Category-specified Value Statements

A value statement such as “she is a good teacher” is categoryspecified, i.e., the criteria of evaluation are specified as those that are applicable to a given category, in this case the category of teachers. In this study of categoryspecified value statements, certain categories are identified that cannot be used to specify value aspects. Special attention is paid to categories that are constituted by functional characteristics. The logical properties of value statements that refer to such categories are shown to differ significantly from the corresponding properties in social choice theory.     

Why the social sciences are irreducible

Synthese - It is often claimed that the social sciences cannot be reduced to a lower-level individualistic science. The standard argument for this position (usually labelled explanatoryholism) is...     

Active Powers and Passive Powers – Do Causal Interactions Require Both?

Philosophia - Many powers metaphysicians postulate both active and passive powers, understood as distinct kinds of intrinsic causal properties of objects. I argue that the category of passive power...     

The role of short-term memory capacity and task experience for overconfidence in judgment under uncertainty

Research with general knowledge items demonstrates extreme overconfidence when people estimate confidence intervals for unknown quantities, but close to zero overconfidence when the same intervals are assessed by probability judgment. In 3 experiments, the authors investigated if the overconfidence specific to confidence intervals derives from limited task experience or from short-term memory limitations. As predicted by the naive sampling model (P. Juslin, A. Winman, & P. Hansson, 2007), overconfidence with probability judgment is rapidly reduced by additional task experience, whereas overconfidence with intuitive confidence intervals is minimally affected even by extensive task experience. In contrast to the minor bias with probability judgment, the extreme overconfidence bias with intuitive confidence intervals is correlated with short-term memory capacity. The proposed interpretation is that increased task experience is not sufficient to cure the overconfidence with confidence intervals because it stems from short-term memory limitations.     

Cluster randomized trials with treatment noncompliance

Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed.     

The multilevel latent covariate model: a new, more reliable approach to group-level effects in contextual studies

In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach, in which the observed (manifest) group mean is assumed to be perfectly reliable. This article demonstrates mathematically and with simulation results that this MMC approach can result in substantially biased estimates of contextual effects and can substantially underestimate the associated standard errors, depending on the number of L1 individuals per group, the number of groups, the intraclass correlation, the sampling ratio (the percentage of cases within each group sampled), and the nature of the data. To address this pervasive problem, the authors introduce a new multilevel latent covariate (MLC) approach that corrects for unreliability at L2 and results in unbiased estimates of L2 constructs under appropriate conditions. However, under some circumstances when the sampling ratio approaches 100%, the MMC approach provides more accurate estimates. Based on 3 simulations and 2 real-data applications, the authors evaluate the MMC and MLC approaches and suggest when researchers should most appropriately use one, the other, or a combination of both approaches.     

Local Change

An agent can usually hold a very large number of beliefs. However, only a small part of these beliefs is used at a time. Efficient operations for belief change should affect the beliefs of the agent locally, that is, the changes should be performed only in the relevant part of the belief state. In this paper we define a local consequence operator that only considers the relevant part of a belief base. This operator is used to define local versions of the operations for belief change. Representation theorems are given for the local operators.     

Simultaneous factor analysis of dichotomous variables in several groups

A new method is proposed for a simultaneous factor analysis of dichotomous responses from several groups of individuals. The method makes it possible to compare factor loading pattern, factor variances and covariances, and factor means over groups. The method uses information from first and second order proportions and estimates the model by generalized least-squares. Hypotheses regarding different degrees of invariance over groups may be evaluated by a large-sample chi-square test.The research of the first author was supported partly by the Bank of Sweden Tercentenary Foundation under project Structural Equation Models in the Social Sciences, project director Karl G. Jöreskog, and partly by the Swedish Research Council in the Humanistic and Social Sciences under project Analysis of Contingency Tables, project director Bengt Muthén.     

The Application of Latent Curve Analysis to Testing Developmental Theories in Intervention Research

The effectiveness of a prevention or intervention program has traditionally been assessed using time-specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time-specific mean levels can be both limiting and potentially misleading when the behavior of interest is developing systematically over time. It is argued here that there are both theoretical and statistical advantages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed technique of latent curve (LC) analysis is reviewed and extended to a true experimental design setting in which subjects are randomly assigned to a treatment intervention or a control condition. LC models are applied to both artificially generated and real intervention data sets to evaluate the efficacy of an intervention program. Not only do the LC models provide a more comprehensive understanding of the treatment and control group developmental processes compared to more traditional fixed-effects models, but LC models have greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power estimates under a variety of conditions and assumptions that can provide much needed information for the planning and design of more powerful but cost-efficient intervention programs for the future.     

Intensive family therapy: a way to change family functioning in multi-problem families

TA single group treatment outcome study of intensive family therapy (IFT) is presented. One hundred and nine families from five Swedish units participated and results on family measures for eighty-six of these families are reported. This multi-centre study is the largest so far of this treatment model. The units offered a full-day multi-impact treatment programme for families during an intensive period of approximately one month preceded by a period of extensive planning. Measures used were the self-rating 'Family Climate' and Family Relation Scale and observer-rated CRS-Turbo and the Beavers' System Scales. Significant changes in the direction towards a better family climate and a higher family functioning occurred. Given the very difficult circumstances of these multi-problem families the results are considered promising.