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The IBM Hybrid Cloud team is returned at it with yet one more win for design. I’m excited to announce that our design crew has been awarded the 2018 red Dot: communique Design Award for IBM SPSS information within the Interface Design class. This award is a continuation of the design achievements we have considered this past year, together with the A’Design Awards, IF Design Awards, and others. i am thrilled to look the difficult work of our designers and IBM Design continue to shine and make a change in commercial enterprise utility.
First developed within the 1990’s, the crimson Dot Award has been the revered overseas seal of striking design satisfactory. Designers, businesses, and businesses from forty five different countries took half during this year’s competition, totaling over eight,600 entries that underwent a 24 member jury.
“All those that development in the course of the difficult adjudication method to garner a pink Dot have each reason to be pleased with themselves, as the jury offers our award simplest to creations of excessive design exceptional. This makes me all of the greater delighted to congratulate the laureates sincerely on their hard-earned success.” — Professor Dr. Peter Zec, founder and CEO of the red Dot Award
Receiving this award became incredibly entertaining for our group and we're honored to be among the winners. here is an important fulfillment for our designers who labored on this product, and they faced an interesting and challenging adventure in working on this product.what is IBM SPSS?
IBM SPSS facts is a magnificent records analysis tool that is one of the most universal information applications. due to the fact that its inception in 1968, SPSS statistics has been revamped and redeveloped distinctive instances. Now the design crew at IBM has taken on the project of growing a very clean consumer adventure.
during this newest redecorate of IBM SPSS data, we implemented design pondering concepts by means of working intently with our clients and making sure this modernized version of SPSS data aligns with their wants. Our optimum aim became to create a magnificent device that isn't most effective easy and intuitive to make use of, however that our users can delight in.Our crew and Design strategy
The IBM SPSS design crew is a component of the IBM Design Studios in Boeblingen, Germany. The group is composed of a various neighborhood, with many members originating from different international locations and cultures. Some participants of the team had some background with records while others were working in this field for the primary time.
Following the principles of IBM Design thinking (study > mirror > Make), our team implemented a redesign that brings a far better center of attention on users for SPSS information. The design group carried out intensive analysis on the consumer base of SPSS statistics as a way to see how the application can improved meet their needs. The latest user base tiers from less skilled clients equivalent to students to extra expert clients reminiscent of information scientists or company experts. A key perception from the group’s analysis turned into that much less skilled users had been intimidated each by means of the math work and the complexity of the application.
the new designs focused on simplifying workflows, cutting back the overall complexity of the UI and interactions, and offering freshmen a simple on-boarding to information and to the product. a different important characteristic within the redecorate was a working towards book led through a character named Simon, who serves as an in-application ebook, assisting beginner users be aware distinctive capabilities and achieve their desires quicker.
The crew faced some entertaining challenges in redesigning a product of such complexity, and one that has also been around for thus decades. a huge success of the designers turned into making the product attainable and attractive to new users devoid of alienating decade-long, experienced users.a look Into the Future
The preview version of our new IBM SPSS data event was launched in March 2018, and made accessible to the public as a trial on the IBM think convention is Las Vegas, and considering June 26 , the new UI is commonly attainable to all SPSS information subscribers. This preview is simply the initial step, providing probably the most used statistical analyses, and basic capabilities for facts preparation, for presentation and for reporting results. Over here months the crew could be working so as to add extra aspects and capabilities with a purpose to meet experience wants of all of our consumer companies.not simply Updating — Redesigning
i am so extremely joyful to see a different Hybrid Cloud design team get hold of an international award for their work. IBM SPSS information is yet another example of how design is making an important change in the success of our products. As we continue to use design to create extra relatable and effective items, we are capable of give our clients the experiences that they need and want. I’m thrilled and proud to monitor the change that our design group is making on earth of commercial enterprise application, and that i can’t wait to look how we proceed to affect the lives of our clients.Award Winners:
I attended IBM’s inaugural suppose experience in Las Vegas ultimate week. This event, IBM’s biggest (estimated 30,000+ attendees!), focused on making your enterprise smarter and included keynotes and classes on such themes as artificial intelligence, records science, blockchain, quantum computing and cryptography. i used to be invited with the aid of IBM as a guest to share some insights from the perspective of a data scientist. beneath are just a few highlights of the experience.records Science the use of IBM SPSS
IBM SPSS is IBM’s set of predictive analytics products that tackle the total analytical procedure, from planning to records collection to evaluation, reporting and deployment. IBM celebrated the 50th anniversary of IBM SPSS with their new beta release of IBM SPSS statistics 25, the greatest beta liberate in its heritage. The up-to-date version contains new trends like e-book-equipped charts, MS workplace integration, Bayesian information and advanced records. additionally, they added a brand new person interface which is pretty slick.
