Notes: Three population structures are considered. The contributions of the causal site for both the traits range from 0.0025 to 0.01. Powers are estimated on 1,000 replicates. See notes in Table 1 for sample sizes.Abbreviations: T12, the proposed test for bivariate analysis; T1, the proposed test for only the first trait; T2, the proposed test for only the second trait.

The correlation coefficient is a commonly used criterion to measure the strength of a linear relationship between the two quantitative variables. For a bivariate normal distribution, numerous procedures have been proposed for testing a precise null hypothesis of the correlation coefficient, whereas the construction of flexible procedures for testing a set of (multiple) precise and/or interval hypotheses has received less attention. This paper fills the gap by proposing an objective Bayesian testing procedure using the divergence-based priors. The proposed Bayes factors can be used for testing any combination of precise and interval hypotheses and also allow a researcher to quantify evidence in the data in favor of the null or any other hypothesis under consideration. An extensive simulation study is conducted to compare the performances between the proposed Bayesian methods and some existing ones in the literature. Finally, a real-data example is provided for illustrative purposes.

The dataset used in the paper is a bivariate Gaussian likelihood example with uncorrelated priors.

Example of a contingency table for which we want to compute a bivariate relationship.

Background: Clean water is an essential part of human healthy life and wellbeing. More recently, rapid population growth, high illiteracy rate, lack of sustainable development, and climate change; faces a global challenge in developing countries. The discontinuity of drinking water supply forces households either to use unsafe water storage materials or to use water from unsafe sources. The present study aimed to identify the determinants of water source types, use, quality of water, and sanitation perception of physical parameters among urban households in North-West Ethiopia.

Methods: A community-based cross-sectional study was conducted among households from February to March 2019. An interview-based a pretested and structured questionnaire was used to collect the data. Data collection samples were selected randomly and proportional to each of the kebeles' households. MS Excel and R Version 3.6.2 were used to enter and analyze the data; respectively. Descriptive statistics using frequencies and percentages were used to explain the sample data concerning the predictor variable. Both bivariate and multivariate logistic regressions were used to assess the association between independent and response variables.

Results: Four hundred eighteen (418) households have participated. Based on the study undertaken,78.95% of households used improved and 21.05% of households used unimproved drinking water sources. Households drinking water sources were significantly associated with the age of the participant (x2 = 20.392, df=3), educational status(x2 = 19.358, df=4), source of income (x2 = 21.777, df=3), monthly income (x2 = 13.322, df=3), availability of additional facilities (x2 = 98.144, df=7), cleanness status (x2 =42.979, df=4), scarcity of water (x2 = 5.1388, df=1) and family size (x2 = 9.934, df=2). The logistic regression analysis also indicated that those factors are significantly determining the water source types used by the households. Factors such as availability of toilet facility, household member type, and sex of the head of the household were not significantly associated with drinking water sources.

Conclusion: The uses of drinking water from improved sources were determined by different demographic, socio-economic, sanitation, and hygiene-related factors. Therefore, ; the local, regional, and national governments and other supporting organizations shall improve the accessibility and adequacy of drinking water from improved sources in the area.

An example of combining ANOVA terms for bivariate principle component data to create the ANODIS F-statistic where N is the total number of samples drawn and K, the number of assemblages compared.

