When you go looking for me, I am not there : description by absence

When women don’t have access to public voices, their stories may be told through symbols and sewing, publicly viewed but understood only by an audience of intimates. My research builds upon my May 2016 residency in Assisi, Italy, and explores description through absence. Punto Assisi, an embroidery tradition predating the Renaissance, is still practised by women of Assisi. Uniquely, the subject matter is empty of detail. The negative space in Punto Assisi work can be seen as echoing the absence of information about the makers. Invisible and indispensable, women and their work have provided the fabric of human society throughout history, yet the names and faces of female artists and artisans are rarely documented. This embroidery style resonated with my interest in women's work and how ubiquitous and anonymous it is. Based on the concept of drawing with thread to manifest content, I explore description through absence, and honour the unknown makers of this art. Studio practice revealed insight into materiality, imagery, form design and palette. The haptic process of sewing gave insight into a universality of the experience of making, a connection crossing time, place and culture. The experience of the maker is highly individual and takes place in diverse contexts. The maker and their experience may be unknown, except to self, however the outcome, the product or the artwork may be indexical of a place, time or the maker, known or unknown. As such, unknown women makers have a presence in their works. The negative space in the uncoloured linen yields a presence and materiality that allows us to engage with what isn’t there. Absence is made material. Materiality, memory, narrative, and identity are themes emerging from this project. In my contemporary application of the style constraints yielded creative freedom. In absence, I found description.Master of Arts (Visual and Performing Arts) (Research

The Choice Between Fixed and Random Effects Models: Some Considerations for Educational Research

We discuss fixed and random effects models in the context of educational research and set out the assumptions behind the two approaches. To illustrate the issues, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference of the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. When the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.fixed effects, random effects, multilevel modelling, education, pupil achievement

The Choice between fixed and random effects models: some considerations for educational research.

We discuss the use of fixed and random effects models in the context of educational research and set out the assumptions behind the two modelling approaches. To illustrate the issues that should be considered when choosing between these approaches, we analyse the determinants of pupil achievement in primary school, using data from the Avon Longitudinal Study of Parents and Children. We conclude that a fixed effects approach will be preferable in scenarios where the primary interest is in policy-relevant inference about the effects of individual characteristics, but the process through which pupils are selected into schools is poorly understood or the data are too limited to adjust for the effects of selection. In this context, the robustness of the fixed effects approach to the random effects assumption is attractive, and educational researchers should consider using it, even if only to assess the robustness of estimates obtained from random effects models. On the other hand, when the selection mechanism is fairly well understood and the researcher has access to rich data, the random effects model should naturally be preferred because it can produce policy-relevant estimates while allowing a wider range of research questions to be addressed. Moreover, random effects estimators of regression coefficients and shrinkage estimators of school effects are more statistically efficient than those for fixed effects.fixed effects, random effects, multilevel modelling, education, pupil achievement

Effects of living near a new urban motorway on the travel behaviour of local residents in deprived areas: Evidence from a natural experimental study

We evaluated the effects of a new motorway built through deprived neighbourhoods on travel behaviour in residents. This natural experiment comprised a longitudinal cohort (n=365) and two cross-sectional samples (baseline n=980; follow-up n=978) recruited in 2005 and 2013. Adults from one of three study areas - surrounding the new motorway (South), an existing motorway (East), or no motorway (North) - completed a previous day travel record. Adjusted two-part regression models examined associations between exposure and outcome. Compared to the North, cohort participants in the South were more likely to undertake travel by any mode (OR 2.1, 95% CI 1.0–4.2) at follow-up. Within the South study area, cohort participants living closer to a motorway junction were more likely to travel by any mode at follow-up (OR 4.7, 95% CI 1.1–19.7), and cross-sectional participants living closer were more likely to use a car at follow-up (OR 3.4, 95% CI 1.1–10.7), compared to those living further away. Overall, the new motorway appeared to promote travel and car use in those living nearby, but did not influence active travel. This may propagate socioeconomic inequalities in non-car owners

Methods for analysing emerging data sources to understand variability in traveller behaviour on the road network

This thesis argues that while simplifications are a necessary part of the modelling process, there is a lack of empirical research to identify which types of variability should be included in our models, and how they should be represented. This research aims to develop methodologies to undertake empirical analyses of variability on the road network, focusing specifically on traveller behaviour. This is particularly timely given the emergence of rich new data sources. Firstly, a method is proposed for examining predictable differences in daily link flow profiles by considering both magnitude and timing. Unlike previous methods, this approach can test for statistically significant differences whilst also comparing the shapes of the profiles, by applying Functional Linear Models to transportation data for the first time. Secondly, a flexible, data-driven method is proposed for classifying road users based on their trip frequency and spatial and temporal intrapersonal variability. Previous research has proposed methodologies for identifying public transport user classes based on repeated trip behaviour, but equivalent methods for data from the road network did not exist. As there was not an established data source to use, this research examines the feasibility of using Bluetooth data. Spatial variability is examined using Sequence Alignment which has not previously been applied to Bluetooth data from road networks, nor for spatial intrapersonal variability. The time of day variability analysis adapts a technique from smart card research so that all observations are classified into travel patterns and, therefore, systematic and random variability can be measured. These network- and traveller-focused analyses are then brought together using a framework which uses them concurrently and interactively to gain additional insights into traveller behaviour. For each of the methods proposed, an application to at least one year of real world data is presented

