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The Theory Crisis in Criminology: Causes, Consequences, and Solutions

We demonstrate how many of the theoretical issues identified in psychology parallel both recognized and unrecognized issues in criminological theory. We discuss ways to address the theory crisis in criminology and mitigate its contribution to the larger reproducibility crisis.

Published onJul 03, 2024
The Theory Crisis in Criminology: Causes, Consequences, and Solutions


Criminology has recently begun to grapple with concerns about the replication crisis, but current efforts focus on addressing methodological and analytical concerns along with questionable research practices. Causes of the replication crisis, however, occur at every link in the derivation chain, from theory to empirical test to interpretation of results to reporting practices. Psychology—which has been wrestling with its own replication crisis since at least the 2010s—has begun to recognize these early-chain causes and thus has turned its attention towards the “theory crisis”: the finding that most psychological theories are imprecise to the point of being unfalsifiable. We demonstrate how many of the theoretical issues identified in psychology parallel both recognized and unrecognized issues in criminological theory. Then, we discuss ways to address the theory crisis in criminology and mitigate its contribution to the larger reproducibility crisis in criminology and the social sciences. 


In the past five to ten years, criminology has begun to grapple with the fact that, like psychology, it may be experiencing its own replication crisis (e.g., Barnes et al., 2020; Chin et al., 2023; Farrington et al., 2019; Lösel, 2018; Pridemore et al., 2018). There is reason to suspect that many, if not most, of our studies are underpowered (Barnes et al., 2020), p-hacked (Wooditch et al., 2020) or otherwise produced with questionable research practices (QRPs; Chin et al., 2023), which calls into question many of our research findings. Though this reflection represents an important step in the right direction, criminology is behind in recognizing that many of the problems identified early in psychology’s replication crisis lie downstream from a bigger issue: weak, vague theories.

Conceptually clear theories are a critical part of the scientific process, guiding the development of hypotheses and appropriate measurements (Meehl, 1967, 1978). Research founded on well-formed theory is also more likely to replicate (Nosek et al., 2022)—possibly because it increases the a priori likelihood of the derived hypotheses being true (Scheel et al., 2021) and limits a variety of research degrees of freedom that have been found to increase false positives, such as flexible dependent variables or atheoretical covariates (Simmons et al., 2011).

Criminology, however, has not yet grappled with the theory crisis to the extent it has the replication crisis, with few even acknowledging its existence (e.g., Bruinsma, 2016; Niemeyer et al., 2022). Yet, as a social science, criminology shares many of the same features as psychology, including its theoretical shortcomings. In this paper, we argue that many of the problems psychologists have identified in their own discipline mirror problems criminology currently experiences. We then propose solutions drawn from psychology’s theory crisis to improve the theoretical rigor—and therefore replicability—of criminology.

Replication Crisis in Psychology

The 2010s proved to be a pivotal point in the development of psychology as a scientific discipline (Nosek et al., 2022). In the wake of papers such as Ioannidis (2005) claiming that most published research findings are wrong and Bem (2011) purportedly demonstrating the existence of psychic abilities—all while employing then-standard research techniques—psychologists began to interrogate their research practices. Their primary focus was empirical in nature, with special attention paid to false positives and p-values (e.g., Benjamin et al., 2018; Lakens et al., 2018; Simmons et al., 2011). Concerned with how widespread the issue of false positives might be, psychologists began mass replications of landmark psychological findings; only 36% replicated (Open Science Collaboration, 2015). Now referred to as the “replication crisis,” this series of events spurned the discipline to inspect their standards for good science and begin revising practices accordingly (Nosek et al., 2012, 2022; Nosek & Bar-Anan, 2012).

