. 1. 1 Principal Components Analysis 17 3. 1. 2 Factor analysis 21 3. 1. 3 Cronbach Coefficient Alpha 26 3. 2
Grouping information on countries 28 3. 2. 1 Cluster analysis 28 3. 2. 2 Factorial k-means analysis 34 3. 3 Conclusions
and factor analysis 56 6. 1. 2 Data envelopment analysis and Benefit of the doubt 59 Benefit of the doubt approach 60 6. 1. 3 Regression approach
81 7. UNCERTAINTY AND SENSITIVITY ANALYSIS 85 7. 1 Set up of the analysis 87 7. 1. 1 Output variables of interest 87 7. 1. 2
7. 1. 5 Normalisation 88 7. 1. 6 Uncertainty analysis 89 7. 1. 7 Sensitivity analysis using variance-based techniques 91 7
and aggregating indicators into a composite and test the robustness of the composite using uncertainty and sensitivity analysis.
or imprecise assessment and use uncertainty and sensitivity analysis to gain useful insights during the process of composite indicators building, including a contribution to the indicators'quality definition and an appraisal of the reliability of countries'ranking.
and sensitivity analysis to increase transparency and make policy inference more defensible. Section 8 shows how different visualization strategies of the same composite indicator can convey different policy messages.
Factor analysis and Reliability/Item Analysis (e g. Coefficient Cronbach Alpha) can be used to group the information on the indicators.
Cluster analysis can be applied to group the information on constituencies (e g. countries) in terms of their similarity with respect to the different sub-indicators.
Clearly the ability of a composite to represent multidimensional concepts largely depends on the quality and accuracy of its components.
Different weights may be assigned to indicators to reflect their economic significance (collection costs, coverage, reliability and economic reason), statistical adequacy, cyclical conformity, speed of available data, etc.
such as 12 weighting schemes based on statistical models (e g. factor analysis, data envelopment analysis, unobserved components models), or on participatory methods (e g. budget allocation, analytic hierarchy processes).
thus higher weight could be assigned to statistically reliable data (data with low percentages of missing values, large coverage, sound values).
Uncertainty analysis and sensitivity analysis is a powerful combination of techniques to gain useful insights during the process of composite indicators building,
A combination of uncertainty and sensitivity analysis can help to gauge the robustness of the composite indicator
Sensitivity analysis (SA) studies how much each individual source of uncertainty contributes to the output variance. In the field of building composite indicators, UA is adopted more often than SA (Jamison and Sandbu, 2001;
The iterative use of uncertainty and sensitivity analysis during the development of a composite indicator can contribute to its well-structuring
etc. and to replicate sensitivity tests. 2. 1 Requirements for quality control As mentioned above the concept of quality of the composite indicators is not only a function of the quality of its underlying data (in terms of relevance, accuracy, credibility, etc.)
Factor analysis and Reliability/Item Analysis can be used complementarily to explore whether the different dimensions of the phenomenon are balanced well-from a statistical viewpoint-in the composite indicator.
The use of cluster analysis to group countries in terms of similarity between different sub-indicators can serve as:(
Cluster analysis could, thereafter, be useful in different sections of this document. The notation that we will adopt throughout this document is the following. tq
and how the interpretation of the components might be improved are addressed without further ado in the following section on Factor analysis. 3. 1. 2 Factor analysis Factor analysis (FA) has similar aims to PCA.
Principal components factor analysis is preferred most in the development of composite indicators (see Section 6), e g.
Assumptions in Principal Components Analysis and Factor analysis 1. Enough number of cases. The question of how many cases (or countries) are necessary to do PCA/FA has no scientific answer
Although social scientists may be attracted to factor analysis as a way of exploring data whose structure is unknown,
Principal components factor analysis (PFA), which is the most common variant of FA, is a linear procedure.
Note, however, that a variant of factor analysis, maximum likelihood factor analysis, does assume multivariate normality. The smaller the sample size, the more important it is to screen data for normality.
Moreover, as factor analysis is based on correlation (or sometimes covariance), both correlation and covariance will be attenuated when variables come from different underlying distributions (ex.,
a normal vs. a bimodal variable will correlate less than 1. 0 even when both series are ordered perfectly co).