i was brought to SPSS facts in college and have used it for each one of my analysis initiatives given that then. To be sincere, SPSS facts has aged better than I actually have! I have already begun using the new edition and am pretty excited concerning the new aspects and user interface. i will document about event in a later put up. try SPSS with a free 14-day trial.improving the client event
fresh studies have estimated that forty five% of agents are expected to enhance using artificial intelligence for consumer experience in the next three years, and fifty five% of marketers are focused on optimizing the customer journey to increase customer loyalty. additionally, eighty five% of all customer interactions with a company can be managed with out human interplay via 2020.
client adventure administration (CXM) is the technique of knowing and managing clients’ interactions with and perceptions about the company/company. IBM is aware that improving the client event is more and more becoming facts-intensive activity, and using the combined energy of facts and these days’s processing capabilities can support corporations model the strategies that affect the customer adventure. I attended a few sessions to study how IBM is leveraging the energy of IBM Watson to aid their purchasers with Watson Commerce and Watson client journey Analytics solutions. These solutions use the vigour of synthetic intelligence (e.g., predictive analytics) to enrich how groups can better control client relationships to enhance client loyalty and stream their company ahead.records Science Meets enhanced Analytics and Augmented truth
I saw a good demonstration of the intersection of records science, enhanced analytics and augmented reality. Getting from records to insights is the goal of information science efforts and, as facts sources continue to grow, we are able to need more suitable how you can get to those insights. Aginity is working with H2O.ai to reveal the way to enrich your predictions through augmenting public data with more suitable information (with derived attributes) and improved analytics to make superior predictions. using baseball facts, Ari Kaplan of Aginity stated that the advancements in predictive models could translate into thousands and thousands of greenbacks per participant. whereas his demo concentrated on the use of these applied sciences in baseball facts, the ideas are generalizable to any industry vertical, together with finance, healthcare and media.
on the equal demonstration station, Alfredo Ruiz, lead of the Augmented truth application at IBM Analytics, confirmed me how his team (IBM Immersive Insights) is incorporating augmented fact into information Science journey to aid organizations stronger be mindful their ever-increasing statistics units. I’m eager for seeing how his efforts in marrying augmented reality and facts science progress.
I had the privilege of interviewing Ari Kaplan of Aginity who talked about the work he is doing to increase how Aginity and H2O.ai is enhancing the records science process. check out what he has to claim under.
Don’t omit this interview with Ari Kaplan, a true “Moneyball” and neatly customary around important League Baseball, as he talks concerning the latest computing device discovering technologies powering nowadays’s baseball selections, and check out the incredible demo.
Posted by IBM information Science on Thursday, March 22, 2018statistics Science is a team recreation
I had the probability to talk with with many business experts who come to records science from a special point of view than I do. while I center of attention primarily on the statistics and mathematics points of facts science, many of my records peers method information science from a technological and programming attitude. basically, for an upcoming podcast, Dez Blanchfield and that i had been interviewed with the aid of Al Martin of IBM Analytics to discuss our respective roles in information science. This conversation turned into a lively one, and that i am eager for reliving that night as soon as the podcast is released. The base line is that records science requires such a diverse skill set that you just actually need to work with different americans who can complement your competencies.
