Data analysis methods in optometry Part 4: an introduction to analysis of variance (ANOVA)

In any investigation in optometry involving more that two treatment or patient groups, an investigator should be using ANOVA to analyse the results assuming that the data conform reasonably well to the assumptions of the analysis. Ideally, specific null hypotheses should be built into the experiment from the start so that the treatments variation can be partitioned to test these effects directly. If 'post-hoc' tests are used, then an experimenter should examine the degree of protection offered by the test against the possibilities of making either a type 1 or a type 2 error. All experimenters should be aware of the complexity of ANOVA. The present article describes only one common form of the analysis, viz., that which applies to a single classification of the treatments in a randomised design. There are many different forms of the analysis each of which is appropriate to the analysis of a specific experimental design. The uses of some of the most common forms of ANOVA in optometry have been described in a further article. If in any doubt, an investigator should consult a statistician with experience of the analysis of experiments in optometry since once embarked upon an experiment with an unsuitable design, there may be little that a statistician can do to help.

Learning and change in interorganizational networks:the case for network learning and network change

The ALBA 2002 Call for Papers asks the question ‘How do organizational learning and knowledge management contribute to organizational innovation and change?’. Intuitively, we would argue, the answer should be relatively straightforward as links between learning and change, and knowledge management and innovation, have long been commonly assumed to exist. On the basis of this assumption, theories of learning tend to focus ‘within organizations’, and assume a transfer of learning from individual to organization which in turn leads to change. However, empirically, we find these links are more difficult to articulate. Organizations exist in complex embedded economic, political, social and institutional systems, hence organizational change (or innovation) may be influenced by learning in this wider context. Based on our research in this wider interorganizational setting, we first make the case for the notion of network learning that we then explore to develop our appreciation of change in interorganizational networks, and how it may be facilitated. The paper begins with a brief review of lite rature on learning in the organizational and interorganizational context which locates our stance on organizational learning versus the learning organization, and social, distributed versus technical, centred views of organizational learning and knowledge. Developing from the view that organizational learning is “a normal, if problematic, process in every organization” (Easterby-Smith, 1997: 1109), we introduce the notion of network learning: learning by a group of organizations as a group. We argue this is also a normal, if problematic, process in organizational relationships (as distinct from interorganizational learning), which has particular implications for network change. Part two of the paper develops our analysis, drawing on empirical data from two studies of learning. The first study addresses the issue of learning to collaborate between industrial customers and suppliers, leading to the case for network learning. The second, larger scale study goes on to develop this theme, examining learning around several major change issues in a healthcare service provider network. The learning processes and outcomes around the introduction of a particularly controversial and expensive technology are described, providing a rich and contrasting case with the first study. In part three, we then discuss the implications of this work for change, and for facilitating change. Conclusions from the first study identify potential interventions designed to facilitate individual and organizational learning within the customer organization to develop individual and organizational ‘capacity to collaborate’. Translated to the network example, we observe that network change entails learning at all levels – network, organization, group and individual. However, presenting findings in terms of interventions is less meaningful in an interorganizational network setting given: the differences in authority structures; the less formalised nature of the network setting; and the importance of evaluating performance at the network rather than organizational level. Academics challenge both the idea of managing change and of managing networks. Nevertheless practitioners are faced with the issue of understanding and in fluencing change in the network setting. Thus we conclude that a network learning perspective is an important development in our understanding of organizational learning, capability and change, locating this in the wider context in which organizations are embedded. This in turn helps to develop our appreciation of facilitating change in interorganizational networks, both in terms of change issues (such as introducing a new technology), and change orientation and capability.

Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter

Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words' sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.

Adapting sentiment lexicons using contextual semantics for sentiment analysis of twitter

Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words’ sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.

SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure. © 2014 Springer International Publishing.

