Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study (...) at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions. (shrink)

Cognitive neuroscience has inspired a number of methodological advances to extract the highest signal-to-noise ratio from neuroimaging data. Popular techniques used to summarize behavioral data include sum-scores and item response theory. While these techniques can be useful when applied appropriately, item dimensionality and the quality of information are often left unexplored allowing poor performing items to be included in an itemset. The purpose of this study is to highlight how the application of two-stage approaches introduces parameter bias, differential item functioning (...) can manifest in cognitive neuroscience data and how techniques such as the multiple indicator multiple cause model can identify and remove items with DIF and model these data with greater sensitivity for brain–behavior relationships. This was performed using a simulation and an empirical study. The simulation explores parameter bias across two separate techniques used to summarize behavioral data: sum-scores and IRT and formative relationships with those estimated from a MIMIC model. In an empirical study participants performed an emotional identification task while concurrent electroencephalogram data were acquired across 384 trials. Participants were asked to identify the emotion presented by a static face of a child across four categories: happy, neutral, discomfort, and distress. The primary outcomes of interest were P200 event-related potential amplitude and latency within each emotion category. Instances of DIF related to correct emotion identification were explored with respect to an individual’s neurophysiology; specifically an item’s difficulty and discrimination were explored with respect to an individual’s average P200 amplitude and latency using a MIMIC model. The MIMIC model’s sensitivity was then compared to popular two-stage approaches for cognitive performance summary scores, including sum-scores and an IRT model framework and then regressing these onto the ERP characteristics. Here sensitivity refers to the magnitude and significance of coefficients relating the brain to these behavioral outcomes. The first set of analyses displayed instances of DIF within all four emotions which were then removed from all further models. The next set of analyses compared the two-stage approaches with the MIMIC model. Only the MIMIC model identified any significant brain–behavior relationships. Taken together, these results indicate that item performance can be gleaned from subject-specific biomarkers, and that techniques such as the MIMIC model may be useful tools to derive complex item-level brain–behavior relationships. (shrink)

Opaque value chains as well as environmental, ethical and health issues and food scandals are decreasing consumer trust in conventional agriculture and the dominant food system. As a result, critical consumers are increasingly turning to community-supported agriculture (CSA) to reconnect with producers and food. CSA is often perceived as a more sustainable, localized mode of food production, providing transparent production or social interaction between consumers and producers. This enables consumers to observe where their food is coming from, which means CSA (...) is considered suitable for building trust in food (production). However, it remains unclear how exactly members’ trust in ‘their’ farmers is built. To determine the factors that predict members’ trust in CSA and its farmers, and the importance of these factors when compared to each other, we conducted a quantitative study among CSA members in Germany and applied a multiple regression model (n = 790). The analysis revealed that trust in CSA and its farmers is influenced by “reputation”, “supply of information”, “direct social interaction” and the “duration of CSA membership”. Other factors such as the “certification status of the CSA farm” and “attitudes toward organic certification” did not significantly predict trust. We conclude that producers’ willingness to be transparent already signals trustworthiness to CSA members and is more important to members than formal signals. Other actors within the food system could learn from CSA principles and foster a transition toward a more regionalized value-based food system to help restore agriculture’s integrity. (shrink)

The world is getting increasingly interconnected. For this reason, schools should apply strategies to develop students’ civic skills and global civic engagement. The purpose of this paper is to examine the influence of multicultural exposure, multicultural interaction, social media usage, and study abroad experience on global civic engagement. The correlational survey model was applied for the study. The participants were selected through cluster random sampling during the 2018-2019 academic year. This study was implemented with 425 high school students who (...) attend private schools. The authors used Global Citizenship Scale (Morais & Ogden, 2011) and Multicultural Experience Assessment (Aytug, Kern, & Dilchert, 2018). The findings showed that multicultural exposure, multicultural interaction, and social media usage were positively correlated to global civic engagement. In addition, multicultural exposure, multicultural interaction, social media usage, and study abroad experience significantly predicted global civic engagement. (shrink)

