So where did the data you're working with come from? Data can be generated from multiple sources. But mostly consists of two types, observational and experimental. How the data are generated is important because it determines what kinds of conclusions we can draw from the data that we're working with. There's one question we can ask to determine whether our data can be considered observational or experimental. And that question is was the explanatory variable manipulated? By manipulated we mean that the values of the variable are controlled by the researcher. For example, suppose you participated in a study in which you completed a questionnaire by responding to questions about your level of exercise. In this case, you're reporting the amount of exercise that you do. The data generated from this type of study is called observational data because the researcher is not manipulating your level of exercise. You're simply reporting how much you exercise. On the other hand, if you are participating in a study in which researchers asked you to exercise at a certain level for a week, then compare you to other individuals who are asked to not exercise at all for the same week. The data generated from this type of study are what we call experimental data. Because the researchers manipulating the amount of exercise that you do. So if our answer to the question, was the explanatory variable manipulated, is no, then we're working with observational data. Observational data can be generated in a number of ways. One way that observational data can be generated is through data reporting. Data reporting is the process of collecting and organizing data, typically to monitor a process or phenomenon. Data reporting does not collect data with any kind of specific hypotheses in mind. Although it's often analyzed later on to test specific hypothesis. Data reporting can tell you what is happening, but it's data analysis that can tell you why it's happening. An example, individual states may report rates of diseases to the Centers for Disease Control and the Centers for Disease Control can combine the information from each date, and from all over the world, into a data set that allows them to monitor global disease rates. Data reporting can also occur when you go the doctor's office, and they record information from your visit into an electronic medical record. This information is primarily collected for monitoring and reporting purposes. However, electronic medical record data can be compiled into a database that can be analyzed to gain insight into disease processes, health behaviors, and other health-related phenomena. The Gapminder dataset that some of you are working with consists of data collected through data reporting. Observational data can also be collected through surveys of samples of individuals in the population. Much of our data are collected this way. In these survey research studies, a sample of individuals or observations is drawn from the population, and are asked to respond to questions posed by researchers, either in an interview or on a questionnaire. Responses to survey questions provide the data that we can analyze. Sample survey data is typically collected with certain hypotheses in mind to test. However, we often use existing sample survey data to test secondary hypotheses or to mine the data for additional insights. If you're working with the NESARC data set or the AddHealth dataset or the Outlook On Life dataset. You're working with observational data generated by survey research studies. Observational data might also be collected by simply observing the values of a variable or variables of interest as they naturally occur. For example, a researcher might observe families at play on the beach, and measure the number of times parents touch or speak to their children. Another example of an observational study is when surface weather observations such as temperature, humidity, wind speed, and precipitation are collected in order to predict future weather patterns or other climatological events. Such as drought and flooding. In both of these cases, family behavior observation and weather observation, the collection of data does not involve any interaction, or intervention, with the process as it is occurring.