This idea that records science is a group sport turned into put on full screen in an exciting session during which a couples therapist (Trisha Mahoney) helped get to the bottom of an argument between a knowledge science leader (Shadi Copty) and IT chief (Ryan Arbow). Asking probing questions, the counselor published that the facts science and IT leader have been at odds as a result of a lack of verbal exchange. She delivered them to IBM’s records Science adventure, an enterprise statistics science platform that enables them to effortlessly collaborate, use accurate open supply equipment and get their models into creation quicker.Analytics: Your competitive expertise
For me, IBM believe 2018 become all about making your enterprise smarter via analytics. basically, research shows that companies which are improved in a position to convey the vigour of analytics to undergo on their enterprise problems should be in an improved place to outperform their analytics-challenged rivals. This theory become illustrated via keynotes, classes and conversations. by using bringing distinct records science experts collectively to leverage the tools and techniques of AI and desktop/deep learning will support you circulate your company ahead. in case you were unable to attend the experience, which you could watch replays of many of the keynotes right here.
(Disclosure: IBM assisted me with shuttle fees to IBM feel 2018.)
IBM remaining week announced that the edition 22 unlock of SPSS statistics could be the last liberate of the product to have any part that runs on gadget i (IBM i). it's going to also be the ultimate liberate with the means to pull SQL records out of the DB2 for i database, which is arguably more destructive for any IBM shoppers that may need to run some statistical evaluation on their DB2/400 facts.
SPSS records is mature and multi-faceted utility equipment that offers clients a variety of superior statistical analysis capabilities. Surrounding the core evaluation component is a fleet of more than 50 add-on products that, among different issues, supply record distribution capabilities.
SPSS information has never run on IBM i itself, even when it became owned by SPSS. before it was purchased by means of IBM, SPSS supported the IBM i platform with just two leading product lines: the show off OLAP and Reporting tools (some of which have been got with the aid of aid/methods) and the Clementine facts mining application. however the core SPSS information equipment changed into always supported on more “mainstream” structures, like Unix, Linux, windows, and the S/390 mainframe, which is where SPSS statistics received its beginning so many years ago.
however, IBM did, apparently, support the SPSS Collaboration and Deployment features part on the IBM i platform, which is entertaining in its own correct. IBM rarely (if ever) touted this skill, which isn't extraordinary when you consider that it’s a narrowly concentrated niche product that plays a assist role to other items. In any adventure, IBM announced that the Collaboration and Deployment functions part in SPSS facts version 22 will not support the “device I” (a reputation that IBM stopped the use of in 2010).
most likely extra damaging is the indisputable fact that SPSS information version 22 marks the final edition of the application that should be able to use DB2 for i (DB2/400) as a supply for SQL information, even if the core SPSS records software is working (as it have to) on one more server platform.
IBM i specialists are aware of relocating construction facts from DB2/four hundred onto different structures where it may also be analyzed. however, interestingly so few SPSS data purchasers have been the usage of this capacity that it now not made experience to retain it.
obviously there are alternative ways to get information off the IBM i server and into SPSS facts. after all, the SQL statistics average is meant to deliver stronger statistics interoperability amongst systems. however the proven fact that it’s now not worth IBM’s effort to maintain this SQL channel open to the IBM i server isn't a great signal.
For extra tips see application Announcement 213-309.
There’s No “i” In French Open Tennis
assist/programs Buys show off BI items from IBM
IBM to buy SPSS for $1.2 Billion
SPSS alterations information Miner’s identify, Drops system i help
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This article first appeared in IEEE Software magazine and is brought to you by InfoQ & IEEE Computer Society.
Embedded analytics and statistics for big data have emerged as an important topic across industries. As the volumes of data have increased, software engineers are called to support data analysis and applying some kind of statistics to them. This article provides an overview of tools and libraries for embedded data analytics and statistics, both stand-alone software packages and programming languages with statistical capabilities. I look forward to hearing from both readers and prospective column authors about this column and the technologies you want to know more about. —Christof Ebert
Big data has emerged as a key concept both in the information technology and the embedded technology worlds.1 Such software systems are characterized by a multitude of heterogeneous connected software applications, middleware, and components such as sensors. The growing usage of cloud infrastructure makes available a wealth of data resources; smart grids, intelligent vehicle technology, and medicine are recent examples of such interconnected data sources. We’re producing approximately 1,200 exabytes of data annually, and that figure is only growing.2,3 Such a massive amount of unstructured data presents enormous and mounting challenges for business and IT executives.
