On a Skew Bimodal Normal–Normal distribution fitted to the Old-Faithful geyser data

The Bimodal Normal distribution introduced by Alavi (2011) is a symmetric distribution where its variance is three times the variance of the corresponding normal distribution. Azzalini (1985) introduced the univariate Skew Normal distribution to model asymmetry data. In this paper the Skew Bimodal Normal–Normal distribution is introduced as a skew-symmetric distribution generated by the cumulative function of standard normal. Some properties of the distribution and some methods for generating data from this distribution are introduced. The maximum likelihood estimation of parameters is obtained. The distribution is fitted to the Old Faithful Geyser data.     

Predictive analysis for joint progressive censoring plans: a Bayesian approach

Comparative lifetime experiments are of particular importance in production processes when one wishes to determine the relative merits of several competing products with regard to their reliability. This paper confines itself to the data obtained by running a joint progressive Type-II censoring plan on samples in a combined manner. The problem of Bayesian predicting failure times of surviving units is discussed in details when parent populations are exponential. Two real data sets are analyzed in order to illustrate all the inferential procedures developed here. When destructive experiments under a censoring scheme finished, the researchers are usually interested to estimate remaining lifetimes of surviving units for sequel experiments. Findings of this paper are useful for these purposes specially when samples are non-homogeneous such as those taken from industrial storages.     

A flexible fixed-sequence testing method for hierarchically ordered correlated multiple endpoints in clinical trials

Statistical approaches for addressing multiplicity in clinical trials range from the very conservative (the Bonferroni method) to the least conservative the fixed sequence approach. Recently, several authors proposed methods that combine merits of the two extreme approaches. Wiens [2003. A fixed sequence Bonferroni procedure for testing multiple endpoints. Pharmaceutical Statist. 2003, 2, 211–215], for example, considered an extension of the Bonferroni approach where the type I error rate (α)$(\alpha )$ is allocated among the endpoints, however, testing proceeds in a pre-determined order allowing the type I error rate to be saved for later use as long as the null hypotheses are rejected. This leads to a higher power of the test in testing later null hypotheses. In this paper, we consider an extension of Wiens’ approach by taking into account correlations among endpoints for achieving higher flexibility in testing. We show strong control of the family-wise type I error rate for this extension and provide critical values and significance levels for testing up to three endpoints with equal correlations and show how to calculate them for other correlation structures. We also present results of a simulation experiment for comparing the power of the proposed method with those of Wiens’ and others. The results of this experiment show that the magnitude of the gain in power of the proposed method depends on the prospective ordering of testing of the endpoints, the magnitude of the treatment effects of the endpoints and the magnitude of correlation between endpoints. Finally, we consider applications of the proposed method for clinical trials with multiple time points and multiple doses, where correlations among endpoints frequently arise.     

Applying a Systemic Procedure to Locate Career Decision‐Making Difficulties

Locating clients' career decision‐making difficulties is one of the first steps in career counseling. The authors demonstrate the feasibility and utility of a systematic 4‐stage procedure for locating and interpreting career decision‐making difficulties by analyzing responses of 626 college students (collected by Tai, 2007) to the Career Decision‐making Difficulties Questionnaire (CDDQ; Gati, Krausz, & Osipow, 1996). In addition, the responses of 2 students selected from this group are individually analyzed and interpreted. The procedure makes it possible to map the various career decision‐making difficulties of undergraduate students and tailor the appropriate career counseling intervention to the unique needs of clients.     

Financial decision making in collective society—A field test on Israeli kibbutz members and city residents

This comparison of Israeli kibbutz members to city residents examines how of individualistic/collective society affects financial decision making. Findings revealed that kibbutz members are more risk averse and discount the future more than city residents, undermining the assumption that collective society accords a safety net. We claim that the collective financial management of the kibbutz reduces individuals’ financial knowledge and experience, causing them to be more risk averse in financial decisions, and thus overpowering the safety net offered by the collective society. In addition, we argue that despite privatization, individuals still operate according to collective ideas in handling their personal finances, but less than before the onset of the privatization process.     

An approach to setting up a national customer satisfaction index: the Jordan case study

The aim of this paper was to develop a national customer satisfaction index (CSI) in Jordan and to derive its theory using generalized maximum entropy. During the course of this research, we conducted two different surveys to complete the framework of this CSI. The first one is a pilot study conducted based on a CSI basket in order to select the main factors that comprise the Jordanian customer satisfaction index (JCSI). Based on two different analyses, namely nonlinear principal component analysis and factor analysis, the explained variances in the first and second dimensions were 50.32 and 16.99% respectively. Also, Cronbach coefficients α in the first and second dimensions were 0.923 and 0.521, respectively. The results of this survey suggests the inclusion of loyalty, complaint, expectation, image and service quality as the main CS factors of our proposed model. The second study is a practical implementation conducted on the Vocational Training Corporation in order to evaluate the proposed JCSI. The results indicated that the suggested components of the proposed model are significant and form a good fitted model. We used the comparative fit index and the normed fit index as goodness-of-fit measures to evaluate the effectiveness of our proposed model. Both measures indicated that the proposed model is a promising one.     

Textual data transformations using natural language processing for risk assessment

Underlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause–effect scenarios with minimal manual interpretation. This study applies natural language processing and text-mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion-related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain-specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.     

“There is no future in it”: Pandemic and ride-hailing hustle in Africa

This article examines the impact of the COVID-19 pandemic on ride-hailing drivers in Africa. It argues that, although ride-hailing offers paid work to some African workers, the commodified and informalized nature of this work results in poor job quality, the effects of which were greatly amplified during the pandemic. Drawing on a mixed methods approach involving in-depth interviews with ride-hailing drivers in Nairobi and digital ethnography, it also provides accounts of drivers' hustles to demonstrate strategies of resilience, reworking and resistance among informal workers. The article concludes by highlighting the need for adequate regulatory frameworks and on-the-ground solidarity networks to ensure decent working conditions, and to push back against precarity in the gig economy.     

The importance of sector‐stereotypical images in relation to job pursuit intentions

This study broadens the concept of employer image and demonstrates the relevance of applicants’ sector image when looking for employment in either nonprofit or for‐profit organizations. We apply the Stereotype Content Model to conceptualize image through perceived competence and warmth and use multilevel analyses on data from final‐year Master’s students. Results show the importance of the sector‐stereotypical image and are partially in line with our hypotheses. Job pursuit intentions for specific organizations are predicted by both the organizations’ as well as the respective sectors’ perceived warmth and competence. Pursuit intentions for the for‐profit sector are predicted only by the image of that sector. However, the intention to pursue employment in the nonprofit sector is predicted by both its competence and warmth as well as by the perceived warmth of its organizations. These findings are discussed in view of a trickle‐down effect and compared to previous findings on sector image among consumers.