Elemental analysis of glass and ink by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser induced breakdown spectroscopy (LIBS)

The necessity of elemental analysis techniques to solve forensic problems continues to expand as the samples collected from crime scenes grow in complexity. Laser ablation ICP-MS (LA-ICP-MS) has been shown to provide a high degree of discrimination between samples that originate from different sources. In the first part of this research, two laser ablation ICP-MS systems were compared, one using a nanosecond laser and another a femtosecond laser source for the forensic analysis of glass. The results showed that femtosecond LA-ICP-MS did not provide significant improvements in terms of accuracy, precision and discrimination, however femtosecond LA-ICP-MS did provide lower detection limits. In addition, it was determined that even for femtosecond LA-ICP-MS an internal standard should be utilized to obtain accurate analytical results for glass analyses. In the second part, a method using laser induced breakdown spectroscopy (LIBS) for the forensic analysis of glass was shown to provide excellent discrimination for a glass set consisting of 41 automotive fragments. The discrimination power was compared to two of the leading elemental analysis techniques, μXRF and LA-ICP-MS, and the results were similar; all methods generated >99% discrimination and the pairs found indistinguishable were similar. An extensive data analysis approach for LIBS glass analyses was developed to minimize Type I and II errors en route to a recommendation of 10 ratios to be used for glass comparisons. Finally, a LA-ICP-MS method for the qualitative analysis and discrimination of gel ink sources was developed and tested for a set of ink samples. In the first discrimination study, qualitative analysis was used to obtain 95.6% discrimination for a blind study consisting of 45 black gel ink samples provided by the United States Secret Service. A 0.4% false exclusion (Type I) error rate and a 3.9% false inclusion (Type II) error rate was obtained for this discrimination study. In the second discrimination study, 99% discrimination power was achieved for a black gel ink pen set consisting of 24 self collected samples. The two pairs found to be indistinguishable came from the same source of origin (the same manufacturer and type of pen purchased in different locations). It was also found that gel ink from the same pen, regardless of the age, was indistinguishable as were gel ink pens (four pens) originating from the same pack.

Analysis of Housing Partnerships using the Balanced Scorecard framework

Housing Partnerships (HPs) are collaborative arrangements that assist communities in the delivery of affordable housing by combining the strengths of the public and private sectors. They emerged in several states, counties, and cities in the eighties as innovative solutions to the challenges in affordable housing resulting from changing dynamics of delivery and production. ^ My study examines HPs with particular emphasis upon the identification of those factors associated with the successful performance of their mission of affordable housing. I will use the Balanced Scorecard (BSC) framework in this study. The identification of performance factors facilitates a better understanding of how HPs can be successful in achieving their mission. The identification of performance factors is significant in the context of the current economic environment because HPs can be viewed as innovative institutional mechanisms in the provision of affordable housing. ^ The present study uses a mixed methods research approach, drawing on data from the IRS Form 990 tax returns, a survey of the chief executives of HPs, and other secondary sources. The data analysis is framed according to the four perspectives of BSC: the financial, customer, internal business, and learning and growth. Financially, revenue diversification affects the financial health of HPs and overall performance. Although HPs depend on private and government funding, they also depend on service fees to carry out their mission. From a customer perspective, the HPs mainly serve low and moderate income households, although some serve specific groups such as seniors, homeless, veterans, and victims of domestic violence. From an internal business perspective, HPs’ programs are oriented toward affordable housing needs, undertaking not only traditional activities such as construction, loan provision, etc., but also advocacy and educational programs. From an employee and learning growth perspective, the HPs are small in staff size, but undertake a range of activities with the help of volunteers. Every part of the HP is developed to maximize resources, knowledge, and skills in order to assist communities in the delivery of affordable housing and related needs. Overall, housing partnerships have played a key role in affordable housing despite the housing market downturn since 2006. Their expenses on affordable housing activities increased despite the decrease in their revenues.^

A comparison of upper division performance of community college transfer students to native university students

