Are Theories Actually Practical?

Patrick S. Forscher

“There’s nothing as practical as a good theory.”

This quote is often attributed to the person hailed as the “father of social psychology”, Kurt Lewin. In social psychology textbooks, the quote is used to justify a mode of social psychology whereby theories that are developed and tested in the lab “naturally” and “inherently” lead to useful social applications (Billig, 2015).

Here I will argue that theory is not always helpful for solving practical social problems. This is because fixating overmuch on theory can lead to what I call theory blindness, a singular focus on the aspects of the problem that are relevant to the theory at the expense of other aspects that are just as important when the problem is considered in its entirety. I review a case study that illustrates the dangers of theory blindness and close with an argument for a focus on the practical problem as it exists in its original context rather than a focus on any particular theory.

Theory application as a model for practical impact

The dominant model of how social psychology comes to have a useful impact on society, at least as portrayed in social psychology textbooks (Billig, 2015), is what I will call the theory application model. In this model, theory development may be somewhat informed by a particular social problem, but theory testing happens largely in tightly controlled environments, such as the laboratory or an online survey platform. The process proceeds something like this:

  1. The social psychologist develops a theory. The theory can come from many sources: introspection, personal experience, the literature, or perhaps as a response to current events. Although this is rare in social psychology, the theory may even follow a process of formal theory development. These theories may be somewhat informed by a social problem, but not necessarily the problem in its full, original context.
  1. The social psychologist tests hypotheses derived from the theory in tightly controlled environments. The theory is then used to derive hypotheses that, in social psychology at least, are usually tested using experimental methods, most often in the laboratory or a tightly controlled online platform like Qualtrics. The results of these experiments are used to support the theory, refine it, and add boundary conditions. The usual justification for the high degrees of control in this step is that this control is necessary to isolate and manipulate the psychological processes that are relevant to the theory (Billig, 2015).
  1. The social psychologist applies the theory to a practical problem as a way to test its generalizability. Once theory-derived hypotheses have been tested in tightly controlled environments, the social psychologist may choose to conduct a study (for example, a field experiment) specifically designed to address some practical social problem. Usually, this exercise is framed as an application of theoretical principles already established in the previous two steps; the implication is that the study cannot directly refute the theory but merely demonstrate how its principles generalize to new, somewhat uncontrolled and messy settings.

Theory highlights some things and de-emphasizes others

The theory application model of practical impact illustrates how theory might, at least in principle, help solve social problems. Psychological theories provide working models of how psychological processes produce behavior. Insofar as individual behaviors “add up” to produce a social problem, psychological theories can highlight the psychological processes that produce those individual behaviors. Theories therefore also provide ready explanations for social problems and, due to their usefulness in identifying causal mechanisms behind individual behaviors, a guidebook for intervention via disrupting those mechanisms.

For example, the appraisal theory of emotion argues that emotions are produced by a person’s interpretations of events – in other words, their appraisals. This theory highlights these appraisals as a cause of emotion. The implication is that, to solve problems related to widespread negative emotions, such as the emotional fallout of a natural disaster, one should look to identify and change people’s interpretations of the natural disaster. This can lead scientists to develop and deploy specialized measures of people’s appraisals of the natural disaster and apply interventions that target those appraisals. Under the spotlight of appraisal theory, aspects of a social problem related to appraisals become valid observations while other aspects of the problem become de-emphasized.

Herein lies the danger of the theory application model of practical impact: if the theory does not fully capture the problem’s original context, it can leave important aspects of the problem outside its theoretical spotlight. For example, the lens of appraisal theory might cause well-meaning scientists who wish to provide mental health assistance in the wake of a natural disaster to neglect the real mental burdens caused by the economic and social fallout of the disaster. Focusing too much on people’s appraisals of a natural disaster as a cause of poor mental health is a demeaning way to help people who have just lost their homes and loved ones. In addition to leading to misguided forms of help, theory blindness can have real material costs if lives depend on the help’s effectiveness. In the most extreme situations, these costs can include lost lives and livelihoods.

