 Jos olet interesting in research methods, it's useful to know where you can learn more about what methods are actually used in your field, and where to get advice if you're stuck with a problem. Of course, if you're taking a course, then it's a good idea to ask the course stuff, but there are also other options available, and you should familiarize yourself with these options already during the course. During the course. The first option that's available is that many academic organizations offer research methods training, or have interest groups for research methods. I'm a member of the Academy of Management Research Methods Division and that's one great place to learn about different methodologies. This is their website that contains some resources, but more importantly these people organize trainings at conferences and you can participate if you're a member. So consider if you're a member of the Academy of Management, consider joining the research methods division. Of course other academic organizations have similar interest groups or units or divisions or groups, whatever they're called. The Academy of Management Research Methods Division also organizes pre-conference workshops or professional development workshops. Here are some examples. For example, there are these kind of sessions where you can meet people who know a lot about methods. This is a, for example, if you like the paper by Akunis and Wunderberg, then you can go and see Bob Wunderberg and ask questions from him. You may also want to ask questions from other people that are experts in methods or some fields of methods. So there are these open settings where people who know generally about methods come and you can talk to them. You can even take your paper with them or give a regression table to them and ask them what can I do about this and then you get one on one feedback. So that's a very useful way of seeing people informally. These conferences also host a number of introductions to certain topics or workshops or short courses about topics. For example, I like to teach outliers based on the article by Herman Akunis and his students. So they have been presenting this article of how to identify, define and handle outliers, a half-day workshop for multiple years. So if you want to know more about outliers or ask questions, how you should deal with outliers in your research, then this kind of workshop is very valuable. Of course, there are tens of other workshops as well that are equally high quality. Then outside conferences you may also want to have a place to ask for help. If you take a course, then using the course forum is a good idea, but there are public forums that you can also ask. Here are some examples. SCMnet is a 25-year-old email list that has a couple of thousand, if I remember correctly, 3,500 members that are either interested in methods so that they can learn more or are experts in methods, particularly structural ecosystem modelling, but also others. So you can post your questions if you have a data-analytical question like how do I interpret the chi-square statistic in structural ecosystem model, then you can post it there. And you will certainly get lots of responses. You will find out that there are often multiple different perspectives on an issue and then you will learn more and then you can make your own judgement on which advice to follow. Even if your question is not related to structural ecosystem modelling, this is a big list of people, then you can ask what's the best place to ask questions about R and somebody on the list will know. Then we have the academic management research methods division list. That's a bit smaller. There's a few hundred people, maybe a thousand people, and the questions are less specific to data analysis problems, but they are more about research design. Like you have a problem of whether you want to or whether you should include a control or not, that's a good place to ask. Then if you are interested in learning about particular software, software typically has some forms. For example, Stata has a Stata list at ORC and people with all levels of expertise in Stata ask questions there. Like you could ask how do I get started or you could say ask my multilevel model doesn't convert. Here is the output, what should I do? And there will be people who are willing to answer your questions. So there are many places on the internet that you can ask these questions. Because the email lists are pretty large, it guarantees that there is some of self-censoring. So if someone is going to answer your question and they know that it's going to go to thousands of their colleagues, they want to be sure that their email response is well written and correct. That's a good thing for you if you ask questions. Then it's also possible to take courses. I recommend also looking outside your own department. One of the best courses that I have taken was in a psychological department. And that really taught me a lot of things. And also when you learn methods for other applications, then it gives you a broader picture of what you can do with them in your own field as well. And then there are also online courses. I recommend Coursera. But one problem with online courses is that basically anyone with a microphone can record online video and put a price tag on it and post it on some services. So some of these services that are available online are not their high quality. They rely on just having a large amount of students to take a course for free and the course may not be any good. The problem is that if you're just looking at the course, you may not know. So be aware of, I call these scams because some of these courses are taught by people who may not know the subject area as well as they should. So when you're looking at whether to take an online course, there are two things that you should look at. First is the person and expert. So if you are taking an econometrics course, then if it's run by an econometrics professor, then it's probably a better course than if it's run by a marketing professor. This is of course another bullet proof criteria and there are some really good marketing professors out there. But looking at whether the specialty of the person is the thing that they're teaching is a good indication. Another thing that you can look at is whether it's branded by a university. So if a course is branded by a university instead of being branded as a person teaching a course online, then that gives some credibility to the course. Because universities don't want anyone to be teaching incorrect things using their brand. I recommend particularly the Duke University courses on Coursera. Data analysis and statistical inference is a great course. They use R and they cover the basics and then they go more advanced regression model. They don't really cover research design as much as they cover the basics of statistics. But that's a really, really valuable course. It's one of the most highly rated, massive online courses ever. Then another nice way of learning about things is to do examples online. University of California Los Angeles Statistics Department has these data analysis examples. This is their old website, so the new website is a bit different. But the idea is that they have a list of different statistical analysis that you could apply for data. And they have different web pages for different statistical software. They have data, they have SAS, which is a bit older software that I can't recommend anymore. Then there is a SPSS that I don't recommend, although it's commonly used. There's M+, which is a Specialized Package for Socrates on modeling, and there is R. So you can compare how a particular analysis would be executed in these five different software packages. And also these websites