These efforts implemented much-needed scientific reforms, but the emphasis on empiricism—such as statistical techniques (Gelman, 2015; Kruschke & Liddell, 2018), sample sizes (LeBel et al., 2017), and research degrees of freedom (Simmons et al., 2011)—could not sufficiently address the discipline’s issues. Pre-registration, for instance, proved difficult to implement because scholars struggled to precisely articulate their predictions. They instead relied on vague generalizations that did little to limit the flexibility of their analyses (Szollosi & Donkin, 2019). As a result, other “crises” were identified, such as the generalizability crisis (Yarkoni, 2022) and the applicability crisis (IJzerman et al., 2020; Schoenegger & Pils, 2023), but one that has most contended for attention over the replication crisis is the theory crisis.

Theory Crisis

Though the theory crisis is relatively recent, concerns about psychological theory are not. In the 1980s, for instance, psychologists explored theory integration as a means of improving psychology and unifying science, but with little success. Werthiemer (1988), for example, discussed how seemingly contradictory and competitive theories might be: (1) intertranslatable and amenable to integration; (2) contradictory and impossible to integrate; and (3) superficially translatable, but the process of integration would ruin the integrity of at least one of the theories being integrated. In other words, integration was potentially possible in some circumstances but mostly a fraught effort. Additionally, Kalmar and Sternberg (1988) tied difficulties integrating psychological theories to their origins in logical positivism/empiricism, the emphasis on theory competition, and the common predictions made by competing theories that encourage psychologists to emphasize areas of disagreement over consensus. Consequently, this approach led to the segregation of theories as psychologists focused discovery on the unique insights one theory has over another, locking researchers into narrows ways of conceptualizing a phenomenon under investigation.

More recently, concerns about theory have thus resurfaced in direct response to the replication crisis, when some scholars began to argue that the deficiencies in methodology, analysis, and publication were, in part, products of poorly-articulated theories (Fiedler, 2017; Muthukrishna & Henrich, 2019). As noted by Meehl (1990) over three decades ago, hypothesis testing requires scientific theory, methods, and analysis to be part of a sound “derivation chain,” where the theory constrains the methods, which constrains the analysis, which constrains the interpretation. If any link in this chain is weak, the outcome is uninterpretable. Therefore, efforts to reform methods, analysis, and publication practices are moot if the theoretical foundations of the discipline are unsound. When taking stock of their theories, some psychologists concluded that they were, in fact, lacking appropriate rigor and declared that psychology was facing a theory crisis (Oberauer & Lewandowsky, 2019).

Initial efforts at reform focused primarily on formalization. Scholars argued that verbal models were too flexible and ambiguous (Szollosi & Donkin, 2019)—in other words, they were “weak” (Meehl, 1967, 1978)—to be falsifiable (Scheel, 2022). The solution, then, was to increase their precision via formal or computational modeling. So-called “formal” models were usually mathematical or logical-symbolic in nature (Eronen & Romeijn, 2020; Gray, 2017; Navarro, 2021), while computational models relied on modeling theory with computer code and simulations (e.g., Guest & Martin, 2021). Both required psychologists to have clear, precise expectations of how variables would be (causally) related to one another before they could even begin hypothesis testing (van Rooij & Baggio, 2020).

Though logical in theory, some argued that most subdisciplines of psychology were not sufficiently developed to allow for formal models or, by extension, hypothesis testing (Scheel et al., 2021). The reason other disciplines have been successful at developing formal models is because they have a stock of stable, reproducible phenomena; most of psychology does not (Scheel, 2022; Scheel et al., 2021). Psychologists must first agree on what phenomena exist before they can begin to establish precise relationships between variables, which will then allow for formal model development (Eronen & Bringmann, 2021; Oberauer & Lewandowsky, 2019). Despite slow efforts towards improvement, proponents of theory reform still face resistance, and there has yet to be the same flurry of development seen in the wake of the replication crisis (van Rooij, 2022).

