United Grain Grower Case free essay sample

Joined Grain Grower Case Risk Management United Grain Grower Case Risk Management PREFACE United Green Growers (UGG) is an organization who offers business types of assistance to ranchers in Canada and markets farming items around the world. UGG attempted to separate itself from contenders by making items with brand names a d by giving on-going administrations to clients. During the last piece of the 1990s, some UGG’s directors began to scrutinize the allure of overseeing unadulterated hazard and money related hazard independently. UGG began by shaping a hazard the executives board, comprising of the CEO, CFO, chance chief, treasurer, consistence supervisor (for ware exchanging), and administrator of corporate review administrations. This advisory group, alongside various UGG workers, at that point met with an agent from Willis (hazard the executives expert) for a meeting to generate new ideas to distinguish the firm’s significant dangers. This procedure distinguished 47 presentation regions, from which six were picked for additional examination and evaluation. The six dangers were: 1. We will compose a custom article test on Joined Grain Grower Case or on the other hand any comparative theme explicitly for you Don't WasteYour Time Recruit WRITER Just 13.90/page Ecological obligation 2. The impact of climate on grain volume 3. Counterparty hazard (providers or clients not satisfying agreements) 4. Credit chance 5. Item hazard and premise chance 6. Stock Risk (harm to items in stock) The investigation directed by Willis Risk Solutions prompted the end that, of the six dangers initially distinguished, UGG’s principle wellspring of unmanaged chance was from the climate. As indicated by Willis research if climate hazard evacuated, UGG’s benefit would have been progressively steady: Having measured their presentation to climate chance, UGG needed to settle on some solution for it. They investigated a few alternatives: 1. Maintenance * Advantage: * No expense related with moving it to another person. * Disadvantages: * Higher credit loan cost. * UGG need to hold additional value capital as a pad against startling low incomes. * Suppliers and clients couldn't depend on for administration and top notch items because of precarious income 2. Climate subordinates * Advantage: * Zero misfortune if contract structure could consummately cover all the dangers

Paper on Abuse Reporting Essay Example | Topics and Well Written Essays - 500 words

Paper on Abuse Reporting - Essay Example This paper will gracefully an outline of the kid detailing laws of the province of California. This paper will additionally talk about these laws comparable to my contemplations on privacy and the obligation to shield others from hurt. It will allude to moral measures of human assistance s experts and different codes of morals. The Office of Child Abuse and Prevention under the California Department of Social Services, is liable for executing the youngster misuse and disregard announcing law. As indicated by a Summit (1983), California instituted its first kid misuse announcing law in 1963. Prior laws ordered that physical maltreatment should just be accounted for by doctors. The meaning of youngster misuse has anyway been extended by the various alterations that have been occurring throughout the years. Laws have likewise explained the methods for announcing different classes of kid misuse (Summit, 1983). In California, there are sure experts who are required to report known cases or associated cases with kid misuse. Be that as it may, different residents who are not legally necessary to do so may likewise be permitted to do as such. Commanded reports and specialists should subsequently be kept refreshed on changes made to these laws every once in a while with the goal that they can be very much educated on what is expected of them under all conditions (Gil, 2001). The California laws on misuse announcing accommodate why detailing is important, what ought to be accounted for, who should report, when revealing ought to be done and to whom it ought to be finished. The laws additionally give methods of recognizing rates that should report and signs to pay special mind to realize when to report. There are rules to decide doubt of the different types of misuse, for example, physical maltreatment, sexual maltreatment, disregard and psychological mistreatment among others (Summit, 1983). Corresponding to my musings on classification and securing the privileges of others, I am of the conclusion that it

Appraches in Business Psychology Essay Example | Topics and Well Written Essays - 500 words

Appraches in Business Psychology - Essay Example It is impacted by understanding. To see that a cerebrum structure or a mind procedure is associated with sexual orientation doesn't really infer that cerebrum contrasts cause sex contrasts. Sexual orientation contrasts in cerebrum structure may result from sex contrasts in learning, experience, and socialization. All the components of an association †the individuals, capacities, rules, overseeing authority, income and thoughts †must be pulled together around arrangement of essential policy centered issues. Along these lines, it gets important to recognize the issues with the goal that legislative issues in an association might be analyzed in unique terms. What issues are these? There are a few issues, yet the worry of this paper is the issue on the sexual orientation. Individuals are related inside the association; they should remain in a connection to one another. Truth be told, the individuals are the most significant component of the association. In the event that the association is a pyramid, at that point they were the base; in this manner, they are the very establishment of the gathering. Specialists may be at the top coordinating and driving the association yet they ought to have an intensive comprehension of this component to ready to run the association easily. Are for the most part individuals set on equivalent balance? Or then again are some better than the rest? In all actuality, regardless of how awful it is by all accounts, individuals would consistently be ordered by class in a general public where they have a place. This reality would reveal to us that business chiefs, so as to decipher their strategic vision, should consistently direct class investigation. Rowe (1994) affirmed that â€Å"the measures we mark as ecological (counting such focal ones as social class) may shroud hereditary variation† (p. 5). Furthermore, how do qualities make the earth? To Rowe, The appropriate response is that the qualities may develop a sensory system - and that hormones and synapses may then persuade practices bringing about the emotional overhaul of a domain.

