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Poverty is one of the most significant economic and social problems that European countries have to face. In recent years, it has become widely accepted that poverty is a multidimensional concept and now many studies use indicators of deprivation to examine the phenomenon. The focus on financial resources alone does not capture people’s quality of life as being poor means a lack of access to resources enabling a minimum standard of living and participation in the society within which one belongs. The paper aims to discuss this issue.

Using a longitudinal component (2006-2010) of EU Statistics on Income and Living Conditions data on 26 European countries, the author apply a second-order confirmatory factor analysis to estimate deprivation. To describe the patterns of change over time and to evaluate the role of household characteristics in deprivation level, the author employ a set of multilevel growth curve models.

Three findings clearly stand out from my analysis. First, there is great variability in deprivation between European countries. Second, European countries show various patterns of change in deprivation over time. Third, households with different characteristics have quite different deprivation levels; moreover, the impact of household characteristics on deprivation can vary over time and between countries.

This paper sheds light on the importance of analysing deprivation from a longitudinal perspective and that financial resources alone does not capture people’s quality of life.

In the last five years, there has been an increased interest across the globe, and in the United Kingdom in particular, to define, conceptualise and measure learning gains. The concept of learning gains, briefly summarised as the improvement in knowledge, skills, work-readiness and personal development made by students during their time spent in higher education, has been hailed by some as an opportunity to measure ‘excellence’ in teaching. This chapter will review some of the common definitions and the methods employed in research on learning gains. Secondly, we will provide a critical evaluation of the computational aspects of learning gains (e.g., raw gain, normalised gain). Finally, we will critically reflect upon the lessons learnt and what is not yet known in terms of learning gains.

The purpose of this paper is to evaluate the violence-reduction effects following an FBI-led gang takedown in South Central Los Angeles.

The time series impact of the intervention was estimated using a Bayesian diffusion-regression state-space model designed to infer a causal effect of an intervention using data from a similar (non-targeted) gang area as a control.

A statistically significant 22 percent reduction in violent crime was observed, a reduction that lasted at least nine months after the interdiction.

The research method does make assumptions about the equivalency of the control area, though statistical checks are employed to confirm the control area crime rate trended similarly to the target area prior to the intervention.

The paper demonstrates a minimum nine-month benefit to a gang takedown in the target area, suggesting that relatively long-term benefits from focused law enforcement activity are possible.

Longer-term crime reduction beyond just the day of the intervention can aid communities struggling with high crime and rampant gang activity.

Few FBI-led gang task force interventions have been studied for their crime reduction benefit at the neighborhood level. This study adds to that limited literature. It also introduces a methodology that can incorporate crime rates from a control area into the analysis, and overcome some limitations imposed by ARIMA modeling.

This paper aims to introduce latent growth curve modeling (LGCM) as a statistical technique to analyze repeated measures of longitudinal data to researchers in hospitality management.

First, the basics and extensions of LGCM are explained. Second, this paper reviews three existing empirical hospitality research studies that could have benefitted from LGCM but did not use this methodology. Third, this paper provides an overview of two specific illustrative examples of how the current authors have already used LGCM for hospitality research.

Based on explaining the basics of LGCM, delineating two examples using LGCM method and presenting new research avenues that would use LGCM to advance theoretical knowledge, this paper shows how LGCM represents a leap forward in the promotion of more rigorous research in hospitality management.

This paper is the first in hospitality to call for research based on LGCM and provide hands-on demonstrations and an agenda for this methodology.

Examining multilevel effects of financial and social performance of microfinance institutions (MFIs), the authors aim to investigate microfinance mission drift from the trend effect. The authors also seek to move the literature forward by decomposing the performance variance at different levels and examining whether and how much each level of analysis matters.

Growth curve modeling and variance decomposition analysis were conducted using a dataset consisting of 17,953 observations of 2,902 microfinance institutions in 122 countries from 1999 to 2017.

The study's result shows no evidence of mission drift in the microfinance industry. While MFIs improve their economic returns, they also increase the depth of outreach. In addition, firm-level heterogeneity is the dominant effect which explains 44% of the variance in microfinance financial performance (ROA) and 39% of the variance in social performance (Depth of outreach). The country-level is more critical in explaining financial performance (ROA) than social performance (Depth of outreach), accounting for 11 and 32% of the total variance, respectively. In particular, the interplay between the country-level and organizational-category level accounts for 9 and 11% of the total variance in financial performance (ROA) and social performance (Depth of outreach), respectively.

