Causal Chain Model Research Articles

Toward a theory of motivational interviewing.

The widely disseminated clinical method of motivational interviewing (MI) arose through a convergence of science and practice. Beyond a large base of clinical trials, advances have been made toward "looking under the hood" of MI to understand the underlying mechanisms by which it affects behavior change. Such specification of outcome-relevant aspects of practice is vital to theory development and can inform both treatment delivery and clinical training. An emergent theory of MI is proposed that emphasizes two specific active components: a relational component focused on empathy and the interpersonal spirit of MI, and a technical component involving the differential evocation and reinforcement of client change talk. A resulting causal chain model links therapist training, therapist and client responses during treatment sessions, and posttreatment outcomes.

A differentiated account of the role of trust in consumers' acceptance of genetically modified foods in Germany

This paper proposes a sequential model to contrast the causal chain and associationist models on the role of trust in GM food acceptance. Data from 60 laddering interviews suggest the role of trust in evaluating the perceived consequences of GM food production and consumption is small and that the link between risk and trust is rather weak, too. Lending further support to the relevancy of the proposed model, the data support the hypothesis that respondents who are likely to accept GM food raise trust issues significantly less often than respondents who are unlikely to accept GM food. The analysis further identifies the segment with intermediate acceptance likelihood as most important for the future uptake of the technology.

Why we should not believe every lesson Andrew Moravcsik teaches us: A response

This article is a response to Andrew Moravcsik’s “What Can We Learn from the Collapse of the European Constitutional Project?”, published in No. 2, Vol. 47 (2006) of the PVS. In our reply we focus on three main points. First, we argue that Moravcsik’s apologia of the status quo does not convince in light of the challenges that a European Union currently with 27 member states and increasing heterogeneity is facing. Second, we discuss his causal chain model linking participation, deliberation and political legitimacy. We argue that Moravcsik confuses causality with conditionality. By doing so, he exaggerates claims of normative political science about the causal relationship between participation, deliberation and legitimacy, and makes it an unjustifiably easy target for critique. Third, we critically examine Moravcsik’s notion of democracy in order to show that his view of democracy as guaranteeing “certain social goods” brings about the risk of producing a theory of democracy without democracy.

Railroad noise: economic valuation and policy

In developed countries noise annoyance is an important source of environmental concern. Research on noise annoyance caused by railroad traffic is relatively underdeveloped. Here, a causal chain model is presented in which railroad traffic density, noise emission, noise immission and noise annoyance are causally related. Noise level, habituation and railroad usage are determinant factors. Noise annoyance causes social and economic costs, such as property value depreciation. Policy measures, aimed at reducing social and economic costs, are incorporated in various stages of the causal model. These measures can be subdivided into noise regulation and direct prevention measures. Stricter threshold values lead to higher total costs, but may lower social costs per capita. Economic feasibility of policy measures is usually analyzed by means of a cost-benefit case study. Methods of analysis used are diverse and ad hoc. Therefore, results of different case studies are not easily compared in terms of research synthesis.

The rise and fall of public sector plant breeding in the United Kingdom: a causal chain model of basic and applied research and diffusion

This paper examines the barley and wheat breeding programmes of the Plant Breeding Institute (PBI), which was the most successful public plant breeding institute in the UK, until privatization in 1987. The PBI's shares in barley and wheat seed sales are explained, showing that the success with barley was largely a matter of serendipity, whereas the wheat programme followed a more normal pattern. For wheat, the causal chain, or recursive, model decomposes the well-documented link between research expenditures and increases in agricultural productivity into three stages. These are the effects of R&D expenditures on basic research output, measured by publications, the effect of publications and applied R&D expenditures on trial plot yields, and the diffusion of the trial plot technologies, which raises yields on farms. Applying the model to the PBI's wheat varieties allows estimation of the lag structures. In contrast to the results for aggregate agricultural research, for a single plant breeding programme alone there is a considerable lead time before there is any response, followed by a lag distribution only a few years long. The returns to the R&D investments are calculated from the causal chain model, from single equation estimates and by evaluating the yield advantage of the PBI varieties. All three approaches give consistent results, which show that the returns to barley and wheat alone were sufficient to support the entire PBI budget and still give rates of return to applied research of between 14 and 25%. The return to the basic science expenditures of the John Innes Institute has a lower bound of 17%, but must have been even higher than for the PBI if the other Institutes were taken into account. The paper concludes by commenting on the effects of the privatization of the PBI.