The regression model is generally utilized in several fields of study because of its applications. Regression is an extremely incredible approach; it builds up a connection between dependent and independent variables. We have addressed a powerful computational model by utilizing dengue information joined with fuzzy multiple linear regression. Information is accumulated on dengue fever through the survey. This paper is centered on the comparison of the crisp method with fuzzy multiple linear regression, (...) and then, the utilization of a fuzzy multiple regression method is explained after the comparison. We have used multiple regression and then converted the said technique into three fuzzy cases. The effectiveness of the fuzzy multiple regression model is measured by numerical computation and comparison of both techniques. 2020 Mathematics Subject Classification. Primary 30C45; 30C50; 30C80; Secondary 11B65, 47B38. (shrink)

With the widespread application of information technology and the continuous deepening of my country’s education informatization construction, the educational field has increasingly higher requirements for teachers’ informatization quality. The advancement of the process of education informatization depends to a large extent on the level of teachers’ own level of informatization and has gradually become an important indicator to measure the informatization of education. The main body of education and the decisive factor are teachers. The scientific and reasonable evaluation of informatization (...) education is an important content of today’s education informatization. This paper uses regression analysis to evaluate the teacher education information system and uses mathematical statistics to establish a linear regression model for the city and analyze and calculate it. Through the quantitative analysis of the evaluation index system of the education informatization level, the multiple linear regression analysis method was used to establish the linear regression mathematical model of the school education informatization level on each secondary index, and the influence of various indicators on the school education informatization level was analyzed to establish a scientific evaluation system to evaluate the literacy of teachers’ informatization education and to give suggestions for improving the level of school education informatization. The design of the evaluation system is more scientific and reasonable, the regression error is small, the results obtained are more accurate, and the impact of nonlinear errors is basically eliminated. (shrink)

Wheat is the most imperative crop for man feeding and is planted in numerous countries under rainfed conditions in semiarid zones. It is necessary for decision-makers and governments to predict the yield of rainfed wheat before harvest and to determine the effect of the major factors on it. Different methods have been suggested for forecasting yield with various levels of accuracy. One of these approaches is the statistical regression model, which is simple and applicable for regions with scarce (...) data available. Since the weather is the most important factor affecting the production of wheat, particularly in rainfed cultivation, regression models using weather parameters are very common. However, the coefficients of these models are location based and should be determined locally. Therefore, in this research, backward multiple linear regression technique based on relative importance metrics was used to determine the most important effective weather parameters on rainfed wheat productions in Fars Province, south of Iran, during 2006–2013. The influence of each parameter in the final model was analyzed using the values of LMG relative importance metric. The result indicated that sunshine hours had the biggest LMG and, therefore, was the most effective parameter. Also, among the other considered parameters, rainy days, minimum relative humidity, and average relative humidity with LMG values of 21.97%, 21.69%, and 21.62%, respectively, had the most effects on rainfed wheat yield in the studied area. All parameters except for the sunshine hours positively affected rainfed wheat yield. The most important reason for the significance of these parameters can be the prevailing dry and semidry climate in the southern areas of Iran. The proposed model for determination of weather parameters effects on rainfed wheat could be a great guidance and aid for different stakeholders such as farmers, decision-makers, and governments. (shrink)

In order to study the influence of different entrepreneurial psychological leadership styles on organizational learning ability and organizational performance, and to provide theoretical basis for the improvement of organizational benefits of entrepreneurial enterprises in the future, 421 general managers, middle managers, and grass-roots managers of 350 small and medium-sized private enterprises in Beijing were surveyed by questionnaire in two forms: online and on site. Then, a hypothesis model of the relationship between different entrepreneurial psychological leadership styles and organizational learning (...) ability and organizational performance was constructed. The correlation between transformational, transactional, and laissez-faire psychological leadership style and organizational performance and organizational learning ability was analyzed using multiple regression models. AMOS 7.0 software was used to simulate and verify the model. The results show that transformational psychological leadership style is positively correlated with organizational learning ability, financial performance, and growth performance; transactional psychological leadership style is positively correlated with organizational financial performance, growth performance, and organizational learning ability; there is no correlation between laissez-faire psychological leadership style and organizational financial performance and growth performance, but there is a significant positive correlation between laissez-faire psychological leadership style and organizational learning ability. The covariance between the error terms of the internal and external potential variables of the model is positive, and the factor load value of each potential variable and the observed variable is between 0.5 and 1, which shows that transformational and transactional psychological leadership styles have more positive influence on organizational financial performance and learning ability than laissez-faire psychological leadership styles. The hypothesis model of the relationship between entrepreneurial psychological leadership style and organizational learning ability and organizational performance conforms to the adaptation criteria and is feasible. (shrink)

