The clinical pain acceptance Q-sort: a tool for assessment and facilitation of pain acceptance

The topic of pain acceptance can be clinically difficult to raise in a respectful way. This article introduces a method of managing the topic of pain acceptance in daily clinical practice: The clinical pain acceptance Q-sort. The Q-sort procedure comprises 13 small cards with printed statements concerning pain acceptance on the one side, score numbers on the other side. The procedure involves the patient handling and prioritizing the statements in a personally meaningful order. Both quantitative and qualitative use of the tool is possible. The method has a three-fold outcome: (1) topics of pain acceptance are presented in a multi-faceted way for the chronic pain patient, (2) an approximate assessment of the level of pain acceptance issues is offered to the clinician, and (3) good opportunities for a therapeutic discussion on pain acceptance are made available. The clinical pain acceptance Q-sort procedure may positively contribute to daily clinical work with pain acceptance in a straightforward way. The method provides options for assessment of pain acceptance, for better understanding of the patient, and for clinical training in psychological pain management.     

Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform Information

The standard error (SE) stopping rule, which terminates a computer adaptive test (CAT) when the SE is less than a threshold, is effective when there are informative questions for all trait levels. However, in domains such as patient-reported outcomes, the items in a bank might all target one end of the trait continuum (e.g., negative symptoms), and the bank may lack depth for many individuals. In such cases, the predicted standard error reduction (PSER) stopping rule will stop the CAT even if the SE threshold has not been reached and can avoid administering excessive questions that provide little additional information. By tuning the parameters of the PSER algorithm, a practitioner can specify a desired tradeoff between accuracy and efficiency. Using simulated data for the Patient-Reported Outcomes Measurement Information System Anxiety and Physical Function banks, we demonstrate that these parameters can substantially impact CAT performance. When the parameters were optimally tuned, the PSER stopping rule was found to outperform the SE stopping rule overall, particularly for individuals not targeted by the bank, and presented roughly the same number of items across the trait continuum. Therefore, the PSER stopping rule provides an effective method for balancing the precision and efficiency of a CAT.     

Measurement scales and scale independence in the analytic hierarchy process

One approach to evaluate the relative performance of decision alternatives with respect to multiple criteria is provided by the analytic hierarchy process. The method is based on pairwise comparisons between attributes, and several numerical measurement scales for the ratio statements have been proposed. The choice of measurement scale is re‐examined, and new arguments supporting the measurement scale of geometric progression are derived. Separately from the measurement scale considerations, the effects of the scale parameter in geometric measurement scale are also studied. By using a regression model for pairwise comparisons data, it is shown that the statistical inference does not depend on the value of the scale parameter in the case of a single pairwise comparison matrix. It is also shown when the scale independence of statistical inference can be achieved in a decision hierarchy. This requires the use of the geometric‐mean aggregation rule instead of the traditional arithmetic‐mean aggregation. The results of the case study demonstrate that the measurement scale and the aggregation rule have potentially large impacts on decision support. Copyright © 2000 John Wiley & Sons, Ltd.     

A Framework for the Improvement of Test Interpretation in Counseling

Frequent counselor errors in test interpretation include presenting test results too soon, interpreting them without reference to clearly defined criteria, erroneously assuming their validity, and failing to present them in terms the student can understand. These errors can be reduced through emphasis on the student's felt need for information, helping him state his questions operationally, basing interpretive statements on empirical validity, and by communicating test results in an understandable manner. Observations of behavior to determine student acceptance of the interpretation as well as self-report to determine recall and understanding of the interpretation should be used. The focus of evaluation should be on student recall, understanding, and acceptance of predictions derived from test results.     

