The next series of readings will allow you to understand more about: conceptualizing and operationalizing research, relationships between variables and different types of hypotheses. First, the readings will explain how to develop questions or hypotheses. This process begins with conceptualization, or the mental process where fuzzy and imprecise concepts are made more specific and precise. In social or business research, this is especially important as the questions are often quite broad in scope. For example, how would you conceptualize prejudice, or love, or satisfaction, in a way that you could study these concepts? We will also consider operationalization. This is the development of specific inquiry procedures that will result in verifiable observations. Conceptualization and operationalization take us one step closer to learning what we wish to know. Pay careful attention to these concepts as you are submerged in your readings. As you continue in your readings, we're going to look more deeply at variables and the relationship between them. Correlation is a term that you will see that just means, that as one thing changes, another thing changes as well. There is some sort of relationship between them. We want to know whether the relationship is positive or negative and how strong the relationship is. In a positive correlation, as the value of the independent variable increases, the value of the dependent variable increases as well. For example, it could be that college students who graduate with a bachelor's degree will earn more money within the next 10 years than people who do not hold a bachelor's degree. With a negative correlation, or what we sometimes call an inverse relationship, as the value of the independent variable increases, the value of the dependent variable decreases, and vice versa. For example, it could be that people who make less than $30,000 annually have more major dental problems than those who make $30,000 or more annually. We will also talk about the ideas of confounding variables and causality. Confounding variables are unmeasured third variables that may be the cause of the outcome you are measuring. They make it difficult to plan if the confounding or the independent variable caused a change. For example, suppose we find that the more educated people are, the fewer dental problems they have. But does education directly cause a reduction in dental problems? Probably not, but it might very well be that education does cause higher income and higher income results in people being able to go to a dentist and get their teeth cleaned more often and people with cleaner teeth have fewer dental problems. So really, it's income that's a key factor here, not education. Income is said to be a confounding variable. Both temporal order and correlation are indicators that we should look for but they alone are not enough to prove anything. Very often, one or more factors, confounding variables, are the ultimate cause of many things. The readings will go through this process in more detail. Then, we will move on to research questions and hypotheses. Often, an inquiry process begins with a very broad question called the central question. For example, the question might be: How do customers become interested in buying an iPhone? The question is quite broad so to really gather information, we could ask a series of sub questions that focuses down to more answerable questions. Those sub questions may be answered using qualitative or quantitative inquiry methods. Here's an example: Is there a relationship between the number of advertisements for iPhones shown during the Superbowl and the number of iPhones purchased the following week? Often, the relationship questions are stated in the form of a hypothesis, rather than in the form of a question. The sub question could be restated as something like this: There is no relationship between the number of advertisements for iPhones shown during the Superbowl and the number of iPhones purchased the following week, or: If six or more advertisements for iPhones are shown during the Superbowl, the number of iPhones purchased the following week is at least 20% higher than the week before the Super Bowl. In this case, the first example stands for something we call a null hypothesis. It basically says there's no relationship between the independent variable, the number of ads shown, and the dependent variable, the number of iPhones purchased. The second example is what we would call a directional hypothesis. It predicts a positive relationship, meaning more ads, more purchases. We could have written a non-directional hypothesis such as: If six or more advertisements for iPhones are shown during the Superbowl, the number of iPhones purchased the following week is significantly different from the week before the Super Bowl. Finally, we will come back to the ideas of conceptualization and operationalization. Putting these two terms together in order to gather useful information, you first figure out what you're going to measure through the process of conceptualization and then you figure out how to measure it through operationalization. In other words, we set up procedure we will use so that we are consistent in how we get measurements. The readings will provide a more detailed summary of how the measurements are performed. Glossaries are also provided for all the definitions. Now jump in and let's get started.