Rohypnol (aka Flunitrazepam, Roofies) Fast Facts

Rohypnol (otherwise known as Flunitrazepam, Roofies) Fast Facts Rohypnol is the exchange name for Flunitrazepam, a medication that goes about as a soothing, muscle relaxant, sleep inducing, and upper. While Flunitrazepam is called Rohypnol when promoted by Roche, it is likewise sold by different organizations under the names Darkene, Flunipam, Flunitrazepam, Fluscand, Hipnosedon, Hypnodorm, Ilman, Insom, Nilium, Silece, and Vulbegal. Rohypnol might be taken as a pill or the pill might be squashed and grunted or added to a food or drink. What Does Rohypnol Resemble? Rohypnol is accessible as a pill, however the pill might be squashed and blended into food or beverages or it might be broken up in fluid and infused. The current type of the medication is engraved with 542 and provided as a 1-milligram portion in an olive green, oval tablet which contains a blue color that should be noticeable if the medication is added to a beverage. Prior to that, Rohypnol was sold as a white 2-milligram tablet. For what reason Do People Use Rohypnol? As a physician endorsed prescription, Rohypnol is utilized as a pre-sedative medicine and as a transient treatment for sleep deprivation. It might be utilized to treat the downturn coming about because of the utilization of cocaine, methamphetamine, and different energizers. As a recreational medication, Rohypnol (roofies) might be seen at dance club, gatherings, and raves. The medication has been utilized regarding assault and theft to cripple the person in question and keep the person in question from reviewing the wrongdoing. Rohypnol might be utilized to end it all. What Are the Effects of Rohypnol Use? The impacts of Rohypnol use might be felt inside 15 to 20 minutes of organization and may keep going for more than 12 hours. Side effects related with the utilization of Rohypnol incorporate tiredness, brought down circulatory strain, muscle unwinding, cerebral pain, visual aggravations, unsteadiness, slurred discourse, poor response time, disarray, memory impedance, annoyed stomach, maintenance of pee, tremors, and bad dreams. One symptom related with Rohypnol utilize is retroactive amnesia, where the individual who took the medication cannot recollect occasions that happened while affected by the medication. In spite of the fact that Rohypnol is a depressant, it might create edginess, chattiness, or forceful conduct. An overdose of Rohypnol produces sedation, disabled discourse and equalization, respiratory discouragement, and conceivably unconsciousness or passing. Why Is Rohypnol Illegal in the United States? It is illicit to make, sell, or use Rohypnol in the United States since taking it can create physiological and mental reliance and benzodiazepine withdrawal disorder. The medication is lawful in different nations (e.g., Mexico) and is pirated into the US through the mail or other conveyance administrations.