Propositional Reasoning that Tracks Probabilistic Reasoning

This paper concerns the extent to which uncertain propositional reasoning can track probabilistic reasoning, and addresses kinematic problems that extend the familiar Lottery paradox. An acceptance rule assigns to each Bayesian credal state p a propositional belief revision method ${\sf B}_{p}$ , which specifies an initial belief state ${\sf B}_{p}(\top)$ that is revised to the new propositional belief state ${\sf B}(E)$ upon receipt of information E. An acceptance rule tracks Bayesian conditioning when ${\sf B}_{p}(E) = {\sf B}_{p|_{E}}(\top)$ , for every E such that p(E)?>?0; namely, when acceptance by propositional belief revision equals Bayesian conditioning followed by acceptance. Standard proposals for uncertain acceptance and belief revision do not track Bayesian conditioning. The ??Lockean?? rule that accepts propositions above a probability threshold is subject to the familiar lottery paradox (Kyburg 1961), and we show that it is also subject to new and more stubborn paradoxes when the tracking property is taken into account. Moreover, we show that the familiar AGM approach to belief revision (Harper, Synthese 30(1?C2):221?C262, 1975; Alchourrón et al., J Symb Log 50:510?C530, 1985) cannot be realized in a sensible way by any uncertain acceptance rule that tracks Bayesian conditioning. Finally, we present a plausible, alternative approach that tracks Bayesian conditioning and avoids all of the paradoxes. It combines an odds-based acceptance rule proposed originally by Levi (1996) with a non-AGM belief revision method proposed originally by Shoham (1987).     

Scientific Realism and the Meanings of Theoretical Terms

In contemporary philosophy of science, there are many interesting arguments for and against scientific realism with regard to the meaningfulness and truthfulness of theoretical statements. Some anti-realists hold that since many important concepts in scientific theories have no specific referents, the relevant theoretical statements are therefore either false or meaningless. In this essay, I join the debates concerning the plausibility of scientific realism by focusing on two intertwined issues： first, that of how we can we explicate the meaningfulness of theoretical statements, especially statements pertaining to unobservable objects, and second, that of the meaningfulness of theoretical statements for our acceptance of scientific realism.     

New aspects of the probabilistic evaluation of hypotheses and experience

The probabilistic corroboration of two or more hypotheses or series of observations may be performed additively or multiplicatively . For additive corroboration (e.g. by Laplace's rule of succession), stochastic independence is needed. Inferences, based on overwhelming numbers of observations without unexplained counterinstances permit hyperinduction , whereby extremely high probabilities, bordering on certainty for all practical purposes may be achieved. For multiplicative corroboration, the error probabilities (1 - Pr) of two (or more) hypotheses are multiplied. The probabilities, obtained by reconverting the product, are valid for both of the hypotheses and indicate the gain by corroboration.. This method is mathematically correct, no probabilities > 1 can result (as in some conventional methods) and high probabilities with fewer observations may be obtained, however, semantical independence is a prerequisite. The combined method consists of (1) the additive computation of the error probabilities (1 - Pr) of two or more single hypotheses, whereby arbitrariness is avoided or at least reduced and (2) the multiplicative procedure . The high reliability of Empirical Counterfactual Statements is explained by the possibility of multiplicative corroboration of “all-no” statements due to their strict semantical independence.     

Probabilistic Subset Conjunction

The authors introduce subset conjunction as a classification rule by which an acceptable alternative must satisfy some minimum number of criteria. The rule subsumes conjunctive and disjunctive decision strategies as special cases. Subset conjunction can be represented in a binary-response model, for example, in a logistic regression, using only main effects or only interaction effects. This results in a confounding of the main and interaction effects when there is little or no response error. With greater response error, a logistic regression, even if it gives a good fit to data, can produce parameter estimates that do not reflect the underlying decision process. The authors propose a model in which the binary classification of alternatives into acceptable/unacceptable categories is based on a probabilistic implementation of a subset-conjunctive process. The satisfaction of decision criteria biases the odds toward one outcome or the other. The authors then describe a two-stage choice model in which a (possibly large) set of alternatives is first reduced using a subset-conjunctive rule, after which an alternative is selected from this reduced set of items. They describe methods for estimating the unobserved consideration probabilities from classification and choice data, and illustrate the use of the models for cancer diagnosis and consumer choice. They report the results of simulations investigating estimation accuracy, incidence of local optima, and model fit. The authors thank the Editor, the Associate Editor, and three anonymous reviewers for their constructive suggestions, and also thank Asim Ansari and Raghuram Iyengar for their helpful comments. They also thank Sawtooth Software, McKinsey and Company, and Intelliquest for providing the PC choice data, and the University of Wisconsin for making the breast-cancer data available at the machine learning archives.     

