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You’re conducting statistical research. What tools can you use to make it more effective?

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Statistical research is the process of collecting, analyzing, and interpreting data to answer questions, test hypotheses, and draw conclusions. It can be applied to various fields, such as science, engineering, business, education, and social sciences. To conduct statistical research effectively, you need to use appropriate tools that can help you design your study, perform calculations, visualize your results, and communicate your findings. In this article, we will introduce some of the most common and useful tools for statistical research and how they can enhance your work.

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Everton Valério

Analista de Dados | Informa??es Gerenciais | Estatística | SQL | SAS | Power BI
Jennifer Buck, PhD, MSW

Director of Quantitative Research
Scott Bishop

Medical-Grade Wearables, Physiotherapy, Quality Assurance

1 Data collection tools

Before you can analyze data, you need to collect it. Data collection tools are methods or instruments that help you gather, organize, and document data from different sources. Depending on your research question and design, you may use tools such as surveys, interviews, experiments, observations, or secondary data. Data collection tools should be reliable, valid, and ethical, meaning that they measure what they intend to measure, produce consistent and accurate results, and respect the rights and privacy of the participants or data providers. You should also consider the feasibility, cost, and time required for each tool.

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Scott Bishop

Medical-Grade Wearables, Physiotherapy, Quality Assurance
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From a regulatory perspective, TypeForm holds a lot of heavyweight ISO certificates. They really are doing a fantastic job of making beautiful surveys, with a hidden layer of data integrity that any health authority or mega corp could use for patient and staff/employee surveys.

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Muhammad Hassan Khan

Data Analyst | Gen AI | ETL | ML | Python | R | Power BI | Data Engineer | Statistical Analyst | Reporting Automation | SILVER Medallist
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Use tools like Google Forms, Excel, R/Python, and survey apps for efficient data collection. Employ visualization tools, mobile apps, and EDC systems, considering research needs.

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2 Data analysis software

Once you have collected your data, you need to analyze it. Data analysis software are programs or applications that help you perform statistical operations, such as descriptive statistics, inferential statistics, regression analysis, hypothesis testing, or machine learning. Data analysis software can handle large and complex datasets, automate calculations, and generate outputs in various formats. Some of the most popular data analysis software are R, Python, SPSS, SAS, and Excel. Each software has its own advantages and disadvantages, such as ease of use, functionality, flexibility, and compatibility. You should choose the software that best suits your needs and preferences.

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Everton Valério

Analista de Dados | Informa??es Gerenciais | Estatística | SQL | SAS | Power BI
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Python e R s?o ótimas ferramentas para prosseguir em uma pesquisa, pois s?o ferramentas gratuitas com muitos pacotes estatísticos a disposi??o. Claro, todas as citadas no texto entregam muito em quest?o de estatística e análise de dados, mas, para um iniciante eu indicaria Python, por sua grande comunidade e material gratuito disponível.

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Jennifer Buck, PhD, MSW

Director of Quantitative Research
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If you only need to run simple analysis - for instance, calculating descriptive statistics -- excel or google sheets should be just fine. Once you start diving into inferential statistics or latent variable modeling, I recommend R. It's free, and while there is a learning curve for those of us who aren't super familiar with coding, it's a really versatile tool that allows you to dig into a wide variety of analyses as much or as little as you'd like. Displayr is another option. It's a point and click program, but fully based in R, so there's capacity to customize the code. I've found Displayr to be a great tool -- allowing me to quickly run most of the analyses I need and visualize them in an accessible way.

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Scott Bishop

Medical-Grade Wearables, Physiotherapy, Quality Assurance
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Business-wise: leverage your lowest common denominator's knowledge and build from there. Excel or Google sheets is the highest uptake so use it and consider purchasing co- pilot. You can also learn DAX, and then find someone to help you link in to bigger and bigger data sets. After that, R and Python can take you even further, and can be integrated with DAX via Power BI. Now you have a pyramid of data flowing from "big" to "granular", and multiple skills-tasks alignment slots.

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3 Data visualization tools

After you have analyzed your data, you need to visualize it. Data visualization tools are techniques or tools that help you create graphical representations of your data, such as charts, graphs, maps, or dashboards. Data visualization tools can help you explore your data, identify patterns and trends, communicate your results, and persuade your audience. Data visualization tools should be clear, accurate, and relevant, meaning that they convey the main message, avoid distortion or misinterpretation, and match the purpose and context of your research. You can use data visualization tools within your data analysis software or use standalone tools, such as Tableau, Power BI, or Google Data Studio.

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4 Data communication tools

Finally, you need to communicate your data. Data communication tools are methods or tools that help you present and share your data with others, such as reports, papers, articles, slides, or podcasts. Data communication tools should be concise, coherent, and compelling, meaning that they provide the essential information, follow a logical structure and flow, and engage and inform the audience. You should also consider the format, style, and tone of your data communication tools, depending on your target audience, medium, and objective. You can use data communication tools within your data analysis or visualization software or use other tools, such as Word, PowerPoint, or Canva.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Muhammad Hassan Khan

Data Analyst | Gen AI | ETL | ML | Python | R | Power BI | Data Engineer | Statistical Analyst | Reporting Automation | SILVER Medallist
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To enhance statistical research effectiveness, utilize tools like mobile data collection apps (ODK Collect), electronic data capture systems (REDCap), sensor technologies, collaboration platforms (Slack), and database management systems (MySQL). Tool selection can be tailored to specific research needs and collaboration preferences

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You’re conducting statistical research. What tools can you use to make it more effective?

1

2

3

4

5

1 Data collection tools

2 Data analysis software

3 Data visualization tools

4 Data communication tools

5 Here’s what else to consider

Statistics

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You’re conducting statistical research. What tools can you use to make it more effective?

1

2

3

4

5

1 Data collection tools

2 Data analysis software

3 Data visualization tools

4 Data communication tools

5 Here’s what else to consider

Statistics

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