Current Practices in Data Analysis Procedures in Psychology: What Has Changed?

From means to meaning in the study of sex/gender differences and similarities

The incorporation of sex and gender (S/G) related factors is commonly acknowledged as a necessary step to advance towards more personalized diagnoses and treatments for somatic, psychiatric, and neurological diseases. Until now, most attempts to integrate S/G-related factors have been reduced to identifying average differences between females and males in behavioral/ biological variables. The present commentary questions this traditional approach by highlighting three main sets of limitations: 1) Issues stemming from the use of classic parametric methods to compare means; 2) challenges related to the ability of means to accurately represent the data within groups and differences between groups; 3) mean comparisons impose a results' binarization and a binary theoretical framework that precludes advancing towards precision medicine. Alternative methods free of these limitations are also discussed. We hope these arguments will contribute to reflecting on how research on S/G factors is conducted and could be improved.

Intelligent Framework for Early Detection of Severe Pediatric Diseases from Mild Symptoms

Children's health is one of the most significant fields in medicine. Most diseases that result in children's death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research aims to develop a machine learning (ML) framework to detect the severity of disease in children. The proposed framework helps in discriminating children's urgent/severe conditions and notifying parents whether a child needs to visit the emergency room immediately or not. The model considers several variables to detect the severity of cases, which are the symptoms, risk factors (e.g., age), and the child's medical history. The framework is implemented by using nine ML methods. The results achieved show the high performance of the proposed framework in identifying serious pediatric diseases, where decision tree and random forest outperformed the other methods with an accuracy rate of 94%. This shows the reliability of the proposed framework to be used as a pediatric decision-making system for detecting serious pediatric illnesses. The results are promising when compared to recent state-of-the-art studies. The main contribution of this research is to propose a framework that is viable for use by parents when their child suffers from any commonly developed symptoms.

The influence of journal submission guidelines on authors' reporting of statistics and use of open research practices: Five years later

Changes in statistical practices and reporting have been documented by Giofrè et al. PLOS ONE 12(4), e0175583 (2017), who investigated ten statistical and open practices in two high-ranking journals (Psychological Science [PS] and Journal of Experimental Psychology-General [JEPG]): null hypothesis significance testing; confidence or credible intervals; meta-analysis of the results of multiple experiments; confidence interval interpretation; effect size interpretation; sample size determination; data exclusion; data availability; materials availability; and preregistered design and analysis plan. The investigation was based on an analysis of all papers published in these journals between 2013 and 2015. The aim of the present study was to follow up changes in both PS and JEPG in subsequent years, from 2016 to 2020, adding code availability as a further open practice. We found improvement in most practices, with some exceptions (i.e., confidence interval interpretation and meta-analysis). Despite these positive changes, our results indicate a need for further improvements in statistical practices and adoption of open practices.

Improving the quality of reporting findings using computer data analysis applications in educational research in context

Data analysis is an important step in the research process as it influences the quality and standard of reporting research findings. Based on a review of the content of 255 doctorate theses, the use of computer applications for data analysis in educational research was assessed. It was feasible to assess how extensively used and accepted computer packages had become in educational research using an aspect of the diffusion of innovations theory as part of the conceptual framework. The results showed that the use of computer applications to analyse data was more prevalent among researchers using quantitative and mixed-methods research methodologies than among qualitative educational researchers. Educational researchers have not yet fully adopted innovative computer data analysis techniques in their research. It is evident that they use traditional technologies more than computer applications in their research. Name dropping of the computer applications used without employing the language or visualisations features provided by the applications was rife. This article bridges the gap between methodological scholarship and the use of computer applications in data analysis. It illuminates the potential of computer software to enhance the quality of the reporting of findings. The article aims to contribute to improvements in the standard of research reporting and the attributes of the graduates. The practical methodological advice in this article is aimed at guiding researchers who consider using computer packages in data analysis, irrespective of their methodological orientation. It stimulates debate on the use of computer applications in data analysis.

Repeated measures ANOVA and adjusted F-tests when sphericity is violated: which procedure is best?

Introduction

One-way repeated measures ANOVA requires sphericity. Research indicates that violation of this assumption has an important impact on Type I error. Although more advanced alternative procedures exist, most classical texts recommend the use of adjusted F-tests, which are frequently employed because they are intuitive, easy to apply, and available in most statistical software. Adjusted F-tests differ in the procedure used to estimate the corrective factor ε, the most common being the Greenhouse-Geisser (F-GG) and Huynh-Feldt (F-HF) adjustments. Although numerous studies have analyzed the robustness of these procedures, the results are inconsistent, thus highlighting the need for further research.

Methods

The aim of this simulation study was to analyze the performance of the F-statistic, F-GG, and F-HF in terms of Type I error and power in one-way designs with normal data under a variety of conditions that may be encountered in real research practice. Values of ε were fixed according to the Greenhouse-Geisser procedure (ε ^ ). We manipulated the number of repeated measures (3, 4, and 6) and sample size (from 10 to 300), with ε ^ values ranging from the lower to its upper limit.

