 Yksi kupeessaan suunnan lähelläväntöä, ja esim. tuosta todella kokemaamiseksi olisi se, että tämä on yksi lähelläntöä, jolla on yhdistettäväntöä ei ole yhdistettäväntöä. Joten, mitä on yhdistettäväntöä? Yhdistettäväntöä on tietysti hieman, että voimme numeroa lähellä betausi samaan wearin kanssa. Jos täällä on eri eriin omistuja lähelläntöä, himäimmäistä ja eri reikumattomakasta. Päätäväntöä on se, että kaikki lähelläntöä, kuten klassiikassa teoriin, he menevät the same thing. Here the same thing is T and the indicators all capture T plus some random noise and this is the statistical model for unidimensional measurement. This is also known as the factor analysis model. So why is unidimensionality important? Why can't you just summarize these things into a single score? There are reasons, reason can be understood with an example. So let's take a look at this example. We have a score defined as a person's height plus person's weight and we take a sum we call the person's size. So the problem is which one is bigger, the tall and skinny person or the short and fat person? We cannot really say because the concept of a big person relates to both of those concepts. You can say that the person is big if they are tall and big if they are just heavy. And let's also say my size is about 250 using this score. So what does it really tell about me? I could be a tall and skinny person if we want to study whether I'm a good athlete or not, being tall and skinny or being short and fat. They probably have different performance consequences. If I'm tall and skinny, I could be a good long distance runner. If I'm short and fat, I could be good in sports that require strength for example. So these are two different kinds of people. We cannot say that these are equivalent. The idea of unidimensionality and unidimensionality scale is that we can summarize all relevant information about the construct being studied with one number. With person's size we need two numbers. At least two numbers, height and weight. We can also have shoe size and whatever, but the height and weight are the two most important ones. Why is this important? It is because if we try to theorize the causes or consequences of our person's size score, the causes and consequences of being heavy and being tall are different. For example, if I would go to try out in a basketball team, the coach would say that I'm too small for basketball. My size is 250, then the average size of a person in the team is 300. So I'm too small, so how do I make myself larger? I could eat more, in which case my size would become bigger. I'm a fat, but I would be a fat person and I still would make it to the team because the others would be tall and skinny in the team or anyway tall. The same thing, we can influence only one of those. So if you're saying that you have a cause of person's size, then at least for the adult population, the only way you can influence your size is by changing your weight, by eating more and exercising less. So these concepts of height and weight are distinct dimensions of the high level concept of person's size. It doesn't make any sense to take some of those. This is relevant for business research because you oftentimes see constructs such as intravenient orientation, which is defined as three main dimensions of innovativeness, productivity and risk taking. You have a product mix called a marketing mix consisting of product, price, place and promotion and then you measure each of those dimensions separately. Sometimes researchers still generate one score that is supposed to summarize those dimensions. That makes as much sense as summarizing person's size as the sum of height and weight.