The purpose of this study was to examine the hypothesis that no differences existed in the upper division performance of academically excellent community college transfer students when compared to native university students. The relationship of enrollment patterns such as skipped terms, dropped terms, summer session utilization, college of major, credits attempted, credits received, test scores, and current status were also studied.^ The data were collected through a hand analysis of 673 student transcripts which provided the information for a database designed specifically for this study. The subjects were 229 transfers from Miami-Dade Community College and 444 natives from Florida International University. The students all began their studies in the lower division in the Fall term of 1982, 1983 or 1984 and eventually transferred to the upper division at FIU. This longitudinal study followed the upper division performance and enrollment patterns through the Spring term of 1991.^ Data analysis included chi-square for all categorical and numerical variables; t-tests were performed for the numerical variables. Correlation coefficients, Two-Way Analysis of Variance and Three-Way Crosstabulations were also used when indicated. There were significant differences among the upper division performance of community college transfer students and native university students for the graduation rate and the GPA range. A significant difference was also found between the math and essay CLAST scores, number of summer terms utilized, number of terms to graduation, current enrollment status, and credits attempted and received for the groups. ^

Advanced Placement and American education: A Foucauldian analysis of the Advanced Placement Program of the college board

Advanced Placement is a series of courses and tests designed to determine mastery over introductory college material. It has become part of the American educational system. The changing conception of AP was examined using critical theory to determine what led to a view of continual success. The study utilized David Armstrong's variation of Michel Foucault's critical theory to construct an analytical framework. Black and Ubbes' data gathering techniques and Braun and Clark's data analysis were utilized as the analytical framework. Data included 1135 documents: 641 journal articles, 421 newspaper articles and 82 government documents. ^ The study revealed three historical ruptures correlated to three themes containing subthemes. The first rupture was the Sputnik launch in 1958. Its correlated theme was AP leading to school reform with subthemes of AP as reform for able students and AP's gaining of acceptance from secondary schools and higher education. The second rupture was the Nation at Risk report published in 1983. Its correlated theme was AP's shift in emphasis from the exam to the course with the subthemes of AP as a course, a shift in AP's target population, using AP courses to promote equity, and AP courses modifying curricula. The passage of the No Child Left Behind Act of 2001 was the third rupture. Its correlated theme was AP as a means to narrow the achievement gap with the subthemes of AP as a college preparatory program and the shifting of AP to an open access program. ^ The themes revealed a perception that progressively integrated the program into American education. The AP program changed emphasis from tests to curriculum, and is seen as the nation's premier academic program to promote reform and prepare students for college. It has become a major source of income for the College Board. In effect, AP has become an agent of privatization, spurring other private entities into competition for government funding. The change and growth of the program over the past 57 years resulted in a deep integration into American education. As such the program remains an intrinsic part of the system and continues to evolve within American education. ^

Comparison of Cardiovascular Disease Risk Factors between Single and Partnered Mothers: Data from the 2006 Health Survey of Adults and Children in Bermuda

Women are a high-risk population for cardiovascular diseases (CVD); however relationships between CVD and subpopulations of mothers are sparse. A secondary data analysis of the 2006 Health Survey of Adults and Children in Bermuda was conducted to compare the prevalence of CVD risk factors in single (n=77) and partnered (n=241) mothers. A higher percentage of single mothers were Black (p25 kg/m2 (p=0.01) and reported high blood pressure (p=0.004) and high cholesterol (0.017). Single mothers were nearly three times (OR=2.66) more likely to experience high blood pressure and two times (OR= 2.22) more likely to have high cholesterol. Single mothers may benefit from nutrition education programs related to lowering CVD risk.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Development and Application of Covalent-Labeling Strategies for the Large-Scale Thermodynamic Analysis of Protein Folding and Ligand Binding

Thermodynamic stability measurements on proteins and protein-ligand complexes can offer insights not only into the fundamental properties of protein folding reactions and protein functions, but also into the development of protein-directed therapeutic agents to combat disease. Conventional calorimetric or spectroscopic approaches for measuring protein stability typically require large amounts of purified protein. This requirement has precluded their use in proteomic applications. Stability of Proteins from Rates of Oxidation (SPROX) is a recently developed mass spectrometry-based approach for proteome-wide thermodynamic stability analysis. Since the proteomic coverage of SPROX is fundamentally limited by the detection of methionine-containing peptides, the use of tryptophan-containing peptides was investigated in this dissertation. A new SPROX-like protocol was developed that measured protein folding free energies using the denaturant dependence of the rate at which globally protected tryptophan and methionine residues are modified with dimethyl (2-hydroxyl-5-nitrobenzyl) sulfonium bromide and hydrogen peroxide, respectively. This so-called Hybrid protocol was applied to proteins in yeast and MCF-7 cell lysates and achieved a ~50% increase in proteomic coverage compared to probing only methionine-containing peptides. Subsequently, the Hybrid protocol was successfully utilized to identify and quantify both known and novel protein-ligand interactions in cell lysates. The ligands under study included the well-known Hsp90 inhibitor geldanamycin and the less well-understood omeprazole sulfide that inhibits liver-stage malaria. In addition to protein-small molecule interactions, protein-protein interactions involving Puf6 were investigated using the SPROX technique in comparative thermodynamic analyses performed on wild-type and Puf6-deletion yeast strains. A total of 39 proteins were detected as Puf6 targets and 36 of these targets were previously unknown to interact with Puf6. Finally, to facilitate the SPROX/Hybrid data analysis process and minimize human errors, a Bayesian algorithm was developed for transition midpoint assignment. In summary, the work in this dissertation expanded the scope of SPROX and evaluated the use of SPROX/Hybrid protocols for characterizing protein-ligand interactions in complex biological mixtures.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.

