need to believe?
Illogical Reasoning
Illogical reasoning refers to arguments that are not logical. It is not logical, for example, for you to say that the reason I am grumpy today is because of what I will have for breakfast tomorrow. This is not logical because it is not possible for a cause of something (my grumpiness) to come after it. It must come before. If I am grumpy right after breakfast today, you might logically say that one reason for it might be my breakfast. But not what I am going to have for breakfast tomorrow.
One form of illogical reasoning is the false dichotomy—the claim that there are only two options in an argument when, in fact, there are more. Consider this conversation the author once had with a friend. The friend asked, “Do you believe in doing a lot of testing of elementary school students?” I replied, “Yes, I do.” The friend responded, “Don’t you believe in instilling a love of learning?” The tone of voice suggested that this friend did not think that you could believe in testing and also believe in instilling a love of learning. He was presenting only two options as being possible when there were more.
According to this false dichotomy, if you believe in testing you must not believe in instilling a love of learning. But there are four alternatives here, not just two. You could possibly (1) believe in testing and not believe in instilling a love of learning, (2) not believe in testing but believe in instilling a love of learning, (3) not believe in testing and also not believe in instilling a love of learning, and (4) believe in testing and also believe in instilling a love of learning. By the way, my position is the last one: I believe in both. I do not think that you fail to believe in instilling a love of learning if you believe in testing.
My friend had entered this conversation with a perception that entailed incomplete information. There were four alternatives rather than two. This is the false dichotomy, which is a false claim that there are only two options (yes/no) when, in fact, there are more options logically in the argument.
There are other forms of illogical reasoning and other ways we make mistakes in decision making when we avoid the spirit of scientific inquiry. The next section will begin our review of this spirit and show how it helps with practice decisions.
Science as a Way of Knowing
A social scientist is someone who applies the principles and methods of science to learn more about social phenomena. This might include being able to describe the students in a club with enough clarity to draw some basic conclusions about what the members are like. It might include examining whether there is a relationship between variables (e.g., Do people who regularly exercise have fewer illnesses than those who do not?). It might include the examination of whether a social program is effective (e.g., Is there a lower rate of absenteeism in schools that employ the strengths model of schooling than in schools that do not?). Science employs certain methods of inquiry that are different from other ways of searching for meaning. These methods are designed to reduce human error in observation.
What Do You Already Know?
In this section are a few situations that will illustrate various aspects of the use of science to inform decisions. You will review each and decide if there is a problem.
Let’s examine the following situation:
A group of researchers started their study process with the purpose of determining the extent to which clients treated for depression have shown improvement in their levels of depression. They collected scores on a depression scale for a group of clients in treatment for depression. They collected these scores both before and after a service was provided that was designed to reduce depression. These scores showed improvement from the first to the last measurement. They were subjected to statistical analysis and were found to be statistically significant. The researchers concluded that these clients had experienced improvement with regard to depression, but they were not clear about the population to whom these results could be generalized because these clients had not been selected on a random basis from a larger population.
Do you see any problems with this situation? It is a summary you may have read from your search of the literature with regard to evidence about the treatment of a condition like depression. There are no major flaws here. The process started with a purpose, data were collected consistent with that purpose, and conclusions were drawn that were consistent with the data analyzed.
You saw a reference to statistical analysis, which will be discussed many times in this book, but you are not expected to fully understand it at present. But you surely have seen references to statistical analysis of data and concepts such as “statistical significance.” These clients showed improvement, and the level of their improvement cannot be easily explained by chance (because the data were found to be statistically significant). If your data can be explained by chance, you cannot conclude that they can be taken seriously.
A reference was made to the generalization of data results, another concept that will be discussed many times in the chapters of this book. It was noted that the clients had not been selected at random, a basis for scientific generalization. It is likely that you have seen numerous references to random samples in your review of the literature. That is the superior form of sampling and strengthens the extent to which you can generalize your findings to persons who were not in your study.
Now, let’s examine another situation.
A group of researchers had the purpose of determining if after-school tutoring was effective in the improvement of grades for middle school students. Grades were compared for the period of time before tutoring was offered and measured again after tutoring was completed. The results showed that the average student improved but that statistical significance was not achieved (i.e., these results can too easily be explained by chance). These researchers concluded that tutoring is an effective way to improve grades for middle school students.
Do you see a problem with this situation? You need an appreciation of the theme of chance to grasp the lesson provided here. These researchers treated the data seriously in their conclusions even though the data failed to be statistically significant. The proper conclusion would be that the study failed to find an improvement in grades. What they discovered was chance. If chance can explain your data, you cannot take them seriously with regard to your study conclusions.
What do you think of the following situation?
Some fellow students conduct a study of students in one social work research class and find that the majority of them are identified with the Democratic Party. They conclude that there are more people in this university who identify as Democratic than Republican.
The problem here is easy to identify. You cannot generalize from a small sample of people in one class in a university to the entire university. The issue of generalization is discussed with regard to the nature of the sample that you employ. A small class of students in one specific place is not a random sample. And you would need a much larger sample than this to generalize to the entire university.
Now examine this situation.
Paul is a social work research student who starts the process of conducting research by deciding that he would like to conduct interviews of his fellow students. He has not yet decided on the purpose of his study.
What is the problem here? Well, Paul has put the cart before the horse. He has decided on the methods he will use to conduct his study before he decides on the purpose of the study. The purpose should come first. Why? Different purposes suggest different types of methods to be used. You need to know the purpose