This study aimed at determining B.S. Biology students’ comprehension of the Bioethics principles and conviction schemas towards bioethical issues; as well as at identifying predictors for comprehension of bioethical principles and convictions and creating model constructs of predictors which are fit for the data. One-hundred sixteen Filipino Biology majors were pre and post-tested for comprehension of bioethics principles and convictions towards bioethical issues. Predictors for comprehension and convictions among personal and family background factors, global and primary personality factors, and (...) mental ability were identified through step-wise multiple regression. Constructed path models were tested for goodness of fit using Structural Equation Modeling. Results showed that case study discussion and the bioethics course had positive effects on comprehension of the bioethics principles and on convictions towards bioethical issues. Predictors for comprehension of Bioethics principles before intervention were verbal ability, warmth, and exposure to media. Sex and openness to change were predictors for pre-convictions. Post-conviction predictors were anxiety, self-control, extraversion, liveliness, and perfectionism. Four out of five model constructs were accepted based on model fit criteria. In conclusion, personal factors, i.e., sex and personality factors are contributors in bioethical convictions; not cognitive factors like mental ability or comprehension of Bioethics principles; implying that bioethical convictions are already pre-conceived and that empathy-based experiences may strengthen convictions towards bioethical issues. (shrink)

Ozone is one of the pollutants with most negative effects on human health and in general on the biosphere. Many data-acquisition networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing problems to manage complexity. In the present paper, multiple-regression techniques and Artificial Neural Network models are (...) applied to approximate the absent ozone values from five explanatory variables containing air-quality information. To compare the different imputation methods, real-life data from six data-acquisition stations from the region of Castilla y León are gathered in different ways and then analyzed. The results obtained in the estimation of the missing values by applying these techniques and models are compared, analyzing the possible causes of the given response. (shrink)

The perceptual-cognitive ability to track multiple moving objects and its contribution to team sports performance has traditionally been studied in the laboratory under non-sports specific conditions. It is thus questionable whether the measured visual tracking performance and the underlying gaze activity reflected the actual ability of team sports players to track teammates and opponents on a real field. Using a Virtual Reality-based visual tracking task, the ability of participants to track multiple moving virtual players as they would do (...) on a soccer field was observed to pursue two objectives. See the influence of different scenario types on the visual tracking performance of soccer compared to non-soccer players. Observe the influence of spatial features of the simulated situations on gaze activity between soccer players and non-soccer players. The linear mixed model regression revealed a significant main effect of the group but no interaction effect between group and the type of trajectories, suggesting that the visual tracking ability of soccer players did not benefit from their specific knowledge when they faced scenarios with real game trajectories. Virtual players’ spatial dispersion and crowding affected the participants’ gaze activity and their visual tracking performance. Furthermore, the gaze activity of soccer players differed in some aspects from the gaze activity of non-soccer players. Assumptions are formulated as to the implication of these results in the difference in visual tracking performance between soccer players and non-soccer players. Overall, using soccer-specific trajectories might not be enough to replicate the representativeness of the field conditions in the study of visual tracking performance. Multitasking constraints should be considered along with motor-cognitive dual-tasks in future research to develop the representativeness of visual exploration conditions. (shrink)

Building on prior research related to impact of information communication technology and operational risk management in the context of medium and small enterprises, the focus of this study was to investigate the relationship between ICT operational risk management and performances of MSEs. To achieve the focus, the research investigated evaluating models for understanding the value of ICT ORM in MSEs. Multiple regression, Repeated-Measures Analysis of Variance and Repeated-Measures Multivariate Analysis of Variance were performed. The findings of the distribution (...) revealed that only one variable made a significant percentage contribution to the level of ICT operation in MSEs, the Payback method. It may thus be inferred that the Payback method is the prominent variable, explaining the variation in level of evaluation models affecting ICT adoption within MSEs. Conclusively, in answering the two questions degree of variability explained and predictors, the results revealed that the variable contributed approximately 88.4% of the variations in evaluation models affecting ICT adoption within MSEs. The analysis of variance also revealed that the regression coefficients were real and did not occur by chance. (shrink)