The Theory Wars: Criminologists Reconciling with Theory

Like psychology, criminology has focused its reform efforts on the empirical causes of the replication crisis (e.g., Barnes et al., 2020; Pridemore et al., 2018; Wooditch et al., 2020) and the publication process (Farrington et al., 2019; Wooditch et al., 2020). Consequently, it has never experienced an acknowledged “theory crisis” in response to issues related to the replication of findings. It did, however, experience an analogous debate in the late 1960s through the mid-1990s surrounding the low explanatory power of criminological theories—with theoretical formalism, theory elaboration, and theory integration proposed as methods to spur theoretical growth in the field, guided by the assumption that theory improvement would necessarily translate to gains in statistical measures of explanatory power (Elliot, 1985; Elliot et al., 1979).

One of the first suggested reforms was to improve theoretical formalism. As in psychology (and likely most social sciences), criminological theories are too imprecise and discursive to yield meaningful tests of theories (Bernard, 1989; Gibbs, 1972, 1985; Proctor, 2010; Proctor & Niemeyer, 2019; Tittle, 1985). In principle, more formal theories might have been able to advance criminology, but in practice, criminologists struggled for similar reasons as psychologists. First, criminologists tend not to examine how they theoretically answer research questions about crime (i.e., metatheorizing) and prefer to proceed directly to answering research questions (Meier, 1985). Second, most criminologists are not trained in theory construction, formalism, and metatheory (Proctor & Niemeyer, 2019). Third, criminologists commonly have alternative career goals that steer them away from theory development, including applying for grants and publishing at the levels required for tenure and promotion (Bernard, 1990).

Finally, as Proctor and Niemeyer note (2019), formalization is tied to a specific type of scientific explanation—called a “covering law approach”—that explains phenomena in terms of laws, relies on logical deductive forms of explanation, and assumes levels of reality exist that are associated with specific scientific disciplines (i.e., physics, chemistry, biology, and social science). A hallmark of this approach is its use of reduction to integrate theories from different scientific fields across levels to achieve a unification of science (e.g., Walsh, 1995, 2002). Unfortunately, it is difficult to reduce emergent, complex phenomena—like those in the biological, psychological, and social sciences—that span multiple levels simultaneously (Proctor & Niemeyer, 2019). Consequently, reduction has never demonstrated itself to be a viable form of theory building in criminology, which raises questions about the more general applicability of formal covering law approaches in the field (Proctor & Niemeyer, 2019).

Also proposed was falsifying individual components of a theory and elaborating upon what was left. As with formalism, this approach seemed initially promising. The traditional model of scientific progress holds that theories are empirically tested and revised in terms of their assumptions, concepts, propositions, and overall organization (Thornberry, 1989). Elaboration, however, is a slow method of discovery (Tittle, 1995) that tends to get bogged down with technical considerations relating to theory and methods (Hirschi, 1989). Moreover, it has not produced much change in existing criminal theories, something potentially linked to the absence of formalism in the field and difficulties with falsification. Bernard (1990) contended that no theory in criminology had been falsified since the 1960s—an echo of Meehl’s (1978) complaint in psychology. He attributed this failure to imprecise criminological theories, the belief that one must falsify whole theories rather than individual propositions, the failure to test some theories at all, the view that empirical evidence could not falsify theories, the embedding of unfalsifiable values into scientific theories, and the fact that the inability to falsify theories leads people to instead abandon them—a process called falsification by atrophy (Dooley & Goodison, 2020).

Given the shortcomings of falsification, some scholars proposed to integrate the best parts of multiple theories into single, more complete theories (for a review, see Krohn & Ward, 2016). In practice, this approach has been used primarily to increase the statistical variance explained by a theory (Elliot, 1985; Elliot et al., 1979, 1985), which does little to expand our substantive understanding of criminological phenomena. Furthermore, integration has been criticized on a number of grounds, the most damning of which is that it fails to integrate each theory’s underlying logical structure (Tittle, 1995). Indeed, Hirschi (1979, 1989) argues that theories containing contradictory assumptions cannot be integrated—and many of our theories were explicitly formulated within an opposition tradition to contradict older ones. Such concerns led to significant debates in the 1970s over, for example, whether differential association theory could be integrated logically with Skinnerarian behaviorism as proposed by Burgess and Akers (1966). Those opposed to the integration claimed the mental concepts in differential association theory (e.g., definitions) precluded the possibility of a logical integration between the approaches (Halbasch, 1979; Jeffery, 1980), while Akers (2009) maintained subsequent elaborations of social learning theory incorporating Bandura’s notion of vicarious learning addressed this problem. In the face of these issues, integration has focused instead on variables that are devoid of causal significance once divorced from their original theories (Tittle, 1995). Given the significance of theory in falsification (Niemeyer et al., 2022), empirical integrations alone are unlikely to promote theory development.

