Hello and welcome to the fifth and final session of this week on the operationalization of your research. Assuming that you participated in previous sessions, you are already able to define the problem statements, research objective, and research question. And you are able to perform a literature review and to develop concepts and a conceptual framework. This session will discuss the operationalization of your research. By the end of this video, you will be able to explain what operationalization is, and why it's useful. You will also be able to operationalize your concepts by turning them into variables and indicators. So, what is operationalization? It is the process of unpacking broad concepts into variables and indicators. Things you can measure and observe. It provides maximum clarity on what the concept means in the context of your research. You can operationalize concepts in two steps. The first step is to unpack concepts into variables. That is; into observable units that vary in their manifestation. It may seem obvious, but it is very important to check if variables indeed vary. In my experience, many people want to study what factors explain, why an urban policy fails. But if a policy has failed, it does not vary in its manifestation. It only fails and it's just not a good variable. Instead, you would have to compare urban policies that fail with those that succeeds or partly succeeds. In the second step, you'll define indicators for each variable. That is; you define concrete ways to measure the relative presence of the variables. You can easily turn a good indicator into interview or survey questions, or you can find data on the indicator in your quantitative data set. Now, let's take a look at an example. This time, I will use an exciting study of Steve Wrights, a student from Tel Aviv in Israel. He noted that the number of traffic jams in Tel Aviv is rapidly increasing as more and more people travel by car. And one of the policies to improve mobility is to promote bicycling. His research question was, what factors explain the level of preference for using a bicycle as a preferred mode of commuting to work in Tel Aviv? From my perspective, this is an interesting and relevant research question, because all cities have to address the issue of mobility. Also, as a proper Dutchman, I like bicycling. So, how does he operationalize his two concepts? Factors which influence bicycling, and bicycling as a preferred mode of transport. Especially the independent variable factors which influence bicycling, it's very fake. It's not easy to operationalize. So, let's try to unpack this independent variable. Based on this literature review, we know the three variables may influence whether people bicycle or not. These three variables are: Geographical condition, Psychological factors, and Trip characteristics. These three variables can indeed vary in the manifestation. For example; Geographical conditions include whether there are bicycle lanes or not. Now, I would like to raise a question. Can you actually empirically observe these three variables geographical conditions, psychological factors, and trip characteristics? Well, yes you can. That is, once you've decided on measurable indicators, because of course, the whole purpose of operationalization is defining how you would like to measure these variables in your own research. And that brings me then to the second step, defining your indicators. Let's look at the indicators for the variable geographical conditions. Based on safety literature review, we know that bicycle infrastructure and weather conditions may both influence whether people bicycle or not. So, safest correctly split is variable into two sub-variables, bicycle infrastructure and weather conditions. Now, we still have to identify measurable indicators for each of these two sub-variables. Let's consider the sub-variable bicycle infrastructure. One of the indicators is whether the route to work has separate bicycle lanes or not. This indicator can be measured on a lighter scale from one to five. The one indicates that the route has no bicycle lanes at all. A score of two indicates a few bicycle lanes. Three, about half of the routes has a bicycle lane. Four, mainly bicycle lanes, and a score of five indicates that the whole route comprises of bicycles lanes. The same indicator and scale have already been used in a previous study of Chato Way, Et al, in 2014 and that tells me that this indicator is tested and likely to be valid. The indicator can also easily be transformed into a question for a survey or a same structured interview. It's a good indicator and this process of identifying indicators and their measurement continues. In total, Steve has identified three indicators for bicycle infrastructure. Three more for weather conditions, and in total, he identified 35 measurable and concrete indicators. Finally, I would like to note that the study includes one other type of variable which I've not yet discussed, control variables. Control variables tests all the factors outside the conceptual framework, which may also impact on your study. You want to keep them constant and unchanged during your research. Steve therefore had to ask himself, what other factors may possibly explain whether people bicycle to work or not. In our example, control indicators include personal characteristics such as age, gender, and income. These personal characteristics may also influence whether people bicycle to work or not, but they are not part of the chosen conceptual framework. We now come to the end of this video, which discusses the operationalization of concepts into variables and indicators and I can tell you, it's a lot of work. But if you've done a good literature review and conceptualization, it's also a lot of fun, because suddenly your research proposal becomes very concrete. This week, you've learned how to prepare a theoretical framework and how to operationalize your research. You are now able to develop a literature review, to develop concepts, a conceptual framework operationalization and its measurements. Well done. The next step of the research cycle is choosing your own research strategy and we will discuss five research strategies; the survey, experiments, classy experiments, desk research and a case study. These will be discussed next week. Goodbye for now.