The digital revolution has made a multitude of text documents from highly diverse perspectives on almost any topic easily available. Accordingly, the ability to integrate and evaluate information from different sources, known as multiple document comprehension, has become increasingly important. Because multiple document comprehension requires the integration of content and source information across texts, it is assumed to exceed the demands of single text comprehension due to the inclusion of two additional mental representations: the integrated situation model (...) and the intertext model. To date, there is little empirical evidence on commonalities and differences between single text and multiple document comprehension. Although the relationships between single text and multiple document comprehension can be well distinguished conceptually, there is a lack of empirical studies supporting these assumptions. Therefore, we investigated the dimensional structure of single text and multiple document comprehension with similar test setups. We examined commonalities and differences between the two forms of text comprehension in terms of their relations to final school exam grades, level of university studies and university performance. Using a sample of n = 501 students from two German universities, we jointly modeled single text and multiple document comprehension and applied a series of regression models. Concerning the relationship between single text and multiple document compr... (shrink)

The purpose of this study is to examine the relationship between contextual work-related factors on the one hand, in terms of job demands (i.e., risk factors) and job resources (i.e., protective factors), and work-family conflict in teachers on the other. Building on the Job Demands-Resources (JD-R) model, we hypothesized that job demands, namely qualitative and quantitative workload, are positively associated with work-family conflict in teachers. Moreover, in line with the buffer hypothesis of the JD-R, we expected job resources, in (...) terms of support from supervisor, job autonomy, and participation in decision making, to affect this association, which is expected to be stronger when job resources are low. The study was conducted in an Italian secondary school. Overall, 122 teachers completed a self-report questionnaire aimed at determining work-family conflict, as well as job demands and resources. The hypothesized relationships were tested using moderated multiple regression. The results of this study largely support our predictions. First, both aspects of workload were positively associated with work-family conflict. Secondly, job resources, including support from supervisor and participation in decision making, buffered this association, which was stronger when resources were low. On the contrary, job autonomy did not buffer the association between workload and work-family conflict. Overall, the results of this study are consistent with the Job Demands-Resources model and contribute to to the understanding of work–family conflict among teachers. More specifically, our study suggests that teachers with high levels of job resources, namely support from supervisor and participation in decision making, can effectively cope with job demands, in terms of both qualitative and quantitative workload, thus preventing negative consequences such as conflict between work and family domains. Interventions aimed at preventing work-family conflict among teachers should encourage organizations to optimize the balance between job demands and resources, as well as the identification and training of the workers at risk of work-family conflict. (shrink)

Multimedia learning research addresses the question of how to design instructional material effectively. Signaling and adding graphics are typical instructional means that might support constructing a mental model, particularly when learning abstract content from multiple representations. Although signals can help to select relevant aspects of the learning content, additional graphics could help to visualize mentally the subject matter. Learners’ prior knowledge is an important factor for the effectiveness of both types of support: signals and added graphics. Therefore, we (...) conducted an experimental study situated in a university course of computer science with N = 124 participants. In our 2 × 2 factorial design, we investigated the effects of signals and illustrating graphics on learning outcomes and their potential interplay. Based on our regression analysis, we revealed prior knowledge as a significant moderator. Although learners with low levels of prior knowledge can profit from all types of help but still gain rather weak learning outcomes, learners with medium levels of prior knowledge profit from the synergy of both helps. With higher levels of prior knowledge, signals were particularly hampering. To improve the understanding of these supportive or hampering effects, a more fine-grained analysis of these processes and motivational effects is necessary. (shrink)

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and treatment follow-up. (...) The Hopkins Verbal Learning Test is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual’s performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo under the Bayesian Regression Models using ‘Stan’ package of R. Comparisons of the fitted models were done using leave-one-out information criteria, Widely applicable information criterion, and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach. (shrink)