Consequences of Theoretical Stagnation

Debates about criminological theory largely ended with Bernard and Snipes’ (1996) call to abandon theory testing in favor of a risk factor approach based on existing theories. In the years since, formalism has become no more present than it was during the debate (Proctor, 2010), elaboration efforts have been stunted by the inability of criminologists to falsify theories (Dooley & Goodison, 2020; Proctor, 2010; Proctor & Niemeyer, 2019), and theory integration is commonly used to generate new theories (Krohn & Ward, 2016) despite the continued problem of contradictory assumptions.

Without a clear resolution to this debate, criminology has become a highly fragmented field (Agnew, 2011; Bruinsma, 2016; Dooley & Goodison, 2020; Walsh, 2002) with competing goals in relation to whether criminology should (1) be a scientific field or not; (2) focus on basic science (if it is a science) or technology (i.e., the development of interventions); and (3) prioritize individual interests over the collective interests of the scientific community (Proctor & Niemeyer, 2019). The first set of competing goals relates to distinctions in the field between mainstream scientific and critical or nonscientific approaches that emphasize social justice and are often opposed to scientific criminology (Agnew, 2011). The second set refers to whether criminology should focus on the development of cumulative scientific theory development (Bernard, 1990; Tittle, 2002) or on an engineering approach that eschews theories for specific research methods, such as randomized controlled trials, to identify interventions (Stevenson, 2023). The third set of competing aims relates to the careerism of criminologists (and academics more broadly), which are often misaligned with the collective goals of science. Academia currently rewards scholars who emphasize research output and career status, which is often incompatible with the slower, more careful efforts necessary for theory development and knowledge advancement.

Taken together, these competing aims and the institutional arrangements that come to reinforce and perpetuate them serve as a possible source of goal displacement in scientific criminology that moves the field away from scientific discovery. Under such conditions, there is little consensus of what criminology should hope to accomplish and what the best institutional arrangement might look like for advancing scientific knowledge. As such, it is not surprising that criminology could be seen as what Collins (1994) labels a low-consensus, slow-discovery scientific field—a claim most evident in Bernard’s (1990) observation that criminological theory textbooks have not changed much since the 1960s, at least regarding their scientific theories.

Table 1: Parallel Problems Identified in Criminology & Psychology







Lack of stable, reproducible phenomena

Niemeyer et al. (2022)


Eronen & Bringmann (2021)

Flexible verbal/discursive models

Gibbs (1972, 1975)

Proctor (2010)

Tittle (1985)

Frankenhuis et al., (2022)

Szollosi & Donkin (2019)

Meehl (1967)

Absence of Metatheory


Proctor & Niemeyer (2019)

Meier (1985)

Goertzen (2008)

Yanchar (1997)

Perverse Incentives

Bernard (1990)

Thomas (2024)

Stewart & Plotkin (2021)

Frankenhuis et al., (2022)




Field Fragmentation

Agnew (2011)

Bruinsma (2016)

Dooley and Goodison (2020) Proctor & Niemeyer (2019) Walsh 2002


Walsh-Bowers (2010)

Yanchar (1997)

Yanchar and Slife (1997)

Theory Atrophy vs. Falsification

Dooley & Goodison (2020)

Meehl (1978)