On the study of statistical intuitions

The study of intuitions and errors in judgment under uncertainty is complicated by several factors: discrepancies between acceptance and application of normative rules; effects of content on the application of rules; Socratic hints that create intuitions while testing them; demand characteristics of within-subject experiments; subjects' interpretations of experimental messages according to standard conversational rules. The positive analysis of a judgmental error in terms of heuristics may be supplemented by a negative analysis, which seeks to explain why the correct rule is not intuitively compelling. A negative analysis of non-regressive prediction is outlined.     

The many faces of counts-as: A formal analysis of constitutive rules

The paper proposes a logical systematization of the notion of counts-as which is grounded on a very simple intuition about what counts-as statements actually mean, i.e., forms of classification. Moving from this analytical thesis the paper disentangles three semantically different readings of statements of the type “X counts as Y in context c”, from the weaker notion of contextual classification to the stronger notion of constitutive rule. These many ways in which counts-as can be said are formally addressed by making use of modal logic techniques. The resulting framework allows for a formal characterization of all the involved notions and their reciprocal logical relationships.     

Justification,excuse, and proof beyond reasonable doubt

The law requires criminal guilt to be proved beyond a reasonable doubt. There are two different approaches to construing this legal rule. On an epistemic approach, the rule is construed in terms of justified belief or knowledge; on a probabilistic approach, the rule is construed in terms of satisfying a probabilistic threshold. An epistemic construction of the rule has this advantage over a probabilistic construction: the former can while the latter cannot excuse the state from blame for a false conviction. This claim rests on an understanding of legal rules, legal justification for a finding of guilt and the central purpose of a criminal trial.     

Practical and scientific rationality: A difficulty for Levi's epistemology

Traditionally scientific rationality has been distinguished from mere practical rationality. It has seemed that it is sometimes rational to accept statements for the purposes of particular practical deliberations even though it would not be rational to count them as having been confirmed by science. Isaac Levi contends that this traditional view is mistaken. He thinks that there should be a single standard of acceptance for all purposes, scientific and practical. The author contends that Levi has given no good reason for identifying scientific with practical rationality. And he argues that Levi's own theory is inconsistent with the thesis that a scientist should use a single standard of acceptance in all his scientific deliberations.     

Generalizing parametric models by introducing trial-by-trial parameter variability: The case of TVA

We identify two biases in the traditional use of Bundesen’s Theory of Visual Attention (TVA) and show that they can be substantially reduced by introducing trial-by-trial variability in the model. We analyze whole and partial report data from a comprehensive empirical study with 347 participants and elaborate on Bayesian model selection theory for quantifying the advantage of trial-by-trial generalization in general. The analysis provides strong evidence of trial-by-trial variation in both the VSTM capacity parameter and perceptual threshold parameter of TVA. On average, the VSTM capacity bias was found to be at least half an item, while the perceptual threshold parameter was found to be underestimated by about 2 ms.     

Trauma-Informed Police Responses to Rape Victims

ABSTRACT

Rape victims can benefit from trauma-informed approaches when reporting rape to police. Police interviewing skill can prevent survivor re-victimization while eliciting useful crime statements. However, rape myth acceptance and police culture may pose obstacles to a trauma-informed approach. Client empowerment, demystification, trigger reduction, and expressed concern for victim safety can be implemented by police agencies. Interdisciplinary collaboration, combating sexual harassment, gender balancing, emotional debriefing of officers, accountability to victims, new reporting methods, and advanced training protocols are elements of a trauma-informed approach.     

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.     

The subjective and irreproducible character of direct experience

Summary The author discusses the subjective and irreproducible character of the method of direct experience, which is the ground of the acceptance of observation statements. It is also shown how this method forms a part of the method of arriving at universal statements on the basis of experience without depriving the latter of its intersubjective and reproducible character.     

Genetic inquiry and ideological thought

It is customary to apply the term “ideology” to political statements and statements about politics believed to be saturated with irrational elements. Since more often than not it is applied to the political science and policies of parties of the extreme, one may suspect that this usage is itself colored by political interests. However, “ideology” can be redefined at the level of a meta‐science that reduces, though it cannot altogether eliminate, the partisan function of language about politics. Ideological thinking can then be shown to involve a logical error that is the counterpart of the genetic fallacy. Surprisingly, in this usage liberal political science, or that with liberal preconceptions, is a more frequent offender against the logic of science than a science of politics with illiberal preconceptions.