Engineering education research - the UK perspective at a time of change

Engineering education in the United Kingdom is at the point of embarking upon an interesting journey into uncharted waters. At no point in the past have there been so many drivers for change and so many opportunities for the development of engineering pedagogy. This paper will look at how Engineering Education Research (EER) has developed within the UK and what differentiates it from the many small scale practitioner interventions, perhaps without a clear research question or with little evaluation, which are presented at numerous staff development sessions, workshops and conferences. From this position some examples of current projects will be described, outcomes of funding opportunities will be summarised and the benefits of collaboration with other disciplines illustrated. In this study, I will account for how the design of task structure according to variation theory, as well as the probe-ware technology, make the laws of force and motion visible and learnable and, especially, in the lab studied make Newton's third law visible and learnable. I will also, as a comparison, include data from a mechanics lab that use the same probe-ware technology and deal with the same topics in mechanics, but uses a differently designed task structure. I will argue that the lower achievements on the FMCE-test in this latter case can be attributed to these differences in task structure in the lab instructions. According to my analysis, the necessary pattern of variation is not included in the design. I will also present a microanalysis of 15 hours collected from engineering students' activities in a lab about impulse and collisions based on video recordings of student's activities in a lab about impulse and collisions. The important object of learning in this lab is the development of an understanding of Newton's third law. The approach analysing students interaction using video data is inspired by ethnomethodology and conversation analysis, i.e. I will focus on students practical, contingent and embodied inquiry in the setting of the lab. I argue that my result corroborates variation theory and show this theory can be used as a 'tool' for designing labs as well as for analysing labs and lab instructions. Thus my results have implications outside the domain of this study and have implications for understanding critical features for student learning in labs. Engineering higher education is well used to change. As technology develops the abilities expected by employers of graduates expand, yet our understanding of how to make informed decisions about learning and teaching strategies does not without a conscious effort to do so. With the numerous demands of academic life, we often fail to acknowledge our incomplete understanding of how our students learn within our discipline. The journey facing engineering education in the UK is being driven by two classes of driver. Firstly there are those which we have been working to expand our understanding of, such as retention and employability, and secondly the new challenges such as substantial changes to funding systems allied with an increase in student expectations. Only through continued research can priorities be identified, addressed and a coherent and strong voice for informed change be heard within the wider engineering education community. This new position makes it even more important that through EER we acquire the knowledge and understanding needed to make informed decisions regarding approaches to teaching, curriculum design and measures to promote effective student learning. This then raises the question 'how does EER function within a diverse academic community?' Within an existing community of academics interested in taking meaningful steps towards understanding the ongoing challenges of engineering education a Special Interest Group (SIG) has formed in the UK. The formation of this group has itself been part of the rapidly changing environment through its facilitation by the Higher Education Academy's Engineering Subject Centre, an entity which through the Academy's current restructuring will no longer exist as a discrete Centre dedicated to supporting engineering academics. The aims of this group, the activities it is currently undertaking and how it expects to network and collaborate with the global EER community will be reported in this paper. This will include explanation of how the group has identified barriers to the progress of EER and how it is seeking, through a series of activities, to facilitate recognition and growth of EER both within the UK and with our valued international colleagues.

Understanding change in the public sector:a local authority case study

Increasingly managers in the public sector are being required to manage change, but many of the models of change which are available to them have been developed from private sector experience. There is a need to understand more about how the change process unfolds in the public sector. A case study of change in one local authority over the period 1974-87 is provided. The events surrounding housing decentralisation and the introduction of community development are considered in detail. To understand these events a twofold model of change is proposed: a short wave model which explains a change project or event; and a long wave model which considers how these projects or events might be linked together to provide a picture of an organisation over a longer period. The short wave model identifies multiple triggers of change and signals the importance of mediators in recognising these triggers. The extent to which new ideas are implemented and the pace of their adoption is influenced by the balance of power within the organisation and the political tactics which are used. Broad phases in the change process can be identified, but there is not a simple linear passage through these. The long wave model considers the way in which continuity and change feed off one another. It suggests that periods of relative stability may be interspersed with more radical transformations as the dominant paradigm guiding the organisation shifts. However, such paradigmatic shifts in local government may be less obvious than in the private sector due to the diverse nature of the former.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

China's economic co-operation related investment:an investigation of its direction and some implications for outward investment

Chinese firms undertake large scale contracted projects in a number of countries under the auspices of economic cooperation. While there are suggestions that these activities are an extension of China's soft power aimed at facilitating Chinese foreign direct investment (FDI) in those countries, often for access to natural resources, there is no systematic analysis of this in the literature. In this paper, we examine China's economic cooperation related investment (ECI) over time. Our results suggest that the pattern of investment is indeed explained well by factors that are used in the stylised literature to explain directional patterns of outward FDI. They also demonstrate that the (positive) relationship between Chinese ECI and the recipient countries' natural resource richness is not economically meaningful. Finally, while there is some support for the popular wisdom that China is willing to do business with countries with weak political rights, the evidence suggests that, ceteris paribus, its ECI is more likely to flow to countries with low corruption levels and, by extension, better institutions.

