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Mixed methods research is commonly defined as the combination and integration of qualitative and quantitative data. However, defining these two data types has proven difficult. In this article, I argue that qualitative and quantitative data are fundamentally different, and this difference is not about words and numbers but about condensation and structure. As qualitative data are analyzed with qualitative methods and quantitative data with quantitative methods, we cannot analyze one type of data with the other type of method. Quantitative data analysis can reveal new patterns, but these are always related to the existing variables, whereas qualitative data analysis can reveal new aspects that are hidden in the data. To consider data as quantitative or qualitative, we should judge these data as end products, not in terms of the process through which they come into being. Thus, quantitizing qualitative data results in quantitative data and the analysis thereof is quantitative, not mixed, data analysis. For mixed data analysis, both real, non-quantitized qualitative data and quantitative data are needed. As these quantitative data may be quantitized qualitative data, the implication is that, contrary to a common view, mixed methods research does not necessarily involve quantitative data collection.

Over the past decades, 'governing by numbers' has taken a flight in the higher education sector. Performance-based budgeting and quality assurance schemes orient universities to new objectives, while rankings have globalised the metrified observation of higher education at large. Where previously no indicators existed, they are being introduced; where indicators already existed, they are being standardised for purposes of comparison. This thematic issue aims to work towards a more comprehensive understanding of the growing diversity of quantification-based instruments in higher education sectors in three European countries. The effects of quantification are noticed at all levels of the higher education system, from policy makers at the top of the regulatory pyramid down to students and academic staff. Yet even quantifiers outside of the regulatory system, such as ranking and metrics organisations, may have an important bearing on the operation of the university organisation and the sector at large. Thus, an entire governance landscape emerges in which actors at various levels turn to numbers for guidance. The articles in this thematic issue analyse the life cycle of such numbers, from their origins, through to their production and finally, their consequences. This editorial outlines the central questions and overarching issues addressed by the thematic issue and introduces its various contributions.

Quantification theories assume that numbers govern and steer a policy field or an organisation. In order to steer successfully, however, the local interpretation of numbers takes centre stage as the meaning of numbers - and thus the way how actors respond to them - varies between systems or sectors. Empirically, this article reviews how a German university makes sense of political numbers and their implicit steering signals, and how quantification alters its organisational structures and reshapes the roles of academics. The article analyses the translation process distinguishing between three levels: the political discourse on university reform; the organisational adaptations; and the effects they have on the professional academic role. The article finds that the university has highly differentiated strategies to respond to the 'governance by numbers,' and that it has established independent number-based steering systems. We also find that such differentiation of programmes makes the university management more flexible, helping it deal with anticipated goal conflicts and unwanted allocative effects, but it also places serious strain on - and potentially overburns - the coordination provided by the university's central administration. We also find that academics have started to align their behavioural strategies towards fulfilling their organisational goals and that they tend to deviate from professional expectations. Discussing these differentiated strategies, this article shows how the differentiation of governance approaches also contributes to the university becoming an 'organisational actor.' These preliminary findings suggest the need for and potential direction of further investigations.

The cybernetic dream of regulatory 'dashboard control' has taken off in the German higher education system. Both government regulators and university managers are engaged in the creation of waves of increasingly fine-grained quantitative data. Yet a wide range of recent case studies of the German higher education sector attest that in spite of this 'datafication' frenzy, the impact of the collected data mass on regulatory and managerial decision-making capacities seems to have remained relatively limited. This article explores why, in spite of the considerable investment in quantitative data infrastructures in the German higher education sector, this did not result in significant overt analytical capacity building. It explores three hypotheses: 1) a legal hypothesis according to which quantification is curbed by legal protections under the Rechtsstaat; 2) a dysfunctionality hypothesis which holds that decision makers reject quantification as a flawed and impracticable pursuit; and 3) an egalitarian federalism hypothesis which argues that Germany's federal states seek to prevent commensurability to avoid comparison and competition. The article finds that, in spite of its inconspicuousness, quantification indeed does inform various central decision-making processes. However, different legal, political, and relational factors prompt decision makers to engage in a hybrid, tempered and, overall, untransparent application of numerical data.

