Assessing item-level fit for the sequential G-DINA model

Estimates of gene effects through joint scaling test of three and six parameter and sequential fit model in five crosses for eleven characters were investigated. It was noticed that simple additive dominance model exhibited lack of good fit for all the traits. So, sequential fit model was searched after eliminating the non-significant parameters of six parameter model. Five parameter sequential fit model was observed for number of primaries per plant (cross 1), number of seeds per capsule (cross 4), oil content (cross 2 and 3), seed yield per plant (cross 1) and chlorophyll content (cross 3). Best fit four parameter sequential model was observed for number of primaries per plant (cross 2) and 1000 seed weight (cross 1). Higher order interactions epistasis or linkage were observed for days to 50 per cent flowering (cross 1, 2, 3, 4, and 5), days to maturity (cross 1, 2, 3, and 4), plant height (cross 3, 4, and 5), number of effective primaries per plant (cross 3, 4, and 5), number of effective capsules per plant (cross 2, 3, 4, and 5), number of seeds per capsule (cross 1, 2, 3, and 5), 1000 seed weight (cross 4 and 5), seed yield per plant (cross 4 and 5), oil content (cross 1, 4, and 5) and chlorophyll content (cross 1, 2, 4, and 5). Differential model schemes for same trait in different crosses were noticed in the present investigation. It was due to different parents involved with variable gene frequency with opposing and reinforcing genetic effects. The magnitude of [d] was relatively small to that of other genetic effects. This indicated that, additive genes are playing a minor role in the inheritance of these traits.

Identifying predictors of early childhood caries among Australian children using sequential modelling: Findings from the VicGen birth cohort study

The modeling of response times using sequential sampling models has a long history. Because choices, confidence judgments, and reaction times are closely linked in perceptual decisions, it seems only natural to simultaneously model these three outcome variables of a decision. In the package dynConfiR, we implemented various sequential sampling models of choice, response time, and decision confidence in R. This paper gives an overview of the package, which provides probability density functions as well as high-level functions for fitting parameters to empirical data, prediction of reaction time and response distributions and simulation of artificial data sets. We describe the mathematical specification of the implemented models and give a detailed description of the implemented likelihood functions. In addition, we outline the workflow for applying the model to empirical data step-by-step: data preprocessing, model fitting, model prediction, quantitative model comparison, and visual assessment of model predictions. Finally, we present results from a parameter and model recovery analyses and assess the precision in calculating probability densities, illustrating the reliability of the implemented computations. Offering intuitive usability and high flexibility, the package is targeted at researchers in the fields of decision-making and confidence and does not require expert-level programming skills.

ABSTRACTThis study investigates the application of ordinal regression models in seismic fragility curve modeling, providing a flexible alternative to the traditional log‐normal distribution function. A comparative analysis is conducted among various ordinal regression approaches, including the traditional Cumulative model as well as alternative methods like Sequential and Adjacent Category models, along with extensions that account for category‐specific effects and heteroscedasticity. These models are applied to bridge damage data from the 2008 Wenchuan earthquake, using both frequentist and Bayesian inference methods. Model diagnostics, including surrogate residuals, are performed to assess model fit and performance. A total of eleven models are examined, from basic forms to those incorporating category‐specific effects and variance heterogeneity. The Sequential model with category‐specific effects, rigorously evaluated using leave‐one‐out cross‐validation, outperforms the traditional Cumulative probit model. The findings highlight significant differences in the predicted damage probabilities, emphasizing the potential of more flexible fragility curve modeling techniques to improve seismic risk assessments. This study underscores the importance of ongoing evaluation and refinement of modeling techniques to enhance the predictive accuracy and applicability of seismic fragility models in performance‐based earthquake engineering.

Allowance for multiple chances to answer constructed response questions is a prevalent feature in computer‐based homework and exams. We consider the use of item response theory in the estimation of item characteristics and student ability when multiple attempts are allowed but no explicit penalty is deducted for extra tries. This is common practice in online formative assessments, where the number of attempts is often unlimited. In these environments, some students may not always answer‐until‐correct, but may rather terminate a response process after one or more incorrect tries. We contrast the cases of graded and sequential item response models, both unidimensional models which do not explicitly account for factors other than ability. These approaches differ not only in terms of log‐odds assumptions but, importantly, in terms of handling incomplete data. We explore the consequences of model misspecification through a simulation study and with four online homework data sets. Our results suggest that model selection is insensitive for complete data, but quite sensitive to whether missing responses are regarded as informative (of inability) or not (e.g., missing at random). Under realistic conditions, a sequential model with similar parametric degrees of freedom to a graded model can account for more response patterns and outperforms the latter in terms of model fit.

