27 research outputs found
ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}
Recent technological advances have provided new settings to enhance
individual-based data collection and computerized-tracking data have became
common in many behavioral and social research. By adopting instantaneous
tracking devices such as computer-mouse, wii, and joysticks, such data provide
new insights for analysing the dynamic unfolding of response process.
ssMousetrack is a R package for modeling and analysing computerized-tracking
data by means of a Bayesian state-space approach. The package provides a set of
functions to prepare data, fit the model, and assess results via simple
diagnostic checks. This paper describes the package and illustrates how it can
be used to model and analyse computerized-tracking data. A case study is also
included to show the use of the package in empirical case studies
Modeling random and non-random decision uncertainty in ratings data: A fuzzy beta model
Modeling human ratings data subject to raters' decision uncertainty is an
attractive problem in applied statistics. In view of the complex interplay
between emotion and decision making in rating processes, final raters' choices
seldom reflect the true underlying raters' responses. Rather, they are
imprecisely observed in the sense that they are subject to a non-random
component of uncertainty, namely the decision uncertainty. The purpose of this
article is to illustrate a statistical approach to analyse ratings data which
integrates both random and non-random components of the rating process. In
particular, beta fuzzy numbers are used to model raters' non-random decision
uncertainty and a variable dispersion beta linear model is instead adopted to
model the random counterpart of rating responses. The main idea is to quantify
characteristics of latent and non-fuzzy rating responses by means of random
observations subject to fuzziness. To do so, a fuzzy version of the
Expectation-Maximization algorithm is adopted to both estimate model's
parameters and compute their standard errors. Finally, the characteristics of
the proposed fuzzy beta model are investigated by means of a simulation study
as well as two case studies from behavioral and social contexts.Comment: 24 pages, 0 figures, 5 table
Mixture polarization in inter-rater agreement analysis: a Bayesian nonparametric index
In several observational contexts where different raters evaluate a set of
items, it is common to assume that all raters draw their scores from the same
underlying distribution. However, a plenty of scientific works have evidenced
the relevance of individual variability in different type of rating tasks. To
address this issue the intra-class correlation coefficient (ICC) has been used
as a measure of variability among raters within the Hierarchical Linear Models
approach. A common distributional assumption in this setting is to specify
hierarchical effects as independent and identically distributed from a normal
with the mean parameter fixed to zero and unknown variance. The present work
aims to overcome this strong assumption in the inter-rater agreement estimation
by placing a Dirichlet Process Mixture over the hierarchical effects' prior
distribution. A new nonparametric index is proposed to quantify
raters polarization in presence of group heterogeneity. The model is applied on
a set of simulated experiments and real world data. Possible future directions
are discussed
A novel CFA+EFA model to detect aberrant respondents
Aberrant respondents are common but yet extremely detrimental to the quality
of social surveys or questionnaires. Recently, factor mixture models have been
employed to identify individuals providing deceptive or careless responses. We
propose a comprehensive factor mixture model that combines confirmatory and
exploratory factor models to represent both the non-aberrant and aberrant
components of the responses. The flexibility of the proposed solution allows
for the identification of two of the most common aberant response styles,
namely faking and careless responding. We validated our approach by means of
two simulations and two case studies. The results indicate the effectiveness of
the proposed model in handling with aberrant responses in social and behavioral
surveys.Comment: 24 pages, 5 figures, 7 table
Measuring Distribution Similarities Between Samples: A Distribution-Free Overlapping Index
Every day cognitive and experimental researchers attempt to find evidence in support of their hypotheses in terms of statistical differences or similarities among groups. The most typical cases involve quantifying the difference of two samples in terms of their mean values using the t statistic or other measures, such as Cohen's d or U metrics. In both cases the aim is to quantify how large such differences have to be in order to be classified as notable effects. These issues are particularly relevant when dealing with experimental and applied psychological research. However, most of these standard measures require some distributional assumptions to be correctly used, such as symmetry, unimodality, and well-established parametric forms. Although these assumptions guarantee that asymptotic properties for inference are satisfied, they can often limit the validity and interpretability of results. In this article we illustrate the use of a distribution-free overlapping measure as an alternative way to quantify sample differences and assess research hypotheses expressed in terms of Bayesian evidence. The main features and potentials of the overlapping index are illustrated by means of three empirical applications. Results suggest that using this index can considerably improve the interpretability of data analysis results in psychological research, as well as the reliability of conclusions that researchers can draw from their studies
A psychometric modeling approach to fuzzy rating data
Modeling fuzziness and imprecision in human rating data is a crucial problem
in many research areas, including applied statistics, behavioral, social, and
health sciences. Because of the interplay between cognitive, affective, and
contextual factors, the process of answering survey questions is a complex
task, which can barely be captured by standard (crisp) rating responses. Fuzzy
rating scales have progressively been adopted to overcome some of the
limitations of standard rating scales, including their inability to disentangle
decision uncertainty from individual responses. The aim of this article is to
provide a novel fuzzy scaling procedure which uses Item Response Theory trees
(IRTrees) as a psychometric model for the stage-wise latent response process.
In so doing, fuzziness of rating data is modeled using the overall rater's
pattern of responses instead of being computed using a single-item based
approach. This offers a consistent system for interpreting fuzziness in terms
of individual-based decision uncertainty. A simulation study and two empirical
applications are adopted to assess the characteristics of the proposed model
and provide converging results about its effectiveness in modeling fuzziness
and imprecision in rating data
Factors associated with providers’ work engagement and burnout in homeless services: A cross‐national study
The complexity of homeless service users' characteristics and the contextual challenges faced by services can make the experience of working with people in homelessness stressful and can put providers' well-being at risk. In the current study, we investigated the association between service characteristics (i.e., the availability of training and supervision and the capability-fostering approach) and social service providers' work engagement and burnout. The study involved 497 social service providers working in homeless services in eight different European countries (62% women; mean age = 40.73, SD = 10.45) and was part of the Horizon 2020 European study "Homelessness as Unfairness (HOME_EU)." Using hierarchical linear modeling (HLM), findings showed that the availability of training and supervision were positively associated with providers' work engagement and negatively associated with burnout. However, results varied based on the perceived usefulness of the training and supervision provided within the service and the specific outcome considered. The most consistent finding was the association between the degree to which a service promotes users' capabilities and all the aspects of providers' well-being analyzed. Results are discussed in relation to their implications for how configuration of homeless services can promote social service providers' well-being and high-quality care.info:eu-repo/semantics/publishedVersio
fIRTree: An Item Response Theory modeling of fuzzy rating data
In this contribution we describe a novel procedure to represent fuzziness in
rating scales in terms of fuzzy numbers. Following the rationale of fuzzy
conversion scale, we adopted a two-step procedure based on a psychometric model
(i.e., Item Response Theory-based tree) to represent the process of answer
survey questions. This provides a coherent context where fuzzy numbers, and the
related fuzziness, can be interpreted in terms of decision uncertainty that
usually affects the rater's response process. We reported results from a
simulation study and an empirical application to highlight the characteristics
and properties of the proposed approach