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Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

The method applied to carry out the study of the transformation of strategic alliances in emerging markets is discussed in this chapter. Specifically, this chapter discusses how the whole study is conducted, i.e. the identification and development of the research issues, the research aim and problem, the development of the conceptual framework, the development of the research method, how the empirical study is analyzed and conclusions are drawn. The specific research strategy applied to conduct the current study is a longitudinal research approach. A short discussion on how the ECE countries are classified into various groups is conducted in the current chapter and a detailed discussion on the classification of ECE countries into different groups is also conducted in chapter eight. Moreover, the logic for adopting a longitudinal research strategy to conduct the current study of the transformation of strategic alliances is also justified. The identification and choice of the case companies, the logic followed in the development of the questionnaire and how much and to what extent the questionnaire is modified are also discussed in detail. The identification of the interviewees, the execution of the interviews and the transcription of the interview materials and the structuring of the cases are also discussed in detail.

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.

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.

Research that tracks low-income populations across educational transitions contains threats to validity that can compromise evidence-based educational policy and practice. The Big Picture Longitudinal Study is a national, multiyear study that follows low-income urban youth who were accepted into college as high school seniors. Triangulating the results of multiple longitudinal data sources showed that reported college aspirations and enrollment intentions were inconsistently and differently reported by students and teachers in the final semester of high school. Relying on a particular data source and time can result in mistakenly equating college aspirations and enrollment behaviors, these findings suggest. In particular, secondary school educators’ inflated assumptions about their students’ college aspirations can obscure the need for supporting multiple pathways to college and work for low-income, first-generation high school seniors.

Using data from the National Longitudinal Transition Study (NLTS) and the National Longitudinal Transition Study-2 (NLTS2), this study compared the post-high school outcomes of young adults with learning disabilities (LD) or emotional/behavioral disorders (EBD) in 1990 and 2005. These cohort comparisons reveal how the results of special education have changed over that time period as evidenced in the post-high school outcomes of nationally representative samples of youth. The extended data collection time period of NLTS2 (2001–2009) also enabled an assessment of the evolution in the post-high school outcomes of young adults with LD or EBD who had been out of high school up to 8 years. The post-high school outcomes considered included high school completion, postsecondary education enrollment and completion, employment status and wages, and community integration as illustrated by living arrangements and criminal justice system involvement. Findings for both the NLTS/NLTS2 cohort comparisons and the longitudinal analyses from NLTS2 indicate progress in efforts to improve outcomes for youth and young adults with LD or EBD but also underscore the work ahead in setting these groups on a path to successful adulthood. Implications for practice and research are discussed.

Economists and sociologists have proposed arguments for why there can exist wage penalties for work involving helping and caring for others, penalties borne disproportionately by women. Evidence on wage penalties is neither abundant nor compelling. We examine wage differentials associated with caring jobs using multiple years of Current Population Survey (CPS) earnings files matched to O*NET job descriptors that provide continuous measures of “assisting & caring” and “concern” for others across all occupations. This approach differs from prior studies that assume occupations either do or do not require a high level of caring. Cross-section and longitudinal analyses are used to examine wage differences associated with the level of caring, conditioned on worker, location, and job attributes. Wage level estimates suggest substantive caring penalties, particularly among men. Longitudinal estimates based on wage changes among job switchers indicate smaller wage penalties, our preferred estimate being a 2% wage penalty resulting from a one standard deviation increase in our caring index. We find little difference in caring wage gaps across the earnings distribution. Measuring mean levels of caring across the U.S. labor market over nearly thirty years, we find a steady upward trend, but overall changes are small and there is no evidence of convergence between women and men.

The present study uses data from the National Longitudinal Study of Adolescent Health (Ad Health) to evaluate the effects of exposure to violent victimization in childhood on adolescent delinquency and subsequent adult criminality.

Using Longitudinal Latent Class Analysis (LLCA), the present study investigates whether there are distinct and diverse longitudinal delinquency trajectories among those exposed to violence in childhood.

Findings from the current study indicate that there are three distinct trajectories of delinquency and offending from age 14 to 27 for both males and females exposed to violence in childhood. Further, it appears that violent victimization in childhood bridges the gender gap in delinquency between males and females. Thus, childhood violent victimization, and the fact that females are victimized by parents/caregivers and romantic partners at higher rates than males, might be partially responsible in explaining the narrowing of the gender gap between male and female offending in the recent decades. At the same time, childhood violent victimization also seems to impact males and females in somewhat different ways. Practically, all female victims stop offending by their late 20s, whereas a fairly large proportion of males exposed to violent victimization in childhood steadily continue offending.

Although this study was able to identify the diverse impacts of violence exposure on engagement in subsequent delinquency, it did not examine the unique contributions of each type of violence on adolescent outcomes or the chronicity of exposure to each of these types of violent victimization. We were also not able to measure all types of violence experiences in childhood, such as exposure to parents’ or caregivers’ intimate partner violence.

While early prevention would be the most desirable option for both genders for the most optimal outcome, the retrospective intervention and treatment programs should be gender-specific. For males, they should heavily focus on providing alternative ways to cope with anger, impulse control and frustration, as well as teach empathy, cognitive problem solving skills, verbal communication skills, and tangible life and job skills. For females, most successful intervention and treatment programs may focus on helping the girls through a transition from adolescence to adulthood while providing mental health, medical, and family support services.

The paper uses a unique methodological approach to identify distinct and diverse longitudinal delinquency trajectories. The findings demonstrate how more resilient individuals (in terms of externalizing behaviors) can bring down the mean scores of delinquency even though many other individuals can be severely affected by violence exposure in childhood.

This chapter employs a longitudinal social network approach to research small group teaching in higher education. Longitudinal social network analyses can provide in-depth understanding of the social dynamics in small groups. Specifically, it is possible to investigate and disentangle the processes by which students make or break social connections with peers and are influenced by them, as well as how those processes relate to group compositions and personal attributes, such as achievement level. With advanced methods for modelling longitudinal social networks, researchers can identify social processes affecting small group teaching and learning.

Theories of trust imply that team trust develops and grows over time, yet relatively few researchers have taken on the challenge of studying team trust in longitudinal research designs. The purpose of this chapter is to provide a concise summary of the existing literature on team trust over time, and to offer a conceptual model of team-level trust development over time to aid future research on this topic.

We draw from the Input–Mediator–Output–Input (IMOI) framework, as well as previous multilevel models of organizational trust development, and published findings from longitudinal team trust studies.

Taking a temporal perspective, we consider how team-level mediators and outcomes can both predict and be predicted by team trust trajectories and feedback loops over time, as well as how those relationships with team trust might change based on the existence of other moderating variables including trust violation and repair.

Future longitudinal team research may use the model as a starting point for investigating the antecedents, as well as the team processes and dynamic emergent states, that can effectively predict trajectories of team trust across various stages of teamwork.

Based on our review of extant literature, we provide several recommendations for training and organizational intervention including the importance of management’s consideration of team-level trust in providing feedback, enhancing cohesion, and mitigating conflict.

We provide insight into the development of team trust trajectories and offer a framework to help guide future longitudinal team trust research.