This study proposes a moderated mediation model based on deontic justice theory to investigate the impact of unethical peer behavior on employee performance. Data were collected in China through two survey studies, with two measurement points in each study. The data in study 1 were obtained from 271 employees of 17 firms, and the data in study 2 were collected from 225 employees of 9 firms. Confirmatory factor analysis was conducted to confirm the factorial validity of the measures employed (...) in the studies. The hypothesized moderated mediation model was tested using hierarchical multiple regression analysis. The results show that employee deontic justice mediates the negative relationship between unethical peer behavior and employee performance, and a stronger ethical climate tends to strengthen this indirect relationship. This study extends previous research by introducing deontic justice theory to explore the unconscious process that relates unethical peer behavior to employee performance. The theoretical and practical implications of this paper are discussed. (shrink)

The purpose of this study is to explore the impact of entrepreneurial leadership on entrepreneurial performance in start-ups. Specifically, a moderated serial mediation model was developed to investigate the mediating role of tacit knowledge sharing and job embeddedness and the moderating effect of career growth opportunities. Data was collected from 376 start-up employees via an online survey platform. Using hierarchical multiple regression and Hayes' PROCESS Macro by SPSS 21.0, and structural equation modeling by AMOS 23.0, support was (...) found for both mediation and moderation effects. Results showed that entrepreneurial leadership significantly positively affects entrepreneurial performance by mediating with tacit knowledge sharing and job embeddedness. Moreover, career growth opportunities moderate the serial mediating effect of tacit knowledge sharing and job embeddedness between entrepreneurial leadership and entrepreneurial performance. This study provides theoretical guidance for entrepreneurial leadership to improve entrepreneurial performance. (shrink)

A statistical method for inference of formal causes was introduced. The procedure, referred to as the method of corresponding regressions, was explained and illustrated using a variety of simulated causal models. The method reflects IV/DV relations among variables traditionally limited to correlational or structural equation analysis. The method was applied to additive, subtractive, multiplicative, recursive and reflected models, as well as models of unrelated and correlated dependent variables. Initial applications to data from physical science, biology, economics, marketing and psychology were (...) developed, with generally supportive results. (shrink)

Systemic lupus erythematosus is an inflammatory autoimmune disease with depression as one of its most common symptoms. The aim of this study is to establish a nomogram prediction model to assess the occurrence of depression in patients with SLE. Based on the Hospital Anxiety and Depression Scale cutoff of 8, 341 patients with SLE, recruited between June 2017 and December 2019, were divided into depressive and non-depressive groups. Data on socio-demographic characteristics, medical history, sociopsychological factors, and other risk factors (...) were collected. Between-group differences in clinical characteristics were assessed with depression as the dependent variable and the variables selected by logistic multiple regression as predictors. The model was established using R language. Marital status, education, social support, coping, and anxiety predicted depression. The nomogram prediction model showed that the risk rate was from 0.01 to 0.80, and the receiver operating characteristic curve analysis showed that the area under the curve was 0.891. The calibration curve can intuitively show that the probability of depression predicted by the nomogram model is consistent with the actual comparison. The designed nomogram provides a highly predictive assessment of depression in patients with SLE, facilitating more comprehensive depression evaluation in usual clinical care. (shrink)

The productivity of researchers and the impact of the work they do are a preoccupation of universities, research funding agencies, and sometimes even researchers themselves. The h-index is the most popular of different metrics to measure these activities. This research deals with presenting a practical approach to model the h-index based on the total number of citations and the duration from the publishing of the first article. To determine the effect of every factor on h, we applied a set (...) of simple nonlinear regression. The results indicated that both NC and D1 had a significant effect on h. The determination of coefficient for these equations to estimate the h-index was 93.4% and 39.8%, respectively, which verified that the model based on NC had a better fit. Then, to record the simultaneous effects of NC and D1 on h, multiple nonlinear regression was applied. The results indicated that NC and D1 had a significant effect on h. Also, the determination of coefficient for this equation to estimate h was 93.6%. Finally, to model and estimate the h-index, as a function of NC and D1, multiple nonlinear quartile regression was used. The goodness of the fitted model was also assessed. (shrink)