On Escaping

On Escaping I have five different takes of this video that were scrapped before I finally figured out what I wanted to say. I think thats how this semester is going, though. Im working on so many different types of projects that it takes me a couple tries to even get in the right mindset to make something for myself. Im still not sure this is good. But its beginning to delve into some of the things Ive been feeling this year about being a senior at MIT, and I think that was enough for me to finally finish and post it. I also gave up scripting this one, so my initial monologue was incredibly long and circuitous. Cutting it down to 7 minutes was a challenge, but if you get bored at around 3 or 4 minutes (as I think, statistically, people do with YouTube videos) I wont blame you. Self-deprecating disclaimers aside Things you will see in this video: angst, pretty shots of Cape Cod, not-so-pretty shots of mac and cheese, an attempt at a timelapse video of the sunset, thoughts on being in a creative slump, an extended vlog section at the end that tries to be less angsty/more relatable, awkward faces Things you wont see in this video: a cohesive narrative of what Ive been doing this semester, good lighting/focusing

Exploratory factor analysis - Free Essay Example

1. Introduction Hirsch (2005) introduced a new indicator for the assessment of the research performance of scientists. The proposed h-index is intended to measure simultaneously the quality and sustainability of scientific output, as well as, to some extent, the diversity of scientific research. The specific index attracted interest immediately and received great attention in the scientometrics literature. Not only it has found a wide use in a very short time, but also a series of articles were subsequently published either proposing modifications of the original h-index for its improvement, or implementations of the newly proposed index. The h-index (sometimes called the Hirsch index or the Hirsch number) is based on the distribution of citations received by a given researchers publications. By definition: A scientist has index h if h of his Np papers have at least h citations each, and the other (Np h) papers have at most h citations each. The index is designed to improve simpler measures such as the total number of citations or publications, to distinguish truly influential (in terms of citations) scientists from those who simply publish many papers. Among the advantages of this index is its simplicity, the fact that it encourages researchers to produce high quality work, the fact that it can combine citation impact with publication activity and that is also not affected by single papers that have many citations. Besides its popularity, a lot of criticism has been raised, too (see, e.g., Adler, Ewing, Taylor, 2009; Schreiber, 2007a; Vinkler, 2007; Meho, 2007), and various modifications and generalizations have appeared (see, e.g., Egghe, 2006a; Jin, Liang, Rousseau, Egghe, 2007; Schreiber, 2007b, 2008b; Sidiropoulos, Katsaros, Manolopoulos 2006; Tol, 2009). The h-index is robust to the numbers of citations received by the papers belonging to the h-core (i.e. the papers receiving h or more citations). To relax this robustness, various modifications have appeared in the literature, e.g. the g-index (Egghe, 2006), the A-index (Jin, 2006), the R-index (Jin et al., 2007), and the hw-index (Egghe and Rousseau, 2008). Since the suggestion of the Hirsch index a lot more h-type variants have been devised in order to overcome this robustness [e.g. the g-index (Egghe, 2006), the A-index (Jin, 2006), the R-index (Jin et al., 2007), and the hw-index (Egghe and Rousseau, 2008)]. However, more and more voices argue against the usefulness of all these measures (see e.g. Bornmann et al., 2009b; Adler, Ewing, Taylor, 2009; Schreiber, 2007a; Vinkler, 2007; Meho, 2007). In the same vein, van Noorden (2010) states that many metrics correlate strongly with one another, suggesting that they are capturing much of the same information about the data they descr ibe. After a comparison of some of the more important variants, Bornmann, Mutz, and Daniel (2008) by performing exploratory factor analysis on a set of some of the most important h-type indices, including the h-index, conclude that indices can be categorized into two basic categories: those that came to the conclusion that essentially there are two types of indices, one type of indices that describe the most productive core of the output of a scientist and tell us the number of papers in the core (p. 836) while and those that the other indices depict the impact of the papers in the core (p. 836). In particular, theAccording to the authors, h-index and the g-index were classified as belonging to the first