Examining data visualization pitfalls in scientific publications.

Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. To address common pitfalls in graphical representations, this paper focuses on identifying and understanding the root causes of misinformation in graphical representations. We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color, shape, size, and spatial orientation. Moreover, a text mining technique was applied to extract practical insights from common visualization pitfalls. Cochran's Q test and McNemar's test were conducted to examine if there is any difference in the proportions of common errors among color, shape, size, and spatial orientation. The findings showed that the pie chart is the most misused graphical representation, and size is the most critical issue. It was also observed that there were statistically significant differences in the proportion of errors among color, shape, size, and spatial orientation.

An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis.

In this article, we propose integrated generalized structured component analysis (IGSCA), which is a general statistical approach for analyzing data with both components and factors in the same model, simultaneously. This approach combines generalized structured component analysis (GSCA) and generalized structured component analysis with measurement errors incorporated (GSCAM) in a unified manner and can estimate both factor- and component-model parameters, including component and factor loadings, component and factor path coefficients, and path coefficients connecting factors and components. We conduct 2 simulation studies to investigate the performance of IGSCA under models with both factors and components. The first simulation study assesses how existing approaches for structural equation modeling and IGSCA recover parameters. This study shows that only consistent partial least squares (PLSc) and IGSCA yield unbiased estimates of all parameters, whereas the other approaches always provided biased estimates of several parameters. As such, we conduct a second, extensive simulation study to evaluate the relative performance of the 2 competitors (PLSc and IGSCA), considering a variety of experimental factors (model specification, sample size, the number of indicators per factor/component, and exogenous factor/component correlation). IGSCA exhibits better performance than PLSc under most conditions. We also present a real data application of IGSCA to the study of genes and their influence on depression. Finally, we discuss the implications and limitations of this approach, and recommendations for future research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Detecting Conditional Dependence Using Flexible Bayesian Latent Class Analysis.

A fundamental assumption underlying latent class analysis (LCA) is that class indicators are conditionally independent of each other, given latent class membership. Bayesian LCA enables researchers to detect and accommodate violations of this assumption by estimating any number of correlations among indicators with proper prior distributions. However, little is known about how the choice of prior may affect the performance of Bayesian LCA. This article presents a Monte Carlo simulation study that investigates (1) the utility of priors in a range of prior variances (i.e., strongly non-informative to strongly informative priors) in terms of Type I error and power for detecting conditional dependence and (2) the influence of imposing approximate independence on model fit of Bayesian LCA. Simulation results favored the use of a weakly informative prior with large variance-model fit (posterior predictive p-value) was always satisfactory when the class indicators were either independent or dependent. Based on the current findings and the additional literature, this article offers methodological guidelines and suggestions for applied researchers.

Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation.

Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach-partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.

The left cerebral hemisphere may be dominant for the control of bimanual symmetric reach-to-grasp movements.

Previous research has established that the left cerebral hemisphere is dominant for the control of continuous bimanual movements. The lateralisation of motor control for discrete bimanual movements, in contrast, is underexplored. The purpose of the current study was to investigate which (if either) hemisphere is dominant for discrete bimanual movements. Twenty-one participants made bimanual reach-to-grasp movements towards pieces of candy. Participants grasped the candy to either place it in their mouths (grasp-to-eat) or in a receptacle near their mouths (grasp-to-place). Research has shown smaller maximum grip apertures (MGAs) for unimanual grasp-to-eat movements than unimanual grasp-to-place movements when controlled by the left hemisphere. In Experiment 1, participants made bimanual symmetric movements where both hands made grasp-to-eat or grasp-to-place movements. We hypothesised that a left hemisphere dominance for bimanual movements would cause smaller MGAs in both hands during bimanual grasp-to-eat movements compared to those in bimanual grasp-to-place movements. The results revealed that MGAs were indeed smaller for bimanual grasp-to-eat movements than grasp-to-place movements. This supports that the left hemisphere may be dominant for the control of bimanual symmetric movements, which agrees with studies on continuous bimanual movements. In Experiment 2, participants made bimanual asymmetric movements where one hand made a grasp-to-eat movement while the other hand made a grasp-to-place movement. The results failed to support the potential predictions of left hemisphere dominance, right hemisphere dominance, or contralateral control.