The fundamental tenet of contemporary sociobiology, namely the assumption of a single process of evolution involving the selection of genes, is critically examined. An alternative multiple-level, multiple-process model of evolution is presented which posits that the primary process that operates via selection upon the genes cannot account for certain kinds of biological phenomena, especially complex, learned, social behaviours. The primary process has evolved subsidiary evolutionary levels and processes that act to bridge the gap between genes and these (...) complex behaviours. The subsidiary levels are development, individual animal learning, and socioculture itself. It is argued that individual learning is pivotal to the derivation and biological analysis of culture. The differences between cultural and noncultural societies are stressed. It is concluded that such a multiple-level model of evolution can form the basis for reconciling opposing sides in the sociobiology debate. (shrink)

A large body of research has examined the relationship between moral intensity (MI) and the four‐component model of morality, typically, by separating MI into its constituent dimensions and regressing them individually against the four‐component model. This approach, however, violates the definition of MI as a single construct. To correct this problem, we develop and test a model of the impact of MI as a single, 6‐item formative construct. We find that when MI is taken into account, moral (...) recognition is not significant. MI does, however, serve as a strong trigger for judgment. We also determine that splitting MI into multiple factors does not significantly affect the results, confirming the assertion that MI is a single, coherent construct. (shrink)

This paper reports the experience of using the PAELLA algorithm as a helper tool in robust regression instead of as originally intended for outlier identification and removal. This novel usage of the algorithm takes advantage of the occurrence vector calculated by the algorithm in order to strengthen the effect of the more reliable samples and lessen the impact of those that otherwise would be considered outliers. Following that aim, a series of experiments is conducted in order to learn how (...) to better use the information contained in the occurrence vector. Using a contrively difficult artificial data set, a reference predictive model is fit using the whole raw dataset. The second experiment reports the results of fitting a similar predictive model but discarding the samples marked as outliers by PAELLA. The third experiment uses the occurrence vector provided by PAELLA in order to classify the observations in multiple bins and fit every possible model changing which bins are considered for fitting and which are discarded in that particular model. The fourth experiment introduces a sampling process before fitting in which the occurrence vector represents the likelihood of being considered in the training data set. The fifth experiment considers the sampling process as an internal step to be performed interleaved between the training epochs. The last experiment compares our approach using weighted neural networks to a state of the art method. (shrink)

Sports energy consumption is a quantitative reflection of physical exercise effect. Combined with different sports modes and students’ physical characteristics, the calculation model of sports energy consumption is put forward. Firstly, the relationship between students’ age, height, weight, gender, and energy consumption is analyzed by using multiple linear regression method, and a linear acceleration model is proposed by combining different exercise methods. The relationship between the integral value of acceleration and energy consumption is analyzed, and a (...) linear integral model based on different motion modes is proposed. Based on the kinetic energy theorem, the student movement energy expenditure is estimated. This paper proposes a human movement recognition method based on hybrid features, which mostly can represent the curve of the second generation wavelet transform edge thinning, and from the edge and texture features of the optimal said human posture, the statistical characteristic of the second generation wavelet transform is subtly trained as image characteristics, learning and recognition of human movement. Then, the motion recognition algorithm is tested, which can effectively identify the common movement patterns of primary and middle school students. Finally, the linear relationship between the estimation results of the model and the calculation results of Meijer is analyzed. The analysis results show that the linear acceleration model proposed in this paper can estimate the energy consumption of primary and middle school students’ motion relatively accurately. (shrink)

Following Brunswik (1952), social judgement theorists have long relied on regression models to describe both an individual's judgements and the environment about which such judgements are made. However, social judgement theory is not synonymous with these compensatory, static, structural models. We compared the characterisations of physicians' judgements using a regression model with that of a non-compensatory process model (called fast and frugal). We found that both models fit the data equally well. Both models suggest that physicians (...) use few cues, that they disagree among themselves, and that their stated cue use is discrepant with the models' stated cue use. However, the fast and frugal model is easier to convey to physicians and is also more psychologically plausible. Implications for how social judgement theorists conceptualise the process of vicarious functioning are discussed. (shrink)

This article offers a characterization of what I call multiple-models juxtaposition, a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena to...

