 Toi pitää puhuta kautta- ja researchers, jossa viewershuvan yhdistys on erityisesti. Puuutina research on research done with numbers. Kun researches with numbers, tenemos tre important questions. The first question is, how do we get the numbers? That is about data collection and research design. Then once we have the numbers, the second question is, what do we do with the numbers? Kann cruel the..! Minds standard deviation son Correlation regression analysis . Multilevel booster m misses all kinds of things! And to understand what we can do with the numbers, We have to answer the third question? What do we want to say with the numbers? So, typically, our analysis has some kind of objective. We could be trying to answer research questions about for example, the relationship between companies profitability relationships between companies profitability and the CO gender, or we could be trying to predict something. For example given some characteristics of a house, how much are we going to have to pay for the house? So statistical analysis can be used for multiple different purposes. So when you see research results such as the regression table here, Täällä on tärkeää kysymys, mitä haluamme sanoa tämän tableon. Joten meidän on täytyy ymmärrää, mitä voimme sanoa tämän tableon, sillä voimme tehdä kokemme. Sitten, mitä on tämän tableon, mitä on varioita, se on tämän, miten saamme tämän numeiden ja myös, mitä haluamme sanoa tämän numeiden. Se on tämän research designin. Ja sitten, jolloin se, mitä tämän tableon on tämän, on tämän, mitä tehdään tämän numeiden. Tietysti, kun meillä on tämän final resulta, olemme already answered these three important questions. Where do we get the numbers? What do we do with the numbers? And what do we want to say with the numbers? And of course these questions are interrelated, so you can't really answer one without another. If you want to do quantitative research, you need to have some basic skills. And my personal opinion is that you need four different skills. All you need to understand the subject of study. So you need to understand what are the most important theories of the phenomenon that you're studying. This is important because a big part in quantitative analysis and research design in quantitative research is about ruling out alternative explanations. If you don't understand the phenomenon, then it's very difficult to rule out the different causes that could be behind your observed data. And it's very difficult then to make any valid causal claims. The second thing that you need to understand is basics of statistical analysis methods. So you have to understand what different techniques do and how the results are interpreted. And that's important for two reasons. First of all, just even if you just knew how to point and click yourself through a software, the interpretation part is actually quite challenging. Also the software sometimes doesn't calculate things in a way that you would like it to do and then you need to do some adjustments and to do those adjustments you have to understand what you're doing. The third thing is research design and application of methods. That refers to skills such as data collection, what data to collect, how do you do, for example, surveys. And also what is the standards of methods in your field because it varies between fields and you have to know what is expected in terms of quality. Then the final thing is use of statistical software. So you have to be able to use a computer to run your analysis. Which one of these is the most important one depends on what you want to specialize in. So no one is a master of all these four and typically it pays off to be really good in one of these and just have basic understanding of others. For example, if you are really great in theory and you are good in research design, you are good in collecting data, then you can always find someone else to help you with the analysis with the statistical methods and use of statistical software parts. Then you can write the paper together or it also works the other way around. If you are really good at statistical analysis methods and use of statistical software and perhaps research design, but you are weak in theory, then you can ask a colleague to help you with the theory part and then you can design the study together. So normally it's a joint effort where you have people with different kinds of skills, but generally the skill set that you need is of this area and you should understand the basics of each of these four. The question that I often face is is quantitative research difficult? And it can be difficult if you start, at least it's difficult to get started. So if you take for example Green's econometrics book and you start studying quantitative research by reading that book, it's nearly impossible to do because you don't have the background knowledge. So I like this metaphor of quantitative research as writing a novel. So to write a novel you first have to go to school to learn how to write things. But just half a year of first grade which will teach you how to write doesn't make you a great novelist. So you're not going to be able to come up with Game of Thrones novel based on just first year degree. Then qualitative research is like doing a painting and anyone, if you give them a pen and a paper can doodle something. And if it's a child that doodles, the parents will even call it art. But just being able to get something on a paper doesn't make you a Picasso. It takes years of training. The reason why people think that quantitative research is difficult is that they cannot write. So you don't have the basic skills of running a correlation, running a regression analysis, then it can be hard. But on the other hand, rolling these basics is not that difficult. Qualitative research on the other hand, people can think that they know how to do qualitative research if you can do some interviews and write some conclusions about those interviews, even if you had no idea about what qualitative research is really about. In practice, quantitative research is easier to publish for a couple of reasons. First of all, particularly if you publish to trade magazines is that people like numbers because we think numbers are more objective than just interpretations from qualitative data. The second one reason is that there is typically one best practice that can be followed. For example, if you do a survey, you apply a factor analysis according to a factor analysis book, then you apply regression analysis according to a regression analysis book. That's quite a mechanistic exercise and it'll get you published. On the other hand, in qualitative research, you need lots more imagination and your own creativity to get a useful result. That can be harder. It's more like an artistic problem and this is more like an engineering problem.