The Theory Crisis in Criminology

Criminology’s theory debates pre-date the theory crisis in psychology, yet there are striking parallels between the issues identified in each field (see Table 1). As noted earlier, both criminology and psychology made early efforts to resolve theoretical issues with integration and failed for very similar reasons. For instance, Werthiemer’s (1988) discussions on the conditions under which theory integration will and will not work mirror similar observations by Kornhauser (1975/1984) and Hirschi (1979) who contend only theories possessing orientations under a common paradigm, such as control theory, are amenable to integration. Psychology and criminology have also historically emphasized theory competition and theoretical disagreement (Hirschi, 1979, 1989; Kalmar &Sternberg, 1988), albeit for somewhat different reasons. Unlike psychology, logical positivism/empiricism never dominated criminological discourse due to its status as an interdisciplinary field, so criminology’s theory competition is tied to historical paradigms associated with philosophical schools of thought. As such, debates and analyses about criminological theories focus on their philosophical assumptions rather than their empirical adequacy (e.g., Akers, 1996, 2009; Costello, 1997, 1998; Hirschi, 1996; Matsueda, 1988). Despite these different origins, the general adoption of theory competition has led both fields to emphasize areas of discovery that are not shared across theories (Kalmar & Sternberg, 1988).

Criminology and psychology also share fundamental scientific shortcomings that make theory building, replication, and the overall accumulation of knowledge especially difficult. Primary issues include the lack of stable, reproducible phenomena (Eronen & Bringmann, 2021; Niemeyer et al., 2022), flexible verbal/discursive models (Bernard, 1989; Frankenhuis et al., 2023; Gibbs, 1972, 1985; Meehl, 1967; Proctor, 2010; Szollosi & Donkin, 2019; Tittle, 1985), and perverse incentives (Frankenhuis et al., 2023; Stewart & Plotkin, 2021; Thomas, 2024). Consequently, both fields suffer from theoretical atrophy (rather than true falsification; Dooley & Goodison, 2020; Meehl, 1978) and fragmentation in the field (Agnew, 2011; Bruinsma, 2016; Dooley & Goodison, 2020; Kalmar & Sternberg, 1988; Proctor & Niemeyer, 2019; Walsh, 2002). While criminology has begun to recognize the replication crisis in general, it has not yet turned its attention to the theory crisis itself. Given the parallels with psychology in indicators of the theory crisis along with the substantive similarities between the fields, criminology would benefit from examining the solutions to the theory crisis that psychology has adopted.

Solutions from the Theory Crisis

Until now, criminologists have borrowed exclusively from psychology’s solutions for the replication crisis—i.e., methodological and empirical improvements (Niemeyer et al., 2022). Yet these efforts, though important, do not address theoretical problems. For instance, even registered reports cannot correct for poor or vague hypothesizing (Scheel, 2022). In this way, there are still significant researcher degrees of freedom in the theoretical interpretation and framing of studies. Consequently, preregistration and other open science reforms cannot solve the theory crisis despite their important methodological and statistical contributions.

Criminologists instead need to look beyond the early empirical and methodological solutions to more recent suggestions for how to improve theory. Like psychology, criminology is a heavily empirical field, with many scholars focused not just on basic empirical research but application. However, theory serves as an essential backbone for all empirical research. As described earlier, high-quality research relies on a strong derivation chain that begins with the clear conceptualization of phenomena followed by specific descriptions of how those phenomena relate to one another. Only with these building blocks in place can one meaningfully derive testable, falsifiable hypotheses, develop and employ appropriate measurements, and deploy methodological and analytical research practices that are robust against questionable research practices.

Fortunately, many theoretical improvements derived from psychology do not require criminologists to suspend their empirical efforts while theoretical knots are untangled. Though there have been many proposed solutions in psychology that may be worth borrowing, we will only touch upon a few: phenomenon detection, epistemic iteration, improved measurement, theory formalism, and division of labor.