Combining Information from Multiple Sources in Bayesian Modeling

Surveys can collect important data that inform policy decisions and drive social science research. Large government surveys collect information from the U.S. population on a wide range of topics, including demographics, education, employment, and lifestyle. Analysis of survey data presents unique challenges. In particular, one needs to account for missing data, for complex sampling designs, and for measurement error. Conceptually, a survey organization could spend lots of resources getting high-quality responses from a simple random sample, resulting in survey data that are easy to analyze. However, this scenario often is not realistic. To address these practical issues, survey organizations can leverage the information available from other sources of data. For example, in longitudinal studies that suffer from attrition, they can use the information from refreshment samples to correct for potential attrition bias. They can use information from known marginal distributions or survey design to improve inferences. They can use information from gold standard sources to correct for measurement error.

This thesis presents novel approaches to combining information from multiple sources that address the three problems described above.

The first method addresses nonignorable unit nonresponse and attrition in a panel survey with a refreshment sample. Panel surveys typically suffer from attrition, which can lead to biased inference when basing analysis only on cases that complete all waves of the panel. Unfortunately, the panel data alone cannot inform the extent of the bias due to attrition, so analysts must make strong and untestable assumptions about the missing data mechanism. Many panel studies also include refreshment samples, which are data collected from a random sample of new

individuals during some later wave of the panel. Refreshment samples offer information that can be utilized to correct for biases induced by nonignorable attrition while reducing reliance on strong assumptions about the attrition process. To date, these bias correction methods have not dealt with two key practical issues in panel studies: unit nonresponse in the initial wave of the panel and in the

refreshment sample itself. As we illustrate, nonignorable unit nonresponse

can significantly compromise the analyst's ability to use the refreshment samples for attrition bias correction. Thus, it is crucial for analysts to assess how sensitive their inferences---corrected for panel attrition---are to different assumptions about the nature of the unit nonresponse. We present an approach that facilitates such sensitivity analyses, both for suspected nonignorable unit nonresponse

in the initial wave and in the refreshment sample. We illustrate the approach using simulation studies and an analysis of data from the 2007-2008 Associated Press/Yahoo News election panel study.

The second method incorporates informative prior beliefs about

marginal probabilities into Bayesian latent class models for categorical data.

The basic idea is to append synthetic observations to the original data such that

(i) the empirical distributions of the desired margins match those of the prior beliefs, and (ii) the values of the remaining variables are left missing. The degree of prior uncertainty is controlled by the number of augmented records. Posterior inferences can be obtained via typical MCMC algorithms for latent class models, tailored to deal efficiently with the missing values in the concatenated data.

We illustrate the approach using a variety of simulations based on data from the American Community Survey, including an example of how augmented records can be used to fit latent class models to data from stratified samples.

The third method leverages the information from a gold standard survey to model reporting error. Survey data are subject to reporting error when respondents misunderstand the question or accidentally select the wrong response. Sometimes survey respondents knowingly select the wrong response, for example, by reporting a higher level of education than they actually have attained. We present an approach that allows an analyst to model reporting error by incorporating information from a gold standard survey. The analyst can specify various reporting error models and assess how sensitive their conclusions are to different assumptions about the reporting error process. We illustrate the approach using simulations based on data from the 1993 National Survey of College Graduates. We use the method to impute error-corrected educational attainments in the 2010 American Community Survey using the 2010 National Survey of College Graduates as the gold standard survey.