This text files aims to explain the procedure to perform both the data handling and the analyses in the paper: Description of the files: 1. Datasets PhenoAsreml.txt contains the observed values for the phenotypes described in the paper PhenoAsreml_scaled.txt contains the scaled (mean of zero and standard deviation of one) of the same phenotypes. THIS DATASET IS USED FOR BIVARIATE ANALYSES WITH 2x2 STRATA PhenoAsreml_scaledLong.txt contains the same information, organized differently because the dataset is reshaped from wide to long format. PhenoAsreml_scaledLong_3x3strata.txt contains the information for BackFat and BodyWeight, THIS DATASET IS USED FOR BIVARIATE ANALYSES WITH 3x3 STRATA PhenoAsreml_scaledLong_3x2strata.txt contains the information for BackFat and Adiponectin, THIS DATASET IS USED FOR BIVARIATE ANALYSES WITH 3x2 STRATA. It is only an example of dataset that needs to be generated in order to obtain all the results presented in the paper BEDERE_2023_Data_PedigreeHens.txt is the pedigree file (individual/sire/dam) traced back over 5 generations 2. Codes and parameter files BEDERE_2023_RScript_handlingdata_Long.R is an R code to reshape PhenoAsreml_scaled into PhenoAsreml_scaledLong and to subset it to generate PhenoAsreml_scaledLong_3x3strata for instance BEDERE_2023_ASREMLScript_bivariate_2x2strata.as (as well as ...3x2strata.as and ...3x3strata.as) are ASReml parameter files used to state the data, model specification and post-hoc calculation to ASReml Please, note that some variance components have been fixed in some analyses when the algorithm was struggling to converge. BEDERE_2023_RScript_BartlettTest.R is an R code to perform the Bartlett test. 2. Results examples Some output files of ASReml are provided to give an example of results for each type of bivariate analysis. The .asr file is the log of the program, explaining how the program ran The .res file is describing the residuals The .pvc file describes the variance components and provides the genetic parameters with their associated standard errors.

Bivariate Bayesian correlates between the Well-being Numerical Rating Scales (WB-NRSs) and the other variables in the study in the non-clinical samples.

Dataset for: Leipold, B. & Loepthien, T. (2021). Attentive and emotional listening to music: The role of positive and negative affect. Jahrbuch Musikpsychologie, 30. https://doi.org/10.5964/jbdgm.78 In a cross-sectional study associations of global affect with two ways of listening to music – attentive–analytical listening (AL) and emotional listening (EL) were examined. More specifically, the degrees to which AL and EL are differentially correlated with positive and negative affect were examined. In Study 1, a sample of 1,291 individuals responded to questionnaires on listening to music, positive affect (PA), and negative affect (NA). We used the PANAS that measures PA and NA as high arousal dimensions. AL was positively correlated with PA, EL with NA. Moderation analyses showed stronger associations between PA and AL when NA was low. Study 2 (499 participants) differentiated between three facets of affect and focused, in addition to PA and NA, on the role of relaxation. Similar to the findings of Study 1, AL was correlated with PA, EL with NA and PA. Moderation analyses indicated that the degree to which PA is associated with an individual´s tendency to listen to music attentively depends on their degree of relaxation. In addition, the correlation between pleasant activation and EL was stronger for individuals who were more relaxed; for individuals who were less relaxed the correlation between unpleasant activation and EL was stronger. In sum, the results demonstrate not only simple bivariate correlations, but also that the expected associations vary, depending on the different affective states. We argue that the results reflect a dual function of listening to music, which includes emotional regulation and information processing.: Dataset Study 1

Same sample: two traits are measured on the same set of samples. Different sample: two traits are measured on the different sets of samples. : parameter of genetic correlation (i.e. proportion of simulated causal variants shared between the two traits). Est.: estimate of genetic correlation from 100 simulations. SE(Obs.): mean of the observed standard errors from 100 simulations. s.e.m.: standard error of the mean (i.e. SE(Obs.)). SE(Approx.): standard error calculated from our approximation theory.

Example of data.

Bivariate associations between soil characteristics and soil-transmitted helminth (STH) detection in soil samples (n = 449) by qPCR.

Note:Combined p value1: Combined p values by joint analyses of the Caucasian discovery and the Caucasian replication samples.Combined p value2: Combined p values by joint analyses of the Caucasian discovery and the Chinese replication samples.−: p value not available.Bold: SNPs that were replicated in the replication samples.