More than 300 samples of Macrolepidoptera have been collected over 24 years at a site in southern England on field courses run for university students. The samples were taken in mercury vapour light traps. They show that numbers have fluctuated markedly between periods of high abundance and periods of low abundance. Species richness in the samples is strongly affected by abundance. Evenness of distribution of numbers between species is higher in samples from woodland than in samples collected over grass, and higher earlier than later in the season. For a series of samples from the same population, MacArthur's overlapping niche and the broken stick resource apportionment models predict a weakly positive regression of the evenness J of a sample on species number, whereas the sequential breakage model predicts a negative regression. The latter implies the highest level of competitive interaction within the moth communities sampled. We find that the data agree with the sequential breakage model, rather than the other two. A weak positive regression was expected in view of the trapping method used but was not found. The fit of the sequential breakage model also implies that species abundance is log normally distributed, which it may be for many reasons. It is argued nevertheless that such comparisons may be of use for detecting competitive interaction, and that it is important to do so in order to judge the validity of predictions about effects of environmental change or human interference on the structure of communities.

More than 300 samples of Macrolepidoptera have been collected over 24 years at a site in southern England on field courses run for university students. The samples were taken in mercury vapour light traps. They show that numbers have fluctuated markedly between periods of high abundance and periods of low abundance. Species richness in the samples is strongly affected by abundance. Evenness of distribution of numbers between species is higher in samples from woodland than in samples collected over grass, and higher earlier than later in the season. For a series of samples from the same population, MacArthur's overlapping niche and the broken stick resource apportionment models predict a weakly positive regression of the evenness J of a sample on species number, whereas the sequential breakage model predicts a negative regression. The latter implies the highest level of competitive interaction within the moth communities sampled. We find that the data agree with the sequential breakage model, rather than the other two. A weak positive regression was expected in view of the trapping method used but was not found. The fit of the sequential breakage model also implies that species abundance is log normally distributed, which it may be for many reasons. It is argued nevertheless that such comparisons may be of use for detecting competitive interaction, and that it is important to do so in order to judge the validity of predictions about effects of environmental change or human interference on the structure of communities.

Converging findings from behavioral, neurophysiological, and neuroimaging studies suggest an integration-to-boundary mechanism governing decision formation and choice selection. This mechanism is supported by sequential sampling models of choice decisions, which can implement statistically optimal decision strategies for selecting between multiple alternative options on the basis of sensory evidence. This review focuses on recent developments in understanding the evidence boundary, an important component of decision-making raised by experimental findings and models. The article starts by reviewing the neurobiology of perceptual decisions and several influential sequential sampling models, in particular the drift-diffusion model, the Ornstein–Uhlenbeck model and the leaky-competing-accumulator model. In the second part, the article examines how the boundary may affect a model’s dynamics and performance and to what extent it may improve a model’s fits to experimental data. In the third part, the article examines recent findings that support the presence and site of boundaries in the brain. The article considers two questions: (1) whether the boundary is a spontaneous property of neural integrators, or is controlled by dedicated neural circuits; (2) if the boundary is variable, what could be the driving factors behind boundary changes? The review brings together studies using different experimental methods in seeking answers to these questions, highlights psychological and physiological factors that may be associated with the boundary and its changes, and further considers the evidence boundary as a generic mechanism to guide complex behavior.

To provide more insight into an individual's response process and cognitive process, this study proposed three mixed sequential item response models (MS-IRMs) for mixed-format items consisting of a mixture of a multiple-choice item and an open-ended item that emphasize a sequential response process and are scored sequentially. Relative to existing polytomous models such as the graded response model (GRM), generalized partial credit model (GPCM), or traditional sequential Rasch model (SRM), the proposed models employ an appropriate processing function for each task to improve conventional polytomous models. Simulation studies were carried out to investigate the performance of the proposed models, and the results indicated that all proposed models outperformed the SRM, GRM, and GPCM in terms of parameter recovery and model fit. An application illustration of the MS-IRMs in comparison with traditional models was demonstrated by using real data from TIMSS 2007.

Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC), and absolute fit indices (RMSEA2, ABS(fcor) and MAX(χ2jj′)). These fit indices were assessed under different CDM settings with respect to Q-matrix misspecification and CDM misspecification. Results showed that relative fit indices selected the correct DINA model most of the times and selected the correct G-DINA model well across most conditions. Absolute fit indices rejected the true DINA model if the Q-matrix was misspecified in any way. Absolute fit indices rejected the true G-DINA model whenever the Q-matrix was under-specified. RMSEA2 could be artificially low when the Q-matrix was over-specified.