Phenomenon Detection. Strong theory requires real, clearly defined phenomena to serve as its foundation; indeed, phenomena are what are being explained by theory (Borsboom et al., 2021). Part of why building strong theory in the social sciences feels so elusive is the difficulty of directly observing and measuring social phenomena (Meehl, 1978). Even subdisciplines within social sciences can disagree on the existence and boundaries of phenomena (Eronen & Romeijn, 2020), which reduces their ability to constrain theory as they do in the natural sciences. Criminology exhibits a similar problem, with empirical tests of theory using idiosyncratic definitions of key concepts (e.g., Proctor et al., 2024). Phenomenon detection and consensus, however, are crucial to building better models and understanding of the social world (Eronen & Bringmann, 2021; Eronen & Romeijn, 2020). Without agreement on the existence and nature of specific phenomena, we cannot build theories about or explained by those phenomena.

One solution, therefore, is to refocus empirical efforts towards descriptive and exploratory work dedicated to identifying concrete phenomena and defining their boundary conditions (Eronen & Bringmann, 2021; Haig, 2013, 2023; Scheel et al., 2021). Such a “bottom-up” approach to research has often been considered the purview of qualitative research, and indeed, qualitative research will be crucial to these early efforts due to the difficulty of quantifying phenomena we have yet to discover (Bringmann et al., 2022). However, quantitative research can be just as effective at phenomenon detection when it eschews traditional null hypothesis significance testing in favor of more descriptive forms of statistical analyses (Haig, 2013). It is particularly important to focus on detecting phenomena grounded in concrete reality rather than statistical artifacts to guard against placing undo effort into investigating the properties of phenomena that do not actually exist (e.g., ego depletion; Hagger et al., 2016).

Epistemic Iteration. Phenomenon detection, however, is not a one-and-done process. Because many of our phenomenon are intangible and detectable only with fallible measures, what we discover may be only a vague first approximation of the true underlying phenomenon. Furthermore, the discovery process can often create concepts and measurements that may or may not be truly distinguishable from pre-existing ones (Hagger, 2014). Therefore, we need to then engage with the process of “epistemic iteration”: identifying a phenomenon and testing different methodologies for capturing it conceptually, thus iteratively updating our understanding of it (Eronen & Bringmann, 2021). Imperfect measures allow us to identify the phenomenon in the first place; theory then expands upon what that phenomenon is and how it relates to other phenomena, which suggests better ways to measure the phenomenon, which then improves the construct validity of the measurement and the conceptual clarity of the construct (Eronen & Bringmann, 2021; Fried et al., 2022). Ultimately, this process identifies the “best” or most accurate and robust version to be used based on context, population, etc., to allow for criminologists to continually improve their understanding of a phenomenon.

Part of why epistemic iteration is so important to theory development is that it helps guard against “concepts” that lump several phenomena together or artificially split a single phenomenon into multiple phenomena (Bringmann et al., 2022; Proctor & Niemeyer, 2019)—creating a problem of “old wine in new bottles.” In criminology, for instance, several terms capture or overlap with what we might call impulsivity: discounting the future (Nagin & Pogarsky, 2004), hot (vs. cool) decision-making (van Gelder, 2013), and self-control (Marcus, 2004). Epistemic iteration would allow criminologists to identify whether these concepts are, in fact, distinct phenomena. If they are not, we would then identify which conceptualization is the most valid, replicable, and robust and discard the rest. However, like with phenomenon detection, epistemic iteration requires criminologists to shift from explanatory and confirmatory research to descriptive and exploratory (i.e., “non-confirmatory”) research.

To engage in epistemic iteration, criminologists can begin by including explicit discussions in the methods section of how phenomena are being conceptualized (Bringmann et al., 2022; Flake & Fried, 2020). At minimum, this practice would allow us to identify when researchers are using the same term for different concepts or different terms for the same concept. Such practices, however, also will likely highlight when conceptual definitions are vague and in need of refinement. Thus, research should also be dedicated to the exploration of specific concepts of interest with the goal of refining those definitions (Bringmann et al., 2022). For instance, Clack and Ward (2020) used a multistage method to clarify the concept anhedonia, a symptom of depression characterized by a diminished or lack of interest/pleasure in most activities. They began with current definitions of anhedonia and assessed whether the currently existing data were of sufficient quality to indicate the existence of the phenomenon. Once they were reasonably confident anhedonia existed in some form, they modeled it at multiple levels (e.g., molecular, neurological, phenomenological) and linked these models together with other theorized etiological causes, leading to an overall improved understanding of anhedonia. One could imagine taking a similar approach to key criminological concepts such as strain (Agnew, 2001), self-control (Marcus, 2004), and deviant behavior.