Advanced Placement and American Education: A Foucauldian Analysis of the Advanced Placement Program of the College Board

Advanced Placement is a series of courses and tests designed to determine mastery over introductory college material. It has become part of the American educational system. The changing conception of AP was examined using critical theory to determine what led to a view of continual success. The study utilized David Armstrong’s variation of Michel Foucault’s critical theory to construct an analytical framework. Black and Ubbes’ data gathering techniques and Braun and Clark’s data analysis were utilized as the analytical framework. Data included 1135 documents: 641 journal articles, 421 newspaper articles and 82 government documents. The study revealed three historical ruptures correlated to three themes containing subthemes. The first rupture was the Sputnik launch in 1958. Its correlated theme was AP leading to school reform with subthemes of AP as reform for able students and AP’s gaining of acceptance from secondary schools and higher education. The second rupture was the Nation at Risk report published in 1983. Its correlated theme was AP’s shift in emphasis from the exam to the course with the subthemes of AP as a course, a shift in AP’s target population, using AP courses to promote equity, and AP courses modifying curricula. The passage of the No Child Left Behind Act of 2001 was the third rupture. Its correlated theme was AP as a means to narrow the achievement gap with the subthemes of AP as a college preparatory program and the shifting of AP to an open access program. The themes revealed a perception that progressively integrated the program into American education. The AP program changed emphasis from tests to curriculum, and is seen as the nation’s premier academic program to promote reform and prepare students for college. It has become a major source of income for the College Board. In effect, AP has become an agent of privatization, spurring other private entities into competition for government funding. The change and growth of the program over the past 57 years resulted in a deep integration into American education. As such the program remains an intrinsic part of the system and continues to evolve within American education.

Elemental Analysis of Glass and Ink by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) and Laser Induced Breakdown Spectroscopy (LIBS)

The necessity of elemental analysis techniques to solve forensic problems continues to expand as the samples collected from crime scenes grow in complexity. Laser ablation ICP-MS (LA-ICP-MS) has been shown to provide a high degree of discrimination between samples that originate from different sources. In the first part of this research, two laser ablation ICP-MS systems were compared, one using a nanosecond laser and another a femtosecond laser source for the forensic analysis of glass. The results showed that femtosecond LA-ICP-MS did not provide significant improvements in terms of accuracy, precision and discrimination, however femtosecond LA-ICP-MS did provide lower detection limits. In addition, it was determined that even for femtosecond LA-ICP-MS an internal standard should be utilized to obtain accurate analytical results for glass analyses. In the second part, a method using laser induced breakdown spectroscopy (LIBS) for the forensic analysis of glass was shown to provide excellent discrimination for a glass set consisting of 41 automotive fragments. The discrimination power was compared to two of the leading elemental analysis techniques, µXRF and LA-ICP-MS, and the results were similar; all methods generated >99% discrimination and the pairs found indistinguishable were similar. An extensive data analysis approach for LIBS glass analyses was developed to minimize Type I and II errors en route to a recommendation of 10 ratios to be used for glass comparisons. Finally, a LA-ICP-MS method for the qualitative analysis and discrimination of gel ink sources was developed and tested for a set of ink samples. In the first discrimination study, qualitative analysis was used to obtain 95.6% discrimination for a blind study consisting of 45 black gel ink samples provided by the United States Secret Service. A 0.4% false exclusion (Type I) error rate and a 3.9% false inclusion (Type II) error rate was obtained for this discrimination study. In the second discrimination study, 99% discrimination power was achieved for a black gel ink pen set consisting of 24 self collected samples. The two pairs found to be indistinguishable came from the same source of origin (the same manufacturer and type of pen purchased in different locations). It was also found that gel ink from the same pen, regardless of the age, was indistinguishable as were gel ink pens (four pens) originating from the same pack.

Secure and Scalable Statistical Computation of Questionnaire Data in R

Collecting data via a questionnaire and analyzing them while preserving respondents’ privacy may increase the number of respondents and the truthfulness of their responses. It may also reduce the systematic differences between respondents and non-respondents. In this paper, we propose a privacy-preserving method for collecting and analyzing survey responses using secure multi-party computation (SMC). The method is secure under the semi-honest adversarial model. The proposed method computes a wide variety of statistics. Total and stratified statistical counts are computed using the secure protocols developed in this paper. Then, additional statistics, such as a contingency table, a chi-square test, an odds ratio, and logistic regression, are computed within the R statistical environment using the statistical counts as building blocks. The method was evaluated on a questionnaire dataset of 3,158 respondents sampled for a medical study and simulated questionnaire datasets of up to 50,000 respondents. The computation time for the statistical analyses linearly scales as the number of respondents increases. The results show that the method is efficient and scalable for practical use. It can also be used for other applications in which categorical data are collected.