Revisiting fixed- and random-effects models: some considerations for policy-relevant education research

The use of fixed (FE) and random effects (RE) in two-level hierarchical linear regression is discussed in the context of education research. We compare the robustness of FE models with the modelling flexibility and potential efficiency of those from RE models. We argue that the two should be seen as complementary approaches. We then compare both modelling approaches in our empirical examples. Results suggest a negative effect of special educational needs (SEN) status on educational attainment, with selection into SEN status largely driven by pupil level rather than school-level factors

Identifying road user classes based on repeated trip behaviour using Bluetooth data

© 2018 The Authors Analysing the repeated trip behaviour of travellers, including trip frequency and intrapersonal variability, can provide insights into traveller needs, flexibility and knowledge of the network, as well as inputs for models including learning and/or behaviour change. Data from emerging data sources provide new opportunities to examine repeated trip making on the road network. Point-to-point sensor data, for example from Bluetooth detectors, is collected using fixed detectors installed next to roads which can record unique identifiers of passing vehicles or travellers which can then be matched across space and time. Such data is used in this research to segment road users based on their repeated trip making behaviour, as has been done in public transportation research using smart card data to understand different categories of users. Rather than deciding on traveller segmentation based on a priori assumptions, the method provides a data driven approach to cluster together travellers who have similar trip regularity and variability between days. Measures which account for the strengths and weaknesses of point-to-point sensor data are presented for (a) spatial variability, using Sequence Alignment, and (b) time of day variability, using Model Based Clustering. The proposed method is also applied to one year of data from 23 fixed Bluetooth detectors in a town in northwest England. The data consists of almost 7.5 million trips made by over 300,000 travellers. Applying the proposed methods allows three traveller user classes to be identified: infrequent, frequent, and very frequent. Interestingly, the spatial and time of day variability characteristics of each user class are distinct and are not linearly correlated with trip frequency. The frequent travellers are observed 1–5 times per week on average and make up 57% of the trips recorded during the year. Focusing on these frequent travellers, it is shown that these can be further separated into those with high spatial and time of day variability and those with low spatial and time of day variability. Understanding the distribution of travellers and trips across these user classes, as well as the repeated trip characteristics of each user class, can inform further data collection and the development of policies targeting the needs of specific travellers

A statistical method for estimating predictable differences between daily traffic flow profiles

It is well known that traffic flows in road networks may vary not only within the day but also between days. Existing models including day-to-day variability usually represent all variability as unpredictable fluctuations. In reality, however, some of the differences in flows on a road may be predictable for transport planners with access to historical data. For example, flow profiles may be systematically different on Mondays compared to Fridays due to predictable differences in underlying activity patterns. By identifying days of the week or times of year where flows are predictably different, models can be developed or model inputs can be amended (in the case of day-to-day dynamical models) to test the robustness of proposed policies or to inform the development of policies which vary according to these predictably different day types. Such policies could include time-of-day varying congestion charges that themselves vary by day of the week or season, or targeting public transport provision so that timetables are more responsive to the day of the week and seasonal needs of travellers. A statistical approach is presented for identifying systematic variations in daily traffic flow profiles based on known explanatory factors such as the day of the week and the season. In order to examine day-to-day variability whilst also considering within-day dynamics, the distribution of flows throughout a day are analysed using Functional Linear Models. F-type tests for functional data are then used to compare alternative model specifications for the predictable variability. The output of the method is an average flow profile for each predictably different day type, which could include day of the week or time of year. An application to real-life traffic flow data for a two-year period is provided. The shape of the daily profile was found to be significantly different for each day of the week, including differences in the timing and width of peak flows and also the relationship between peak and inter-peak flows. Seasonal differences in flow profiles were also identified for each day of the week

Clinical observations associated with proven and unproven cases in the ESCRS study of prophylaxis of postoperative endophthalmitis after cataract surgery

Aims to describe cases of postoperative endophthalmitis in the European Society of Cataract and Refractive Surgeons (ESCRS) study of the prophylaxis of endophthalmitis, compare characteristics of unproven cases and cases proven by culture or polymerase chain reaction, and compare the characteristics with those in other reported series. Twenty-four ophthalmology units in Austria, Belgium, Germany, Italy, Poland, Portugal, Spain, Turkey, and the United Kingdom. Univariable and multivariable logistic regression models were used to analyze data forstatistical association of signs and symptoms in cases with proven or unproven endophthalmitis. Specific data describing characteristics of the cases were compared between the 2 types of cases. Data from 29 endophthalmitis cases were analyzed. Swollen lids and pain were statistically associated with proven cases of endophthalmitis on univariable regression analysis. Multivariable analysis indicated that swollen lids and an opaque vitreous were associated with proven cases. Five cases of endophthalmitis occurred in the cefuroxime-treated groups. No case of streptococcal infection occurred in the cefuroxime-treated groups. However, cases of infection due to streptococci showed striking differences in visual acuity and were associated with earlier onset. Characteristics in the 29 cases parallel results in previous studies, such as the Endophthalmitis Vitrectomy Study, although the addition of a control group in the ESCRS study elicited additional findings. Swollen lids, pain, and an opaque vitreous were statistically associated with proven endophthalmitis cases in the ESCRS study