Big data is defined by four dimensions: volume, source complexity, production rate, and potential number of users. The data needs to be organized to transform the countless bits and bytes into actionable information—the sheer abundance of data won’t be helpful unless we have ways to make sense out of it. Traditionally, programmers wrote code and statisticians did statistics. Programmers typically used a general-purpose programming language, whereas statisticians plied their trade using specialized programs such as IBM’s SPSS (Statistical Package for the Social Sciences). Statisticians pored over national statistics or market research usually only available to select groups of people, whereas programmers handled large amounts of data in databases or log files. Big data’s availability from the cloud to virtually everybody changed all that.
(Click on the image to enlarge it)
As the volumes and types of data have increased, software engineers are called more and more often to perform different statistical analyses with them. Software engineers are active in gathering and analyzing data on an unprecedented scale to make it useful and grow new business models.1 As an example, consider proactive maintenance. We can continuously monitor machines, networks, and processes to immediately detect irregularities and failures, allowing us to correct them before damage occurs or the system comes to a standstill. This reduces maintenance costs in both material cost as well as human intervention. Often, processing and making sense of data is just part of a bigger project or is embedded in some software, configuration, or hardware optimization problem. Luckily, the community has responded to this need by creating a set of tools that bring some of statisticians’ magic to programmers—in fact, these are often more powerful than traditional statistics tools because they can handle volumes that are scales of magnitudes larger than old statistical samples.Technologies for Embedded Analytics and Statistics
There’s a wealth of software available for performing statistical analysis; Table 1 shows the most popular ones. They differ in the statistical sophistication required from their users, ease of use, and whether they’re primarily stand-alone software packages or programming languages with statistical capabilities.
With the exception of D3, all entries in the table provide facilities for carrying out advanced statistics, such as multivariate and time-series analysis, either by themselves or via libraries. Each one, though, has a particular focus that will better suit working on a given target problem. Python’s Pandas package, for instance, has good support for time-series analysis because part of it was written to cater to such analysis regarding financial data.The Python Statistics Ecosystem
The most popular general-purpose programming language for doing statistics today is Python. It’s always been a favorite for scientific computation, and several excellent Python tools are available for doing even complex statistical tasks. The fundamental scientific library in Python is NumPy. Its main addition to Python is a homogeneous, multidimensional array that offers a host of methods for manipulating data. It can integrate with C/C++ and Fortran and comes with several functions for performing advanced mathematics and statistics. Internally, it primarily uses its own data structures, implemented in native code, so that matrix calculations in NumPy are much faster than equivalent calculations in Python. SciPy, which builds on top of NumPy, offers a number of higher-level mathematical and statistical functions. SciPy deals again with NumPy’s arrays; these are fine for doing mathematics but a bit cumbersome for handling heterogeneous data with possibly missing values. Pandas solves that problem by offering a flexible data structure that allows easy indexing, slicing, and even merging and joining (similar to joins between SQL tables). One attractive setup involves using iPython, an interactive Python shell with commandline completion, nice history facilities, and many other features that are particularly useful when manipulating data. Matplotlib can then visualize the results.
The World Bank is a trove of information, and it makes a lot of its data available over the Web. For more sophisticated analysis, the public can download data from the World Bank’s Data Catalog or access it through an API. The most popular dataset is the World Development Indicators (WDI). WDI contains, according to the World Bank, “the most current and accurate global development data available, and includes national, regional and global estimates.” WDI comes in two downloadable forms: Microsoft Excel and commaseparated values (CSV) files. (Because Microsoft Excel files aren’t suitable for programmatic analysis, we deal with the CSV files here.)
Figure 1. A program for calculating World Development Indicators correlations using Python. The program collects the top 30 most measured indicators, calculates the Spearman pairwise correlations, and shows the results graphically.
