Although the ideas behind idiographic research are quite old in philosophy, they were only applied to the sciences at the start of the last century. If we think of famous scientists like Newton or Darwin, they never saw truth as subjective.
They posed laws of science that were objectively true and applicable in all situations. Newton, Darwin, and others operated within the positivist paradigm, which continues to dominate much of science today. When positivists try to establish causality, they try to come up with a broad, sweeping explanation that is universally true for all people.
This is the hallmark of a nomothetic causal explanation. Nomothetic causal explanations are also incredibly powerful because they allow scientists to make predictions about what will happen in the future, with a certain margin of error. Moreover, they allow scientists to generalize —that is, make claims about a large population based on a smaller sample of people or items. Generalizing is important, as we clearly do not have time to ask everyone their opinion on a topic, nor can we look at every interaction in the social world.
We need a type of causal explanation that helps us predict and estimate truth in all situations. Imagine you are working for a community-based non-profit agency serving people with disabilities. You are putting together a report to lobby the state government, and you need to support your argument for additional funding for further community support programs at your agency.
By referring to nomothetic research, you would learn that previous studies have linked community-based programs like yours to positive health and employment outcomes for people with disabilities. Nomothetic research seeks to explain that community-based programs are better for everyone with disabilities. By referring to idiographic research, you would learn what it is like to be a person that is involved in a community-based program by reading their personal accounts of their lived experiences.
Neither kind of causal explanation is better than the other. Deciding to conduct idiographic research means that you will attempt to explain or describe your phenomenon exhaustively while attending to cultural context and subjective interpretations. Deciding to conduct nomothetic research means that you will try to explain what is true for everyone and predict what will be true in the future.
In short, idiographic explanations have greater depth, and nomothetic explanations have greater breadth. Most importantly, social workers understand the value of both approaches to understanding the social world. The social worker also utilizes nomothetic research to apply generalizable knowledge about what typically helps people with substance use disorders, such as minimizing risk factors, maximizing protective factors, and employing evidence-based therapy techniques.
One of my favorite classroom moments occurred early in my teaching career. I had instructed my students to form groups, discuss the research questions they had drafted for their projects, and provide feedback to each other. I overheard one group trying to help a student rephrase their research question. So, are you trying to generalize your potential research findings… or nah?
Answering this question can you understand how to conceptualize and design your research project. If you are trying to generalize, or create a nomothetic causal relationship, then the rest of these statements are likely to be true: you will use quantitative methods, reason deductively, and engage in explanatory research. How can I know all of that? Nomothetic causal relationships aim to generalize.
For phenomena to be generalizable, they must be precisely measured and reduced to universally understood terms, such as mathematics and statistics. On one hand, not all quantitative methods explanatory in nature. On the other hand, nearly all explanatory studies are quantitative. In sum, nomothetic causal relationships use quantitative methods to achieve generalizability and prove cause and effect relationships.
When we talk about x and y, we are talking about the relationships between variables. When one variable causes or contributes to change in another, we have what researchers call independent and dependent variables. For our example on spanking and aggressive behavior, spanking would be the independent variable and aggressive behavior would be the dependent variable.
An independent variable is the cause, and a dependent variable is the effect. Dependent variables depend on independent variables. If you find yourself confused, remember the graphical relationship below. Relationship strength does not refer to the strength of your friendships or a marriage, rather in this context it refers to statistical significance.
The more statistically significant a relationship between two variables is shown to be, the greater confidence we can have in the strength of that relationship. In quantitative research, hypotheses are a nomothetic causal relationship that the researcher expects to demonstrate between the independent and dependent variables. Your prediction should be taken from a theory or model of the social world. For example, you may hypothesize that treating clinical clients with warmth and positive regard is likely to help them achieve their therapeutic goals.
That hypothesis would be using the humanistic theories of Carl Rogers. Using previous theories to generate hypotheses is an example of deductive research. In sum, all nomothetic causal relationships must use deductive reasoning. What is the causal relationship being predicted here? Which is the independent and which is the dependent variable? Sometimes researchers will hypothesize that a relationship will take a specific direction.
As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and support for legalization of marijuana. Thus, as age your independent variable moves in one direction up , support for marijuana legalization your dependent variable moves in another direction down. In addition, positive relationships involve two variables going in the same direction and negative relationships involve two variables going in opposite directions.
If writing hypotheses feels tricky, it is sometimes helpful to draw them out and depict each of the two hypotheses we have just discussed. What happens if you conduct a study to test the hypothesis from Figure 7. In this example, your hypothesis was wrong, but you can still draw valuable information from your incorrect prediction. Your study would challenge what the existing literature says on your topic and therefore demonstrate that more research needs to be done to figure out the factors that impact support for marijuana legalization.
As age increases, support for marijuana legalization decreases. Causal explanation complete, right? Not quite. The main criteria for causality have to do with covariation, plausibility, temporality, and spuriousness. In our example from Figure 7. When variables covary , they vary together.
Both age and support for marijuana legalization vary in our study. Our sample contains people of varying ages and varying levels of support for marijuana legalization. The presence of some correlation between two variables does not mean that a causal relationship between the two is plausible.
Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. Random assignment of participants to treatments in experiments is a powerful causal tool. Be very skeptical of studies that totally equate their concrete measures with their constructs.
Cancerous Human Lung This dissection of human lung tissue shows light-colored cancerous tissue in the center of the photograph. While normal lung tissue is light pink in color, the tissue surrounding the cancer is black and airless, the result of a tarlike residue left by cigarette smoke.
Lung cancer accounts for the largest percentage of cancer deaths in the United States, and cigarette smoking is directly responsible for the majority of these cases. All rights reserved. There are many topics where it is neither possible--nor desirable--to use the experimental method. Most social scientific research is interested in testing causal claims. In fact, most theoretically derived hypotheses implicitly or explicitly assume causal relationships. However, causality is very difficult to prove.
In fact, some believe that causality can never be demonstrated with finality and that the best researchers can do is to generate increasingly compelling evidence that is consistent with causality. There are three widely accepted preconditions to establish causality: first, that the variables are associated; second, that the independent variable precedes the dependent variable in Show page numbers Download PDF.
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