Sample entropy based tests, methods of sieves and Grenander estimation type procedures are known to be very efficient tools for assessing normality of underlying data distributions, in one-dimensional nonparametric settings. Recently, it has been shown that the density based empirical likelihood (EL) concept extends and standardizes these methods, presenting a powerful approach for approximating optimal parametric likelihood ratio test statistics, in a distribution-free manner. In this paper, we discuss difficulties related to constructing density based EL ratio techniques for testing bivariate normality and propose a solution regarding this problem. Toward this end, a novel bivariate sample entropy expression is derived and shown to satisfy the known concept related to bivariate histogram density estimations. Monte Carlo results show that the new density based EL ratio tests for bivariate normality behave very well for finite sample sizes. To exemplify the excellent applicability of the proposed approach, we demonstrate a real data example.

This dataset supports a meta-analytic structural equation modelling (MASEM) study investigating the factors influencing students’ behavioural intention to use educational AI (EAI) technologies. The research integrates constructs from the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB), and Artificial Intelligence Literacy (AIL), aiming to resolve inconsistencies in previous studies and improve theoretical understanding of EAI technology adoption.

Research Hypotheses The study hypothesized that: Students’ behavioural intention (INT) to use EAI technologies is influenced by perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), subjective norm (SN), and perceived behavioural control (PBC), as described in TAM and TPB. AI literacy (AIL) directly and indirectly predicts PU, PEU, ATT, and INT. These relationships are moderated by contextual factors such as academic level (K–12 vs. higher education) and regional economic development (developed vs. developing countries).

What the Data Shows The meta-analytic dataset comprises 166 empirical studies involving over 69,000 participants. It includes pairwise Pearson correlations among seven constructs (PU, PEU, ATT, SN, PBC, INT, AIL) and is used to compute a pooled correlation matrix. This matrix was then used to test three models via MASEM: A baseline TAM-TPB model, An internal-extended model with additional TPB internal paths, An AIL-integrated extended model. The AIL-integrated model achieved the best fit (CFI = 0.997, RMSEA = 0.053) and explained 62.3% of the variance in behavioural intention.

Notable Findings AI literacy (AIL) is the strongest predictor of intention to use EAI technologies (Total Effect = 0.408). PU, ATT, and SN also significantly influence intention. The effect of PEU on intention is fully mediated by PU and ATT. Moderation analysis showed that the relationships differ between developed and developing countries and between K–12 and higher education populations.

How the Data Can Be Interpreted and Used The dataset includes bivariate correlations between variables, publication metadata, sample sizes, coding information, and reliability values (e.g., CR scores). Suitable for replication of MASEM procedures, moderation analysis, and meta-regression. Researchers may use it to test additional theoretical models or assess the influence of new moderators (e.g., AI tool type). Educators and policymakers can leverage insights from the meta-analytic results to inform AI literacy training and technology adoption strategies.

This survey focuses on European's perceptions and expectations on the EU’s action, the EU's fight against terrorism and the mutual defence clause. Most EU citizens declare the EU's action is insufficient in a majority of the fifteen areas suggested to them, and a massive majority of EU citizens would like the EU to intervene more than at present in these areas. On the issue of migration, for example, 66% consider EU action insufficient and 74% would like to see the EU take more action. On the protection of external borders, 61% consider EU action insufficient and 71% would like to see the EU take more action. A second part of this survey focuses on the perceptions and expectations of young Europeans.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Trends" The file compares to previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

BiChroM Raw R Files1. Dataset and tree 2. Raw R files for optimizations 3. Full model optimizations 4. Reduced model optimizations 5. Profile rhoH and Profile rhoW 6. Bivariate profile rhoqH and Bivariate profile rhoqW 7. Raw R files for simulations 8. Simulations number of taxa 9. Simulations for tree heightBiChroMRawRfiles.zip

