Siegfried Macho

Estimating, testing, and comparing specific effects in structural equation models: The phantom model approach.

The phantom model approach for estimating, testing, and comparing specific effects within structural equation models (SEMs) is presented. The rationale underlying this novel method consists in representing the specific effect to be assessed as a total effect within a separate latent variable model, the phantom model that is added to the main model. The following favorable features characterize the method: (a) It enables the estimation, testing, and comparison of arbitrary specific effects for recursive and nonrecursive models with latent and manifest variables; (b) it enables the bootstrapping of confidence intervals; and (c) it can be applied with all standard SEM programs permitting latent variables, the specification of equality constraints, and the bootstrapping of total effects. These features along with the fact that no manipulation of matrices and formulas is required make the approach particularly suitable for applied researchers. The method is illustrated by means of 3 examples with real data sets.

Assessing Mediation in Dyadic Data Using the Actor-Partner Interdependence Model

The assessment of mediation in dyadic data is an important issue if researchers are to test process models. Using an extended version of the actor–partner interdependence model the estimation and testing of mediation is complex, especially when dyad members are distinguishable (e.g., heterosexual couples). We show how the complexity of the model can be reduced by assuming specific dyadic patterns. Using structural equation modeling, we demonstrate how specific mediating effects and contrasts among effects can be tested by phantom models that permit point and bootstrap interval estimates. We illustrate the assessment of mediation and the strategies to simplify the model using data from heterosexual couples.

Probabilistic set-up and the search for probability information in quasi- naturalistic decision tasks

The influence of the probabilistic set-up (i.e., formal aspects of the presented probability information) and of the task domain on the active search for probability information in quasi-natural risky decision tasks was investigated. In each of four tasks (domains: business, medicine, social, epidemic control) 72 subjects chose between a risky alternative and one without risk. There were three conditions in relation to the probabilistic set-up: (a) In the single condition the decision concerned a single case (e.g., one person). In the two multiple conditions (b and c) the decision was for many cases (e.g., 100 persons). In (b) the decision maker had to make an either-or decision (same alternative for all cases). In (c) the subject could assign a proportion of cases to one alternative and the rest to the other one. While the probabilistic set-up had no effect on the search for probabilistic information, the task domain had a strong impact. Risky decision making has been one of the central themes of decision research for decades. Most of the relevant experiments have used and are still using simple lotteries (gambles or bets). In these experiments, the utilities of the consequences and the probabilities of obtaining the consequences are found to be the central

Modeling Associative Recognition: A Comparison of Two-High-Threshold, Two- High-Threshold Signal Detection, and Mixture Distribution Models

A 2-high-threshold signal detection (HTSDT) model, a mixture distribution (SON) model, and 2-high-threshold (HT) models with responses distributed over 1 or several response categories were fit to results of 6 experiments from 2 studies on associative recognition: R. Kelley and J. T. Wixted (2001) and A. P. Yonelinas (1997). HTSDT assumes that associative recognition is based on conscious recollection and familiarity assessment, whereas according to SON and HT, associative information results in a shift of familiarity. The modeling results cast doubt on the prominent role of conscious recollection, and as far as models are valid, parameter estimation suggests 2 processes in associative recognition: a shift in familiarity that is due to associative information and the determination of the source of familiarity of pairs.

Effect of relevance shifts in category acquisition: a test of neural networks.

The effect of changing the validity of stimulus dimensions in the course of category learning was investigated. Contrary to previous experiments on interdimensional relevance shifts, a family resemblance stimulus structure was used that allowed for the direct comparison of performance on items learned before and after the relevance shift. In both experiments, the test performance was dominated by information that was acquired after the relevance shift. The results indicate that the acquired knowledge of the 2 learning phases was not integrated, but knowledge learned before the shift was partly replaced by information acquired after the shift. Simulation of the results by the independent cue model, the configural cue model, and ALCOVE (attention learning covering map) demonstrates that these models must be expanded by a mechanism that inhibits the integration of information acquired before and after the relevance shift.

Analyzing change at the dyadic level: the common fate growth model.

For the study of growth in dyads, methods have been developed to analyze growth at the level of the dyad members. In this article, we present a novel approach that we call the Common Fate Growth Model (CFGM). This model permits an analysis of growth at the level of the dyads when members are either distinguishable (e.g., heterosexual couples) or indistinguishable (e.g., lesbian couples). To estimate the model, we describe the use of structural equation modeling (SEM) for both distinguishable and indistinguishable members. For indistinguishable members and small groups, such as families, we provide details for the use of multilevel SEM (MSEM). For both SEM and MSEM, we address the issue of measurement invariance (MI) and the estimation of group-level means. The models are illustrated with data from couples collected at seven measurement occasions. To aid the estimation of the models, Mplus code and Amos setups are provided.

Cognitive modeling with spreadsheets

Spreadsheet implementations of two different types of cognitive models—a neural network model and a statistical model—are presented. The two examples illustrate how to employ the facilities of spreadsheets, the spreadsheet data structure, array functions, the built-in function library, and the integrated optimizer, for building cognitive models. The two presented models are new extensions of existing models. They are used for simulating data from experiments illustrating that the extended versions are able to explain experimental results that could not be simulated by the original models. The whole simulation study demonstrates that spreadsheets are a handy tool, especially for researchers without programming knowledge who want to build cognitive models and for instructors teaching cognitive modeling.

Recursive retrospective revaluation of causal judgments.

Recursive causal evaluation is an iterative process in which the evaluation of a target cause, T, is based on the outcome of the evaluation of another cause, C, the evaluation of which itself depends on the evaluation of a 3rd cause, D. Retrospective revaluation consists of backward processing of information as indicated by the fact that the evaluation of T is influenced by subsequent information that is not concerned with T directly. Two experiments demonstrate recursive retrospective revaluation with contingency information presented in list format as well as with trial-by-trial acquisition. Existing associative models are unable to predict the results. The model of recursive causal disambiguation that conceptualizes the revaluation as a recursive process of disambiguation predicts the pattern of results correctly.

Feature sampling in detection : Implications for the measurement of perceptual independence

The article presents the feature sampling signal detection (FS-SDT) model, an extension of the multivariate signal detection (SDT) model. The FS-SDT model assumes that, because of attentional shifts, different subsets of features are sampled for different presentations of the same multidimensional stimulus. Contrary to the SDT model, the FS-SDT model enables the estimation of pure perceptual effects that are uncontaminated by strategic attention shifts. The consideration of feature sampling in detection and identification opens a new perspective on the problem of measuring, respectively, the separability and integrality of stimulus dimensions. Disregarding feature sampling as a component process in detection and identification usually results in biased estimations of perceptual independence concepts relevant for judgments of whether stimulus dimensions are processed independently.