Non-Experimental & Experimental Research Alright! It's time to learn something using research by … performing a non-experimental study? Wait, wait, wait! Is it possible to have a non-experimental study? Is that sort of like sugar free candy? Is it something that you're supposed to have that is replaced by something that makes you scratch your head? Before we discuss research designs, though, you need a brief walkthrough of some of the terms I am going to throw at you. A predictor variable is the portion of the experiment that is being manipulated to see if it has an effect on the dependent variable. For example, do people eat more Gouda or cheddar cheese? The predictor variable in this is the type of cheese. Now, every time you eat cheese, you'll think about predictor variables. When I say subjects, I just mean the people in the experiment or the people being studied. Experimental research is when a researcher is able to manipulate the predictor variable and subjects to identify a cause-and-effect relationship. This typically requires the research to be conducted in a lab, with one group being placed in an experimental group, or the ones being manipulated, while the other is placed in a placebo group, or inert condition or non-manipulated group. A laboratory-based experiment gives a high level of control and reliability. Non-experimental research is the label given to a study when a researcher cannot control, manipulate or alter the predictor variable or subjects, but instead, relies on interpretation, observation or interactions to come to a conclusion. Typically, this means the non-experimental researcher must rely on correlations, surveys or case studies, and cannot demonstrate a true cause-and-effect relationship. Non-experimental research tends to have a high level of external validity, meaning it can be generalized to a larger population. Differences So, now that we have the basics of what they are, we can see some of the differences between them. Obviously, the first thing is the very basis of what they are looking at: their methodology. Experimental researchers are capable of performing experiments on people and manipulating the predictor variables. Non-experimental researchers are forced to observe and interpret what they are looking at. Being able to manipulate and control something leads to the next big difference. The ability to find a cause-and-effect relationship is kind of a big deal in the world of science! Being able to say X causes Y is something that has a lot of power. While non-experimental research can come close, nonexperimental researchers cannot say with absolute certainty that X leads to Y. This is because there may be something it did not observe, and it must rely on less direct ways to measure. For example, let's say we're curious about how violent men and women are. We cannot have a true experimental study because our predictor variable for violence is gender. To have a true experimental study we would need to be able to manipulate the predictor variable. If we had a way to switch men into women and women into men, back and forth, so that we could see which gender is more violent, then we could run a true experimental study. But, we can't do that. So, our little experiment becomes a non-experimental study because we cannot manipulate our predictor variable. Nonexperimental research is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both. In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are not. But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This distinction is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this inability does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense. The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?). The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?). The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?). The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?). Types of Nonexperimental Research Nonexperimental research falls into three broad categories: single-variable research, correlational and quasi-experimental research, and qualitative research. First, research can be nonexperimental because it focuses on a single variable rather than a statistical relationship between two variables. Although there is no widely shared term for this kind of research, we will call it singlevariable research. Milgram’s original obedience study was nonexperimental in this way. He was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of single-variable research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the research asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.) As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This detail is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their selfesteem. But this design would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied compares with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied thereby introducing another variable. Research can also be nonexperimental because it focuses on a statistical relationship between two variables but does not include the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both. This kind of research takes two basic forms: correlational research and quasi-experimental research. In correlational research, the researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them. A research methods student who finds out whether each of several middle-school students has been bullied and then measures each student’s self-esteem is conducting correlational research. In quasi-experimental research, the researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions. For example, a researcher might start an antibullying program (a kind of treatment) at one school and compare the incidence of bullying at that school with the incidence at a similar school that has no antibullying program. The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In qualitative research, the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256). Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group. characteristics As previously mentioned, the first characteristic of this type of research is that there is no manipulation of the variables studied. Normally, these are phenomena that have already occurred and are analyzed a posteriori. Apart from this characteristic, other peculiarities present in these designs can be pointed out: - Non-experimental research is widely used when, for ethical reasons (such as giving drink to young people), there is no option to conduct controlled experiments. - No groups are formed to study them, but these are already pre-existing in their natural environments. -The data is collected directly, and then analyzed and interpreted. There is no direct intervention on the phenomenon. - It is very common that non-experimental designs are used in applied research, since they study the facts as they occur naturally. - Given the characteristics presented, this type of research is not valid to establish unequivocal causal relationships. Types Transverse or transectional design This type of non-experimental research design is used to observe and record the data at a specific time and, by its very nature, unique. In this way, the analysis is focused on the effects of a phenomenon that occurs at a particular time. As an example, we can mention the study of the consequences of an earthquake on the housing in a city or the school failure rates in a given year. You can also take more than one variable, turning the study into a more complex one. The transversal design allows to cover diverse groups of individuals, objects or phenomena. At the time of developing them, they can be divided into two different groups: Descriptive The objective is to investigate those incidents and their values, in which one or more variables appear. Once the data is obtained, a description of them is simply made. Causes In these designs, we try to establish the relationships between several variables that have occurred at a given moment. These variables are not described one by one, but rather they try to explain how they are related. Longitudinal design Contrary to what happens with the previous design, in the longitudinal the researchers intend to analyze the changes suffered by certain variables over time. You can also investigate how the relationships between these variables evolve during this period. To achieve this goal it is necessary to collect data at different time points. There are three types within this design: Trend They study the changes that happen in some population in general. Of group evolution The subjects studied are smaller groups or subgroups. Panel Similar to the previous ones but with specific groups that are measured at all times. These investigations are useful to analyze the individual changes together with the group, allowing to know what element has produced the changes in question. Examples In general terms, these designs are prepared for the study of events that have already happened and, therefore, it is impossible to control the variables. They are very frequent in statistical fields of all kinds, both to measure the incidence of some factors and for opinion studies.