Log Data Analysis with ANFIS: A Fuzzy Neural Network Approach

This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research.     

Unobtrusive indicators of culture for organizations: a systematic review

ABSTRACT

We systematically reviewed the literature using unobtrusive measures to study organizational culture. To synthesize, theorize, and evaluate this research, we introduce the concept of an unobtrusive indicator of culture (UIC) for organizations. A UIC measures organizational culture through collecting data without engaging employees, and is conceptualized in terms of cultural artefacts. We identified thirty-five articles, containing 135 distinct UICs, drawn from 16 distinct data sources. UICs coalesced into two groups. First, textual UICs, with culture measured through language patterns in annual reports, employee online reviews, and emails. Second, UICs focusing on organizational practices, for instance, organizational policies or executive rewards. Over two-thirds (68%) of UICs measured values for integrity, results orientation, and clan cultures, and we conjecture that UICs may be most useful for studying aspects of culture sensitive to reporting biases, and benchmarking large samples of organizations. Forty-eight percent of UICs had good or promising construct validity: many were textual UICs, and those focusing on organizational practices were less established. UICs can potentially advance the study of organizational culture, yet must be developed and applied cautiously, with careful consideration of their advantages and limitations, and how they complement existing measurements and conceptualizations of culture.     

Neutrosophic data formation using Gaussian filter based costas coding for wireless communication systems

Outstanding advantages of OFDM helps high data rate communication systems such as Digital Video Broadcasting (DVB) and mobile worldwide interoperability for microwave access (mobile Wi-MAX). But, OFDM system grieves from grave issue of high PAPR. In OFDM system data output is superposition of multiple sub-carriers and leads to big data. In this case some instantaneous power output might increase greatly and become far higher than the mean power of system. In order to make Neutrosophic data for less PAPR criteria, Gaussian filter based Costas coding is proposed. This paper also proposes feeding the Orthogonal Frequency Division Multiplexed signal (OFDM) into a phase accumulator to obtain frequency modulation, with the aim of reducing the peak to average power ratio (PAPR). For associated radar with modulated waveform mentioned here can be used to enhance the performance. It is widely used to analyze the variation of the output waveform further, the implementation results using MATLAB show that the ratio of average power to peak power is reducing considerably in comparing with the conventional method. Consequently; the signal can assume to fade in a frequency selective channel without an equalizer.     

A new goodness-of-fit measure for probit models: Surrogate R2

Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R² used for linear models. Such a measure has long been sought to achieve ‘comparability’ of different empirical models across multiple samples addressing similar social questions. To this end, we propose a novel R² measure for probit models using the notion of surrogacy – simulating a continuous variable $S$ as a surrogate of the original discrete response (Liu & Zhang, Journal of the American Statistical Association, 113, 845 and 2018). The proposed R² is the proportion of the variance of the surrogate response explained by explanatory variables through a linear model, and we call it a surrogate R². This paper shows both theoretically and numerically that the surrogate R² approximates the OLS R² based on the latent continuous variable, preserves the interpretation of explained variation, and maintains monotonicity between nested models. As no other pseudo R², McKelvey and Zavoina's and McFadden's included, can meet all the three criteria simultaneously, our measure fills this crucial void in probit model inference.     

ACADEA—a decision support system for faculty performance reviews

ACADEA, a multi‐criteria decision support system for the performance review of individual faculty, is presented. Developed from the point of view of a department that is facing exogenously as well as self imposed objectives, the support system looks upon the aggregate performance of an academic department as the result of individual faculty member's multi‐criteria evaluations. Five objectives, research output, teaching output, external service, internal service and cost, are operationalized into criteria. The system is applied to a university department with 30 faculty members evaluated over a 3‐year period. The results identify promotional candidates and reveal underlying problems in managerial consistency, departmental sub‐groupings and the incentive structure. The outcomes of the support system are consistent with the position that equity in faculty governance does not necessarily imply equal loads on all tasks. Copyright © 2000 John Wiley & Sons, Ltd.     

Friend or foe? Postdivorce hostility among recently divorced individuals

High levels of hostility often occur during and postdivorce and may significantly affect the quality of life, parent–child relationships, and social functioning of divorcees. Moreover, hostility may predict aggressive and violent behavior. This study sought to (a) compare average general hostility levels of a large sample of Danish divorcees to the norms of the general adult Danish population, (b) compare general hostility levels between male and female divorcees, and (c) investigate the explanatory value of various sociodemographic and divorce-related factors on postdivorce general hostility and whether these factors differ across gender. Cross-sectional baseline data (N = 1,856) from a larger randomized controlled trial study was used in this study. Normative data from a general sample of Danish adults (N = 2,040) was used for comparisons of hostility levels between our study sample and the Danish background population. This study found that male and female divorcees did not report significantly different hostility levels. However, participants reported significantly higher hostility levels postdivorce than the comparative Danish norm sample. Significant predictors of postdivorce hostility were lower age, lower educational level, infidelity as a reason for divorce, higher degree of postdivorce conflict, worse communication with the former spouse, the former spouse as the initiator of the divorce, and new partner status with neither divorcees having a new partner, or only the former spouse having a new partner. The predictive strength of the factors did not differ across gender. The findings may be especially relevant for interventions targeting problematic outcomes postdivorce (e.g., preventing aggressive behavior).     

Comparison of Common Amplitude Metrics in Event-Related Potential Analysis

Abstract

Waveform data resulting from time-intensive longitudinal designs require careful treatment. In particular, the statistical properties of summary metrics in this area are crucial. We draw on event-related potential (ERP) studies, a field with a relatively long history of collecting and analyzing such data, to illustrate our points. In particular, three summary measures for a component in the average ERP waveform feature prominently in the literature: the maximum (or peak amplitude), the average (or mean amplitude) and a combination (or adaptive mean). We discuss the methodological divide associated with these summary measures. Through both analytic work and simulation study, we explore the properties (e.g., Type I and Type II errors) of these competing metrics for assessing the amplitude of an ERP component across experimental conditions. The theoretical and simulation-based arguments in this article illustrate how design (e.g., number of trials per condition) and analytic (e.g., window location) choices affect the behavior of these amplitude summary measures in statistical tests and highlight the need for transparency in reporting the analytic steps taken. There is an increased need for analytic tools for waveform data. As new analytic methods are developed to address these time-intensive longitudinal data, careful treatment of the statistical properties of summary metrics used for null hypothesis testing is crucial.     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.