Factor analysis cannot create valid dimensions (factors) if none exist in the input data. In such cases, factors generated by the factor analysis algorithm will not be comprehensible.
Likewise, the inclusion of multiple definitionally-similar sub-indicators representing essentially the same data will lead to tautological results. 8. Strong intercorrelations are required not mathematically,
but applying factor analysis to a correlation matrix with only low intercorrelations will require for solution nearly as many factors as there are original variables,
thereby defeating the data reduction purposes of factor analysis. On the other hand, too high inter-correlations may indicate a multi-collinearity problem
and collinear terms should be combined or otherwise eliminated prior to factor analysis. (a) The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is a statistics for comparing the magnitudes of the observed correlation coefficients to the magnitudes of the partial correlation coefficients.
The concept is that the partial correlations should not be very large if one is to expect distinct factors to emerge from factor analysis (see Hutcheson and Sofroniou, 1999, p. 224).
A KMO statistic is computed for each individual sub-indicator, and their sum is the KMO overall statistic.
or higher to proceed with factor analysis (Kaiser and Rice, 1974), though realistically it should exceed 0. 80
but common cut off criterion for suggesting that there is a multi 26 collinearity problem. Some researchers use the more lenient cutoff VIF value of 5. 0. c) The Bartlett's test of sphericity is used to test the null hypothesis that the sub-indicators in a correlation matrix are uncorrelated,
Note also, that the factor analysis in the previous section had indicated ENROLMENT as the sub-indicator that shares the least amount of common variance with the other sub-indicators.
Although both factor analysis and the Cronbach coefficient alpha are based on correlations among sub-indicators, their conceptual framework is different. 28 Table 3. 6. Cronbach coefficient alpha results for the 23 countries after deleting one subindicator (standardised values) at-a-time Deleted sub-indicator
2004) Success of software process implementation 3. 2 Grouping information on countries 3. 2. 1 Cluster analysis Cluster analysis (CLA) is given the name to a collection of algorithms used to classify objects
Cluster analysis has been applied in a wide variety of research problems, from medicine and psychiatry to archeology.
cluster analysis is of great utility. 29 CLA techniques can be hierarchical (for example the tree clustering),
or nonhierarchical when the number of clusters is decided ex ante (for example the k-means clustering).
To do so the clustering techniques attempt to have more in common with own group than with other groups, through minimization of internal variation while maximizing variation between groups.
the next step is to choose the clustering algorithm, i e. the rules which govern how distances are measured between clusters.
Single linkage (nearest neighbor. The distance between two clusters is determined by the distance between the two closest elements in the different clusters.
3 6 9 12 15 18 21 Figure 3. 3. Linkage distance versus fusion step in the hierarchical clustering for the technology achievement example.
A nonhierarchical method of clustering, different from the Joining (or Tree) clustering shown above, is the k-means clustering (Hartigan,
1975). ) This method is useful when the aim is that of dividing the sample in k clusters of greatest possible distinction.
Table 3. 8. K-means clustering for the 23 countries in the technology achievement case study Group1 (leaders) Group 2 (potential leaders) Group 3 (dynamic adopters
kmeans clustering (standardized data. Finally, expectation maximization (EM) clustering extends the simple k-means clustering in two ways:
Principal component analysis or Factor analysis) that summarize the common information in the data set by detecting non-observable dimensions.
On the other hand, the relationships within a set of objects (e g. countries) are explored often by fitting discrete classification models as partitions, n-trees, hierarchies, via nonparametric techniques of clustering.
or when is believed it that some of these do not contribute much to identify the clustering structure in the data set,
frequently carrying out a PCA and then applying a clustering algorithm on the object scores on the first few components.
because PCA or FA may identify dimensions that do not necessarily contribute much to perceive the clustering structure in the data and that,
Various alternative methods combining cluster analysis and the search for a low-dimensional representation have been proposed, and focus on multidimensional scaling or unfolding analysis (e g.,
A method that combines k-means cluster analysis with aspects of Factor analysis and PCA is presented by Vichi and Kiers (2001.
A discrete clustering model together with a continuous factorial one are fitted simultaneously to two-way data,
including Factor analysis, Coefficient Cronbach Alpha, Cluster analysis, is something of an art, and it is certainly not as objective as most statistical methods.