category, while the A-index and the R-index certainly belong to the second group. However, Bornmann et al. (2008) recommended a more thorough validation of their factor analysis results by using other data sets, especially from different fields of research. Schreiber, Malesios and Psarakis (2011) have shown that the distinction is not so evident for the citation records of 26 physicists, which were previously analyzed (Schreiber, 2008a and 2010b). Specifically, the authors utilized 7 bibliometric indices similar to the analysis of Bornmann et al. (2008), with the addition of standard indicators of quantity and impact, namely total number of publications n, total number of citations S and average number of citations . In particular, the nearly equal factor loadings for g in the exploratory factor analysis (EFA) of the raw data seemed to confirm verify the assumption (Schreiber, 2010a) that the g-index measures both, the quantity and the impact. However, this was not substantiated by the more comprehensive FA. Significant differences to previous analysesthe findings of Bornmann et al. (2008, 2009a, 2009b) have also been found. On the other hand, the results were mostly in agreement with those of Costas Bordons (2007; 2008) and Hendrix ( 2008). In the current article, we expand the previous analysis of Schreiber et al. (2011), by once again utilizing EFA using this time an augmented database consisting of a set of 17 indicators in addition to the h-index that have been proposed in recent years to improve the h-index, illustrated in detail by Schreiber (2010b). The actual values of these indices and some standard bibliometric indicators can be found in Appendix A and a short description in Appendix B. By this we attempt to clarify the properties and behaviour of the latter indices, by coming up with categorizations to latent items provided by the factor analysis. Moreover, we attempt to interpret the categorization of those indices based on previous research and the properties shared by the indices. In addition we investigate the claim that the g-index can be considered to measure both the actual scientific productivity and the scientific impact of a scientist that the g-index can be classified as a bibliometric index that can measure both the quantity of the productive core and the impact of the productive core, a property not shared by the majority of the other indices. 2. Data The data are from 26 present or former members of the Institute of Physics at Chemnitz University of Technology, including all full and associate professors as well as scientists who have been working as assistants or senior assistants (see Table A1). Data collection period covers the time period between January and February 2007, and were collected Data for the subsequent analysis were compiled between January and February 2007 from the ISI Thomson Web of Science (WoS) database Science Citation Index provided by Thomson Scientific in the Web of Science (WoS) (Schreiber, 2007a). The 26 datasets include the citation records of present or former members of the Institute of Physics at Chemnitz University of Technology, including all full and associate professors as well as scientists who have been working as assistants or senior assistants (see Table A1). The datasets for each researcher are indexed A, B, C, .., Z in conformity with the previous analysis (Schreiber, 2007a). In the current article we utilize 18 Hirsch-type indices, namely w, h(2), h, , A, f, t, g, , m, hw, R, Ä §, à Ã¢â€š ¬, e, s, hT and x (Maxprod). In parallel to the h- and g-indices we also utilize the interpolated and in compliance with the analysis of Schreiber (2010b). In addition the standard bibliometric indicators n, n1, S, c1, and for each dataset are also used.   3. Methodology Overview The statistical methodology of EFA can be used to examine for latent associations to identify the latent structure present in a set of observed variables, called the factors or latent variables. In this way, EFA and reduces dimensionality of the data to a few representative factors. , and therefore summarizes the multivariate information in a simpler form. Our aim with the specific paper, is to provide a valid In this paper we employ EFA in order to derive categorizations of the h-index and some of its variants, by employing EFA. Although the sample size used for the factor analysis can be regarded as relatively small (N=26), recent studies based on simulations have shown that when certain conditions exist the small sample size does not play a very important role and reliable FA results can be obtained. Specifically, presence of high communalities, when combined with a relatively small number of factors, tends to alleviate the effects of small sample sizes (Preacher MacCallum, 2002). (For more