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Event History Modeling, first published in 2004, provides an accessible guide to event history analysis for researchers and advanced students in the social sciences. The substantive focus of many social science research problems leads directly to the consideration of duration models, and many problems would be better analyzed by using these longitudinal methods to take into account not only whether the event happened, but when. The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus. Critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make recommendations regarding the implementation of duration modeling methods.

The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions. On the other hand, sequence analysis (SA) has made increasing inroads into the social sciences to analyze trajectories as holistic “process outcomes.” We propose an original combination of these two approaches called the sequence analysis multistate model (SAMM) procedure. The SAMM procedure allows the study of the relationship between time-varying covariates and trajectories of categorical states specified as process outcomes that unfold over time. The SAMM is a stepwise procedure: (1) SA-related methods are used to identify ideal-typical patterns of changes within trajectories obtained by considering the sequence of states over a predefined time span; (2) multistate event history models are estimated to study the probability of transitioning from a specific state to such ideal-typical patterns. The added value of the SAMM procedure is illustrated through an example from life-course sociology on how (1) time-varying family status is associated with women’s employment trajectories in East and West Germany and (2) how German reunification affected these trajectories in the two subsocieties.

In adult physical rehabilitation, the transformative learning theory presents the basis for a detailed model of Meaning Perspective Transformation in Physical Rehabilitation based on research evidence, with examples and cases, and presents directives for clinical applications. This book draws from the theoretical background of transformative learning in adult education (Mezirow) to discuss the expansion and transfer of knowledge to clinical rehabilitation and community health rehabilitation settings for rehabilitation interventions. It also presents ground breaking findings of a fifteen-year research project on the process of meaning perspective transformation with client/patient groups. The project generated the first model of transformative change in the field of physical rehabilitation, built on the results of successive, externally funded research projects completed for the first time with various client populations undergoing interventions in occupational therapy, physiotherapy, and other physical health rehabilitation disciplines. The book is written for clinicians, educators, and students of physical rehabilitation, as well as for those who are interested in helping a loved one deal with personal change. Published in English.

We examine whether corporate governance matters more for firms facing financial distress. We theorize that financial crisis changes the relative costs and benefits of governance mechanisms and that more independent and smaller boards become more valuable in distressed firms. We further hypothesize that CEO power becomes increasingly beneficial as concentrated power allows the firm to respond more rapidly to the crisis. Event-history analysis of the failure of publicly traded Internet firms over the period 2000–2002 confirms our hypotheses. Our results suggest that the association between governance and survival depends on firm and environmental context and that one-size-fits-all prescriptions for governance mechanisms are therefore likely to be ineffective.

Quotas to promote women's representation in the world's legislatures have spread to more than one hundred countries. The diffusion of gender quotas poses a puzzle since they have often been adopted in countries where women have low status. International influence and inducements best explain quota adoption in developing countries. Promoting gender equality, including through gender quotas, has become a key part of international democracy promotion. The international legitimacy of gender quotas leads them to be adopted through two causal pathways: directly, through postconflict peace operations, and indirectly, by encouraging countries, especially those that depend on foreign aid, to signal their commitment to democracy by adopting quotas. An event history analysis, which controls for other relevant factors, shows that the hypothesized relationships exist. Further support comes from a process-tracing analysis of Afghanistan's 2004 quota.

Reduction of cancer-related disparities requires strategies that link medically underserved communities to preventive care. In this community-based participatory research project, a public library system brought together stakeholders to plan and undertake programs to address cancer screening and risk behavior. This study was implemented over 48 months in 20 large urban neighborhoods, selected to reach diverse communities disconnected from care. In each neighborhood, Cancer Action Councils were organized to conduct a comprehensive dynamic trial, an iterative process of program planning, implementation and evaluation. This process was phased into neighborhoods in random, stepped-wedge sequence. Population-level outcomes included self-reported screening adherence and smoking cessation, based on street intercept interviews. Event-history regressions (n = 9374) demonstrated that adherence outcomes were associated with program implementation, as were mediators such as awareness of screening programs and cancer information seeking. Findings varied by ethnicity, and were strongest among respondents born outside the U.S. or least engaged in care. This intervention impacted health behavior in diverse, underserved and vulnerable neighborhoods. It has been sustained as a routine library system program for several years after conclusion of grant support. In sum, participatory research with the public library system offers a flexible, scalable approach to reduce cancer health disparities.