The curious blindness caused by theory is part of a phenomenon that philosophers of science call the theory-dependence of observation. Theories define what counts as a valid observation. Theories can lead people to develop entire instruments dedicated to the measurement of the processes posited by theory (see Greenwald, 2012, for examples). In the context of a social problem, everything outside the measurement paradigm becomes “noise” that is outside the theory’s scope. When the theory-derived measures are deployed to solve a social problem, these “extraneous” factors can therefore be interpreted as irrelevant to the target problem.

I am not the first to note the peculiar dangers of theory for application. Daniel Kahneman called this phenomenon “theory-induced blindness” and linked it to the mental heuristics and biases that dominated his decision-making research. Curiously, Kurt Lewin himself may not have endorsed theory as a route to application (see Lewin, 1931) – at least in the way social psychologists currently go about it. (Even curiouser, Lewin was also not the originator of the quote that leads this blog.) 

Despite this history of similar critique, I believe the dangers of theory for application are not widely recognized. I therefore illustrate these dangers with a concrete case study.

Case study on Implicit bias

The concept of implicit bias was developed to explain a particular contradiction in the United States: in large, national surveys racial attitudes appeared to consistently improve from the 1960s through the early 1990s (Schuman, Steeh, Bobo, & Krysan, 1997). Yet, despite these improvements, racial disparities remained more stable than many scholars would like (Lee, 2002). These stable patterns in disparities were reflected in the laboratory, where even participants who claimed to value equality seemed to act unfairly when assessed on “unobtrusive” measures of bias (i.e., measures where, from the participant’s perspective, no particular response could be clearly labeled as “prejudice” or “discrimination”; Crosby, Bromley, & Saxe, 1980). Solving the puzzle of why people’s self-reports contradict their laboratory behavior provided an enticing means of making a practical impact.

Dual process models, such as the “prejudice habit model” (Devine, 1989), arose as an explanation for this contradiction. In these models, people’s beliefs had changed throughout the 1960s up until the 1990s, but a fast, relatively automatic mental process had not. When national surveys asked people how they felt about Black people, those surveys measured beliefs. However, the studies of “unobtrusive” bias measured behaviors that were more influenced by the automatic process.

At first, research in the dual process tradition was stymied by the fact that there was no independent measure of the automatic process. This changed with the creation of “implicit measures”, especially the Implicit Association Test. These measures were important for two reasons. First, they gave researchers a tool that purported to quickly, easily, and directly assess the automatic process (which came to be known as “implicit bias”). Second, they created a “palpable experience” (Monteith, Voils, & Ashburn-Nardo, 2005) that, when made widely available through websites like Project Implicit, greatly enhanced the standing of implicit bias in the public imagination.

The result was an explosion of research on implicit bias and a steady increase in the reach of the concepts into the public sphere. This research included observational studies designed to validate the new class of implicit measures and demonstrate the potential consequences of implicit bias (Cunningham, Preacher, & Banaji, 2001). The research also included manipulations designed to investigate the procedures that could change implicit bias (Dasgupta & Greenwald, 2001).

A funny thing happened over the course of this research on implicit bias: the implicit measures, but especially the IAT, became a target of change in their own right. In effect, the IAT became a stand-in for the problems related to social disparities. Thus, the existence of bias on the IAT became a convenient way of representing those disparities, and demonstrating the presence or absence of change on the IAT became a way of demonstrating which procedures might have promise for changing social disparities. 

The result was a cottage industry of interventions and trainings all developed around the concept of implicit bias, especially as represented by the IAT. This has come to a head in the public sphere, where large companies like Starbucks and Delta Airlines have rolled out company-wide training programs focused on implicit or unconscious bias. Governmental agencies have also taken an interest; the New York Police Department has instituted its own training, as has the UK civil service (this program was recently scrapped).

Meanwhile, the evidence is muddled at best that implicit bias does indeed cause real-world disparities, and in some settings there is strong evidence of the importance of non-psychological factors. Take racial disparities in who is subject to police misconduct. In many US jurisdictions, firing problematic officers is extremely difficult because weaker disciplinary oversight is one of the main concessions that police unions have extracted through collective bargaining. This concession makes it difficult to remove officers who are acting out of line, with a disproportionate impact on communities of color. The implication is that reforming systems of collective bargaining might be an effective means of reducing police misconduct, especially in communities of color.