Monitoring of Heart Rate from Photoplethysmographic Signals Using a Samsung Galaxy Note8 in Underwater Environments.

Photoplethysmography (PPG) is a commonly used in determining heart rate and oxygen saturation (SpO2). However, PPG measurements and its accuracy are heavily affected by the measurement procedure and environmental factors such as light, temperature, and medium. In this paper, we analyzed the effects of different mediums (water vs. air) and temperature on the PPG signal quality and heart rate estimation. To evaluate the accuracy, we compared our measurement output with a gold-standard PPG device (NeXus-10 MKII). The experimental results show that the average PPG signal amplitude values of the underwater environment decreased considerably (22% decrease) compared to PPG signals of dry environments, and the heart rate measurement deviated 7% (5 beats per minute on average. The experimental results also show that the signal to noise ratio (SNR) and signal amplitude decrease as temperature decreases. Paired t-test which compares amplitude and heart rate values between the underwater and dry environments was performed and the test results show statistically significant differences for both amplitude and heart rate values (p < 0.05). Moreover, experimental results indicate that decreasing the temperature from 45 °C to 5 °C or changing the medium from air to water decreases PPG signal quality, (e.g., PPG signal amplitude decreases from 0.560 to 0.112). The heart rate is estimated within 5.06 bpm deviation at 18 °C in underwater environment, while estimation accuracy decreases as temperature goes down.

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis.

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.

A Comparative Study on the Performance of GSCA and CSA in Parameter Recovery for Structural Equation Models With Ordinal Observed Variables.

A simulation based comparative study was designed to compare two alternative approaches to structural equation modeling-generalized structured component analysis (GSCA) with the alternating least squares (ALS) estimator vs. covariance structure analysis (CSA) with the maximum likelihood (ML) estimator or the weighted least squares mean and variance adjusted (WLSMV) estimator-in terms of parameter recovery with ordinal observed variables. The simulated conditions included the number of response categories in observed variables, distribution of ordinal observed variables, sample size, and model misspecification. The simulation outcomes focused on average root mean square error (RMSE) and average relative bias (RB) in parameter estimates. The results indicated that, by and large, GSCA-ALS recovered structural path coefficients more accurately than CSA-ML and CSA-WLSMV in either a correctly or incorrectly specified model, regardless of the number of response categories, observed variable distribution, and sample size. In terms of loadings, CSA-WLSMV outperformed GSCA-ALS and CSA-ML in almost all conditions. Implications and limitations of the current findings are discussed, as well as suggestions for future research.

Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error.

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM), where latent variables are approximated by weighted composites of indicators. It has no formal mechanism to incorporate errors in indicators, which in turn renders components prone to the errors as well. We propose to extend GSCA to account for errors in indicators explicitly. This extension, called GSCAM, considers both common and unique parts of indicators, as postulated in common factor analysis, and estimates a weighted composite of indicators with their unique parts removed. Adding such unique parts or uniqueness terms serves to account for measurement errors in indicators in a manner similar to common factor analysis. Simulation studies are conducted to compare parameter recovery of GSCAM and existing methods. These methods are also applied to fit a substantively well-established model to real data.

A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

Effects of a Responsiveness-Focused Intervention in Family Child Care Homes on Children's Executive Function.

Caregiver responsiveness has been theorized and found to support children's early executive function (EF) development. This study examined the effects of an intervention that targeted family child care provider responsiveness on children's EF. Family child care providers were randomly assigned to one of two intervention groups or a control group. An intervention group that received a responsiveness-focused online professional development course and another intervention group that received this online course plus weekly mentoring were collapsed into one group because they did not differ on any of the outcome variables. Children (N = 141) ranged in age from 2.5 to 5 years (mean age = 3.58 years; 52% female). At pretest and posttest, children completed delay inhibition tasks (gift delay-wrap, gift delay-bow) and conflict EF tasks (bear/dragon, dimensional change card sort), and parents reported on the children's level of attention problems. Although there were no main effects of the intervention on children's EF, there were significant interactions between intervention status and child age for delay inhibition and attention problems. The youngest children improved in delay inhibition and attention problems if they were in the intervention rather than the control group, whereas older children did not. These results suggest that improving family child care provider responsive behaviors may facilitate the development of certain EF skills in young preschool-age children.

Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data.

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

Left-dominant temporal-frontal hypercoupling in schizophrenia patients with hallucinations during speech perception.

BACKGROUND: Task-based functional neuroimaging studies of schizophrenia have not yet replicated the increased coordinated hyperactivity in speech-related brain regions that is reported with symptom-capture and resting-state studies of hallucinations. This may be due to suboptimal selection of cognitive tasks. METHODS: In the current study, we used a task that allowed experimental manipulation of control over verbal material and compared brain activity between 23 schizophrenia patients (10 hallucinators, 13 nonhallucinators), 22 psychiatric (bipolar), and 27 healthy controls. Two conditions were presented, one involving inner verbal thought (in which control over verbal material was required) and another involving speech perception (SP; in which control verbal material was not required). RESULTS: A functional connectivity analysis resulted in a left-dominant temporal-frontal network that included speech-related auditory and motor regions and showed hypercoupling in past-week hallucinating schizophrenia patients (relative to nonhallucinating patients) during SP only. CONCLUSIONS: These findings replicate our previous work showing generalized speech-related functional network hypercoupling in schizophrenia during inner verbal thought and SP, but extend them by suggesting that hypercoupling is related to past-week hallucination severity scores during SP only, when control over verbal material is not required. This result opens the possibility that practicing control over inner verbal thought processes may decrease the likelihood or severity of hallucinations.

Symptom dimensions of the psychotic symptom rating scales in psychosis: a multisite study.

The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS.

Symptom changes in five dimensions of the Positive and Negative Syndrome Scale in refractory psychosis.

Refractory psychosis units currently have little information regarding which symptoms profiles should be expected to respond to treatment. In the current study, we provide this information using structural equation modeling of Positive and Negative Syndrome Scale (PANSS) ratings at admission and discharge on a sample of 610 patients admitted to a treatment refractory psychosis program at a Canadian tertiary care unit between 1990 and 2011. The hypothesized five-dimensional structure of the PANSS fit the data well at both admission and discharge, and the latent variable scores are reported as a function of symptom dimension and diagnostic category. The results suggest that, overall, positive symptoms (POS) responded to treatment better than all other symptoms dimensions, but for the schizoaffective and bipolar groups, greater response on POS was observed relative to the schizophrenia and major depression groups. The major depression group showed the most improvement on negative symptoms and emotional distress, and the bipolar group showed the most improvement on disorganization. Schizophrenia was distinct from schizoaffective disorder in showing reduced treatment response on all symptom dimensions. These results can assist refractory psychosis units by providing information on how PANSS symptom dimensions respond to treatment and how this depends on diagnostic category.

A unified approach to multiple-set canonical correlation analysis and principal components analysis.

Multiple-set canonical correlation analysis and principal components analysis are popular data reduction techniques in various fields, including psychology. Both techniques aim to extract a series of weighted composites or components of observed variables for the purpose of data reduction. However, their objectives of performing data reduction are different. Multiple-set canonical correlation analysis focuses on describing the association among several sets of variables through data reduction, whereas principal components analysis concentrates on explaining the maximum variance of a single set of variables. In this paper, we provide a unified framework that combines these seemingly incompatible techniques. The proposed approach embraces the two techniques as special cases. More importantly, it permits a compromise between the techniques in yielding solutions. For instance, we may obtain components in such a way that they maximize the association among multiple data sets, while also accounting for the variance of each data set. We develop a single optimization function for parameter estimation, which is a weighted sum of two criteria for multiple-set canonical correlation analysis and principal components analysis. We minimize this function analytically. We conduct simulation studies to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of functional neuroimaging data to illustrate its empirical usefulness.