In psychology, many different guidelines exist for developing measurement instruments and manipulations of constructs. When conducting empirical research or secondary research, therefore, it is important to have a clear idea, and clearly communicate, which guidelines are followed. However, these guidelines are fragmented, and no universal consensus exists regarding how any construct must be measured or manipulated. Therefore, developing one ‘final’ taxonomy is not feasible or even desirable. At the same time, it is important that people clearly communicate their ‘study-specific taxonomy’. The dct package makes it possible to combine these two goals.

For more background information on the concept underlying the Decentralized Construct Taxonomies, see the reasoning set out in Peters & Crutzen (2017a, 2017b) and Crutzen & Peters (2018). In brief, in those papers the following premises are postulated:

  • Psychological constructs do not exist as such, but instead are useful metaphors that enable communication about, and measurement and manipulation of, aspects of the human psychology.
  • These constructs have verying degrees of specificity and levels of aggregation, and are therefore often represented in theories as influencing or causing one another.

From these premises it follows that is is not feasible (or desirable) to develop one ‘final’ taxonomy of psychological constructs or of manipulations of psychological constructs. Instead, multiple taxonomies exist, each of which has different uses. For example, in behavior change research, one taxonomy has been developed that is well-suited for describing intervention content but that is not useful when developing interventions (Abraham & Michie, 2008), and another that is well-suited for developing interventions, but is in its present form less useful for coding interventions (Kok et al., 2016).

Similarly, different theories that explain behavior postulate different psychological variables as important predictors of behavior. These variables generally overlap with each other: usually between theories, but also within the same theory. For example, the main three determinants in the Reasoned Action Approach are postulated to correlate (Fishbein & Ajzen, 2010), correlations that are empirically almost impossible to distinguish from structural composition (Peters & Crutzen, 2017a).

Not only do psychological variables often capture overlapping aspects of the human psychology, operationalisations often vary between studies, languages, target behaviors, and cultures. For example, the heterogeneity in definitions and measurement instrument of self-efficacy has recently been lamented (Williams & Rhodes, 2016a, 2016b), and a recent review of self-identity operationalisations identified hundreds of different items.

As a consequence of this pluriformity, research syntheses suffer, and in communication about research results, a lot is lost in translation.

However, given that the constructs in health psychology do not correspond to natural kinds (Fried, 2017), there is no way to settle on ‘the best’ definitions or operationalisations. The opinions and preferences of experienced researchers reflect, to an unknown degree, their training; and therefore, convictions from decades ago. Correlation strengths reflect artefacts such as correlated measurement error that render the assumption that correlation strengths can be used to infer ‘the best’ definitions or operationalisations circumspect.

However, at the same time there is a need for consistency in communication.

How can this be realised given that there’s no scientifically justifiable way to identify ‘the best’ definitions or operationalisations?

This is the problem Decentralized Construct Taxonomies aim to solve.

DTCs are plain text files that follow a number of conventions that enable unequivocal communication and reference to specific constructs and definitions, as well as their assumed structural composition (which is still assumed to be equivalent to causal influence for all practical purposes).

A DCT file is a YAML file of the following form:

---
id: intention_xl678lqgr
name: Intention
source:
  name: Reasoned Action Approach
  xdoi: ISBN
  spec: p. 888
def:
  def: Intention Def
  source:
    spec: pp. 8-1
measure:
  instruction: Ask about intention
manipulate:
  instruction: Instruction for manipulating intention directly.
  source:
operationalisation_coding: 
  instruction: If asked about intentions/plans/etc
  source: none
aspect_coding:
  instruction: Expressions of planning/intention/goals
  source: none
parent:
  id: behavior
alters:
  id: motivation
---

References

Abraham, C., & Michie, S. (2008). A taxonomy of behavior change techniques used in interventions. Health Psychology, 27(3), 379–87. doi: 10.1037/0278-6133.27.3.379

Crutzen, R., & Peters, G.-J. Y. (2018). Evolutionary learning processes as the foundation for behaviour change. Health Psychology Review, 12(1), 43–57. doi: 10.1080/17437199.2017.1362569

Add Fried reference from PN discussion

Add RAA book reference

Kok, G., Gottlieb, N. H., Peters, G.-J. Y., Mullen, P. D., Parcel, G. S., Ruiter, R. A. C., … Bartholomew, L. K. (2016). A taxonomy of behavior change methods: an Intervention Mapping approach. Health Psychology Review, 10(3), 297–312. doi: 10.1080/17437199.2015.1077155

Peters, G.-J. Y., & Crutzen, R. (2017a). Pragmatic nihilism: how a Theory of Nothing can help health psychology progress. Health Psychology Review, 11(2). doi: 10.1080/17437199.2017.1284015

Peters, G.-J. Y., & Crutzen, R. (2017b). Confidence in constant progress: or how pragmatic nihilism encourages optimism through modesty. Health Psychology Review, 11(2), 140–144. doi: 10.1080/17437199.2017.1316674

Williams, D. M., & Rhodes, R. E. (2016a). The confounded self-efficacy construct: review, conceptual analysis, and recommendations for future research. Health Psychology Review, 10(2), 113–128. doi: 10.1080/17437199.2014.941998

Williams, D. M., & Rhodes, R. E. (2016b). Reviving the critical distinction between perceived capability and motivation: A response to commentaries. Health Psychology Review, 7199(April), 1–7. doi: 10.1080/17437199.2016.1171729