Theory Construction Methodology: A practical framework for theory formation in psychology

Borsboom, Denny; Maas, Han L. J. van der; Dalege, Jonas; Kievit, Rogier A.; Haig, Brian D.

doi:10.31234/osf.io/w5tp8

Cited by 66 publications

(98 citation statements)

References 36 publications

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“…Clinicians are required to make more rigorous decisions in specifying relations in the case conceptualization, which makes the formalization of idiographic theories a promising avenue to foster more scientific practices in designing patient-tailored treatment. This reasoning is in line with a growing body of literature indicating the need for more rigorous theory development in clinical and social sciences [25,35,[42][43][44][45]. The left part of Fig.…”

Section: The Role Of Computational Models In Bridging the Scientist-psupporting

confidence: 78%

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Burger

Veen

Robinaugh

et al. 2020

BMC Med

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Background: The past decades of research have seen an increase in statistical tools to explore the complex dynamics of mental health from patient data, yet the application of these tools in clinical practice remains uncommon. This is surprising, given that clinical reasoning, e.g., case conceptualizations, largely coincides with the dynamical system approach. We argue that the gap between statistical tools and clinical practice can partly be explained by the fact that current estimation techniques disregard theoretical and practical considerations relevant to psychotherapy. To address this issue, we propose that case conceptualizations should be formalized. We illustrate this approach by introducing a computational model of functional analysis, a framework commonly used by practitioners to formulate case conceptualizations and design patient-tailored treatment. Methods: We outline the general approach of formalizing idiographic theories, drawing on the example of a functional analysis for a patient suffering from panic disorder. We specified the system using a series of differential equations and simulated different scenarios; first, we simulated data without intervening in the system to examine the effects of avoidant coping on the development of panic symptomatic. Second, we formalized two interventions commonly used in cognitive behavioral therapy (CBT; exposure and cognitive reappraisal) and subsequently simulated their effects on the system. Results: The first simulation showed that the specified system could recover several aspects of the phenomenon (panic disorder), however, also showed some incongruency with the nature of panic attacks (e.g., rapid decreases were not observed). The second simulation study illustrated differential effects of CBT interventions for this patient. All tested interventions could decrease panic levels in the system.

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Section: The Role Of Computational Models In Bridging the Scientist-psupporting

confidence: 78%

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Burger

Veen

Robinaugh

et al. 2020

BMC Med

View full text Add to dashboard Cite

show abstract

“…Indeed, there is growing belief that formal theories are needed if we are to avoid problems associated with conflicting empirical results (i.e., the "Replication Crisis"; Collaboration et al, 2015) and move toward an accumulation of knowledge in scientific research (e.g., Muthukrishna & Henrich, 2019;Szollosi & Donkin, 2019;Yarkoni, 2019;Ioannidis, 2014). Accordingly, as a field, psychiatry must grapple not only with methods for the collection and analysis of data, but also methods for the construction of formal theories (Borsboom, van der Maas, Dalege, Kievit, & Haig, 2020).…”

Section: Discussionmentioning

confidence: 99%

Modeling Psychopathology: From Data Models to Formal Theories

Haslbeck¹,

Ryan²,

Robinaugh³

et al. 2019

Preprint

Self Cite

112

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Over the past decade there has been a surge of empirical research investigating mental disorders as complex systems. In this paper, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the Abductive Formal Theory Construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science.

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“…As Cummins put it: "In psychology, we are overwhelmed with things to explain, and somewhat underwhelmed by things to explain them with" (Cummins, 2000). For this reason, many scholars have argued that psychology's attention to statistics and replicability has distracted from a problem that runs much deeper: a crisis of theory (Borsboom, 2013;Borsboom et al, 2020;Cummins, 2000;Guest & Martin, 2020;Haslbeck et al, 2019;Meehl, 1990b;Muthukrishna & Henrich, 2019;Phaf, 2020;Robinaugh, Haslbeck, et al, 2019;Smaldino, 2019;Szollosi et al, 2019;Vaidyanathan et al, 2015;Van Rooij & Baggio, 2020).…”

Section: Data Rich and Theory Poormentioning

confidence: 99%

“…Divergences between real and implied statistical models can then lead to theory revisions Robinaugh et al, 2020). Another advantage of formal theories is that they bring theories into a shared social space: the fact that our model of panic disorder is online means that others can go about modifying, extending, and testing it without having to involve us in the process (Borsboom et al, 2020). Such efforts can be much more difficult with verbal theories, explaining 'adversarial collaborations' in psychology:…”

Section: Putting the Theory Back Into Psychologymentioning

confidence: 99%

“…Somewhat surprisingly, since starting to work on this paper in early 2019, numerous contributions in different areas of psychology have identified this crisis of theory as a crucial challenge moving forward (Borsboom et al, 2020;Burger et al, 2019;Gershman, 2019;Guest & Martin, 2020;Haslbeck et al, 2019;Kellen, 2019;Lakens & Debruine, 2020;Muthukrishna & Henrich, 2019;Oberauer & Lewandowsky, 2019;Robinaugh, Haslbeck, et al, 2019;Savi et al, 2019;Smaldino, 2019;Szollosi et al, 2019;Van Rooij & Baggio, 2020), and both the Journal of Abnormal Psychology and Perspective of Psychological Science have opened calls for special issues on theory.…”

Section: Putting the Theory Back Into Psychologymentioning

confidence: 99%

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Lack of theory building and testing impedes progress in the factor and network literature

Fried¹

2020

Preprint

113

138

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The last decade has brought reforms to improve methodological practices, with the goal to increase the reliability and replicability of effects. However, explanations of effects remain scarce, and a growing chorus of scholars argues that the replicability crisis has distracted from a crisis of theory. In the same decade, the empirical literature using factor and network models has grown rapidly. I discuss three ways in which this literature falls short of theory building and testing. First, statistical and theoretical models are conflated, leading to invalid inferences such as the existence of psychological constructs based on factor models, or recommendations for clinical interventions based on network models. I demonstrate this inferential gap in a simulation study on statistical equivalence: excellent model fit does little to corroborate a theory, regardless of quality or quantity of data. Second, researchers fail to explicate theories about psychological constructs, but use implicit causal beliefs to guide inferences. These latent theories have led to problematic best practices in psychological research where inferences are drawn based on one specific causal model that is assumed, but not explicated. Third, explicated theories are often weak theories: narrative and imprecise descriptions vulnerable to hidden assumptions and unknowns. They fail to make clear predictions, and it remains unclear whether statistical effects corroborate such theories or not. Weak theories are immune to refutation or revision. I argue that these three challenges to theory building and testing are common and harmful, and impede theory formation, failure, and reform. A renewed focus on theoretical psychology and formal models offers a way forward.

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Theory Construction Methodology: A practical framework for theory formation in psychology

Cited by 66 publications

References 36 publications

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Bridging the gap between complexity science and clinical practice by formalizing idiographic theories: a computational model of functional analysis

Modeling Psychopathology: From Data Models to Formal Theories

Lack of theory building and testing impedes progress in the factor and network literature

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