Managing the threat of terrorism in British travel and leisure organizations

Purpose - To study how the threats of terrorism are being handled by a variety of UK companies in the travel and leisure sector in the UK in the post 9/11 era. Design/methodology/approach - A review of the literature of risk management in a world that is perceived to be more risky as a result of the terrorist attacks on the US on 11 September 2001 (9/11) is presented. Describes the application of theories of organizational resilience and institutions to frame an understanding of how managers make sense of terrorism risk and comprehend uncertainty. Reports a qualitative analysis of themes in interviews conducted with 25 managers from 6 unnamed organizations in the aviation industry (3 organizations) and the UK travel and leisure industry (3 organizations), representing a catering supplier, an airport, an airline, a tour company, a convention centre, and an arts and entertainment centre. Findings - The results indicated that the three organizations in the aviation industry prioritize threats from terrorism, whilst the three organizations in the leisure and travel sector do not, suggesting that the managers in the travel and leisure industry apply a probabilistic type of thinking and believe the likelihood of terrorism to be low. Reports that they give precedence to economic concerns and numerous other threats to the industry. Concludes that managers fall prey to the 'ludic fallacy', which conceives all odds as being calculable and hence managers conceive the terrorism risk as low while also expecting institutional factors to pre-empt and control terrorism threats, a reaction which the authors believe to be rather complacent and dangerous. Originality/value - Contributes to the research literature on risk management by revealing the gap in the ability of existing management tools and methodologies to deal with current and uncertain threats facing organizations due to terrorism.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Native language identification (NLID) for forensic authorship analysis of weblogs

This chapter introduces Native Language Identification (NLID) and considers the casework applications with regard to authorship analysis of online material. It presents findings from research identifying which linguistic features were the best indicators of native (L1) Persian speakers blogging in English, and analyses how these features cope at distinguishing between native influences from languages that are linguistically and culturally related. The first chapter section outlines the area of Native Language Identification, and demonstrates its potential for application through a discussion of relevant case history. The next section discusses a development of methodology for identifying influence from L1 Persian in an anonymous blog author, and presents findings. The third part discusses the application of these features to casework situations as well as how the features identified can form an easily applicable model and demonstrates the application of this to casework. The research presented in this chapter can be considered a case study for the wider potential application of NLID.

On stopwords, filtering and data sparsity for sentiment analysis of Twitter

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier's feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and substantially shrinking the feature space

Reducing the risk of sexually transmitted infections in genitourinary medicine clinic patients:A systematic review and meta-analysis of behavioural interventions

Objectives: Are behavioural interventions effective in reducing the rate of sexually transmitted infections (STIs) among genitourinary medicine (GUM) clinic patients? Design: Systematic review and meta-analysis of published articles. Data sources: Medline, CINAHL, Embase, PsychINFO, Applied Social Sciences Index and Abstracts, Cochrane Library Controlled Clinical Trials Register, National Research Register (1966 to January 2004). Review methods: Randomised controlled trials of behavioural interventions in sexual health clinic patients were included if they reported change to STI rates or self reported sexual behaviour. Trial quality was assessed using the Jadad score and results pooled using random effects meta-analyses where outcomes were consistent across studies. Results: 14 trials were included; 12 based in the United States. Experimental interventions were heterogeneous and most control interventions were more structured than typical UK care. Eight trials reported data on laboratory confirmed infections, of which four observed a greater reduction in their intervention groups (in two cases this result was statistically significant, p<0.05). Seven trials reported consistent condom use, of which six observed a greater increase among their intervention subjects. Results for other measures of sexual behaviour were inconsistent. Success in reducing STIs was related to trial quality, use of social cognition models, and formative research in the target population. However, effectiveness was not related to intervention format or length. Conclusions: While results were heterogeneous, several trials observed reductions in STI rates. The most effective interventions were developed through extensive formative research. These findings should encourage further research in the United Kingdom where new approaches to preventing STIs are urgently required.