strategy will depend on the research question, your dependent variable, and other factors, make sure that you spend time understanding your data. The modeling strategy should be appropriate to answer the research question.

In any case, when it comes to research, the most paramount thing to consider is to correctly understand the data being considered for the study. Specifically, it will be best to keep in mind how any variable was collected, captured, and how the data-capturer defined the variable. This goes a long way when it comes to cleaning the data for analysis, merging/stacking several datasets, and presenting the data. These all allow for consistent estimates, internal & external validity, and for making accurate inferences using such data.

Think about establishing a decision criteria checklist to guide the selection of statistical methods. This checklist would include factors like the nature of the data, the project's objectives, and any specific constraints. By systematically applying this checklist, your team can ensure that the chosen methods are the best fit for the project.

Desarrollar un marco de toma de decisiones es una estrategia eficaz para resolver desacuerdos sobre métodos estadísticos. Este marco debe considerar criterios como el alcance del proyecto, características de los datos y resultados deseados. Según Hammond, Keeney y Raiffa (1999), un enfoque estructurado reduce los sesgos y mejora la calidad de las decisiones. Al aplicar este marco, se puede evaluar cada método objetivamente, garantizando que la selección esté alineada con los objetivos del proyecto y no con preferencias personales. Este enfoque lógico y sistemático facilita la toma de decisiones informadas y coherentes, asegurando el éxito del proyecto.

Una POC siempre te permitirá validar el método sin necesidad de llegar a exploración final donde hayas invertido tiempo recursos. Estos mecanismos son útiles cuando quiero explotar alternativas o decantarme por una teoría u otra

Implementar pruebas piloto es una estrategia eficaz para resolver desacuerdos sobre métodos estadísticos. Al probar opciones prometedoras en un entorno controlado, se puede evaluar su eficacia y tomar decisiones basadas en datos. Según Thomke (2003), la experimentación es clave para la innovación y la mejora continua. Las pruebas piloto permiten identificar problemas tempranamente, ofreciendo la oportunidad de ajustar el enfoque antes de una implementación completa. Este proceso minimiza riesgos y optimiza los resultados, garantizando que la metodología seleccionada sea la más adecuada para los objetivos del proyecto, asegurando así su éxito.

Think of adopting a flexibility first mindset where you acknowledge that the initial statistical methods might need adjustments as the project evolves. As real-world data reveals its quirks, remain open to reassessing and tweaking your approach. This adaptability showcases your dedication to the project's success, proving that being flexible is a strength, not a setback.

Una competencia clave es la capacidad de adaptación. Muchas veces un plan sufre variaciones. Una fuente de datos se puede caer o el presupuesto para el proyecto disminuye. Lo principal es no perder de vista el objetivo del análisis, siempre se puede entregar valor igual, adaptando la escala o la duración.

Difficult problems rarely have obvious solutions. Some solutions will only reveal itself once we are deep in the project. Some problems will only reveal itself once we are deep in the project. No matter the amount of planning, flexibility and adaptability will surely play a huge part of the final product.