The rise and fall of public sector plant breeding in the United Kingdom: a causal chain model of basic and applied research and diffusion

AbstractThis paper examines the barley and wheat breeding programmes of the Plant Breeding Institute (PBI), which was the most successful public plant breeding institute in the UK, until privatization in 1987. The PBI's shares in barley and wheat seed sales are explained, showing that the success with barley was largely a matter of serendipity, whereas the wheat programme followed a more normal pattern. For wheat, the causal chain, or recursive, model decomposes the well‐documented link between research expenditures and increases in agricultural productivity into three stages. These are the effects of R&D expenditures on basic research output, measured by publications, the effect of publications and applied R&D expenditures on trial plot yields, and the diffusion of the trial plot technologies, which raises yields on farms. Applying the model to the FBI's wheat varieties allows estimation of the lag structures. In contrast to the results for aggregate agricultural research, for a single plant breeding programme alone there is a considerable lead time before there is any response, followed by a lag distribution only a few years long. The returns to the R&D investments are calculated from the causal chain model, from single equation estimates and by evaluating the yield advantage of the PBI varieties. All three approaches give consistent results, which show that the returns to barley and wheat alone were sufficient to support the entire PBI budget and still give rates of return to applied research of between 14 and 25%. The return to the basic science expenditures of the John Innes Institute has a lower bound of 17%, but must have been even higher than for the PBI if the other Institutes were taken into account. The paper concludes by commenting on the effects of the privatization of the PBI.

Causal Chain Models for the Socioeconomic Career

This paper develops a theoretical model for the socioeconomic career in which income and occupational attainment at successive stages in the life cycle are both modified causal chains. Income is affected only by education, current occupation, and income in the immediate past; occupation is affected only by family background, education, and occupation in the immediate past. Neither is affected by attainments in the more remote past. This dual causal chain model is an extension of Blau and Duncan's single causal chain model for occupational attainments over the life cycle. Previous research suggested that both causal chain patterns were inadequate and so provided support for the alternative historical models proposed by Featherman. But when corrected for attenuation due to measurement error, the crucial data in fact provide strong support for the two causal chain models and evidence against the alternatives.

A Case Study in Prediction: The Market for Watermelons

This paper discusses two forecasting experiments involving models of the watermelon market. The first experiment compares the forecasts of an interdependent model estimated by limited information, single equation with those of a model using least squares reduced form. The second experiment compares the forecasts of the interdependent model with those of a causal chain model. It is found that the forecasts of the interdependent model are generally better than those of the alternative models.

Prediction from Simultaneous Equation Systems and Wold's Implicit Causal Chain Model

According to Wold, information on some of the variables at time t cannot be used for prediction of the values of the remaining variables at t, in a simultaneous equation system. This, however, is not the case with his causal model. This paper considers the stochastic processes underlying the two models, uses the theory of canonical correlation to discuss Wold's criticism, and suggests the type of additional information necessary to remove these objections. It further shows how both these models are complementary to each other. IN A SERIES of papers, Wold [9, 10, 11, 12] has recently proposed a new type of econometric model, which he calls the implicit causal chain model. The main incentive for this model was an attempt to combine the advantages of Tinbergen's causal model with those of the simultaneous equation systems initiated by Haavelmo [3, 4]. This latter model, which has been studied in detail by the Cowles Commission, is also known as the system. Wold has raised some objections, mainly from the point of view of prediction, against the simultaneous equation system; and in order to remedy these, he relaxed some of the restrictions on the correlational properties of the residuals in the simultaneous equation system. In this paper, these two models are discussed from an angle which offers a possibility for reconciling them. It is shown how the merits of both systems can be utilized by effecting some synthesis of them with the help of the stochastic process underlying both models. Furthermore, by using canonical analysis, it is shown how both these models are complementary. It must, of course, be added that the prediction problem has been considered purely on the basis of the stochastic model assumed, and the possible applicability of different representations of such models, for example, in economic contexts, under wider conditions is not discussed. Wold has objected to interdependent models on grounds other than prediction, especially from the point of view of the interpretation of structural parameters as elasticities, etc. The present paper, however, will not deal with this aspect of the problem. Instead, it deals primarily with the stochastic processes underlying economic models, and the demand and supply model, considered in the next section, is only for illustration.

A Generalization of Causal Chain Models (Part III of a Triptych on Causal Chain Systems)

The parting of the ways between causal chain (recursive) and interdependent (nonrecursive) systems is reviewed from the point of view of explanatory relations specified in terms of conditional expectations. On the customary assumptions, a causal chain system is designed so that its relations both in the original form and in the reduced form can be specified in terms of conditional expectations, whereas the relations of interdependent systems allow such specification only in the reduced form. A third type of model is discussed, called conditional causal chains, which formally is similar to interdependent systems, with the important difference that the behavioural relations of the original system are specified in terms of conditional expectations.