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In the evaluation of most interventions in criminal justice settings, evaluators have no control over assignment to treatment and control/comparison conditions, which means that the treated and comparison groups may have differences that lead to biased conclusions regarding treatment effectiveness. Propensity score analysis can be used to balance the differences in the groups, which can be used in a number of ways to reduce biased conclusions regarding effectiveness. A review of propensity scoring studies was conducted for this chapter, where the limited number of evaluations of criminal justice interventions using these methods was identified. Due to the small number of these studies, research was also reviewed if propensity scoring had been employed to evaluate interventions that are similar to those in criminal justice systems. These studies are used as examples to demonstrate how the methods can be used to evaluate criminal justice interventions, the different ways propensity scores can be used to analyse treatment and comparison group differences, and the strengths and limitations of this approach. It is concluded that, while not appropriate for all interventions/settings, propensity score analysis can be useful in criminal justice arenas, at least to investigate the comparability of treatment and comparison groups, with suspected non-comparability being a common weakness of traditional quasi-experimental studies and frequently cited limitation in terms of drawing efficacy conclusions from such evaluations.

This paper investigates the influences of temporary contracts along several dimensions of well-being (physical and mental health, self-assessed health and happiness) for young Italian workers. Our paper contributes to the literature exploring some new aspects of the relationship between temporary jobs and well-being in a country not frequently analysed in previous literature. We focus on the gender gap in the well-being consequences of non-permanent jobs, the influence of financial support by family in reducing well-being effects caused by temporary contracts and the interaction between gender gap and family support. We find that temporary contracts are damaging in terms of psychological health and happiness mostly for young men and individuals without family economic support. On the other hand, women’s mental health is not affected by temporary contracts and they are even better off in terms of their mental health and well-being when receiving family economic support.

The risk-return relationship of real estate equities is of particular interest for investors, practitioners and researchers. The purpose of this paper is to examine, in an asset pricing framework, whether the systematic risk factors play a significantly different role in explaining the returns of listed real estate companies, compared to general equities.

Running the difference test of the Fama-French three-factor and the liquidity-augmented asset pricing model, the authors analyze the effect of the systematic risk factors related to market, size, BE/ME and liquidity in a time-series setting over the period July 1992 to June 2012. By applying the propensity score matching (PSM) algorithm, the authors bypass the “curse of dimensionality” of traditional matching techniques and identify a comparable control sample of general equities, in terms of the relevant firm characteristics of size, BE/ME and liquidity.

The empirical results indicate that European real estate equity returns load significantly differently on the size, value and liquidity factor, while the influence of the market factor seems to be equivalent. In addition, the authors find an economically and statistically significant underperformance of European real estate equities, after accounting for the diverging role of systematic risk factors. Running the conditional time-series regression, the authors further reveal that these findings are predominately caused by the divergent risk-return behavior of real estate equities in economic downturns.

Due to the diverging role of the systematic risk factors in pricing real estate equities, the authors provide evidence of potential diversification benefits for investors and portfolio managers.

This is the first real estate asset pricing study to dissect the unique risk-return relationship of real estate equities by employing propensity score matching.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

In model‐based recognition the 3D models of objects are stored in a model library during an offline phase. During the online recognition phase, a view of the scene is matched with the model library to identify the location and pose of certain library objects in the scene. Aims to focus on the process of 3D modeling and model‐based recognition.

This paper discusses the process of 3D modeling and model‐based recognition along with their potential applications in industry with a particular emphasis on robot grasp analysis. The paper also emphasises the main challenges in these areas and give a brief literature review.

In order to develop an automatic 3D model‐based object recognition system it is necessary to automate the process of 3D modeling and recognition. The challenge in automating the 3D modeling process is to develop an automatic correspondence technique. The core of recognition is the representation scheme. Recognition is an online process. Therefore, representation and matching must be very fast in order to facilitate real time recognition.

There are numerous applications of 3D modeling in a variety of areas ranging from the entertainment industry to industrial automation. Some of its applications include computer graphics, virtual reality, medical imaging, reverse engineering, and 3D terrain construction.