on this see Schreiber et al. 2011). even with very small sample sizes (e.g. N=10), when certain conditions exist. Specifically, presence of high communalities, when combined with a relatively small number of factors, tends to alleviate the effects of small sample sizes (Preacher MacCallum, 2002). Our analysis is a typical example of the above, since communalities are extremely high (way above 0.9 in almost all variables) and the number of factors is very small (2 factors), indicating that the analysis can produce valid and robust results. Bornmann et al. (2008) have utilized a logarithmic transformation to make their data more suitable for the factor analysis, since EFA techniques require that the variables should be approximately normally distributed. In our case there is no need for such transformation, since the non-parametric Kolmogorov-Smirnov test for normality has shown that only 3 out of the 18 items deviate from normality at a 5% level of statistical significance (see Table 1). Due to the small number of datasets one would expect that the index values are better described by Students t -distribution. We have performed the respective Kolmogorov-Smirnov test and the results in Table 1 confirm that untransformed data are even better described by the t -distribution than by the normal distribution. One possible reason for which in contrast to the data of Bornmann et al. (2008) our datasets of most of the 18 indices are approximately normally distributed is the diversity of the status of the selected researchers. Indeed, among the 26 researchers of our dataset there are young researchers with comparatively low index scores as well as senior professors with high values of most of their indices.   On the other hand, Bornmann et al. (2008) study the data of young researchers, whose index values are small and are concentrated within a very narrow field of values, with the direct consequence of giving extremely skewed distributions. Bornmann et al. (2008) have applied a logarithmic transformation to the raw data before utilizing FA, due to that EFA techniques require that all variables should be approximately normally distributed. To test for normality of our data, Table 1   presents results of Kolmogorov-Smirnov test for normality, which indicate that data are adequately normally distributed hence can be forced for conducting FA although are better described by the t-distribution than by the normal distribution . However, it is of interest to check if there are any discrepancies in the results between the raw data and the transformed ones, and thus additionally to the raw data x the logarithmically transformed shifted data (ln(x+1)) and the square-root transformed data were also utilized. The latter transformation was applied in this context by Costas Bordons (2008). Table 1: One-sample Kolmogorov-Smirnov test normal distribution Student distribution Mean Median Std. Dev. D p D p w 3.54 3.5 1.84 0.285 0.029* 0.215 n.s h(2) 5.00 5 1.60 0.230 n.s 0.188 n.s h 14.88 14 6.92 0.186 n.s 0.100 n.s 15.05 14 6.89 0.194 n.s 0.087 n.s A 33.55 29.5 17.8 0.217 n.s 0.096 n.s f 19.23 18 9.59 0.196 n.s 0.096 n.s t 20.92 20 10.44 0.192 n.s 0.120 n.s g 23.96 22 11.99 0.202 n.s 0.094 n.s 24.40 22.4 12.00 0.197 n.s 0.095 n.s m 25.58 23.25 12.95 0.198 n.s 0.107 n.s hw 19.03 17.75 9.20 0.186 n.s 0.092 n.s R 22.18 20.2 10.82 0.199 n.s 0.090 n.s Ä § 19.80 17.55 10.17 0.247 n.s 0.246 n.s à Ã¢â€š ¬ 4.55 2.95 4.93 0.273 0.041* 0.273 0.041* e 16.26 14.3 8.69 0.199 n.s 0.088 n.s s 12.60 10.9 6.64 0.252 n.s 0.252 n.s hT 24.72 22.35 12.32 0.247 n.s 0.247 n.s x 336.7 231 341.3 0.319 0.01* 0.250 n.s *significant at a 5% significance level n.s.: non-significant 3.1 Exporatory Factor Analysis Results We used a least squares factor extraction procedure since it has been argued that the least squares method performs betterwell for small sample sizeswhen using small datasets in comparison to other factor extraction methods such as maximum likelihood (see Ihara and Okamoto, 1985) and a rotated varimax transformation. In order to confirm the suitability of implementing EFA for the specific data and items selected, the EFA gave a value of 0.828 for the Kaiser-Meyer-Olkin (KMO) measure of model adequacy was used (Kaiser, 1974), indicating that the 18 indices are suitable for the factor analysis. It gave an adequate value of 0.828 for the raw data (see Table 2). The results gave also and similar values for the transformed data. Table 2: KMO test Raw indices x ln(x+1) à ¢Ã‹â€ Ã… ¡x KMO 0.828 0.822 0.841 p-value 0.001 0.001 0.001 Both the eigenvalue criterion (according to which one drops any factors with an eigenvalue of less than one) and the scree plot criterion indicated the existence of two major latent structures (factors) as the best solution for