This study’s multilevel analysis of microfinance performances moves the literature forward by responding to the debate on microfinance mission drift and providing a comprehensive overview of both social and financial performance. By focusing on the trend effect, the result of our models shows that MFIs improve both financial and social performance to fulfill dual missions. The microfinance business model becomes sustainable over time. The study's results of country effect and its interaction effect with different organizational categories reveal the prominence of a good policy design on MFI's mission fulfillment.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

To identify the typical multilevel issues in social science, as well as illustrate the theoretical basis, hierarchical models and empirical exemplars of multilevel paradigm.

Hierarchical and multilevel data are extremely common in social systems, but multilevel analysis is constrained by statistical techniques. With the development of social system theory and empirical methods such as hierarchical structure modeling and latent growth modeling, multilevel paradigm can be used to analyze multilevel data. So it is necessary to identify typical multilevel phenomena in social science and discuss multilevel modeling techniques.

This paper identifies four typical multilevel phenomena in social system study: hierarchical and clustered sampling, collective construct research, longitudinal repeated measures, and event history analysis. Hierarchical structure modeling and latent growth modeling are effective multilevel analysis techniques in social science because of their advantages in the integration of social system research.

The quality and availability of multilevel data are the main limitations regarding which model will be applied.

The paper can aid the provision of effective multilevel models to social workers.

This paper provides information on application of multilevel modeling in social science.

Over the last several decades, businesses have faced mounting pressures from diverse stakeholders to alter their corporate operations to become more socially and environmentally responsible. In turn, many firms appear to have responded by implementing more sustainable practices — measuring, documenting, and publishing annual CSR or sustainability reports to showcase how they are addressing important issues in this area, including: resource stewardship, waste management, greenhouse gas emission reductions, fair and safe labor practices, amongst other stakeholder concerns. And yet, research in this domain has not yet systematically examined whether businesses have, on the whole, changed their practices in tandem with the important changes in its institutional context over time. Have corporate CSR initiatives, in fact, been growing over the last 25 years or has the increased attention to CSR actually been much ado about nothing? In this chapter, we review the empirical literature on CSR to uncover that common measures of CSR such as the KLD do not support the concept that CSR practices have increased substantively over the last 25 years. We supplement this historical review by modeling the growth curves of CSR implementation in practice and find that the pace of positive change has indeed been glacial. More alarmingly, we also look at corporate social irresponsibility (CSiR) and find that, contrary to expectations, businesses have become more, not less, irresponsible during this same time period. Implications of these findings for theory are presented as are suggestions for future research in this domain.

The purpose of this paper is to test a multilevel, cross-classified model that seeks to illuminate the dynamic nature of relationships among leadership, teaching quality, and student learning in school improvement. The study's primary goal is to shed light on the paths through which leadership influences student learning. At the school level, the model examines the mediating effect of the school's instructional environment on leadership and student learning. At the classroom level, it examines how instructionally focussed leadership can moderate teacher effects on student learning. Then these multiple paths are examined in a single model that seeks to test and highlight the means by which leadership contributes to school improvement.

The current study employed a multilevel longitudinal data set drawn from 60 primary schools in one state in the USA. Using a cross-classification approach to quantitative modeling, the research analyzes the complex cross-level interactions that characterize school-level and classroom level practices that contribute to school improvement and student learning.

The results illustrate the utility of specifying multilevel relationships when examining the “paths” that link school leadership to student learning. First, leadership effects on student learning were fully mediated by the quality of the school's instructional environment. Second, the findings indicated that the classroom-related paths examined in this study directly influenced the measures of student math achievement. Third, the research found that instructionally focussed school leadership moderated the effect of individual teachers on student learning. Fourth, the results suggest that school leaders can enhance student outcomes by creating conditions that lead to greater consistency in levels of effectiveness across teachers.

The study makes substantive contributions to the global knowledge base on school improvement by testing and elaborating on the “paths” that link school leadership and student learning. More specifically, the findings offer insights into strategic targets that instructional leaders can employ to enhance teacher effectiveness and school improvement. Thus, these results both support and extend findings from prior cross-sectional and longitudinal studies of leadership and school improvement.

This is the first study that has tested a conceptualization of leadership for learning in a single “cross-classified longitudinal model” capable of capturing interactions among leadership, classroom teaching processes and growth in student learning. The research illustrates one “state-of-the-art” methodological approach for analyzing longitudinal data collected at both the school and classroom levels when studying school improvement.