 Reasennin on prosenttia käyttää logiikkia, jota saadaan premissa ja päästään tilaisuudessa. Me ajattavamme erilaisia reasennin kautta- ja kautta-reiseksiä, ja haluamme tehdä teori. Joten katsomme, miten reasennin, kautta-reiseksiä ja kautta-reiseksiä erilaisuuteen ja miten käyttämme teoriin nämä reiseksiä. Joten, mitä teori on? Teori on esim. mitä tapahtuu, miten, miten ja miten, ja teori voi tietysti olla keskusteltu usein prosenttia. Joten voimme sanoa esimerkiksi, että nimenominen asio, kautta-reiseksiä ja kautta-reiseksiä on käyttäjärjestelmä. Joten meillä on kautta-reiseksiä ja kautta-reiseksiä täällä asiaa kautta-reiseksiä. Joten teori on sellaista prosenttia ja kautta-reiseksiä. Joten tämä asia on esim. mitä ne ovat sellaista prosenttia, mitä ne ovat eivät heittävät. Joten teori on esim. teori. Joten, mitä teori on teori, mitä ne ovat eivät heittävät ja mitä ne ovat osaamista prosenttia, mitä ne ovat eivät heittävät, ja miten heidät heittävät. use government services. So if the government service provides you more accurate information, then people are more likely to use it. And then there is one paragraph explanation of how that process plays out. So we have theory here, explanation of particularly what and why happens. And then we have here a hypothesis and the role of the hypothesis here is to be a test of that theory. So the idea is that if this theory is true here, then we should observe this hypothesis here. The process through which we arrive from the theory to the hypothesis is called deduction. So the idea is that this is a general statement, a general theory, and then we infer that if the general statement is true, then we should observe support for a specific hypothesis developed based on the general theory. So it's from general to specific kind of inference. Let's take a look at the CO-gender example that I mentioned in the beginning of the video. So let's assume that we want to make a claim that naming a woman as a CEO causes companies profitability to increase. So our proposition would be that our CO-gender influences company performance. So that's the level of the theory. Then in quantitative research we apply deduction and we think, okay, so how do performance abstract CO-gender is a bit less abstract, but it's not something that we could easily observe. So how do we use logic to come up with some observable things? So we could for example say that performance could be measured with ROA and CO-gender could be measured by checking whether the CEO's name is a man's name or a woman's name. Then we measure data and we calculate some kind of statistical association. Then we infer that if this proposition is true, then this hypothesis must be true as well and then we must observe a statistical association. If we don't observe a statistical association that we hypothesized, then we conclude that the proposition was not correct. Or at least it's not supported using these data. And there are lots more to that, but that's the basic idea. So how do we do it in qualitative research? In qualitative research we don't really use hyperphasies. So that's basically interesting in quantitative studies. So instead of going deductively from theoretical propositions to statistical associations, we go up. So we observe something and then we infer that maybe there is a general tendency for something to happen. So the idea here is that instead of focusing on these variables here, what we focus on in quantitative research, we focus on the process here. So this is not actually, we're not looking at statistical associations. Instead we are looking at causal processes. So what caused what and through which process in your data. Then we infer that because we observed a causal process in one particular case or one small set of cases, then maybe there is a general theory that holds for a larger number of observations. Or larger sample, larger population. So the idea is that we observe a specific instance of a causal process and then we generalize that there could be a more general causal process that occurs also in other settings. And this is the idea. You go from individual observations to more generalizations instead of going from general theory to specific observations. And of course the importance in causal making these kind of inferences and causal claims in qualitative data, it's important that you explain really clearly what is the causal process like and why it happened. Because otherwise you really can't make the proposition which is a causal statement.