Unravelling causal and temporal influences underpinning monitoring systems success: a typological approach

This paper is concerned with the causal and temporal underpinnings of Information Systems (IS) success. It uses a typological approach based on fuzzy-set Qualitative Comparative Analysis (fsQCA) and process tracing. It investigates success across multiple cases of IS adopted for monitoring the disbursement and use of resources within the European Social Fund (ESF). The study unravels the causal mechanisms and temporal pathways underpinning success in these systems. It develops a typological theory of monitoring systems success that reveals the temporal pathways embedded within individual cases, as well as broader theoretical patterns emerging across cases. Theoretical, methodological and practical implications are discussed

A better lemon squeezer? Maxium-likelihood regression with beta-distributed dependent variables

Uncorrectable skew and heteroscedasticity are among the "lemons" of psychological data, yet many important variables naturally exhibit these properties. For scales with a lower and upper bound, a suitable candidate for models is the beta distribution, which is very flexible and models skew quite well. The authors present maximum-likelihood regression models assuming that the dependent variable is conditionally beta distributed rather than Gaussian. The approach models both means (location) and variances (dispersion) with their own distinct sets of predictors (continuous and/or categorical), thereby modeling heteroscedasticity. The location submodel link function is the logit and thereby analogous to logistic regression, whereas the dispersion submodel is log linear. Real examples show that these models handle the independent observations case readily. The article discusses comparisons between beta regression and alternative techniques, model selection and interpretation, practical estimation, and software

Mixed and mixture regression models for continuous bounded responses using the beta distribution

Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite poorly modeled by the normal distribution. The authors extend the beta-distributed generalized linear model (GLM) proposed in Smithson and Verkuilen (2006) to discrete and continuous mixtures of beta distributions, which enables modeling dependent data structures commonly found in real settings. The authors discuss estimation using both deterministic marginal maximum likelihood and stochastic Markov chain Monte Carlo (MCMC) methods. The results are illustrated using three data sets from cognitive psychology experiments

Beta Regression Finite Mixture Models of Polarization and Priming

This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture model approach is superior in this regard to popular methods such as extremity scores, due to its incorporation of three submodels (location, dispersion, and relative composition), each of which can diagnose specific kinds of polarization and related effects. Three examples are elucidated using real data sets

Hierarchical models of simple mechanisms underlying confidence in decision making

Choice confidence is a central measure in psychological decision research, often being reported on a probabilistic scale. Simple mechanisms that describe the psychological processes underlying choice confidence, including those based on error and confirmation biases, have typically received support via fits to data averaged over subjects. While averaged data ease model development, they can also destroy important aspects of the confidence data distribution. In this paper, we develop a hierarchical model of raw confidence judgments using the beta distribution, and we implement two simple confidence mechanisms within it. We use Bayesian methods to fit the hierarchical model to data from a two-alternative confidence experiment, and we use a variety of Bayesian tools to diagnose shortcomings of the simple mechanisms that are overlooked when applied to averaged data. Bugs code for estimating the models is also supplied