Results

Overall, the results showed that the F-statistic becomes more liberal as sphericity violation increases, whereas both F-HF and F-GG control Type I error; of the two, F-GG is more conservative, especially with large values of ε ^ and small samples.

Discussion

If different statistical conclusions follow from application of the two tests, we recommend using F-GG for ε ^ values below 0.60, and F-HF for ε ^ values equal to or above 0.60.

Agent-based null models for examining experimental social interaction networks

We consider the analysis of temporal data arising from online interactive social experiments, which is complicated by the fact that classical independence assumptions about the observations are not satisfied. Therefore, we propose an approach that compares the output of a fitted (linear) model from the observed interaction data to that generated by an assumed agent-based null model. This allows us to discover, for example, the extent to which the structure of social interactions differs from that of random interactions. Moreover, we provide network visualisations that identify the extent of ingroup favouritism and reciprocity as well as particular individuals whose behaviour differs markedly from the norm. We specifically consider experimental data collected via the novel Virtual Interaction APPLication (VIAPPL). We find that ingroup favouritism and reciprocity are present in social interactions observed on this platform, and that these behaviours strengthen over time. Note that, while our proposed methodology was developed with VIAPPL in mind, its potential usage extends to any type of social interaction data.

Evaluation of the extraction of methodological study characteristics with JATSdecoder

This paper introduces and evaluates the study.character module from the JATSdecoder package which extracts several key methodological study characteristics from NISO-JATS coded scientific articles.

STUDY

character splits the text into sections and applies its heuristic-driven extraction procedures to the text of the method and result section/s. When used individually, study.character's functions can also be applied to any textual input. An externally coded data set of 288 PDF articles serves as an indicator of study.character's capabilities in extracting the number of sub-studies reported per article, the statistical methods applied and software solutions used. Its precision of extraction of the reported [Formula: see text]-level, power, correction procedures for multiple testing, use of interactions, definition of outlier, and mentions of statistical assumptions are evaluated by a comparison to a manually curated data set of the same collection of articles. Sensitivity, specificity, and accuracy measures are reported for each of the evaluated functions.

STUDY

character reliably extracts the methodological study characteristics targeted here from psychological research articles. Most extractions have very low false positive rates and high accuracy ([Formula: see text]). Most non-detections are due to PDF-specific conversion errors and complex text structures, that are not yet manageable.

STUDY

character can be applied to large text resources in order to examine methodological trends over time, by journal and/or by topic. It also enables a new way of identifying study sets for meta-analyzes and systematic reviews.

The Association of Social Emotions, Perceived Efficiency, Transparency of the Government, Concerns about COVID-19, and Confidence in Fighting the Pandemic under the Week-Long Lockdown in Shenzhen, China

The outbreak and spread of the COVID-19 pandemic have had a far-reaching impact. The present study investigated the factors primarily affecting the execution of the control measures, including social emotions, concerns about the pandemic, perceived efficiency, transparency of the government in publishing the pandemic-related information, and confidence in fighting the pandemic. Specifically, we examined the differences in these factors across four areas (i.e., lockdown area, control area, prevention area, and safe area) according to different COVID control measures under the week-long lockdown in Shenzhen. We found that social emotions, concerns about the pandemic, perceived efficiency of the government, and confidence in fighting the pandemic were more negative in the lockdown area than that in other areas. More importantly, after controlling for areas and education level of participants, the emotion of optimism, concerns about the COVID-19 pandemic, perceived efficiency, and perceived transparency of the government in releasing COVID-19 relevant information positively predicted confidence in fighting the pandemic, while anger negatively predicted confidence in fighting the pandemic. Therefore, the government and communities could make efforts at effective communication and find innovative approaches to make individuals (especially in the lockdown area) maintain social connections, reduce negative emotions, and enhance confidence in combating the pandemic.

Emotional quality of early education programs improves language learning: A within-child across context design

Observational studies comparing child outcomes in early care and education classrooms of differing quality are often confounded by between-child differences. A within-child design, tracking children across contexts, can identify the effects of quality with less confounding. An analysis of Australian children (N = 1128, mean age 5 years, 48% female, 2.9% Indigenous, ethnicity data unavailable) tracked across pre-K, K, and year 1 (2010-2012) was conducted to assess how changes in observed quality (Classroom Assessment Scoring System) were associated with changes in cognitive development (Woodcock-Johnson III). Thresholds of quality were also investigated. Increases in Emotional Support were associated with improved language development (β = 0.54, 95% CI [0.1-0.99], approximating 2.6 weeks development). Results highlight that emotional quality is an integral and potent component of early learning.

