Narrow down your topic, read what aspects of that topic are quantitative, and are relevant. Make sure it supports(is relevant) your theory. Reference any and all sources you use, credit where credit is due.

Look in primary sourced information published and peer reviewed by reputable people in the field. I would keep studies you are looking at analysis from all relatively the same size so the distributions of information. The less collective information and abundance there is more room for error and inconsistent results. You will go through much research and some you take time to read through may be found irreverent and that OK to omit it. Look for qualifying factor that may effect the data you are looking at.

To process data collect it and consolidate. Check units sources and make sure that the data you are pulling was not a meta-analysis from someone else or you could be overlapping research. This leads to false results. Its like counting your money twice... docent mean you have 2 times as much.

I'm sure there are many tools. But knowing how and what applied stats you want to use and how you want to express your results is your biggest hurdle. you want to make sure if representing yours and other research your are doing it accurately.

There are many great resources..

If you have a university library online they often give you access to primary resources.

Your university librarian can assist with this.

Use google scholar

open access information

science direct

Read secondary and tertiary articles and writings and check the references that they used and you can back track to information that is relevant to your topic.

Hope this helped.

You can check very well written samples on Lancet and guidance for the formatting. You need to set exclusion, inclusion criteria, after collecting your studies and setting your data bases, make your chart, review remaining studies relevant to your research subject, conclude based on that. Make sure to include published and unpublished articles since you need to avoid publication bias. These were few tips i mentioned, hope it helps.

Thank you Jennifer for detailed answer. Can you also mention different rresources and softwares used for data analysis?

Welcome, I did give a few resources at the end, and so did the poster after me, they are all great places to start. You will have to fine tune your resource search for what you need. As for software, I have not used many only for my analysis and that was all done in house and then collaborated later on. However you need to figure out what type of statistical analysis you are going to use then find a software that will work best for you. Id ask someone you known within your field of study they may be better help for that aspect.