This insight comes not from a particular theory, but from a careful examination of the historical and policy environments of US policing. However, an observer who approaches problems in US policing wearing the blinders of dual process theories might miss this important insight, pursuing instead reforms such as mandatory implicit bias training that do not effectively alleviate the suffering caused by police misconduct.

The problem-solving model as an alternative to the theory application model

The police union example illustrates an alternative means of achieving practical impact that is different from theory application model: a focus on a particular, substantive problem as it exists in its original context. The process that I suggest bears some similarities to action research – a research method initially developed by, ironically, none other than Kurt Lewin (Lewin, 1946). My suggested process goes like so:

  1. Analyze the target setting to develop a definition of the target problem. The first step is developing a careful analysis of the problem setting. This analysis should, at a minimum, draw on a consultation with stakeholders, but it can also draw on historical and policy analysis and pre-existing administrative data (if available). Any and every tool that provides insight into the problem is on the table; this stage may therefore require tools from many disciplines and levels of analysis. At this stage, the problem-to-be-solved may not yet be specifically defined. A major goal of this stage is to integrate the various sources of information to come to a more firm definition.
  1. Select measures of the problem and define success and failure using those measures. A definition of the problem is little help if there are no systems for assessing progress in solving the problem. This second stage involves defining the problem in terms of a measure that can be readily deployed in the problem setting. In some cases, no pre-existing monitoring system exists; in those cases, this second stage may involve devising and implementing such a monitoring system.
  1. Define the universe of interventions you can implement. Not all interventions are within the realm of possibility in all target settings; what is feasible will be constrained by pre-existing policy, law, and available resources. This third stage involves surveying the range of possible interventions you can deploy given these practical constraints. The interventions themselves can come from anywhere, whether from psychological theory or from the careful analysis of the target setting developed in the first step.
  1. Implement one or more interventions with a plan for progress monitoring. The final step is to implement one or more of the interventions identified in the third step with a plan for progress monitoring using the measures identified in the third step.

As in action research, these four steps can form a feedback loop: if the intervention tested in the fourth step is unsuccessful, that can form the basis for a new analysis of the target problem in the first step.

Theories may indeed have a role to play at different stages of the problem-solving model. However, and unlike the case with the theory application model, the orientation of the problem-solving model is on a particular problem in its original setting rather than on a theory and its degree of empirical support. The problem setting therefore serves not just as a venue for demonstrating a particular theory’s generality but as the entire focus of the research process. Moreover, the model embraces the “messiness” of the problem setting’s history, politics, and resource constraints rather than attempting to control the messiness away, as understanding this messiness is critical to developing interventions that work.

Conclusion: Theories are not always helpful for generating practical impact

Theory has been enjoying something of a renaissance in psychology research, and a large list of scholars have put forward persuasive arguments that theory development is critical for advancing knowledge about psychology (Muthukrishna & Henrich, 2019; Fried, 2020; Robinaugh et al., 2020; Navarro, 2021; van Rooij & Baggio, 2021 1; 2). As a response to this resurgence, a small but growing list of scholars have started to identify limits to the usefulness of theory in knowledge advancement (Barrett, 2020; Eronnen & Bringmann, 2021).

Personally, I do not dispute the usefulness of theory – if the primary goal is indeed knowledge advancement. If the primary goal is pragmatic, I worry that processes like theory blindness may interfere with a clear-eyed view of the target social problem. For this reason, I believe that a problem-focused approach is a more effective means of achieving pragmatic goals.


These ideas were conceived at the Relationship Preconference at the 2021 meeting of the Society for Personality and Social Psychology. Thanks to Farid Anvari, Hans IJzerman, Daniël Lakens, Duygu Uygun-Tunç, Peder Isager and Leonid Tiokhin for their helpful comments on previous drafts of this blog.

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