Image courtesy of ddpavumba at FreeDigitalPhotos.net

Image courtesy of ddpavumba at FreeDigitalPhotos.net

When scientific methods are used appropriately, that is, when the method matches the question, the results can be extraordinary. We have learned how to send small numbers of people to outer space and large numbers of people from continent to continent. We can build bridges that span great distances and withstand the vagaries of weather. We have also made humongous advances in understanding and treating serious conditions such as poliomyelitis and tuberculosis.

Our achievements in mental health, however, are much less monumental. There is still no widespread consensus on what mental health problems are much less what causes them. And we are still a long way from being able to systematically match treatments with problems to produce high rates of effective and efficient outcomes.

One reason for our lack of substantial progress could be the misalignment of methods and questions. Currently, in many areas, we are using the wrong methods for the questions we are interested in. There is nothing wrong with the methods per se, and there is nothing wrong with the questions being asked. There is something very wrong, however, in expecting methods to answer questions they are not designed to answer. It’s a bit like going to a Chinese restaurant and ordering a pizza. There’s nothing wrong with dining in a Chinese restaurant and there’s nothing wrong with ordering pizza. The problem lies in the mismatch of the ordering and the setting. In research we need to ensure our methods line up with the questions we are asking to guarantee we serve up the best results possible.

When methods are used for inappropriate purposes the quality of the knowledge that is generated is severely compromised. Our current state of fragmented, contentious, and limited understandings may be a direct result of using our research tools to answer questions they were not designed to answer.

An area in which our findings are fundamentally and fatally flawed is in the study of DSM disorders. The investigations go something like this:

  1. allocate people to various diagnostic categories such as depression and schizophrenia according to diagnostic criteria;
  2. look for characteristics that are similar within groups and different between groups;
  3. use the findings as evidence for the legitimacy of the categories.

Research that uses the criteria being investigated as the means for creating the categories to be investigated presupposes an authority of the categories and, so, cannot provide meaningful information about the correctness or otherwise of the categories.

The samples that are recruited in research also influence how widespread the implications of the results of the research might be. People who volunteer for research may be unlike people who don’t volunteer for research in important ways. Perhaps the most important point, though, is that we can never be sure how different they are or in what ways they are different. This isn’t necessarily dreadful but it does mean the results might not apply to all people in any particular population.

If we survey people in a mental health service, for example, and 90% of the people who complete the survey report that, when they were in primary school they were “bedoodled”, are we justified in concluding that 90% of people with mental health problems are bedoodled in childhood? No, we’re not. Even if all of the people accessing this mental health service completed the survey, we would still only know that 90% of the people accessing this mental health service reported experiencing bedoodling. We don’t know anything about people accessing other mental health services and we don’t know anything about people not accessing mental health services. We actually don’t even know how many of the 90% of people reporting that they experienced bedoodling actually were bedoodled. I’m not suggesting that research participants intentionally lie to researchers on any widespread scale but I am suggesting that there might be reasons for endorsing bedoodling on a mental health survey other than the reason that the bedoodling actually occurred. My point here is that it is often an unquestioned assumption that the information participants provide on surveys is completely accurate.

Although techniques can be used to improve the representativeness of samples, a sample will only ever be representative according to certain variables identified as important and relevant by the researcher. We might not even know what the most important variables are to ensure that people who volunteer for research and endorse bedoodling on a survey are representative of people who do not volunteer or to people who do volunteer but who are reluctant to indicate they were bedoodled in childhood. If, for example, we somehow discovered that 90% of the general population also report childhood bedoodling then we wouldn’t have learned very much about the manifestation of severe psychological distress.

We must be careful to ensure that the conclusions we make are appropriate given the information we obtained from the people who participated.

Even when we carefully match our methods and our questions and are also very circumspect about the conclusions we draw, there is still a certain madness in the relationship between the methods we use and the information we want. Statistical methods are designed to help us understand populations. The direction of inference is from the sample to the population (Blampied, 2001). Inferring from a sample to a population, however, is in exactly the opposite direction from what we are generally most interested in. Mostly we want to make inferences about individuals from the research we have conducted. That is, the direction in which we would most like to infer is from the sample to the individual. Statistical methods will not allow us to do that.

So, with very high quality research, we might be able to make conclusions about a certain treatment being effective, in general, for particular problems. We might also be able to conclude that certain experiences from the past are reliably associated, in a general sense, with current problems. The qualifier “in a general sense” is often overlooked but is, in fact, critical. Our current methods do not allow us to specify with any level of precision the likelihood that a given treatment will work with a particular individual. These methods also won’t enable us to predict with high levels of accuracy the extent to which individuals have or do not have certain experiences in their past – or anything else for that matter.

Phil Runkel’s (1990) excellent book Casting Nets and Testing Specimens is a detailed and masterful analysis of some of these problems. Runkel describes statistical approaches to research as “casting nets” methodologies. According to Runkel, statistical methods are the most appropriate means of finding out how much something occurs in a population. Very well conducted statistical research will enable us to infer with specified levels of precision the rate at which we could expect a particular event or characteristic to occur in a population. Statistical methods, however, won’t allow us to specify how likely it is that any particular individual will have that characteristic or will have experienced the specified event. Statistical methods also won’t allow us to learn how certain events relate to particular behaviours.

To understand accurately and precisely how things occur or why things happen in particular ways, we need to use “testing specimens” methodology. In this methodology:

  1. an explanation of why or how something occurs is proposed;
  2. the explanation is expressed in terms that allow a functional model to be built;
  3. the behaviour of the model is compared to the behaviour being investigated.

If there is not a very close match between the actual behaviour and the behaviour of the model then it is assumed that the explanation is wrong and the researcher returns to the drawing board to modify and improve the model.

To make major advances in our understanding and treatment of psychological distress we need to remove the madness from our methods and restrict our use of casting nets methodology to the purposes for which it was designed. We also need to begin to incorporate much more testing specimens methodology into our research practices. We need to demand the construction of functional models rather than relying solely on conceptual or statistical models for the generation of knowledge. Perceptual Control Theory (PCT; www.pctweb.org) is an excellent example of what is possible with a model building approach.

It is madness to use methods to answer questions they were not designed to answer. By using methods for the purposes for which they were designed, mental health research might begin to build its own sturdy bridges between research and practice and shoot for stars that are currently impossibly out of reach.

References

Blampied, N. M. (2001). The third way: Single-case research, training, and practice in clinical psychology. Australian Psychologist, 36(2), 157-163.

Runkel, P. J. (1990). Casting nets and testing specimens: Two grand methods of psychology. New York: Praeger.

Tim Carey

About Tim Carey

Tim is a Professor in Mental Health at the Centre for Remote Health in Alice Springs, Australia where he conducts mental health research and provides a clinical psychology service within the public mental health service. He has a PhD in Clinical Psychology from the University of QLD (Australia) and an MSc in Statistics from the University of St Andrews (Scotland). He has over 100 publications including books, book chapters, and peer-reviewed publications in scientific journals and has presented his work at national and international conferences. Tim has developed a transdiagnostic cognitive therapy called the Method of Levels (MOL) which adopts a patient-centred view of mental health disorders and seeks to help patients resolve the distress underlying particular symptom patterns rather than focussing on the symptoms themselves. He has also pioneered a patient-led system of service delivery in which patients determine the frequency and duration of treatment sessions. His interests in mental health centre around the importance of control to psychological wellbeing and service provision and he prioritises the perspective of the individual in understanding psychological distress and helping in its amelioration.