explaining the variability in the data. The two factors extracted accounted for 97.64%, 96.48% and 97.11% of the total variance in the raw, the log-transformed, and the square-root transformed data, respectively. For the raw data we see that the first factor accounted accounts for the 53.9% of the variance, the second factor for 43.7%. The factor loading matrix of factor loadings for the three models with the 18 indices can be found in Table 3. The corresponding communalities shared by the items are presented in Table 4. Table 3: Varimax rotated loading matrices (applying least squares extraction and Kaiser normalization) for the 3 EFA models with values above 0.7 given in bold face Indices Raw indices x ln(x+1) à ¢Ã‹â€ Ã… ¡x Component Component Component 1 2 1 2 1 2 w 0.711 0.629 0.688 0.588 0.702 0.597 h(2) 0.736 0.629 0.749 0.615 0.748 0.618 h 0.827 0.553 0.864 0.492 0.848 0.520 0.827 0.555 0.866 0.493 0.849 0.521 A 0.499 0.863 0.444 0.895 0.471 0.880 f 0.816 0.572 0.850 0.519 0.835 0.543 t 0.784 0.619 0.809 0.585 0.799 0.599 g 0.685 0.727 0.675 0.735 0.682 0.730 0.691 0.722 0.685 0.726 0.690 0.723 m 0.706 0.649 0.650 0.607 0.686 0.624 hw 0.691 0.721 0.677 0.734 0.686 0.726 R 0.678 0.733 0.675 0.734 0.678 0.733 Ä § 0.798 0.587 0.786 0.599 0.792 0.592 à Ã¢â€š ¬ 0.675 0.704 0.640 0.753 0.659 0.735 e 0.549 0.836 0.494 0.867 0.523 0.852 s 0.831 0.540 0.836 0.531 0.834 0.534 hT 0.835 0.550 0.851 0.523 0.844 0.534 x 0.770 0.591 0.744 0.619 0.762 0.599 Eigenvalues 9.701 7.873 9.614 7.752 9.717 7.763 Table 4: Variance explained by the 3 EFA models Indices Raw indices x ln(x+1) à ¢Ã‹â€ Ã… ¡x w 0.901 0.819 0.849 h(2) 0.938 0.939 0.942 h 0.990 0.989 0.989 0.993 0.994 0.993 A 0.994 0.998 0.997 f 0.994 0.991 0.993 t 0.997 0.997 0.997 g 0.999 0.996 0.998 0.999 0.997 0.998 m 0.919 0.791 0.860 hw 0.999 0.997 0.998 R 0.998 0.995 0.996 Ä § 0.981 0.976 0.978 à Ã¢â€š ¬ 0.951 0.977 0.975 e 0.999 0.996 0.998 s 0.982 0.981 0.981 hT 0.999 0.997 0.998 x 0.942 0.937 0.940 A possible interpretation is complicated, when choosing a value of 0.6 as a cut-off threshold for the factor loadings. Then for the raw data 9 items load on both factors, and only h, , Ä § , f, s, hT, x load on only the first factor, while A and e load strongly on the second factor. This confirms from another viewpoint the observation of Schreiber (2010) that A and e are closely related. This could be so, because these indices are the only ones solely based on h and total number of h-core citations S(h) (The related index R is based entirely on S(h)).   The observation of Schreiber (2010b) that the rank orders for w and h(2) are not very different, is reflected in the FA as both indicators share similar loadings on the two dimensions. Both indices along with h are based directly on citation counts for different core sizes. However, in the current analysis, h exhibits different behavior in comparison to w and h(2), since it loads solely on the first factor.   While A and g are both based on the average number of citations in the FA they appear different since A loads highly on the second factor whereas g loads more evenly on both latent structures. For the indices m, f, t and g depending on different average citation numbers we observe that three of them load on both dimensions, while f loads only on the first dimension. Similarly, the Ä §-index seems to differ from g, R and hw although all of them depend on the square root of the summed number of citations. The results of applying EFA to the transformed indices are very similar to the categorizations given for the raw data (using a cut-off value of 0.6), except that now w and t have shifted and fall into the first category, too. Choosing a threshold level 0.7 leads to a clear separation of all indices to the two dimensions for the raw data. Now, besides A and e, also g, , hw, R, à Ã¢â€š ¬ fall into the second category, the others into the first category. This is also true for the transformed data with the exception of m which is no more attributed to any of the factors. In contrast Bornmann et al. (2008) assign h and g to the same factor (measuring quantity of the research output). We cannot conclude in the wayas Bornmann et al. (2008) did that the first factor relates to the number of papers in the productive core of the researchers outputs, because indices like f and Ä § load on that factor, but are based on the number of citations in the core. On the other hand, all the indices loading on the second factor reflect the impact of the papers in that core, i.e. the quality dimension. The varimax rotation method is an orthogonal rotation method which assumes that the factors in the analysis are uncorrelated. We have additionally to the varimax orthogonal rotation method, utilized an oblique rotation method (specifically