[MUSIC] In the last video, we have looked at the different types of primary data, which you can use for your current study. Here, we are going to start considering secondary data. If you remember, secondary data is a type of data which is not usually collected for the current study. But rather it has been collected by somebody else for some other purpose, and you get access to the data in order to use it for your study. Now usually secondary data is available in different formats or in different scales of measurement. The first type of measurement for secondary data Is the nominal measurement. Here there's no ordering or levels for the different categories which are at a level. So let's think about an example. When you are asked that, okay, you have three different brands, maybe Heinz tomato ketchup. The store brand private level and also preferably enhanced. Out of these three, which one do you prefer most? Of course, here there's no specific ordering of the product which are from which you have to choose your preferred brand. So, this is where nominal data is useful. The second type of secondary data is called ordered. Here, of course, there is some ordering, but importantly, the distances between the different categories are not relevant at all. So again, let's think about our example of tomato ketchup. Again, we have, let's say, three different tomato ketchups, Hunt's, Heinz, and the private label. In this case, you are given a three category scale like, do you prefer it? Do you not prefer it? Or are you indifferent? Here, of course, there is some ordering in the three categories which you are given, but still, there's no specific distance between these categories. It's not the case that you prefer something is exactly half of what you are indifferent. And if you are indifferent, that's exactly half of if you are not preferring the product. So the distances are not at all relevant in this case. The third type of secondary data is called interval data. Here however, the distances between the different categories start to become more meaningful. So let's think about the situation where you have again these three brands of tomato ketchup Heinz, Hunts and the private label. However, in this situation, you are given a seven point scale from one to seven, and you're asked to rate each of these brands on that seven point scale. Of course, here the seven scales refer to the seven different categories which are very important in terms of. So this is where a type of secondary data becomes more useful because you can exactly measure the importance of the different [INAUDIBLE]. The fourth type of secondary data is ratio data. Here there is a natural origin, like zero, and the distances between the categories are becoming more and more meaningful. So again let's think about the example of the three tomato ketchup brands. However here you are probably measuring the sales of the three brands, or maybe the revenues, or maybe the profits the firms earn from the three different brands. In this situation of course it's very important to look at the exact numbers. And that's why this type of secondary data is probably the most prevalent in any kind of any marketing research analysis. So as you'll see as we look at these different types of secondary data. Or the different measurement scales of secondary data Each of them has increasing amount of information compared to the last one. So starting from the interval scale to the ratio scale, everything becomes more important because the distances between the different categories become more relevant. So in the next video, we are going to start looking at different types of data collection methodology in primary data starting from questionnaire design, and then going into different psychological scales of measurement. Thank you. [MUSIC]