However, such an approach can only go so far. As noted above, Clack and Ward (2020) were only able to proceed with their exploration of anhedonia because they were able to trust the currently existing data on its clinical presentation. For other phenomena, the data may be less trustworthy or insufficiently precise. In such a situation, progress would require the development of better data collection instruments and methods.

Improved Measurement and Methodology. To engage in phenomenon detection, epistemic iteration, and the broader construction of theories, one must have reliable and valid methods (Smaldino, 2019). Though in a mature science, choosing the appropriate measure would occur after one has derived a clear hypothesis from a well-articulated theory, testing for the presence of a phenomenon requires a way to measure its existence. In fact, the accumulation of precisely measured phenomena allows for the development and improvement of theories (Smaldino, 2019). Moreover, accumulating evidence on the nature of a phenomenon becomes difficult, if not impossible, when scholars studying that phenomenon define and measure that construct in meaningfully different ways (Fried, 2020). Though diversification of measurement is important (Dale et al., 2022), if scientists are supposedly trying to study the same concept, the measures should still attempt to measure the same underlying concept. Thus, improving the construct validity of our measurements is an important step in strengthening our theories (Flake & Fried, 2020). Additionally, validation of existing measures of phenomena should be conducted using new measures to verify the phenomenon further (Haig, 2013, 2023), and measurement instruments should be carefully calibrated (Haig, 2013).

In addition to improving the methods and measurements we already have, theory would benefit from the development of new methods (Dale et al., 2022). New methods allow for the collection of novel data, which may detect previously unknown phenomena and allow us to more precisely measure pre-existing phenomena (Collins, 1994). For instance, the invention of the telescope allowed for astronomers to make observations and measurements that were essential to the development of astronomy. In the social sciences, this task is considerably more challenging given the intangibility of what we wish to measure (Flake & Fried, 2020; Meehl, 1978; Smaldino, 2019), yet for that very reason, criminology and other social sciences would benefit from such innovations.

Theory Formalism. Problems later in the derivation chain (e.g. false positive findings) often indicate issues earlier in the chain (e.g. accurate phenomenon detection), which begs the question of whether the social sciences are mature enough to be engaged in hypothesis testing at all (IJzerman et al., 2020; Scheel et al., 2021). According to Scheel et al. (2021),

…efforts to formalize hypothesis tests have led researchers to directly experience the repercussions of testing immature theories: Tightening the screws on the testing machinery has had the unexpected effect of making psychological scientists aware that they may not be ready to test hypotheses. (p. 746)

This represents a breakdown in the derivation chain where theory has become decoupled from methods and measurement. There should be a link between theory and the methods and measurements employed in the empirical test (Scheel et al., 2021; Smaldino, 2019). According to Smaldino (2019), cognitive psychology has higher successful replication rates of significant findings compared to social psychology due to possession of better theories to inform the research—something which criminology should aim to emulate.

Unfortunately, criminology’s conceptual and operational flexibility currently identify it as too immature to engage properly in hypothesis testing. However, as criminologists increase their efforts at phenomena detection and appropriate measurement, formalizing theories will prove crucial to criminology’s advancement. Formalization efforts even have some value now because, in some instances, there may be concrete, identified phenomena in other disciplines that we can borrow for our own theories. One way to formalize theory is by modeling each theoretical assumption and hypothesis as a testable mathematical relationship (Eronen & Romeijn, 2020). Another way is with computational models, which share a similar structure of mapping relationships between component constructs (Grahek et al., 2021). These models also can be used to fit data to latent constructs used in psychology to predict phenomena (Grahek et al., 2021). In instances where we cannot yet model theories mathematically or computationally, the mere effort of attempting to formalize our theories will help identify where we have vague, poorly defined concepts, which empiricists can then target via phenomenon detection and epistemic iteration.