Available software packages (e g. STATISTICA, SAS, SPSS) allow for different variations of these techniques. The different variations of each technique can be expected to give somewhat different results
The advantage of the EM is its broadness (it can be used for a broad range of problems, e g. variance component estimation or factor analysis),
The summary indicators are obtained by means of factor analysis, in which each component of the regulatory framework is weighted according to its contribution to the overall variance in the data.
coverage, reliability and economic reason), statistical adequacy, cyclical conformity, speed of available data, etc. In this section a number of techniques are presented ranging from weighting schemes based on statistical models (such as factor analysis, data envelopment analysis, unobserved components models),
to participatory methods (e g. budget allocation or analytic hierarchy processes). Weights usually have an important impact on the value of the composite
thus higher weight could be assigned to statistically reliable data (data with low percentages of missing values, large coverage, sound values).
and factor analysis Principal component analysis (PCA) and more specifically factor analysis (FA)( Section 3) group together sub-indicators that are collinear to form a composite indicator capable of capturing as much of common information of those sub-indicators as possible.
For a factor analysis only a subset of principal components are retained (let's say m), the ones that account for the largest amount of the variance.
Rotation is a standard step in factor analysis, it changes the factor loadings and hence the interpretation of the factors leaving unchanged the analytical solutions obtained ex-ante and ex-post the rotation.
In the extreme case of perfect collinearity among regressors the model will not even be identified. It is argued further that
since it would imply separating the correlation due to the collinearity of indicators from the correlation of error terms
The role of the variability in the weights and their influence in the value of the composite will be the object of the section on sensitivity analysis (section 7). Table 6. 6. Weights for the sub-indicators obtained using 4 different methods:
equal weighting (EW), factor analysis (FA), budget allocation (BAL), and analytic hierarchy process (AHP) Patents Royalties Internet Tech exports Telephones Electricity Schooling University EW 0. 13 0. 13 0. 13
(or geometric) aggregations or non linear aggregations like the multi-criteria or the cluster analysis (the latter is explained in Section 3). This section reviews the most significant ones. 6. 2. 1 Additive methods The simplest
and Factor analysis is employed usually as a supplementary method with a view to examine thoroughly the relationships among the subindicators.
compatibility between aggregation and weighting methods. 27 Compensability of aggregations is studied widely in fuzzy sets theory, for example Zimmermann and Zysno (1983) use the geometric operator
and not importance coefficients 7. Uncertainty and sensitivity analysis The reader will recall from the introduction that composite indicators may send misleading,
A combination of uncertainty and sensitivity analysis can help to gauge the robustness of the composite indicator,
Uncertainty Analysis (UA) and Sensitivity analysis (SA. UA focuses on how uncertainty in the input factors propagates through the structure of the composite indicator
()c Rank CI will be an output of interest studied in our uncertainty sensitivity analysis. Additionally, the average shift in countries'rank will be explored.
and sensitivity analysis (both in the first and second TAI analysis), targeting the questions raised in the introduction on the quality of the composite indicator.
We anticipate here that a scatter-plot based sensitivity analysis will allow us to track which indicator when excluded affects the output the most.
such as the variance and higher order moments, can be estimated with an arbitrary level of precision that is related to the size of the simulation N. 7. 1. 7 Sensitivity analysis using variance-based techniques A necessary step
when designing a sensitivity analysis is to identify the output variables of interest. Ideally these should be relevant to the issue tackled by the model,
In the following, we shall apply sensitivity analysis to output variables()c Rank CI, and S R, for their bearing on the quality assessment of our composite indicator.
2000a, EPA, 2004), robust, model-free techniques for sensitivity analysis should be used for non linear models.
Variance-based techniques for sensitivity analysis are model free and display additional properties convenient for the present analysis:
and to explain 92 they allow for a sensitivity analysis whereby uncertain input factors are treated in groups instead of individually they can be justified in terms of rigorous settings for sensitivity analysis,
as we shall discuss later in this section. How do we compute a variance based sensitivity measure for a given input factor i X?