The focus of this empirical study is to examine the interceding effect of Perceived Organizational Support (POS) between Emotional Intelligence (EI) and Job Satisfaction (JS). For this purpose, random data of 300 respondents was collected from primary public schools of four districts of Punjab, Pakistan i.e. Lahore, Sahiwal, Okara and Nankana Sahib through self-administrative questionnaires. The time legged approach is used in collection of data to decrease self-biasness of responses. Sequential Equation modeling is applied to test the effectiveness and fitness of model. The results of the study suggest that job satisfaction is positively affected by emotional intelligence Perceived Organizational Support (POS) also influences the relationship between emotional intelligence and job satisfaction. Practical implications and future directions are also provided. Key terms: Job Satisfaction, Emotional Intelligence, Perceived Organizational Support

Accurate prediction of power demand and generation is crucial for modern energy systems to efficiently allocate resources and facilitate energy trading. The integration of artificial intelligence (AI) and machine learning techniques has significantly improved the precision of power forecasting. This study focuses on the application of Artificial Neural Networks (ANN) for forecasting power generation in the Eastern Coast region of Malaysia, with a specific emphasis on solar power. The research methodology involves collecting and analyzing historical power data, weather data, and relevant variables. ANN models are trained, validated, and tested on a selected power grid to assess their accuracy and predictive capabilities. The expected outcomes aim to include the development of a precise power generation forecasting model, providing valuable insights for decision-makers to optimize energy operations and seamlessly integrate renewable sources. Additionally, the study explores potential challenges, limitations, and best practices associated with ANN-based power forecasting. The dataset covers the period from 2020 to 2023, with variables such as average output power, ambient temperature, PV module temperature, global horizontal irradiance, and wind speed recorded at 30-minute intervals. The architecture of the ANN model, implemented using the Keras framework, is described as a Sequential model with layers utilizing the 'ReLU' activation function. Model evaluation employs metrics like root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) on the test set, offering insights into the model's overall fit, average deviation, and sensitivity to outliers. Results reveal strong correlations between PV module temperature, irradiance, and AC power generated.

Background and Objective: Traditional assessment methods have been criticized for neglecting the cognitive processes required by test-takers to provide correct responses. To address these issues, cognitive diagnostic models have been introduced by researchers. This study aimed to explore and apply cognitive diagnostic models to estimate the psychometric properties of published eighth-grade math questions from TIMSS 2019. Methods and Materials: The research method employed in this study is mixed-methods, conducted with a retrofitting approach to existing tests. The statistical population of this study consisted of all Iranian eighth-grade students who participated in the TIMSS 2019 study. The population size included 1,095,026 students from 23,895 schools. The sample consisted of 5,980 Iranian eighth-grade students from 220 schools, selected through a two-stage cluster sampling method, with an average age reported as 14.1 years. Findings: Using a qualitative method, 16 skills across four content domains—knowledge, application, and reasoning—were identified and the Q-matrix was developed. Then, in the quantitative section, the fit of the DINA model as a compensatory model, the DINO model as a non-compensatory model, and the G-DINA model as a general model were examined with data from students' responses to math questions in blocks 1, 2, 3, and 5. The findings showed that in terms of model fit, the G-DINA model had the weakest fit, while the DINA model had the best fit. Absolute fit indices of the models also indicated that the DINA model was better, and the DINO model did not fit. Relative fit indices of the models showed no significant difference between the G-DINA model and the other two models, and overall, the non-compensatory DINA model demonstrated the best fit. Conclusion: The results indicated that the DINA model had the best fit with the data and can be considered the optimal model for analyzing the eighth-grade mathematics questions in TIMSS 2019.

Long distance trip has been increased and travel patterns are complicated due to diversity of transportation mode. Evolution of public transport which has made traveler more comfortable also contribute this phenomenon. So, prediction of transportation demand is becoming far more difficult and important than in the past. To impove the prediction of demand, this study suggests 2-phases modal split model that reflect characteristic of inter-regional inter-modal trip into main mode trip and access/egress trip. After that, each multi-nominal logit model is calibrated by trip characteristic. After that, optimum model is selected which is the highest model fitness and significant statistically. The result shows that generic variables and separation of time variables are suitable for both models. Model fitness of optimum model is over 0.2 and all variables are significant statistically. Traveler who takes main mode prefer auto to transit and who takes access/egress mode prefer transit to auto. The result of validation for value of time shows reasonable value comparing with standard guideline on KDI. Comparison with existing models also shows improved outcome of explanation. In conclusion, 2-phases model can explain and describe travel pattern better than existing models.

A Sequential Algorithm to Identify the Mixing Endpoints in Liquids in Pharmaceutical Applications By Akriti Saxena, M.S. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Biostatistics at Virginia Commonwealth University. Virginia Commonwealth University, 2009 Major Director: V. Ramakrishnan, Ph.D. Associate professor Department of Biostatistics The objective of this thesis is to develop a sequential algorithm to determine accurately and quickly, at which point in time a product is well mixed or reaches a steady state plateau, in terms of the Refractive Index (RI). An algorithm using sequential non-linear model fitting and prediction is proposed. A simulation study representing typical scenarios in a liquid manufacturing process in pharmaceutical industries was performed to evaluate the proposed algorithm. The data simulated included autocorrelated normal errors and used the Gompertz model. A set of 27 different combinations of the parameters of the Gompertz function were considered. The results from the simulation study suggest that the algorithm x is insensitive to the functional form and achieves the goal consistently with least number of time points.