The WDI CSV bundle is a 42.5-Mbyte zipped archive. After downloading and unzipping it, you’ll see that the main file is called WDI_Data.csv. A good way to get an overview of the file contents is to examine it interactively. Because we’ll be using Python, the best way to interact with the tools that we’ll use is by launching a session of iPython, and then loading the data:
In : import pandas as pd
In : data = pd.read_csv(“WDI_Data.csv”)
The result, in data, is a DataFrame containing the data. Think of a DataFrame as a two-dimensional array with some extra features that allow for easy manipulation. In a DataFrame, data is organized in columns and an index (corresponding to the rows). If we enter
In : data.columns
we’ll get an output that shows the names of the columns: the country name, the code for that country, an indicator name, and an indicator code. These are followed by columns for each year from 1960 to 2012. Similarly, if we enter
In : data.index
we’ll see that the data contains 317,094 rows. Each row corresponds to the values of one particular indicator for one country for the years 1960 to 2012; years without values in a row indicate no measurement in that year for that indicator in that country. Let’s see first, how many indicators there are
In : len(data[‘Indicator Name’].unique())Out: 1289
and second, how many countries there are
In : len(data[‘Country Name’].unique())Out: 246
Now we have a problem to solve: Are the indicators independent among themselves, or are some of them related to others?
Because we measure indicators by year and by country, we must more precisely define the problem by de-ciding which parameters to retain as constant. In general, we get the best statistical results as our samples increase. It makes sense then to rephrase the problem: For the year in which we have most measurements, are the most measured indicators independent among themselves, or as some of them related to others? By “most measured indicators,” we mean those that have been measured in more countries. It turns out that we can find the answer to the question in about 50 LOC. Figure 1 contains the full program.
Lines 1–10 are imports of the libraries that we’ll be using. Line 11 reads the data. In line 13, we give the number of most measured indicators that we would like to examine. In line 15, we find the zero-based position of the first column with yearly measurements. After that, we’re able in line 17 to find the column with the most measurements (the year 2005). We then remove all data for which measurements aren’t available. In lines 20–26, we get the most measured indicators.
The actual statistical calculations start from line 28, where we prepare a table of ones to hold the result of the correlation values between each pair of indicators. In the loop that follows, we calculate each pairwise correlation and store it in the table we prepared. Finally, in lines 41–52, we display the results on screen and save them to a PDF file (see Figure 2). We take care to reverse the vertical order of the correlation matrix so that the most important indicator comes on the top of the matrix (lines 41 and 49).
The diagonal has perfect correlation—as it should, because we’re examining the same indicators. In addition to that, we do see that there are indicators that correlate with each other—some positively, even strongly so, and some negatively or very negatively.
Figure 2. World Development Indicators correlations matrix with Python created from the program in Figure 1.More Advanced Components in the Python Ecosystem
As Python has attracted interest from the research community, several specialized tools have emerged. Among them, Scikit-learn builds on NumPy, SciPy, and matplotlib and offers a comprehensive machine-learning toolkit. For very big datasets that follow a hierarchical schema, Python offers PyTables, which are built on top of the HDF5 library. This is a hot topic, and DARPA awarded US$3 million in 2013 to Continuum Analytics as part of the XDATA program to develop further Python data analytics tools. You can expect the ecosystem to keep evolving steadily over the next few years.The R Project for Statistical Computing
R is a language for doing statistics. You can think of Python bringing statistics to programmers and R bringing statisticians to programming. It’s a language centered on the efficient manipulation of objects representing statistical datasets. These objects are typically vectors, lists, and data frames that represent datasets organized in rows and columns. R has the usual control flow constructs and even uses ideas from object-oriented programming (although its implementation of object orientation differs considerably from the concepts we find in more traditional object-oriented languages). R excels in the variety of statistical libraries it offers. It’s unlikely that a statistical test or method isn’t already implemented in an R library (whereas in Python, you might find that you have to roll out your own implementation). To get an idea of what it looks like, Figure 3 shows the same program as Figure 1 and adopts the same logic, but using R instead of Python. Figure 4 shows the results.
Figure 3. A program similar to that in Figure 1 that calculates World Development Indicators correlations using R.Combining, Federating, and Integrating Embedded Analytics Technologies
The examples we give in this article are typical of the way different applications can be merged to handle big data. Data flows from the source (in some raw format) to a format acceptable to our statistical package. The package must have some means of manipulating and querying data so that we can get the data subsets that we want to examine. These are subject to statistical analysis. The results of the statistical analysis can be rendered in textual form or as a figure. We can perform this process on a local computer or via the Web (in which case data crunching and processing is performed by a server, and parameters, results, and figures go through a Web browser). This is a powerful concept, because a host of different settings, from an ERP framework to car diagnostic software, can export their data in simple formats like CSV—in fact, we would see a warning sign whenever we encounter a piece of software that doesn’t allow exporting to anything but closed and proprietary data formats.