The price of copper is unstable but it is considered an important indicator of the global economy. Changes in the price of copper point to higher global growth or an impending recession. In this work, the forecasting of the spot prices of copper from the New York Commodity Exchange is studied using a machine learning method, support vector regression coupled with different model schemas (recursive, direct and hybrid multi-step). Using these techniques, three different time series analyses are built (...) and its performance are compared. The numerical results show that the hybrid direct-recursive obtains the best results. (shrink)

Traxler, Pickering, and Clifton found that ambiguous sentences are read faster than their unambiguous counterparts. This so-called ambiguity advantage has presented a major challenge to classical theories of human sentence comprehension because its most prominent explanation, in the form of the unrestricted race model, assumes that parsing is non-deterministic. Recently, Swets, Desmet, Clifton, and Ferreira have challenged the URM. They argue that readers strategically underspecify the representation of ambiguous sentences to save time, unless disambiguation is required by task demands. (...) When disambiguation is required, however, readers assign sentences full structure—and Swets et al. provide experimental evidence to this end. On the basis of their findings, they argue against the URM and in favor of a model of task-dependent sentence comprehension. We show through simulations that the Swets et al. data do not constitute evidence for task-dependent parsing because they can be explained by the URM. However, we provide decisive evidence from a German self-paced reading study consistent with Swets et al.'s general claim about task-dependent parsing. Specifically, we show that under certain conditions, ambiguous sentences can be read more slowly than their unambiguous counterparts, suggesting that the parser may create several parses, when required. Finally, we present the first quantitative model of task-driven disambiguation that subsumes the URM, and we show that it can explain both Swets et al.'s results and our findings. (shrink)

According to recent empirical research (Transparency International, 2013), nearly 15% of people worldwide admitted to having paid a bribe in an educational setting. Given the nature of corruption,...

The city of Kruja (Albania)contains three types of dwellings that date back to different periods of time: the historic ones, the socialist ones, the modern ones. This paper has to deal only with the historic building's typology. The questionnaire that is applied will be considered for the development of mathematical regression based on specific data for this category. Variation between the relevant variables of the questionnaire is fairly or inverse-linked with a certain percentage of influence. The aim of this (...) study is to have better insights into the important role of building occupancy, people's lifestyle, economic level, and the quality of life of the residents in the inner medieval citadel, the physics characteristics, and the energy efficiency of the historical dwellings. The variables such as the number of residents, quality of life, the surface of the dwellings, heating instruments and time spent in the dwelling, current living conditions, monthly bills, level of satisfaction, restoration, and building improvements, interact with each other with probabilities with different positive or negative percentages. (shrink)

The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small simulation (...) study, which illustrates the impact of priors on final model estimates. The findings from the simulation study highlight the importance of conducting a sensitivity analysis of priors. This concept is further extended through an interactive Shiny App that we developed. The Shiny App allows users to explore the impact of various forms of priors using empirical data. We introduce this Shiny App and thoroughly detail an example using a simple multiple regression model that users at all levels can understand. In this paper, we highlight how to determine the different settings for a prior sensitivity analysis, how to visually and statistically compare results obtained in the sensitivity analysis, and how to display findings and write up disparate results obtained across the sensitivity analysis. The goal is that novice users can follow the process outlined here and work within the interactive Shiny App to gain a deeper understanding of the role of prior distributions and the importance of a sensitivity analysis when implementing Bayesian methods. The intended audience is broad and can include those with limited exposure to Bayesian methods or the specific model presented here. (shrink)

