What is the best statistical software for analyzing quantitative data in primary research studies?

Quantitative studies are a powerful tool to understand the world around us such as in public health, social science, education, data science, econ and much more to make informed decisions. It allows researchers to objectively measure and analyze data to test and make predictions based on trends and patterns. I use it both for my teaching and AI work for enterprises. I conducted a quantitative study to evaluate the impact of different teaching methods on student achievement when I taught a fairly large introductory programming course. The results showed that my teaching method that involved experiential learning led to significantly better student achievement. This insight would not have been possible to gain without quantitative data.

Simple: Python and R! They are free and open source. There's loads of information on the internet for any task, issue, or curiosity. And, they are the most flexible for all backgrounds and datasets. But the best part is that they don't require an institutional license (unlike MATLAB and STATA), so once you learn the language, you can use it for the duration of your career.

All throughout my graduate thesis and dissertation work, I have been extremely dependent on SPSS. SPSS is a user-friendly statistical software widely used in pharmaceutical sciences research. It offers a range of statistical procedures and a user-friendly interface, making it suitable for users with limited programming skills. Advantages: User-friendly interface, extensive statistical capabilities, popular in pharmaceutical sciences, and good for basic to intermediate analyses. Considerations: Proprietary software (paid license required), less flexible for custom analyses, and may not be suitable for very complex statistical modeling.

The choice of statistical software for analyzing quantitative data in primary research studies depends on your needs and familiarity. IBM SPSS is user-friendly, comprehensive, and great for complex analyses, but can be costly. Excel is widely available, budget-friendly, and suitable for basic analyses, but has limitations for advanced statistics and data visualization. Consider your research complexity, budget, and expertise when choosing between them. Many researchers use both for different purposes.

There are many good softwares in the market for analysis of experimental data, some of the good software is JMP, MiniTab, etc.

Choosing the best software for a given task depends on the user's skills. As someone with a coding background, I prefer to use Python and R because they are free and have vast online resources. However, having coding skills is essential to use either of them. The choice between the two will depend on factors such as the size and format of the data.

Choosing the best statistical software depends on the research topic, types of data to analyse and hypothesis testing. In my academic experience with clinical trials in sport science I mostly used IBM SPSS and it was resolutive to understand data, validate assumptions, draw final, accurate, conclusions and redact manuscripts.

There is no clear cut winner. I have been using STATA and excel and the combination has served me well. Although STATA has its limitations, but these limitations serves as an opportunity to adapt and improvise, a skill that may be overlooked while working with more "versatile" softwares. Using basic softwares for primary datasets also leaves room for the imagination for simplifying data as much as possible. However, python is extremely handy for setting up automation and data scrapping workflows.

I use R statistical software and it is absolutely the best for computing large data files via agricola package. Also I am using SPSS statistica and it is more user friendly option. Hope this helps!

In comparing statistical software, think about Wikipedia versus an Encyclopedia. Much like Wikipedia, R and Python, are free, draw upon the wisdom of the masses, and in turn, offer tremendous flexibility to fit different statistical models from all of the contributed libraries. Commercial "Encyclopedia" packages (e.g., SAS, Stata, MPLUS) offer model fitting procedures that have been vetted but come at a price and offer less flexibility. R vs Python: Python offers the most flexibility but has a steeper learning curve than R. Commercial packages are more user friendly, and each have their strengths: MPLUS for SEM, SAS for longitudinal and clinical trial analyses, and Stata for causal inference and other health policy analyses.

I Consider 1. Purpose: Some software is industry-specific. Ease of Use: Beginners prefer intuitive interfaces, while seasoned analysts opt for code-based platforms. 2. Data Capacity: Ensure it handles your dataset's size. 3. Techniques Offered: Confirm it supports necessary tests and models. 4. Visualization: Graphical representation capabilities are vital. 5. Extensibility: Check for add-ons or packages to enhance functionality. 6. Cost: Balance between budget constraints and feature requirements. 7. Community: Active user communities can offer support and shared resources. 8. Integration: It should mesh with other tools you utilize. 9. Updates: Ensure the software has regular development for bug fixes and new features.

Comparison is purpose dependent- For students, it is cost-dependent For industry, it is acceptability-dependent Other factors can be- time, visualization, data

Also important for those new in the field is the openness and accessibility of the developer community. Spreadsheet software like Excel may have a lot of users, but most of them are not using it for quantitative data analysis and answers to specific questions may be hard to come by. More specialized software may not have a large community and/or may charge for access. A good proxy for how easy it will be to have you questions answered is to browse StackExchange forums for each software you are considering.

