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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 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.

Purpose: The death of a child can elicit enduring and intense parental grief. Additionally, as parents are both confronted with the loss of their child, interpersonal processes come into play. This study aimed to examine the change in reported levels of grief among bereaved parents individually and at a couple-level. The authors examined the differences in grief trajectories between mothers and fathers and whether the reported level of grief of one partner predicts the other partner’s reported level of grief.

Design/methodology/approach: Our longitudinal study included 229 bereaved couples who completed the Inventory of Complicated Grief at 6, 13, and 20 months post-loss.

Findings: A latent growth curve analysis showed that parents reported consistently high average grief levels, mothers reported higher grief levels than fathers, and all parents reported a similar small decline in grief. A cross-lagged panel analysis showed that the grief of one parent affected the grief of the other parent with similar strength. Our results held regardless of the child’s gender and age, but an expected loss was associated with a lower grief level 6 months post-loss and a smaller decline in reported levels of grief.

Originality/value: These findings highlight bereaved parents as a particularly vulnerable population, increase our understanding of change in parental grief over time and of the interdependence of grieving in bereaved couples.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

The objectives of this study are threefold: first, to analyze the development of intentions of individuals over time; second, to explore potential gender differences in intention development; and third, to analyze the relatedness of the initial level and development of the antecedents of intentions to the initial level and the development of intentions.

Ajzen's Theory of Planned behavior is applied. Longitudinal data were collected in the fall of 2010, 2011 and 2012 in seven different universities of applied sciences, with students representing seven different study fields. In our data, there are 192 individuals with all three measurement waves and 104 individuals with two measurement waves. The analysis of change on multi-wave panel data is done using latent growth curve analysis with structural equation modeling.

Our empirical results are threefold. First, entrepreneurial intentions of higher education seem to decrease during their studies. Second, there is a gender difference in the initial level of entrepreneurial intentions and how intentions develop over time. Third, the initial level of intentions does not affect the future development of intentions.

In sum, the authors believe that the paper makes an important contribution to the field of entrepreneurial education by concluding that intention development in higher educational context is not a simple matter, but a rather complicated process during which young people can realize their true potential vis-à-vis entrepreneurial opportunities. From an educators’ point of view, such realization generally means a decrease in an individual's entrepreneurial intentions, which is a phenomenon that does not provide much encouragement for educators. On the other hand, one of the aims of any entrepreneurship education is to give younger people a more realistic picture about entrepreneurship. When someone is willing to start a new business in this kind of context, the authors, as educators, can be a degree more confident that such an individual is not launching his/her venture because of idealistic dreams.

By using a longitudinal design, the paper is one of the first to provide empirical evidence about the intention development over time. Ultimately, the paper hopes to have added richness to the ongoing discussion among academics and educators alike regarding the importance of intention development in entrepreneurship education.

Every “structural model” is defined by the set of covariance and mean expectations. These expectations are the source of parameter estimates, fit statistics, and substantive interpretation. The recent chapter by Cortina, Pant, and Smith-Darden ((this volume). In: F. Dansereau & F. J. Yammarino (Eds), Research in multi-level issues (vol. 4). Oxford, England: Elsevier) shows how a formal investigation of the data covariance matrix of longitudinal data can lead to an improved understanding of the estimates of covariance terms among linear growth models. The investigations presented by Cortina et al. (this volume) are reasonable and potentially informative for researchers using linear change growth models. However, it is quite common for behavioral researchers to consider more complex models, in which case a variety of more complex techniques for the calculation of expectations will be needed. In this chapter we demonstrate how available computer programs, such as Maple, can be used to automatically create algebraic expectations for the means and the covariances of every structural model. The examples presented here can be used for a latent growth model of any complexity, including linear and nonlinear processes, and any number of longitudinal measurements.

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.

This study aims to contribute to entrepreneurial intention research by examining the theory of planned behavior model in a longitudinal follow-up of the same individuals from a point at which they were studying until six to eight years after graduation and the link between entrepreneurial intention and actual behavior. The objectives of the paper are as follows: to examine the development and temporal stability of entrepreneurial intention and to examine the link between entrepreneurial intention and actual start-up behavior in a longitudinal setting.

The data for this research originate in Finland and consist three data collection waves between years 2008 and 2018. In the second wave, 282 respondents were reached, and in the third wave, 89 respondents were reached. For examining the stability of entrepreneurial intention, latent growth curve modeling was used. In addition, a logistic regression analysis was conducted to examine the link between intention and behavior.

The results suggest that entrepreneurial intention is a stable construct over time. High and low levels of entrepreneurial intention remain quite stable. Entrepreneurial intention measured during study time significantly explains entrepreneurial behavior both after one to three years (Exp (B) 2,069***) and after six to eight years (Exp (B) 1,830*). Gender and role models are significant factors in predicting entrepreneurial behavior.

This study provides new information on the stability of entrepreneurial intention in a rare longitudinal setting. The study verifies the value of intention measures in predicting entrepreneurial behavior in the long term.

The purpose of this study is to examine the changes in global worker preferences for job attributes and incentives at the national cultural level, knowledge that is key to the architecture of effective human resource systems.

A Bayesian latent growth curve (LGC) model was used to test data from the World and European Values Surveys from the time periods 1981 to 2005.

Results (based on Bayesian LGC analysis) indicate that employee-related beliefs about what is attractive in a job do change considerably over time, with pay, then employment security having the highest priorities. Equally important, demand for good pay and a job that matches one’s abilities are the fastest growing global worker desires.

These findings of national culture’s active evolution in the workplace indicate a need for scholars, leaders in practice – especially human resource managers – to rethink our cultural conceptualizations for model building and global job design and reward systems.

In response to the Three-Step-Approach (TSA) that Cortina, Pant, and Smith-Darden (this volume) have suggested, Chan (this volume) expressed his reservations regarding the usefulness of a procedure that explicitly ignores measurement considerations and does not include mean scores. In this reply, we argue that the purpose of TSA is heuristic in nature and does not involve statistical testing of assumptions. In this spirit, the software, illustrated by Grimm and McArdle (this volume), rounds out our more conceptual considerations.