tell you how you interpret the results when you would apply the analysis. So that's quite useful. They have lots of examples from textbooks. For example, I think the prestige data that I use in a different video is analyzed in one of these examples. Then it's always a good idea to read books. And if you want to learn about statistical analysis methods, instead of finding a book that says something about everything, you should find the best book about the particular methods that you want to use. Here are some of my favorites. For regression analysis, I like Woolridge's introduction on econometrics. And Cohen's regression book is a classic as well, although I think a proper econometrics book is better than Cohen's book. Then if you want to do logistic regression analysis, then Horseman and Lemonshow is a classic. So Stanley Lemonshow is also teaching a course on logistic regression analysis on course error using data. So you can follow that course. And for mixed effects regression analysis, Sophie Rabehets gets a book on a multi-level, along with little more using data, it's a great applied book. And if you want to do surveys, there are a couple of books that I recommend. So depending on what you want to do, find the best book about the particular design. Let's assume that you want to do a survey project where you send out questionnaires with multiple index scales and multiple indicator scales. So you want to analyze them with factor analysis and you want to analyze the scale scores with regression analysis. So what you do is that you find a book about survey sampling growth. You find a book about survey instrumentation by Dilman. You find a book about measurement of factor analysis. For example, Develis is a great book. And then you find a book about regression analysis. You study those books while you do your project and you will learn as you do. Yet another great resource is research methods journals. So you probably follow some journals that deal with the topic of your study. But there are also journals that are about research methods. And these fall into two broad categories. The first category is technical research methods journals that focus on the development of new techniques. And you will know these journals based because you can't understand what they're talking about. So these are mathematical journals that present simulation studies and things like that that are meant for people who develop analysis techniques for others. So they're not really for applied researchers. Then there's the other category, applied research methods journals such as organizational research methods and psychological methods that are primarily aimed for researchers who are interested in applying techniques to their field. For example, the organizational research methods, the editorial statement says that it's meant for advancing the understanding of research methods and the current practice of research methods in the field of management. And psychological method is the same, but it's for psychologists. These journals are meant for researchers who have a PhD done using quantitative techniques. So it's not the best place to learn the basics. But once you learn the basics, following one of these research methods journals is a good way of keeping up with what's going on in the field and what are the best, latest and greatest things that you could apply. One nice way of following these journals is that many journals present their articles as an RSS feed. So it's here. You can Google a bit more about these RSS feeds. But the idea is that this link provides a list of articles that the journal publishes and you can use an RSS reader software to subscribe to this link. And then once you have subscribed every time this journal publishes new articles you will be notified and you will get the abstract and the title and the authors and then you can decide whether you want to read it or not. So here is my RSS feeder. I use Feedly, which is an online software. I log in and I click on those RSS links and I subscribe them to Feedly. And this is my academic feed, all my journal articles. I also follow some others and I follow some 20, 30 different journals. I change this quite frequently. So I know what's going on around the topics that I study and around the methods that I use. So this is a great way, the RSS reader, to keep up with your field and you should subscribe to also some research methods journal. If nothing else, subscribe to Organizational Research Methods. And as a disclaimer, I'm on the editorial board. Then you should work with more experienced people. So your supervisor is a great resource if he or she knows about quantitative techniques. Also some other colleagues could be good resources for you. If you have data analysis problem, it may be a good idea to ask somebody who is more experienced than you to become a co-author to help you with the analysis. And if you have a good idea, then people tend to agree with this kind of request. Then you can also ask your supervisor if you're a very beginner, if he or she has data sets that would need to be analyzed and that you can practice with. Then at some point you start your own project. So for me when I was starting doing our learning research methods, I started by reading a book and I understood basically nothing from the book and I was not motivated to read the book at all. I did a project. I decided that I will do a survey study. I did the study and then I applied structural ecosystem modeling because I saw that other people applied that as well. Then I did something. I got some results. I submitted the paper to a conference. I got the reviewer comments back and then the reviewer comments said that the author really doesn't seem to understand what he's doing. And the reviewer of course was right because I was just applying method by looking at what others had done with the same method and without really knowing what I was doing. But the good thing was that that little project really motivated me by indicating what things I should know that I did not and then I started studying about those particular things and eventually building up my competence. So working on data sets while at the same time studying at least that's how it worked for me as well. I mentioned the psychology department course about structural ecosystem modeling. That's the best one that I have taken. In that course I was all the time applying the techniques that they taught in the class on my own data set the nights after the course and in the mornings I could ask the instructor some questions that are like how do I apply this technique to my data? What does this statistic or this result mean in the context of my data? Also if you are going to be investing a lot of effort in data collection plan well ahead because the quality of your study is mostly determined by how well you sample and do you measure the right things and do you measure the things correctly. If you omit for example a control variable in a survey study it's nearly impossible to collect it afterwards. So planning a lot before actually doing is a good advice if you want to do high quality research without redoing a lot. Then finally there are two kinds of answers to all scientific questions. Sometimes somebody tells you that you don't need to care about heteroskedasticity, you don't need to care about this thing or that thing. Just apply this technique and you're going to be fine in all situations. Such simple answers are unfortunately not often right. Sometimes we have a complex problem and the complex problem requires a complex solution. So you have to actually go and study. But then studying things will produce better research and it will make you, it's also more motivating for you when you know that you can trust your results.