Working for a nonprofit, we have definitely have to consider cost as well as staff access. Nonprofits can experience staff change over and changes in funding streams, which can impact the overall budget. Unfortunately, statistical software is often not a priority line item and can therefore become too expensive for an organization to keep. Free software, such as Google docs, Microsoft office, the center for disease, controls, free program, and survey monkey provide foundational computations, but depending on the work and sophistication of the assessment and evaluation component of the program, a more advanced software may be needed.

When evaluating statistical software for quantitative data analysis, consider the following: Functionality: Supports a variety of statistical tests. Usability: Tailored to your skill level. Data Handling: Efficiently manages large datasets. Visualization: Offers precise data interpretation tools. Cost: Balance between affordability and features. Reliability: Ensures accurate, consistent results. Support: Availability of help and active user community. Integration: Works well with other tools you use. Updates: Indicates ongoing improvements.

1. Statistical and graphical capabilities specific to the statistical analysis tasks or features you need the software to handle. 2. Look for packages that are commonly used in your field or industry. 3. Ease of use and learning curve: Is the user interface intuitive and user-friendly? Is there extensive documentation or online resources available to support learning? 4. Data handling and preprocessing capabilities for large datasets, various file formats. 5. Reproducibility features that allow you to save and share code, create reports, or generate automatic scripts. 6. Availability of trial or demo versions for evaluation purposes.

R data analysis software. Free, open source and easy. using R programming language, an open-source language used for statistical computing or graphics. This programming language is often used in statistical analysis and data mining. It can be used for analytics to identify patterns and build practical models.

You can learn statistical softwares by: -Enrolling on shortcourses that teach about statistical softwares/Research Methods -Leverage on educative Youtube Channels.I find one channel such as StataCorp LLC being very useful -Consult experts in the field for guidance and mentorship. -Join Peer learning programs to learn from each other.

To learn statistical software as a physician and clinical researcher: Identify Needs: Choose relevant software (e.g., SPSS, R, SAS). Online Courses: Join platforms like Coursera for beginner courses tailored for healthcare. Practical Application: Regularly practice using sample datasets to understand functionalities. Engage in Workshops: Attend workshops at institutions or conferences for hands-on experience. Collaboration: Work with statisticians for direct insights. Utilize Online Forums: Use resources like Stack Overflow for troubleshooting. Apply in Real Research: The best learning comes from using the software in actual projects. Mastery comes from continuous learning and real-time application.

Play, play and play some more. Ask interesting questions and seek to answer them. Have weather patterns changed in your area in a statistically significant way over the last 50 years? Find out! What about energy use? Or the cost of living? Whatever questions you're interested in, for work or leisure, go and answer them in a quantitative way. And incorporate the tools you want to use - whether python, R, whatever - into your regular workflow. If you want to learn python, but you use Excel as a crutch, do everything you can to completely switch. Learn by doing, and do as much as possible.

Practice, practice, practice Most of them are self-taught They have guidebooks, and videos on using the specific software for purposes

Let’s normalize the use of statistical software in community based/ nonprofit organizations. Taking this into consideration, nonprofits could benefit from discounted cost options, smaller utilization packages and potential support as a benefit for non-profit organizations. This could also be accomplished by large academic or healthcare institutions sponsoring the cost for nonprofits by allowing access to their research database free of cost.

Talk to a statistician, ideally during the design phase of your research study. Your research protocol should include details of the analyses you intend to perform. Performing appropriate analyses is more important than choosing software.

Look at the software that is preferred for your work area and incorporate that in your research Practice, reach out to tech support for clarity Keep Learning as more software come up

Programming is one of the skills that you don't have to necessarily be good at to succeed. Just like every other skill, it takes time to master. I was extremely bad at programming during my engineering years. I loathed the blue screen and bright yellow font of C++. My teacher wasn't good either. Along the years, I realized I was more of a visual learner. I had to see first and then do it myself for me to figure things out. This started by working with LabVIEW and it gradually translated into MATLAB, Ruby on rails and then to Python. The best thing about learning programming for research is that you can do it from the comfort of your home at the time of your ease and at your pace. YouTube is the best resource whether for R or Python!

How do I choose the right statistical software for my research study? 1. Determine the type of data you have: The type of data you have will determine the type of statistical analysis you need to perform. 2. Consider your budget: Statistical software can be expensive, so it’s important to check if the product you chose offer free trials or academic discounts, before making a purchase. 3. Evaluate the features and capabilities of each product before purchasing. 4. Consider the ease of use: choose a product that has a user-friendly interface. 5. Consider compatibility of the product with your system and other software programs you use. 6. Consult with experts in your field or statisticians in case of doubt.