PurposeWhy do collaborations between direct competitors fail and what are the risks? To enhance our understanding about the sources that generate diverse risks of failure of coopetition (cooperation between competitors), this study examined the effect of influential power each partner exercise in the industry on the unexpected dissolution of joint venture (JV) between direct competitors.Design/methodology/approachThis study employed event history analysis to test the hypotheses using a sample of 188 coopetitive JVs between 2001 and 2010 and data on their survival for the following 5-year window.FindingsThe results show that as firms become more powerful within the industry, they have more incentives to cooperate with competitors to maintain their current JVs than to pursue their self-interest through opportunistically dissolving collaborations, which ultimately reduces the risk of failure of coopetition.Originality/valueScholars have often considered why firms cooperate with competitors and how coopetition creates value for firms but paid less attention regarding why coopetition unexpectedly dissolves. Based on transaction cost economics and the situational assumption of mixed-motive interaction, this study provides insight into how the different levels of influence each partner can exercise within the industry cause firms to have different incentives to cooperate with competitors.

Background Survival analysis and effect of covariates on survival time is a central research interest. Cox proportional hazards regression remains as a gold standard in the survival analysis. The Cox model relies on the assumption of proportional hazards (PH) across different covariates. PH assumptions should be assessed and handled if violated. Our aim was to investigate the reporting of the Cox regression model details and testing of the PH assumption in survival analysis in total joint arthroplasty (TJA) studies. Methods We conducted a review in the PubMed database on 28th August 2019. A total of 1154 studies were identified. The abstracts of these studies were screened for words “cox and “hazard*” and if either was found the abstract was read. The abstract had to fulfill the following criteria to be included in the full-text phase: topic was knee or hip TJA surgery; survival analysis was used, and hazard ratio reported. If all the presented criteria were met, the full-text version of the article was then read. The full-text was included if Cox method was used to analyze TJA survival. After accessing the full-texts 318 articles were included in final analysis. Results The PH assumption was mentioned in 114 of the included studies (36%). KM analysis was used in 281 (88%) studies and the KM curves were presented graphically in 243 of these (87%). In 110 (45%) studies, the KM survival curves crossed in at least one of the presented figures. The most common way to test the PH assumption was to inspect the log-minus-log plots ( n  = 59). The time-axis division method was the most used corrected model ( n  = 30) in cox analysis. Of the 318 included studies only 63 (20%) met the following criteria: PH assumption mentioned, PH assumption tested, testing method of the PH assumption named, the result of the testing mentioned, and the Cox regression model corrected, if required. Conclusions Reporting and testing of the PH assumption and dealing with non-proportionality in hip and knee TJA studies was limited. More awareness and education regarding the assumptions behind the used statistical models among researchers, reviewers and editors are needed to improve the quality of TJA research. This could be achieved by better collaboration with methodologists and statisticians and introducing more specific reporting guidelines for TJA studies. Neglecting obvious non-proportionality undermines the overall research efforts since causes of non-proportionality, such as possible underlying pathomechanisms, are not considered and discussed.

Event studies examine stock price movements around corporate events. These events can be voluntary firm announcements (e.g., new product introduction, alliance formation, channel restructuring) or announcements made by other entities such as regulatory bodies (e.g., FDA approval) or competitors (e.g., new market entry). The event study methodology was developed by finance researchers but has been widely adopted in other fields, including marketing. We review the manner in which event studies have been used in the marketing literature and summarize the current state of knowledge about the design and interpretation of event studies. We provide guidelines for researchers who use this methodology and for readers who draw inferences from results obtained from event studies, and we highlight a few areas where the methodology can be leveraged to help us better understand the financial value of marketing actions.

This article offers an applied review of key issues and methods for the analysis of longitudinal panel data in the presence of missing values. The authors consider the unique challenges associated with attrition (survey dropout), incomplete repeated measures, and unknown observations of time. Using simulated data based on 4 waves of the Marital Instability Over the Life Course Study (n = 2,034), they applied a fixed effect regression model and an event-history analysis with time-varying covariates. They then compared results for analyses with nonimputed missing data and with imputed data both in long and in wide structures. Imputation produced improved estimates in the event-history analysis but only modest improvements in the estimates and standard errors of the fixed effects analysis. Factors responsible for differences in the value of imputation are examined, and recommendations for handling missing values in panel data are presented.