IntroductionAlcohol-related problems increase the probability of frequent emergency department (ED) use. In this study, we compared the direct healthcare expenses incurred during a single visit among frequent and non-frequent ED users and analyzed the impact of alcohol-related issues in healthcare costs arising from ED usage.MethodsThe study relied on secondary analyses of economic data from a 1:1 matched case–control study with the primary aim of identifying the clinical characteristics of hospital ED frequent users in a Mediterranean European environment with a public, universal, and tax-funded health system. The participants ranged in age from 18 to 65 years and underwent ED visits at a high-complexity Spanish hospital (cases ≥5 times, controls <5) from December 2018 to November 2019. Each case was matched to a control with the same age, gender, and date of attendance at the ED. Clinical data and direct healthcare costs for a single ED visit were obtained by a retrospective review of the first electronic medical register. Costs and duration of stay were compared between cases and controls using paired-samples t-tests, and ED users with and without alcohol-related problems were compared using bivariate (independent-samples t-tests, one-way analysis of variance, Chi square tests, and multiple linear regression) and multivariate analyses (multiple linear regression models with backward stepwise selection algorithm, and dependent variable: total mean direct costs).ResultsAmong 609 case–control pairs (total n = 1218), mean total healthcare direct costs per ED visit were 22.2% higher among frequent compared with non-frequent users [mean difference 44.44 euros; 95% confidence interval (CI) 13.4–75.5; t(608) = 2.811; p = 0.005]. Multiple linear regression identified length of stay, triage level, ambulance arrival, and the specialty discharging the patient as associated with total healthcare costs for frequent users. In bivariate analyses, a history of alcohol-related problems was associated with a 32.5% higher mean total healthcare costs among frequent users [mean difference 72.61 euros; 95% confidence interval 25.24–119.97; t(320.016) = 3.015; p = 0.003].ConclusionThe findings confirm the high cost of frequent ED use among people with alcohol-related problems, suggesting that costs could be reduced through implementation of intervention protocols.

Results of bivariate analysis to compare CDI versus those non-CDI patients.

Data and code to reproduce the analyses in "Resolution and the Detection of Cultural Dispersals: development and application of spatiotemporal methods in Lowland South America"

Abstract

Inferring episodes of expansion, admixture, diffusion, and/or migration in prehistory is at present undergoing a resurgence in macro-scale archaeological interpretation. In parallel to this renewed popularity, expanding access to computational tools and datasets has seen the use of aggregated radiocarbon datasets for the study of dispersals also increasing. This paper advocates for developing reflexive practice in the application of radiocarbon dates to prehistoric dispersals, by reflecting on the quality and qualities of the underlying data, particularly chronometric uncertainty, and framing dispersals explicitly in terms of hypothesis testing. This paper draws on cultural expansions within South America and employs two emblematic examples, the Arauquinoid and Tupiguarani traditions, to develop an analytical solution that not only incorporates chronometric uncertainty in bivariate regression but, importantly, tests whether the datasets provide statistically significant evidence for a dispersal process. The analysis, which the paper provides the means to replicate, identifies fundamental issues with resolution and data quality that impede identification of pre-Columbian cultural dispersals through simple spatial gradients of radiocarbon data. The results suggest that reflexivity must be fed back into theoretical frameworks of prehistoric mobility for the study of dispersals, in turn informing the construction of more critical statistical null models. As a first step, alternative models of cultural expansion should be formally considered alongside demographic models.Inferring episodes of expansion, admixture, diffusion, and/or migration in prehistory is at present undergoing a resurgence in macro-scale archaeological interpretation. In parallel to this renewed popularity, expanding access to computational tools and datasets has seen the use of aggregated radiocarbon datasets for the study of dispersals also increasing. This paper advocates for developing reflexive practice in the application of radiocarbon dates to prehistoric dispersals, by reflecting on the quality and qualities of the underlying data, particularly chronometric uncertainty, and framing dispersals explicitly in terms of hypothesis testing. This paper draws on cultural expansions within South America and employs two emblematic examples, the Arauquinoid and Tupiguarani traditions, to develop an analytical solution that not only incorporates chronometric uncertainty in bivariate regression but, importantly, tests whether the datasets provide statistically significant evidence for a dispersal process. The analysis, which the paper provides the means to replicate, identifies fundamental issues with resolution and data quality that impede identification of pre-Columbian cultural dispersals through simple spatial gradients of radiocarbon data. The results suggest that reflexivity must be fed back into theoretical frameworks of prehistoric mobility for the study of dispersals, in turn informing the construction of more critical statistical null models. As a first step, alternative models of cultural expansion should be formally considered alongside demographic models.