Fire safety regulations in many countries require Fire Risk Assessment to be carried out for buildings such as workplaces and houses in multiple occupation. This duty is imposed on a \"Responsible Person\" and also on any other persons having control of buildings in compliance with the requirements specified in the regulations. Although regulations only require a qualitative assessment of fire risk, a quantitative assessment is an essential first step for performing cost-benefit analysis of alternative fire strategies to comply with the regulations and selecting the most cost-effective strategy. To facilitate this assessment, various qualitative, semi-quantitative and quantitative techniques of fire risk assessment, already developed, are critically reviewed in this book and some improvements are suggested. This book is intended to be an expanded version of Part 7: Probabilistic risk assessment, 2003, a Published Document (PD) to British Standard BS 7974: 2001 on the Application of Fire Safety Engineering Principles to the Design of Buildings. Ganapathy Ramachandran and David Charters were co-authors of PD 7974 Part 7. Quantitative Risk Assessment in Fire Safety is essential reading for consultants, academics, fire safety engineers, fire officers, building control officers and students in fire safety engineering. It also provides useful tools for fire protection economists and risk management professionals, including those involved in fire insurance underwriting.

Parenting during the COVID-19 pandemic is highly challenging, with parents having to meet various demands simultaneously. An increase in adverse childhood experiences (ACEs) has been widely predicted, but empirical evidence is still scarce. This study aimed to (1) generate representative data on pandemic-related stress, parental stress, general stress, parental subjective and mental health, and the occurrence of ACEs; (2) identify risk factors for an increase in ACEs, and (3) provide qualitative data on parents' experiences. A representative survey was conducted in Germany in August 2020 with 1024 parents of underage children (Mage = 41.70, 50.9% female). More than 50% of parents reported being stressed by social distancing and the closure of schools and childcare facilities. Parental stress increased significantly during the pandemic (d = 0.21). Subgroups of parents also reported very high levels of depressive symptoms (12.3%) and anxiety (9.7%). Up to one-third of the sample reported ACEs in the child's lifetime. In this group, 29.1% reported an increase in children witnessing domestic violence during the pandemic, and 42.2% an increase verbal emotional abuse. These families were characterized by higher parental stress, job losses, and younger parent and child age. Positive aspects of the pandemic related primarily to personal or family life (e.g. slower pace of life, increase in family time). While some parents coped well, a particularly negative pattern was observed in a subgroup of families that experienced an increase in ACEs. Parental stress emerged as important target point for interventions addressing the negative sequelae of the pandemic.

On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic 1 . The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective 2 , but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings 3 . Here, using epidemiological data on COVID-19 and anonymized data on human movement 4 , 5 , we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776–164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44–94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world. A model of the effects of different non-pharmaceutical interventions on the spread of COVID-19 in China suggests that a strategy involving the rapid implementation of a combination of interventions is most effective.

The metal-matrix composites/nano-composites (MMCs/MMNCs) reinforced with hard ceramic particulates have received a tremendous attention due to their potential improvements in physical and mechanical performances. In the present work, we have comprehensively collected currently available experimental data sets of Al-based MMCs/MMNCs and have carried out thorough analyses to quantitatively address the impacts of the reinforcement volume fractions, reinforcement particle sizes, and metal-matrix grain sizes on their mechanical properties including the yield strength, ultimate strength, and strain to failure of composites. We also performed a quantitative analysis on the strengthening mechanisms of Al MMNCs to reveal that the grain refinement can play a major role in increasing the strength of composites. Al-based MMC or MMNC materials generally exhibited an indirect relationship between the strength increase and strain-to-failure increase. The results include a critical comparison for the mechanical performance of particulate-reinforced composites for both pure and alloyed Al matrices to elucidate the contemporary status of Al MMC and MMNC materials.

The use of cardiac PET, and in particular of quantitative myocardial perfusion PET, has been growing during the last years, because scanners are becoming widely available and because several studies have convincingly demonstrated the advantages of this imaging approach. Therefore, there is a need of determining the procedural modalities for performing high-quality studies and obtaining from this demanding technique the most in terms of both measurement reliability and clinical data. Although the field is rapidly evolving, with progresses in hardware and software, and the near perspective of new tracers, the EANM Cardiovascular Committee found it reasonable and useful to expose in an updated text the state of the art of quantitative myocardial perfusion PET, in order to establish an effective use of this modality and to help implementing it on a wider basis. Together with the many steps necessary for the correct execution of quantitative measurements, the importance of a multiparametric approach and of a comprehensive and clinically useful report have been stressed.

With the broader availability of panel data, fixed effects (FE) regression models are becoming increasingly important in sociology. However, in some studies the potential pitfalls of these models may be ignored, and common critiques of FE models may not always be applicable in comparison to other methods. This article provides an overview of linear FE models and their pitfalls for applied researchers. Throughout the article, we contrast FE and classical pooled ordinary least squares (OLS) models. We argue that in most cases FE models are at least as good as pooled OLS models. Therefore, we encourage scholars to use FE models if possible. Nevertheless, the limitations of FE models should be known and considered.