The usefulness of I s, Ti S, also for the case of non-independent input factors, is linked also to their interpretation in terms of settings for sensitivity analysis.
we show in Figure 7. 2 a sensitivity analysis based on the first order indices calculated using the method of Sobol'(1993) in its improved version due to Saltelli (2002).
Rep. of Variance of country rank Non-additive Expert selection Weighting Scheme Aggregation System Exclusion/Inclusion Normalisation Figure 7. 2. Sensitivity analysis results
, Rep. of Total effect sensitivity index Expert Weighting Aggregation Exclusion/Inclusion Normalisation Figure 7. 3. Sensitivity analysis results based on the total effect indices.
The sensitivity analysis results for the average shift in 100 ranking output variable (Equation 7. 2) is shown in Table 7. 2. Interactions are now between expert selection and weighing,
there is not much hope that a robust index will emerge, not even by the best provision of uncertainty and sensitivity analysis.
Robustness and sensitivity The iterative use of uncertainty and sensitivity analysis during the development of a composite indicator can contribute to its well-structuring.
Uncertainty and sensitivity analysis are suggested the tools for coping with uncertainty and ambiguity in a more transparent and defensible fashion.
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Zimmermann H. J. and Zysno P. 1983) Decisions and evaluations by hierarchical aggregation of information, Fuzzy sets and Systems, 10, pp. 243-260.129 APPENDIX TAI is made of a relatively small
Cluster analysis...74 European Competitiveness in KETS ZEW and TNO EN 4 Error! Unknown document property name.
Cluster analysis...125 4. 3. 1. Micro-and Nanoelectronics Europe: The Grenoble cluster...126 4. 3. 2. Micro-and Nanoelectronics Canada:
Cluster analysis...173 5. 3. 1. Industrial biotechnology cluster Europe: Cambridge (United kingdom...174 5. 3. 2. Technology cluster Non-Europe:
Cluster analysis...218 6. 3. 1. Photonics Europe: The Optical Technologies Berlin-Brandenburg cluster (Optecbb...219 6. 3. 2. Photonics Non-Europe:
Cluster analysis...269 7. 3. 1. Advanced Materials Europe: Wallonia's Plastiwin cluster...269 7. 3. 2. Technology cluster non-Europe:
All this complicates to foresee future market development and results in low accuracy of forecasts.
which further limits the accuracy of market forecasts. Chapter 2 Methodological Issues EN 41error! Unknown document property name.
Fourthly, establishing the accuracy of past market forecasts is complicated by either a lack of clear definitions of the technologies and products for
) The tagging exercise was undertaken retroactively resulting in a full coverage of all patents related to nanotechnology.
Cluster analysis Nanotechnology has the potential to impact and shape many other industries through its multiple application possibilities.
While regional or national clustering has certainly its merits and can be an important driver for advance in nanotechnology,
Cluster analysis Clustering can be viewed from three angles: production locations, research activity and investments indicating future (production) location.
Cluster analysis The geographical distribution of industrial biotechnology clusters can be summarised in four regions: West-and North Europe, American West coast, American East coast, and East asia.
Cluster analysis On a global level, production is located (increasingly in low-cost countries, predominantly in Asia. In 2005 Japan represented 32 percent, Europe 19 percent North america 15 percent, Korea 12 percent and Taiwan 11 percent of world production.
but also to local and national governmental initiatives that promote regional clustering activities (Sydow et al.,2007).
Cluster analysis Advanced materials clusters can be found all over the globe, but mainly in North america, Europe, Japan, Australia,
and its geography is spread across all the five Walloon provinces with an extended coverage to the Brussels region (see figure below).
Nordicity Group (2006), Regional/local industrial clustering: Lessons from abroad, Ottawa: National Research Council Canada.
4) Patent applications under the PCT (Patent Cooperation Treaty), at international phase, designating the EPO by country of residence of the inventor (s).(5) The coverage of the Rest of the World is not uniform for all indicators.
Historical Evolution of Creative Clusters‘Clustering'is a term that can be applied to a variety of human, animal, biological and scientific states.
Within this clustering the university and business enterprise may be supplemented by an incubation partner, typically separate from the main university campus and on the outskirts of the university town or city.