To analyze your data in any way you will, you must first have access to it. So you should by all means select technologies that facilitate the exchange of data, either by simple export mechanisms or via suitable calls, for instance through a REST (representational state transfer) API.
Data is getting bigger all the time, so you must investigate whether the tool you’re considering will be able to handle your data. It’s not necessary for you to be able to process all the data in main memory. For instance, R has the big memory library, which lets us handle huge datasets by using shared memory and memory-mapped files. Also, make sure that the software package can handle not only big input but also big data structures: if table sizes are limited to 32-bit integers, for instance, you won’t be able to handle tables with 5 million entries.
In the examples above, the alert reader will have noticed that we’ve spent more code manipulating the data to bring it to the appropriate format for statistical analysis than on the statistical analysis per se, which was provided anyway by functions already written for us. Our examples were somewhat trite, so these ratios of preprocessing to actual processing might have been especially top-heavy, but the examples highlight the fact that data manipulation is usually as important (and demanding) as the analysis. In effect, real proficiency in R and NumPy/SciPy doesn’t come from mastery of statistics but from knowing how to work efficiently with the data structures they offer. And this is essentially work for programmers, not statisticians. Further reading is available elsewhere.4-7
Figure 4. World Development Indicators correlations matrix with R.References
1C. Ebert and R. Dumke, Software Measurement, Springer, 2007.2K. Michael and K.W. Miller, eds., Computer, vol. 46, no. 6, 2013.3T. Menzies and T. Zimmermann, eds., IEEE Software, vol. 30, no. 4, 2013.About the Authors
Panos Louridas is a consultant with the Greek Research and Technology Network and a researcher at the Athens University of Economics and Business. Contact him at firstname.lastname@example.org or email@example.com.
Christof Ebert is managing director at Vector Consulting Services. He’s a senior member of IEEE and is the editor of the Software Technology department of IEEE Software. Contact him at firstname.lastname@example.org.
This article first appeared in IEEE Software magazine. IEEE Software's mission is to build the community of leading and future software practitioners. The magazine delivers reliable, useful, leading-edge software development information to keep engineers and managers abreast of rapid technology change.
Temegne and Ngome; JEAI, 16(5): 1-8, 2017; Article no.JEAI.34111
2. Temegne NC, Mouafor BI, Ngome AF.
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cassava (Manihot esculentaCrantz)
collected in the humid forest and guinea
savannah agro-ecological zones of
Cameroon. Greener Journal of Agricultural
3. Ngome AF, Amougou MFC, Tata PI,
Ndindeng SA, Mfopou MYC, Mapiemfu-
Lamare D, et al. Effects of cassava
cultivation on soil quality indicators in the
humid forest of Cameroon. Greener
Journal of Agricultural Sciences. 2013;3:
4. FAO. Gestion de la fertilité des sols pour la
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subsaharienne. FAO (Food and Agriculture
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5. Howeler RH. Cassava mineral nutrition
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JM, Bellotti AC. (ed.). Cassava, biology,
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6. Temegne NC, Ngome AF, Fotso KA.
Influence de la composition chimique du
sol sur la teneur en éléments nutritifs et le
rendement du manioc (Manihot esculenta
Crantz, Euphorbiaceae) dans deux zones
agroécologiques du Cameroun.
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Chemical Sciences. 2015;9(6):2776-2788.
7. Akanza Kouadjo AP, Yao-Kouame A.
Fertilisation organo-minérale du manioc
(Manihot esculenta Crantz) et diagnostic
des carences du sol. Journal of Applied
Biosciences. 2011;46:3163–3172. French.
8. Ukaoma AA, Ogbonnaya CI. Effect of
inorganic mineral nutrition on tuber yield of
cassava (Manihot esculentaCrantz) on
marginal ultisol of South Eastern Nigeria.
Academia Journal of Agricultural
Research. 2013;1(9): 172-179.