Provides information on 3D modeling which constitutes an important part of computer vision or robot vision.

In this paper, we review the process of “3D modeling” and “model‐based recognition” along with their potential industrial applications. We put a particular emphasis on the case scenario of robot grasp analysis for which 3D model‐based object recognition seems to be a more palpable choice compared with the conventional tactile sensors solutions. We also put a particular emphasis on the main challenges in the areas of 3D modeling and model‐based recognition and give a brief literature review of the latest research that was carried out to respond to these challenges.

Various types of short training programmes exist in Germany. This article aims to evaluate short training courses for welfare recipients and to detect which programme type works best with respect to different outcome indicators.

The author measures the impact of six short training programmes on the participants. She uses propensity score matching and large‐scale administrative data to find suitable comparison groups. She compares treatment to non‐participation as well as participation in different training types directly.

The results demonstrate that in‐firm training has large positive effects on individual employment prospects and stability. Furthermore, classroom skill training is more effective than other classroom types while application training is rather ineffective. Treating some of the application training participants with an alternative sub‐programme would improve the effectiveness.

The results show which training types work best. However, the results do not imply macro or cost‐benefit effects.

Several of the short training courses, mostly occupation‐specific sub‐programmes, are short and relatively inexpensive options to activate welfare recipients.

The paper analyses the effects of six short training programmes for welfare recipients that have not been analysed before. The sensitivity of the results is examined using a wide range of estimators and different outcomes.

The purpose of this paper is to develop a cost model by the variable sampling interval and optimization of the average cost per unit of time. The paper considers an economic–statistical design of the X̅ control charts under the Burr shock model and multiple assignable causes were considered and compared with three types of prior distribution for the mean shift parameter.

The design of the modified X̅ chart is based on the two new concepts of adjusted average time to signal and average number of false alarms for X̅ control chart under Burr XII shock model with multiple assignable causes.

The cost model was examined through a numerical example, with the same cost and time parameters, so the optimal of design parameters were obtained under uniform and non-uniform sampling schemes. Furthermore, a sensitivity analysis was conducted in a way that the variability of loss cost and design parameters was evaluated supporting the changes of cost, time and Burr XII distribution parameters.

The economic–statistical model scheme of X̅ chart was developed for the Burr XII distributed with multiple assignable causes. The correlated data are among the assumptions to be examined. Moreover, the optimal schemes for the economic-statistic chart can be expanded for correlated observation and continuous process.

The economic–statistical design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects; one of the proper alternatives may be the Burr XII distribution which is quite flexible. Yet, in Burr distribution context, only one assignable cause model was considered where more realistic approach may be to consider multiple assignable causes.

This study presents an advanced theoretical model for cost model that improved the shock model that presented in the literature. The study obviously indicates important evidence to justify the implementation of cost models in a real-life industry.

As the provision of public services in many advanced welfare states has increasingly come to be marked by competition, social enterprises have actively been encouraged by governments to become involved in the delivery of public services. While the evaluation of complex public health interventions has arguably become increasingly more sophisticated, this has not been the case where social enterprise is concerned: evaluation of the actual impacts of social enterprises remains significantly underdeveloped by comparison. This study aims to support the establishment of a robust evidence base for the use of social enterprise as a policy instrument.

This paper assesses the potential of three methodological approaches common in the evaluation of complex public health interventions and applies them to the complex realm of community-led social enterprise.

Only through the involvement of different comparator groups, based on the research questions addressed, would it be possible to disentangle the embedded characteristics of organisations such as social enterprises. Each of the methods adopted in this research is time-consuming and resource-intensive and requires the researcher to possess advanced skills. Public officials should recognise the complexity and resource-intensive nature of such evaluation and resource it accordingly. If the aim of policymakers is to understand the added value of social enterprise organisations, an integrative research approach combining different research methods and design should be implemented to improve generalisability.

This study applies a range of favoured approaches to evaluate “complex” public health interventions include systematic reviews, realist evaluation and quasi-experimental investigation. However, such evaluation approaches have rarely been applied before in the context of social enterprise.