promax oblique rotation with least squares extraction) which in contrast to varimax does not require the factors to be uncorrelated. Such oblique rotation techniques have been favored against the use of orthogonal rotations There are several studies proposing the use of oblique rotation instead of orthogonal rotation methodology (see e.g. McCroskey and Young, 1979). The value of the promax rotation exponent k was set to 4 since that value provided more interpretable results (Tataryn, Wood and Gorsuch, 1999). Table 5: Promax oblique rotated loading matrices for the 3 EFA models with values above 0.5 given in bold face Indices Raw indices x ln(x+1) à ¢Ã‹â€ Ã… ¡x Component Component Component 1 2 1 2 1 2 w 0.599 0.384 0.595 0.347 0.612 0.343 h(2) 0.642 0.361 0.669 0.336 0.669 0.336 h 0.866 0.148 0.963 0.037 0.922 0.085 0.864 0.151 0.966 0.037 0.923 0.085 A 0.022 0.978 -0.067 1.055 -0.026 1.020 f 0.829 0.190 0.916 0.093 0.879 0.135 t 0.731 0.298 0.794 0.234 0.769 0.260 g 0.463 0.574 0.445 0.595 0.458 0.581 0.478 0.559 0.470 0.571 0.477 0.563 m 0.571 0.424 0.517 0.410 0.561 0.403 hw 0.479 0.559 0.450 0.590 0.468 0.571 R 0.446 0.591 0.447 0.593 0.449 0.589 [1] Ä § 0.784 0.233 0.744 0.277 0.764 0.255 à Ã¢â€š ¬ 0.468 0.545 0.374 0.654 0.416 0.611 e 0.132 0.885 0.039 0.964 0.085 0.926 s 0.884 0.123 0.883 0.125 0.887 0.120 hT 0.881 0.135 0.915 0.098 0.902 0.112 x 0.733 0.266 0.659 0.347 0.707 0.294 Eigenvalues 16.462 15.516 16.018 14.998 16.269 15.233 Applying a threshold value 0.5 the results in Table 5 provide a clear distinction of the indices, in full compliance with the results of varimax rotation (when using the threshold 0.7). 3.2 Expanded Set In an effort to further categorize h-type variants into indices based on quantity and quality Bornmann, Mutz, Daniel, Wallon and Ledin (2009) have re-run the EFA of Bornmann et al. (2008) including the standard bibliometric measures n and S. Along the same lines, we re-ran our EFA including besides n also other bibliometric measures, as in Schreiber (2010), namely the number of cited publications n1, the average number of citations per article = S/n, the highest number of citations c1, and the average number of citations in the elite set defined by Vinkler (2009) as the most cited nà Ã¢â€š ¬=à ¢Ã‹â€ Ã… ¡n papers. In this way we intend similarly to Bornmann et al. (2009b) a categorization of the indices to the quantity dimension (expressed by n and n1) and the impact dimension (expressed by and c1). The results of the EFA using the least squares extraction method and the varimax rotation with Kaiser normalization are presented in Tables 6, 7 and 8. Once again, the results suggested a factor structure with only two factors having an eigenvalue larger than 1, which both explain 96.1% of the variability in the data. Table 6: KMO test Raw indices x ln(x+1) à ¢Ã‹â€ Ã… ¡x KMO 0.66 0.716 0.657 p-value 0.001 0.001 0.001 Table 7: Varimax rotated loading matrices for the 3 EFA models with values above 0.685 given in bold face Indices Raw indices x ln(x+1) à ¢Ã‹â€ Ã… ¡x Component Component Component 1 2 1 2 1 2 w 0.685 0.648 0.678 0.591 0.687 0.605 h(2) 0.696 0.665 0.730 0.627 0.721 0.640 h 0.767 0.618 0.833 0.521 0.804 0.565 0.769 0.617 0.837 0.520 0.807 0.563 A 0.496 0.857 0.473 0.869 0.491 0.859 f 0.763 0.629 0.827 0.539 0.799 0.580 t 0.738 0.665 0.790 0.601 0.769 0.627 g 0.658 0.753 0.682 0.731 0.676 0.737 0.662 0.750 0.689 0.725 0.681 0.733 m 0.659 0.692 0.623 0.624 0.658 0.648 hw 0.666 0.745 0.682 0.730 0.681 0.732 R 0.647 0.763 0.674 0.738 0.666 0.746 Ä § 0.806 0.590 0.821 0.565 0.819 0.569 à Ã¢â€š ¬ 0.674 0.707 0.675 0.720 0.686 0.710 e 0.541 0.835 0.516 0.848 0.535 0.837 s 0.834 0.550 0.862 0.504 0.853 0.519 hT 0.812 0.580 0.856 0.515 0.840 0.540 x 0.788 0.585 0.781 0.584 0.799 0.569 n1 0.949 0.195 0.950 0.195 0.951 0.190 n 0.958 0.149 0.966 0.143 0.964 0.142 c1 0.346 0.843 0.302 0.853 0.316 0.847 0.369 0.926 0.377 0.927 0.378 0.926 0.107 0.938 0.157 0.952 0.139 0.953 Eigenvalues 11.13 10.975 11.742 10.188 11.614 10.423 Table 8: Variance explained by the 3 EFA models Indices Raw indices x ln(x+1) à ¢Ã‹â€ Ã… ¡x w 0.889 0.809 0.837 h(2) 0.926 0.926 0.930 h 0.970 0.965 0.965 0.972 0.971 0.969 A 0.981 0.979 0.979 f 0.977 0.974 0.974 t 0.987 0.985 0.986 g 0.999 0.999 0.999 0.999 0.999 0.999 m 0.914 0.778 0.852 hw 0.998 0.998 0.998 R 0.999 0.999 0.999 Ä § 0.998 0.993 0.995 à Ã¢â€š ¬ 0.954 0.974 0.976 e 0.990 0.986 0.987 s 0.998 0.997 0.997 hT 0.996 0.998 0.997 x 0.963 0.951 0.961 n1 0.938 0.941 0.940 n 0.940 0.953 0.950 c1 0.830 0.819 0.817 0.993 0.999 0.999 0.891 0.931 0.927 From Table 7 we see that by selecting a threshold between 0.674 and 0.685, we get a clear distinction of all the raw indices, with the first dimension of the EFA comprising w, h(2), h, , f, t, Ä §, s, hT, x, n1, n while