For example, these individuals have a close focus on the front office and innovation: 65%are engaged highly in helping develop new products
and systems of engagement (revenue-generating and firmly in the front office). Exceptional CIOS can play both of these roles,
He's now more closely engaged with the front office of the business, acts as the link between the firm's global IT strategy and its local implementation,
digital transformation 51%Discussing business performance with the executive management team 53%17%Seat at top table Involvement in innovation 50%Relationships to succeed Focus on front office IT-intensive industry CIO
The CIO of a major Chinese insurance company explains how her firm is working closely with the front office to give them the mobile tools they need to boost sales.
and making sure the front office understands what's possible. They also need to think differently in terms of integrating digital activities into their analog activities.
however, will require you to build tight relationships across the front office starting with the CMO
which already demonstrate a‘clustering'effect where there is a strong base of companies and research activity (e g.
and Contact Centres (front office: includes both inbound and outbound call services including problem resolution, information provision, technical support (through from Tier 1 to Tier 3) marketing, sales lead generation,
Wayne Danielson of the University of Texas applied artificial intelligence (AI) to create an early tool for generating computer-written haikus.
when learning analytics and artificial intelligence are used effectively to optimize and customize student engagement and learning in real time (Fournier, 2011).
While an increasing number of MOOCS integrate artificial intelligence and expert systems to provide student feedback and learning customization,
the ability of these systems to function effectively is limited largely to courses designed to advance subject matter mastery.
Educators need to develop new assessment methods using the unique capabilities of digital technology, from algorithms to artificial intelligence.
Digital tools using artificial intelligence can enable real-time customization of learning as they are beginning to do with some MOOCS.
The coalescence of learning analytics and artificial intelligence holds promise. Consider the case of Narrative Science (Northwestern university Innovation and New Ventors Office, 2014.
and insights through its proprietary artificial intelligence authoring system. The algorithms the system uses are highly effective
Analyses Structural equation modeling using LISREL 8. 52 was employed for validation of the scales through confirmatory factor analysis and for hypothesis testing.
For construct validation, a two-phase confirmatory factor analysis approach was conducted, as suggested by Anderson and Gerbing (1988).
Modern Factor analysis. Chicago, IL: University of chicago Press. Hax, A, . and N. Majluf (1991). The Strategy Concept and Process.
Particular attention was paid to the coverage of long distance routes serving large freight volumes by all transport modes apart from air.
The Commission does not guarantee the accuracy of the data included in this study. Neither the Commission nor any person acting on the Commission's behalf may be held responsible for the use
A provisional thematic clustering of DSI organisations is emerging, grouping activities into 6 macro clusters that capture the way DSI is growing and developing:(
and to use the Openspending API. Although the Openspending project has a strong focus on government finance,
open APIS, and citizen science such as Open Data Challenge and Open Cities that provide citizens with better public services,
social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning and e-petitions. The main technological trends in DSI 0100 200 300 400 Arduino Smart Citizen Kit Fairphone Safecast OPEN NETWORKS Tor Confine Guifi. net Smart
and from the environment The explosion of new types of data analytics and machine learning means that it is no longer only government
and standardised APIS is crucial for open innovation, as developers are able to access and use public data
The Internet ecosystem today is highly centralised The current Internet is dominated by a handful of mainly US companies that control all the layers of the ecosystem (app store, cloud, machine learning, devices),
Foray John Goddard Xabier Goenaga Beldarrain Mikel Landabaso Philip Mccann Kevin Morgan Claire Nauwelaers Raquel Ortega-Argilés Disclaimer The responsibility for the accuracy
existence and coverage of training on entrepreneurship and creative problem-solving; autonomy and transparency of education and research organisations;
Frequent coverage in the media helped the project to resonate in the local business community; Pilot projects:
through radio, television and newspaper coverage (iv) the distribution of customised brochures (v) the creation of a specialised project web site and (vi) the use of iconic companies in the region as ambassadors for the project.
DAE has set ambitious targets for high speed internet infrastructure across the Union by 2020: 100%coverage of EU households at 30 Mbps minimum+50%take-up subscriptions
identifying the needs for reaching ambitious population coverage and take-up targets of next generation networks (over 30 Mbps),
what other quantitative and qualitative information/methods have informed the strategy (e g. cluster analysis, value chain analysis,
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