Psychological Perturbation Data on Attitudes Towards the Consumption of Meat

Hoekstra, Ria H. A.; Kossakowski, Jolanda J; Maas, Han L. J. van der

doi:10.5334/jopd.37

Cited by 4 publications

(12 citation statements)

References 7 publications

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“…The dataset used here is a dataset collected by Hoekstra et al (2018) and comprises 30 participants who completed a questionnaire that measures their attitude toward meat consumption. The questionnaire consists of 11 items that focused on the cognitive (six items) and affect (five items) side of one's attitude toward meat consumption.…”

Section: Application To Empirical Perturbation Datamentioning

confidence: 99%

“…Table 2 gives an overview of the items, including the means, standard deviation and the mean change between the baseline and the perturbation environment. All raw data, including the questionnaire and hypothetical scenarios, are published online (Hoekstra et al, 2018).…”

Section: Application To Empirical Perturbation Datamentioning

confidence: 99%

“…These are (empirical) data where some perturbation has taken place. Real-world examples include a pre-posttest comparison with therapy as a perturbation (see, e.g., Kossakowski et al, 2021), or experimental designs in which participants are presented with hypothetical scenarios to change their attitude toward a construct (see Hoekstra et al, 2018, for an empirical example).…”

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confidence: 99%

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The search for causality: A comparison of different techniques for causal inference graphs.

Kossakowski¹,

Waldorp²,

Maas³

2021

Psychological Methods

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Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research.

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Section: Application To Empirical Perturbation Datamentioning

confidence: 99%

Section: Application To Empirical Perturbation Datamentioning

confidence: 99%

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confidence: 99%

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The search for causality: A comparison of different techniques for causal inference graphs.

Kossakowski¹,

Waldorp²,

Maas³

2021

Psychological Methods

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“…The data we use involves the topic of meat consumption and is fully described in Hoekstra et al (2018). The design resembles the graph perturbation method of Section 3.…”

Section: Application To Psychology Datamentioning

confidence: 99%

“…and measure the same set of attitudes again. And so on, for each of the different attitudes (Hoekstra et al, 2018). We get a collection of data sets where each of the variables has been intervened on by hypothetical scenarios, including observation without an intervention.…”

Section: Introductionmentioning

confidence: 99%

Perturbation graphs, invariant prediction and causal relations in psychology

Waldorp,

Kossakowski,

van der Maas

2021

Preprint

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Networks (graphs) in psychology are often restricted to settings without interventions. Here we consider a framework borrowed from biology that involves multiple interventions from different contexts (observations and experiments) in a single analysis. The method is called perturbation graphs. In gene regulatory networks, the induced change in one gene is measured on all other genes in the analysis, thereby assessing possible causal relations. This is repeated for each gene in the analysis. A perturbation graph leads to the correct set of causes (not necessarily direct causes). Subsequent pruning of paths in the graph (called transitive reduction) should reveal direct causes. We show that transitive reduction will not in general lead to the correct underlying graph. There is however a close relation with another method, called invariant causal prediction. Invariant causal prediction can be considered as a generalisation of the perturbation graph method where including additional variables (and so conditioning on those variables) does reveal direct causes, and thereby replacing transitive reduction. We explain the basic ideas of perturbation graphs, transitive reduction and invariant causal prediction and investigate their connections. We conclude that perturbation graphs provide a promising new tool for experimental designs in psychology, and combined with invariant prediction make it possible to reveal direct causes instead of causal paths. As an illustration we apply the perturbation graphs and invariant causal prediction to a data set about attitudes on meat consumption.

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Formalizing psychological interventions through network control theory

Stocker,

Koppe,

Reich

et al. 2023

Sci Rep

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Despite the growing deployment of network representation to comprehend psychological phenomena, the question of whether and how networks can effectively describe the effects of psychological interventions remains elusive. Network control theory, the engineering study of networked interventions, has recently emerged as a viable methodology to characterize and guide interventions. However, there is a scarcity of empirical studies testing the extent to which it can be useful within a psychological context. In this paper, we investigate a representative psychological intervention experiment, use network control theory to model the intervention and predict its effect. Using this data, we showed that: (1) the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network control theoretic metrics (average and modal controllability), (2) the size of change following intervention negatively correlates with a whole-network topology that quantifies the “ease” of change as described by control theory (control energy), and (3) responses after intervention can be predicted based on formal results from control theory. These insights assert that network control theory has significant potential as a tool for investigating psychological interventions. Drawing on this specific example and the overarching framework of network control theory, we further elaborate on the conceptualization of psychological interventions, methodological considerations, and future directions in this burgeoning field.

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Psychological Perturbation Data on Attitudes Towards the Consumption of Meat

Cited by 4 publications

References 7 publications

The search for causality: A comparison of different techniques for causal inference graphs.

The search for causality: A comparison of different techniques for causal inference graphs.

Perturbation graphs, invariant prediction and causal relations in psychology

Formalizing psychological interventions through network control theory

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