Institution
Rider University
Education•Lawrenceville, New Jersey, United States•
About: Rider University is a education organization based out in Lawrenceville, New Jersey, United States. It is known for research contribution in the topics: Dosimetry & Creativity. The organization has 881 authors who have published 1934 publications receiving 50752 citations.
Topics: Dosimetry, Creativity, Dosimeter, Population, Order statistic
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors examined three potential predictors in a sample of 102 college students: addictive qualities of nonsuicidal self-injury, impulsivity, and dissociation.
Abstract: This study aimed to identify predictors of unintentionally severe injury during nonsuicidal self-injury (NSSI). The authors examined 3 potential predictors in a sample of 102 college students: addictive qualities of NSSI, impulsivity, and dissociation. Both impulsivity and addictive qualities of NSSI were associated with unintentionally severe injury during NSSI. In a logistic regression, only addictive qualities of NSSI emerged as a significant predictor of unintentionally severe injury during NSSI. Implications for counseling and research are discussed.
13 citations
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01 Aug 2011TL;DR: In this paper, it was shown that for any fixed m ≥ 2, X.............. 1 has Geometric distribution if and only if the n-th upper weak record value is the nth upper strong record value.
Abstract: Let $${\{X_n, n\geq 1\}}$$
be a sequence of independent and identically distributed non-degenerated random variables with common cumulative distribution function F. Suppose X
1 is concentrated on 0, 1, . . . , N ≤ ∞ and P(X
1 = 1) > 0. Let $${X_{U_w(n)}}$$
be the n-th upper weak record value. In this paper we show that for any fixed m ≥ 2, X
1 has Geometric distribution if and only if $${X_{U_{w}(m)}\mathop=\limits^d X_1+\cdots+X_m ,}$$
where $${\underline{\underline{d}}}$$
denotes equality in distribution. Our result is a generalization of the case m = 2 obtained by Ahsanullah (J Stat Theory Appl 8(1):5–16, 2009).
13 citations
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TL;DR: In this paper, a method is provided which very small firms can use to create and implement a structured assessment tool that builds on observation of critical incidents to illustrate the differences between poor, average, and good performance.
Abstract: Purpose – The purpose of this paper is to improve assessment and feedback processes in the training practices of very small firms, thereby improving the firms' human capital.Design/methodology/approach – The paper reviews research and practice on effective assessment and feedback.Findings – Based on this paper, human resources are increasingly seen as a potential source of sustained competitive advantage, and well‐trained workers can boost the performance of even very small firms. Hence, a method is provided which very small firms can use to create and implement a structured assessment tool that builds on observation of critical incidents to illustrate the differences between poor, average, and good performance.Practical implications – The paper shows that readers can use the provided tools to assess and improve employee performance, thereby enhancing their firm's competitive position.Originality/value – This paper can be used by very small firms to evaluate employee performance and provide employees with...
13 citations
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13 citations
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TL;DR: It is proposed that structured activities requiring students to focus on individual parts of research papers, even on a single figure, are beneficial in a literature‐centered advanced undergraduate course, because they promote the deep reading that is critical to scientific discourse.
13 citations
Authors
Showing all 892 results
Name | H-index | Papers | Citations |
---|---|---|---|
James Chih-Hsin Yang | 127 | 606 | 90323 |
Feng Chen | 95 | 2138 | 53881 |
Vijay Mahajan | 75 | 188 | 24381 |
John J. Bochanski | 68 | 166 | 39951 |
Victor H. Denenberg | 56 | 253 | 11517 |
David G. Kirsch | 56 | 284 | 13992 |
Greg G. Qiao | 55 | 344 | 11701 |
Robert Kaestner | 51 | 282 | 8399 |
John Baer | 45 | 124 | 6649 |
Geoffrey S. Ibbott | 45 | 290 | 8663 |
David S Followill | 43 | 271 | 7881 |
Mark Oldham | 41 | 215 | 6107 |
Michael Gillin | 39 | 147 | 4671 |
Shiva K. Das | 37 | 182 | 5588 |
Hope Corman | 34 | 133 | 3882 |