9. Bakayoko S, Nindjin C, Dao D, Tschannen,
Girardin O, Assa A. Fumure organique et
productivité du manioc. (Manihot esculenta
Crantz) en Côte D’ivoire. Agronomie
Africaine. 2007;19 (3):271–279. French.
10. Uwah DF, Effa EB, Ekpenyong LE, Akpan
IE. Cassava (Manihot esculenta Crantz)
performance as influenced by nitrogen and
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Journal of Animal and Plant Sciences.
11. Mathias L, Kabambe VH. Potential to
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African Journal of Plant Science.
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of Nigeria. International Journal of
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Laboratory methods of soil and plant
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14. Heanes DL. Determination of organic C in
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18. Agbaje GO, Akinlosotu TA. Influence of
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Imagine that you’re taking a test in a large public hall. Obviously, your knowledge and confidence will determine your score, but could the number of people around you have an influence too? According to psychologists Stephen Garcia from the University of Michigan and Avishalom Tor from the University of Haifa, the answer is yes. They have found that our motivation to compete falls as the number of competitors rises, even if the chances of success are the same.
The simple act of comparing yourself against someone else can stoke the fires of competition. When there are just a few competitors around, making such comparisons is easy but they become more difficult when challengers are plentiful. As a result, the presence of extra contenders, far from spurring us on by adding extra challenge, can actually have the opposite effect. Garcia and Avishalom call this the “N-effect” and they demonstrated it through a number of experiments.
First, they showed that US students tended to score more highly in SAT tests in states where there were fewer people on average at each testing venue. For each state, they compared SAT scores in 2005 with the total number of test-takers divided by the number of venues, and adjusted the figures for factors such as education budget, general performance on the SATs and so on. A similar analysis of scores from the Cognitive Reflection Test (CRT) revealed the same pattern – a greater density of test-takers led to lower average scores.
Obviously, this is a very crude analysis. For a start, crowded testing venues could also be rife with distractions that could lie behind a dip in performance. Garcia and Avishalom knew that they had to come up with better evidence, so they ran an experiment.
They approached 74 students on their own, and asked them to complete a short quiz as quickly and accurately as possible. They were told that they were up against either 10 or 100 other students, and the top 20% would receive the princely sum of five dollars. Those who were pitched against a hypothetical 9 contenders completed the quiz in 29 seconds – significantly faster than the 33 seconds taken by those who were competing against 99.
This is clear example of the N-effect, of people behaving with different intents depending on how many others they thought they were competing against (none of whom were actually present). Garcia and Avishalom believe that the N-effect depends on people’s propensity to compare themselves against their peers. The easier and more tangible those comparisons are, the more fuel there is for competition.
To demonstrate that, they told 50 students that they would have a week to win $100 by adding as many Facebook friends as possible. They found that the students felt more motivated to compete when facing 10 competitors compared to 10,000, and they were also more likely to compare themselves against the others within the smaller contest. The number of competitors predicted the students’ motivations to compete, but that association disappeared after adjusting for their tendency to compare themselves with others.
This same experiment allowed them to rule out the possibility that the students were more motivated in the smaller group, simply because they thought the task would be easier. They certainly felt that way (albeit wrongly – in both cases, the prizes went to the top 20% and the students understood that) but it didn’t affect their behaviour. Adjusting for this perception of difficulty didn’t strongly affect the link between number of competitors and motivation.
Garcia and Avishalom admit that there are probably many other factors that lie behind the N-effect (and you may want to posit your own theories in the comments) but certainly, sizing yourself up against your peers is one of them. It’s also unclear how far the effect extends. What are the smallest group sizes where the effect becomes apparent? When groups get larger, do you need larger differences to stimulate the effect? And does the effect apply across all forms of competition?
The answers to these questions will have to wait, but for now, Garcia and Avishalom suggest a couple of areas where the N-effect should be considered. In competitive workplaces (such as sales teams), workers may be more motivated and productive if they work in small branch offices rather than in one large, central location. In the education sector, the N-effect suggests that students may try harder in smaller classrooms (quite apart from the benefits of increased individual attention), and that they may even score slightly higher in exams if they are tested in smaller venues.
Reference: Garcia, S., & Tor, A. (2009). The -Effect: More Competitors, Less Competition Psychological Science DOI: 10.1111/j.1467-9280.2009.02385.x
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