Another, more immediately useable method of theory formalization is mechanistic theory development. Unlike mathematical and computational models, mechanistic approaches address causal connections between concepts and tether them to concrete, real things (Haig, 2023). Phenomena are causally explained through the component concrete entities—i.e., things or agents—and activities they are engaged in (Craver, 2007; Glennan, 2017; Haig, 2023). The detail provided by unpacking the phenomena into its component parts and their organization allows for a precise understanding of how the phenomenon is produced (Craver, 2007; Glennan, 2017; Grahek et al., 2021). In turn, this precision allows for control of the phenomenon instead of just explanation or prediction (Craver, 2007; Glennan, 2017). Additionally, understanding how mechanisms work beyond surface-level phenomena allows for better theory falsification (Grahek et al., 2021).

Division of Labor. One difficulty in enacting the above solutions is that they require researchers to have an abundance of distinct, hard-to-acquire skill sets: theory development, mathematical modeling, quantitative and qualitative methodological expertise, psychometric development and testing. For a single researcher to master all those skills would be difficult, if not impossible, which may explain why psychologists and criminologists have been reluctant to adopt them (Forscher et al., 2023; Haeussler & Sauermann, 2016).

One solution to this problem would be to allow (perhaps even encourage) criminologists to specialize in one of three branches: theory, measurement, or empirics. Currently, all criminologists are expected to engage in empirical research, with theory and measurement or methodological development as supplemental focuses for a minority of scholars. However, there is precedent for allowing researchers to specialize. Physics is the quintessential example, with physicists being exclusively theoretical or empirical. However, a somewhat analogous division exists in psychology, with psychometricians focused on the development and validation of measures being distinct from other empirical psychologists.

Were criminologists to commit to a similar division of labor, the above solutions would become much more tractable. Empiricists would refine our stock of phenomena by adding, removing, and refining what we know exists. Methodologists would then develop increasingly sophisticated ways of detecting these phenomena, creating a virtuous circle. Theoreticians could then propose how these phenomena relate and produce other phenomena, which would imply clear, falsifiable hypotheses the empiricists would test using the methodologists’ best measures. With the results, theoreticians would update the theories, and the cycle would continue. Though such a cycle may be technically feasible now, allowing specialization would remove career-related barriers (such as concern over whether non-empirical papers would be sufficiently valued) and allow for mastery of one’s given branch.


Though criminology’s origins lie primarily with sociology, as a social science, it shares just as many similarities with psychology—including its shortcomings. Psychology’s very public replication crisis should, therefore, be recognized as the canary in the coal mine. Like psychology, criminology engages in QRPs, has poorly defined concepts, and has theories so vague that we cannot reasonably engage in null hypothesis testing or other confirmatory theory questions. The question is not whether criminology is likely to face a similar theory crisis as psychology but whether we will acknowledge that we currently are facing such a crisis and move to act.

Fortunately, psychology has given us a strong starting point for improvement, with ways for scholars of all persuasions to participate. Empiricists, especially qualitative scholars, can refocus their efforts on detecting real, concrete phenomena, while those with a more theoretical bent can begin formalizing current theories using the abundance of resources that are now available (e.g., Proctor & Niemeyer, 2020; Smaldino, 2020; van Rooij & Baggio, 2021). We should learn from our past mistakes and the mistakes of psychology. These are changes we could implement immediately, and if we want criminology to have a meaningful impact on public policy and our overall understanding of crime and deviance, we should not delay.

Authors Notes

This work was supported by the Air Force Office of Scientific Research, funding award FA9550-23-1-0453.

Disclaimer: The views expressed in this publication are those of the author and do not reflect the official policy or position of the US Air Force, Department of Defense, or the U.S. Government. PA#: USAFA-DF-2024-453

This work contains no conflicts of interest for any contributing author.


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