A, g, , m, hw, R, à Ã¢â€š ¬, e, c1,   , load on the second factor. The high loadings of n and n1 on the first factor and and on the second factor, mean that by including these standard bibliometric indicators into the analysis we have successfully enforced a distinction separation of between the quantity and the quality dimension. Results of the promax oblique rotation (with k = 3 and least squares extraction) in Table 9 show once again a more distinct separation to the two dimensions. Table 9: Promax oblique rotated loading matrices for the raw indices with values above 0.54 given in bold face Indices Raw indices x Component 1 2 w 0.548 0.481 h(2) 0.554 0.497 h 0.667 0.402 0.670 0.401 A 0.206 0.839 f 0.656 0.419 t 0.607 0.477 g 0.462 0.628 0.469 0.621 m 0.493 0.550 hw 0.477 0.613 R 0.443 0.645 Ä § 0.730 0.348 à Ã¢â€š ¬ 0.506 0.561 e 0.273 0.789 s 0.785 0.284 hT 0.742 0.333 x 0.710 0.350 n1 1.104 -0.222 n 1.139 -0.284 c1 0.023 0.895 0.012 0.988 -0.326 1.134 Eigenvalues 17.577 17.441 The obtained results suggest that the g-index (accordingly also ) contributes more in measuring the quality dimension, whereas the h-index (and accordingly ) measures mostly the quantity dimension. To achieve an even clearer categorization of the indices we have performed the analysis including also the total number of citations S, as this specific metric has been also utilized by Bornmann et al. (2008). The contribution of the indices to the two factors shown in Table 10 yields a clear distinction in full agreement with Table 7, if again the threshold value 0.685 is used. Table 10: Varimax rotated loading matrices for the raw indices with values above 0.685 given in bold face Indices Raw indices x Component 1 2 w 0.686 0.646 h(2) 0.694 0.664 h 0.767 0.616 0.769 0.616 A 0.499 0.855 f 0.763 0.627 t 0.739 0.663 g 0.659 0.752 0.663 0.748 m 0.661 0.691 hw 0.668 0.743 R 0.648 0.761 Ä § 0.807 0.588 à Ã¢â€š ¬ 0.681 0.703 e 0.543 0.834 s 0.835 0.548 hT 0.813 0.579 x 0.794 0.581 n1 0.951 0.192 n 0.959 0.146 S 0.782 0.581 c1 0.354 0.840 0.372 0.924 0.106 0.939 Eigenvalues 11.795 11.261 A rather surprising result is that S exhibits higher loading on the first factor, rather than on the second factor on which the other indicators that are based on the citations load strongly. That was already observed by Schreiber et al. (2011), and might be explained by the assumption that S correlates more strongly with n than with , since more papers attract more citations. This may also be an indication that S is not the best indicator for measuring quality. The same argument applies to Ä §, because it is proportional to . Thus it loads strongly on the first factor just like S. Most distinctive (except from the standard bibliometric indices) in terms of very high loadings are A and e belonging clearly in the group of indices measuring the impact of the productive core and Ä §, s, x and hT measuring the number of papers in the productive core. 4. Conclusions In this paper we have examined the relationship of the h-index with other related indices measuring research performance using exploratory factor analysis. We have shown, that for our dataset consisting of a wide variety of bibliometric indices, for most of the investigated indices a distinction was evident to one of the two basic dimensions of scientific performance, namely the quality and quantity of scientific output. In summary, two different groups of indices were identified according to the results of EFA. Generally, there was strong indication based on the results of the conducted EFA that most of the indices cannot be fully categorized in any of the two factors. However, for some of the indices there is a stronger tendency to describe the quantity of the productive core. Among these indices are the w, h(2), h, , f, t, Ä §, s, hT, and x. Especially for the h-index, both quantity and impact of articles are taken into account, however the analysis suggests that quantity of publications plays the most important role. In the same manner, for other indices there is a stronger tendency to describe the impact of the productive core, including the A, g, , m, hw, R, à Ã¢â€š ¬ and e. These results also confirm the results of Schreiber (2010a), who based on theoretical arguments suggests that g, A and R belong to the same category of indices, and contrast the different classifications between g and A, R by Bornmann et al. (2008). Nevertheless, the present investigation adds to the results derived by Schreiber et al. (2011), by generalizing the preliminary findings obtained using a set of 7 indices, this time by including most of the important h-type indices proposed to correct insufficiencies of the Hirsch index.

Book Report I Beat the Odds - 1156 Words

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