Prescribing of Psychoactive Drugs for Older People in Nursing Homes: An Analysis of Treatment Culture

Background There is increasing interest in how culture may affect the quality of healthcare services, and previous research has shown that ‘treatment culture’—of which there are three categories (resident centred, ambiguous and traditional)—in a nursing home may influence prescribing of psychoactive medications. Objective The objective of this study was to explore and understand treatment culture in prescribing of psychoactive medications for older people with dementia in nursing homes. Method Six nursing homes—two from each treatment culture category—participated in this study. Qualitative data were collected through semi-structured interviews with nursing home staff and general practitioners (GPs), which sought to determine participants’ views on prescribing and administration of psychoactive medication, and their understanding of treatment culture and its potential influence on prescribing of psychoactive drugs. Following verbatim transcription, the data were analysed and themes were identified, facilitated by NVivo and discussion within the research team. Results Interviews took place with five managers, seven nurses, 13 care assistants and two GPs. Four themes emerged: the characteristics of the setting, the characteristics of the individual, relationships and decision making. The characteristics of the setting were exemplified by views of the setting, daily routines and staff training. The characteristics of the individual were demonstrated by views on the personhood of residents and staff attitudes. Relationships varied between staff within and outside the home. These relationships appeared to influence decision making about prescribing of medications. The data analysis found that each home exhibited traits that were indicative of its respective assigned treatment culture. Conclusion Nursing home treatment culture appeared to be influenced by four main themes. Modification of these factors may lead to a shift in culture towards a more flexible, resident-centred culture and a reduction in prescribing and use of psychoactive medication. 

Maintaining legacy data: saving Belfast Harbour (UK) tide-gauge data (1902-2010)

Tide gauge data are identified as legacy data given the radical transition between observation method and required output format associated with tide gauges over the 20th-century. Observed water level variation through tide-gauge records is regarded as the only significant basis for determining recent historical variation (decade to century) in mean sea-level and storm surge. There are limited tide gauge records that cover the 20th century, such that the Belfast (UK) Harbour tide gauge would be a strategic long-term (110 years) record, if the full paper-based records (marigrams) were digitally restructured to allow for consistent data analysis. This paper presents the methodology of extracting a consistent time series of observed water levels from the 5 different Belfast Harbour tide gauges’ positions/machine types, starting late 1901. Tide-gauge data was digitally retrieved from the original analogue (daily) records by scanning the marigrams and then extracting the sequential tidal elevations with graph-line seeking software (Ungraph™). This automation of signal extraction allowed the full Belfast series to be retrieved quickly, relative to any manual x–y digitisation of the signal. Restructuring variably lengthed tidal data sets to a consistent daily, monthly and annual file format was undertaken by project-developed software: Merge&Convert and MergeHYD allow consistent water level sampling both at 60 min (past standard) and 10 min intervals, the latter enhancing surge measurement. Belfast tide-gauge data have been rectified, validated and quality controlled (IOC 2006 standards). The result is a consistent annual-based legacy data series for Belfast Harbour that includes over 2 million tidal-level data observations.

Work-to-family spillover: Gender differences in Hungary

It is crucial to understand the role that labor market positions might play in creating gender differences in work–life balance. One theoretical approach to understanding this relationship is the spillover theory. The spillover theory argues that an individual’s life domains are integrated; meaning that well-being can be transmitted between life domains. Based on data collected in Hungary in 2014, this paper shows that work-to-family spillover does not affect both genders the same way. The effect of work on family life tends to be more negative for women than for men. Two explanations have been formulated in order to understand this gender inequality. According to the findings of the analysis, gender is conditionally independent of spillover if financial status and flexibility of work are also incorporated into the analysis. This means that the relative disadvantage for women in terms of spillover can be attributed to their lower financial status and their relatively low access to flexible jobs. In other words, the gender inequalities in work-to-family spillover are deeply affected by individual labor market positions. The observation of the labor market’s effect on work–life balance is especially important in Hungary since Hungary has one of the least flexible labor arrangements in Europe. A marginal log-linear model, which is a method for categorical multivariate analysis, has been applied in this analysis.