Tree Inference: Response Time and Other Measures in a Binary Multinomial Processing Tree, Representation and Uniqueness of Parameters

A Multinomial Processing Tree (MPT) is a directed tree with a probability associated with each arc and partitioned terminal vertices. We consider an additional parameter for each arc, a measure such as time. Each vertex represents a process. An arc descending from a vertex represents selection of a process outcome. A source vertex represents processing beginning with stimulus presentation and a terminal vertex represents a response. An experimental factor selectively influences a vertex if changing the factor level changes parameter values on arcs descending from that vertex and no others. Earlier work shows that if each of two factors selectively influences a different vertex in an arbitrary MPT it is equivalent to one of two simple MPTs. Which applies depends on whether the two selectively influenced vertices are ordered by the factors or not. A special case, the Standard Binary Tree for Ordered Processes, arises if the vertices are ordered and the factor selectively influencing the first vertex changes parameter values on only two arcs. We derive necessary and sufficient conditions, testable by bootstrapping, for this case. Parameter values are not unique. We give admissible transformations for them. We calculate degrees of freedom needed for goodness of fit tests.

Small Relative Age Effect Appears in Professional Female Italian Team Sports

The relative age effect (RAE) concerns those (dis)advantages and outcomes resulting from an interaction between the dates of selection and birthdates. Although this phenomenon is well known in a male context, limited data are available in female sports. Thus, the aim of this study was to quantify the prevalence and magnitude of the RAE in a female Italian context at the professional level in basketball, soccer, and volleyball. A total of 1535 birthdates of elite senior players were analyzed overall and separately between early and late career stages. Chi-square goodness-of-fit tests were applied to investigate the RAE in each sport. An asymmetry in birthdates was observed in all sports (Crammer’s V ranged = 0.10–0.12). Players born close to the beginning of the year were 1.62 and 1.61 times more likely to reach first and second Italian divisions of soccer and volleyball, respectively, than those born in the last part of the year. A small over-representation of female athletes born close to the beginning of the year is evident at the senior professional level in all Italian investigated team sports. In soccer, this trend was more evident in the first stage of a senior career.

Building Brand Loyalty Based Brand Characteristic on Middle Eastern Culinary Msme Products

This research aims to determine the effect of brand characteristic variables, including brand reputation, brand predictability, and brand competence, on brand loyalty among MSME consumers of the Kak Fiet brand. The research sample was drawn using a non-probability sampling method called purposive sampling. One hundred twenty-seven respondents participated in this research. The data used in this research were primary data collected through questionnaires. Data analysis included validity and reliability tests, classical assumption tests, path coefficient tests, the goodness of fit tests, and hypothesis testing using the SmartPLS program. The results show that 1) brand reputation has a significant effect on brand loyalty; 2) brand predictability has no significant effect on brand loyalty; and 3) brand competence has a significant effect on brand loyalty. It demonstrates that the hypotheses that brand reputation has a significant effect on brand loyalty and that brand competence has a significant effect on brand loyalty are confirmed. In contrast, the hypothesis that brand predictability has a significant effect on brand loyalty is not proven. This means that not all brand characteristic elements have a significant effect on brand loyalty on Kak Fiet MSME products.

Analyzing the Best Fitted Probabilistic Model for the Seasonal Rainfall Data in Khulna Region of Bangladesh

There are several pieces of research on the statistical modeling of rainfall data in Bangladesh. Since all the seasons of a year do not receive a similar amount of rainfall, hence one single statistical model might not be able to explain the pattern of rainfall at any season of a year. According to the climatologists, Bangladesh has four seasons which are Monsoon, Post-monsoon, Summer, and Winter based on the geographical characteristics of this country. This paper aims to determine the best-fitted probability distribution model for the monthly rainfall data of each particular season in the Khulna district of Bangladesh using the rainfall data of the Khulna region from 1951 to 2018. Very commonly used seven continuous distributions- Normal, Weibull, Gamma, Log-normal, Exponential, Cauchy, and Logistic distributions were used to model the data and to evaluate the performances of the distributions, three non-parametric goodness-of-fit tests were conducted, and AIC, BIC values were calculated. Parameters of the distributions were estimated by the maximum likelihood method. The best-fit result of each season was taken as the distribution with the lowest AIC and BIC values. Among the seven distributions, the Gamma distribution showed the best-fit results of the monthly rainfall data for the Monsoon, Post-Monsoon, and Winter Season, and the Weibull distribution showed the best-fit result for Summer Season.

Estimating the Best-Fitted Probability Distribution for Monthly Maximum Temperature at the Sylhet Station in Bangladesh

The estimation of a suitable probability model depends mainly on the features of available temperature data at a particular place. As a result, existing probability distributions must be evaluated to establish an appropriate probability model that can deliver precise temperature estimation. The study intended to estimate the best-fitted probability model for the monthly maximum temperature at the Sylhet station in Bangladesh from January 2002 to December 2012 using several statistical analyses. Ten continuous probability distributions such as Exponential, Gamma, Log-Gamma, Beta, Normal, Log-Normal, Erlang, Power Function, Rayleigh, and Weibull distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the temperature data, several goodness-of-fit tests were applied, including the Kolmogorov-Smirnov test, Anderson-Darling test, and Chi-square test. The Beta distribution is found to be the best-fitted probability distribution based on the largest overall score derived from three specified goodness-of-fit tests for the monthly maximum temperature data at the Sylhet station.

Performance of the Reported and Intended Behavior Scale among Colombian Adolescents

Objective: To establish the Reported and Intended Behavior Scale (RIBS) psychometric performance, a mental disorder-related stigma measurement, among Colombian adolescents. Methods: A validation study was carried out with 350 students aged between 10 and 17, 53.7% of whom were girls. The RIBS has two sub-scales -reported behaviors and intended behaviors, with four items each. Frequencies were estimated for reported behaviors, whereas internal consistency (Cronbach's alpha and McDonald's omega) and confirmatory factor analysis (CFA) were measured for intended behaviors. Results: The reported behavior sub-scale ranged from 10.0 to 24.9%, whereas the intended behavior sub-scale presented a Cronbach's alpha of 0.88 (CI95% 0.86-0.90) and a McDonald's omega of 0.88. For the CFA, KMO was 0.81; Bartlett chi squared, 771.1 (df=6, p=0.01); and Eigen value, 2.95 that explained 73.9% of the total variance. For the goodness-of-fit tests, chi squared was 21.9 (df=2, p=.001); RMSEA, 0.17 (CI90% 0.11-0.24); CFI, 0.97; TLI, 0.92; and SMSR, 0.03. Conclusions: The RIBS can measure reported behaviors, and the intended behavior sub-scale shows high internal consistency. However, the dimensionality of the intended behavior sub-scale presents modest goodness-of-fit indexes. These findings need further replications.