Software review: The JATSdecoder package-extract metadata, abstract and sectioned text from NISO-JATS coded XML documents; Insights to PubMed central's open access database

JATSdecoder is a general toolbox which facilitates text extraction and analytical tasks on NISO-JATS coded XML documents. Its function JATSdecoder() outputs metadata, the abstract, the sectioned text and reference list as easy selectable elements. One of the biggest repositories for open access full texts covering biology and the medical and health sciences is PubMed Central (PMC), with more than 3.2 million files. This report provides an overview of the PMC document collection processed with JATSdecoder(). The development of extracted tags is displayed for the full corpus over time and in greater detail for some meta tags. Possibilities and limitations for text miners working with scientific literature are outlined. The NISO-JATS-tags are used quite consistently nowadays and allow a reliable extraction of metadata and text elements. International collaborations are more present than ever. There are obvious errors in the date stamps of some documents. Only about half of all articles from 2020 contain at least one author listed with an author identification code. Since many authors share the same name, the identification of person-related content is problematic, especially for authors with Asian names. JATSdecoder() reliably extracts key metadata and text elements from NISO-JATS coded XML files. When combined with the rich, publicly available content within PMCs database, new monitoring and text mining approaches can be carried out easily. Any selection of article subsets should be carefully performed with in- and exclusion criteria on several NISO-JATS tags, as both the subject and keyword tags are used quite inconsistently.

Report Quality of Generalized Linear Mixed Models in Psychology: A Systematic Review

Generalized linear mixed models (GLMMs) estimate fixed and random effects and are especially useful when the dependent variable is binary, ordinal, count or quantitative but not normally distributed. They are also useful when the dependent variable involves repeated measures, since GLMMs can model autocorrelation. This study aimed to determine how and how often GLMMs are used in psychology and to summarize how the information about them is presented in published articles. Our focus in this respect was mainly on frequentist models. In order to review studies applying GLMMs in psychology we searched the Web of Science for articles published over the period 2014–2018. A total of 316 empirical articles were selected for trend study from 2014 to 2018. We then conducted a systematic review of 118 GLMM analyses from 80 empirical articles indexed in Journal Citation Reports during 2018 in order to evaluate report quality. Results showed that the use of GLMMs increased over time and that 86.4% of articles were published in first- or second-quartile journals. Although GLMMs have, in recent years, been increasingly used in psychology, most of the important information about them was not stated in the majority of articles. Report quality needs to be improved in line with current recommendations for the use of GLMMs.

Conspiracy Beliefs, Rejection of Vaccination, and Support for hydroxychloroquine: A Conceptual Replication-Extension in the COVID-19 Pandemic Context

Many conspiracy theories appeared along with the COVID-19 pandemic. Since it is documented that conspiracy theories negatively affect vaccination intentions, these beliefs might become a crucial matter in the near future. We conducted two cross-sectional studies examining the relationship between COVID-19 conspiracy beliefs, vaccine attitudes, and the intention to be vaccinated against COVID-19 when a vaccine becomes available. We also examined how these beliefs predicted support for a controversial medical treatment, namely, chloroquine. In an exploratory study 1 (N = 409), two subdimensions of COVID-19 conspiracy beliefs were associated with negative attitudes toward vaccine science. These results were partly replicated and extended in a pre-registered study 2 (N = 396). Moreover, we found that COVID-19 conspiracy beliefs (among which, conspiracy beliefs about chloroquine), as well as a conspiracy mentality (i.e., predisposition to believe in conspiracy theories) negatively predicted participants' intentions to be vaccinated against COVID-19 in the future. Lastly, conspiracy beliefs predicted support for chloroquine as a treatment for COVID-19. Interestingly, none of the conspiracy beliefs referred to the dangers of the vaccines. Implications for the pandemic and potential responses are discussed.

Conspiracy Beliefs, Rejection of Vaccination, and Support for hydroxychloroquine: A Conceptual Replication-Extension in the COVID-19 Pandemic Context

Many conspiracy theories appeared along with the COVID-19 pandemic. Since it is documented that conspiracy theories negatively affect vaccination intentions, these beliefs might become a crucial matter in the near future. We conducted two cross-sectional studies examining the relationship between COVID-19 conspiracy beliefs, vaccine attitudes, and the intention to be vaccinated against COVID-19 when a vaccine becomes available. We also examined how these beliefs predicted support for a controversial medical treatment, namely, chloroquine. In an exploratory study 1 (N = 409), two subdimensions of COVID-19 conspiracy beliefs were associated with negative attitudes toward vaccine science. These results were partly replicated and extended in a pre-registered study 2 (N = 396). Moreover, we found that COVID-19 conspiracy beliefs (among which, conspiracy beliefs about chloroquine), as well as a conspiracy mentality (i.e., predisposition to believe in conspiracy theories) negatively predicted participants' intentions to be vaccinated against COVID-19 in the future. Lastly, conspiracy beliefs predicted support for chloroquine as a treatment for COVID-19. Interestingly, none of the conspiracy beliefs referred to the dangers of the vaccines. Implications for the pandemic and potential responses are discussed.