BackgroundAwareness of aging brings to light one’s own perceived behavioral, physical, and cognitive changes associated with getting older. Personality and physical illness are each related to two components of awareness of aging: attitudes toward own aging, and awareness of age-related changes. Here, we move beyond main effects to examine how personality and arthritis interact with respect to awareness of aging.Materials and Methods296 participants completed online self-report questionnaires of personality, arthritis, ATOA, and AARC gains and losses.ResultsWe ran three hierarchical multiple (...) regression models to test how personality traits and arthritis interacted to predict ATOA, AARC gains, and AARC losses, respectively. Higher extraversion was related to positive ATOA, and higher openness was related to positive ATOA and more AARC gains. Higher neuroticism was related to negative ATOA, more AARC losses, and less AARC gains. We found a main effect for arthritis, where those with arthritis reported more AARC losses. Lastly, we found a significant interaction between arthritis and agreeableness when predicting AARC gains. Among those with low agreeableness, people with arthritis reported significantly more AARC gains compared to those without arthritis.ConclusionPersonality and arthritis are each important for awareness of aging. Overall, our study suggests that for those with arthritis, it is especially important to consider behavioral and cognitive factors related to agreeableness, as they may be important means of promoting positive views on aging. (shrink)

A questionnaire study was performed among Swedish organic livestock farmers to determine their view of animal welfare and other ethical issues in animal production. The questionnaire was sent to 56.5% of the target group and the response rate was 75.6%. A principal components analysis (exploratory factor analysis) was performed to get a more manageable data set. A matrix of intercorrelations between all pairs of factors was computed. The factors were then entered into a series of multiple regression models (...) to explain five dependent variables. Respondents were well educated and had long experience of farming. 81% were full-time farmers. They generally had a very positive attitude towards organic animal husbandry. They considered allowing animals their natural behavior a central aim, which is in accordance with organic philosophy. Farmers tended to be less approving of concepts like animal rights, dignity, and intrinsic value. When analyzing correlations between the factors, two groups of farmers emerged that were only partially correlated, representing different attitudes and behavioral dispositions. These may be interpreted as two subpopulations of organic livestock farmers in Sweden: those who saw organic farming as a lifestyle (``pioneer attitude'''') and entrepreneurs, who considered making money and new challenges more important. Their view of animal welfare differed. While the pioneers considered natural behavior a key issue, this was less important to the entrepreneurs, who also had a more approving attitude towards invasive operations such as castration and were more critical of the organic standards. (shrink)

Compared with traditional statistics, only a few social scientists employ Bayesian analyses. The existing software programs for implementing Bayesian analyses such as OpenBUGS, WinBUGS, JAGS, and rstanarm can be daunting given that their complex computer codes involve a steep learning curve. In contrast, this paper introduces a new open software for implementing Bayesian network modelling and analysis: the bayesvl R package. The package aims at providing an intuitive gateway for beginners of Bayesian statistics to construct and analyse mathematical models in (...) social sciences... (shrink)

A judgement analysis of people's social inferences of attitudes and ability was conducted. University students were asked to infer the liberalness ( N = 60; Study 1) or intelligence ( N = 40; Study 2) of targets seen in pictures. Multiple regression analyses revealed that attractiveness was the most important cue for predicting inferences of liberalness, while an ethnic cue (i.e., being Asian) was the most important cue for judgements about intelligence. Results also showed that a single-cue (...) class='Hi'>model was less susceptible to overfitting, but significantly less accurate than a multiple-cue model in predicting participant's intelligence judgements. Although the multiple regression models suffered a degree of overfitting, cross validation showed that they continued to have significant predictive value when applied to new data. Furthermore, a “random partner” method (comparing each participant's own regression equation with that of another, randomly selected, participant) provided evidence of significant idiosyncratic variation in the way intelligence judgements were made. (shrink)

Water quality control helps in the estimation of water bodies and detects the span of pollutants and their effect on the neighboring environment. This is why the water quality of the northern part of Lake Manzala has been studied here from January to March, 2016. This study aims to model and create a program for linear and nonlinear regression of the water elements in Lake Manzala to assess and predict the water quality. Water samples have been extracted from (...) various depths, and physio-chemical properties and heavy metal concentrations have been evaluated. This study has proposed a new algorithm for predicting water quality called “Algorithm of Estimation Regression Model”. On the contrary, in renewable energy applications, statistical modeling and forecasting the solar radiation remains a significant issue with detect to reinforce power management. A new proposed method for forecasting the average Monthly Global Solar Energy in Queensland, Australia, is called, Converting Data Set into Markov Model. It was used to obtain Markov transition probability matrices for three and six states of the solar energy. The proposed forecasting method yielded accurate results with minimal error. (shrink)