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Over the last decade, latent growth modeling (LGM) utilizing hierarchical linear models or structural equation models has become a widely applied approach in the analysis of change. By analyzing two or more variables simultaneously, the current method provides a straightforward generalization of this idea. From a theory of change perspective, this chapter demonstrates ways to prescreen the covariance matrix in repeated measurement, which allows for the identification of major trends in the data prior to running the multivariate LGM. A three-step approach is suggested and explained using an empirical study published in the Journal of Applied Psychology.

This chapter introduces four research methods that are not covered in the previous chapters. They are (1) non-parametric statistics, (2) interpretive structural modeling, (3) analytic hierarchy process, and (4) data envelopment analysis. The methods are discussed with examples. The discussion, however, is introductory; so we urge the reader to go through the pertinent references for details.

This research examines how social media helps increase the dynamic capabilities of health organizations. Using the concepts of “technical fitness” and “evolutionary fitness,” the research focuses on changes in: (1) the number of “likes”; (2) the amount of discussions in these pages; and (3) enabling factors for (1) and (2) over time.

Missouri Hospital Association (MHA) provides a sample of 164 hospitals. The Facebook pages of these hospitals are analyzed. Specifically, the number of “likes” and the amount of discussions are readily available for analysis. Data collection was conducted at two time points.

At time 1, 39% of the hospitals had an official Facebook page, increased to 47% at time 2. On average, there was a 22.2% increase in likes and a 4.0% increase in pages of discussions. Whether measured by staffs or patients’ capacity, size is a significant factor that contributes to the increase in likes and discussions. Yet, the location of a hospital — measured by urbanity — is statistically insignificant. Qualitative analyses suggest that certain patient groups particularly welcomed social media. Beside, pictures, videos, and “happy news” tended to increase usage of social media for hospital stakeholders.

Social media can help health organizations fulfill the social needs of their patients. This research applies several useful concepts. In further study, researchers may examine how hospitals optimize staffs’ and patients’ inputs. Besides, comparing how hospitals’ social media platforms operate differently in other states or countries may yield findings with practical implications.

In this study, we aimed at examining the unique and interactive effects of peer violence in cyberspace on adolescents’ emotion regulation and socioemotional adjustment, as well as the mediational role of resilience in the link between adolescent’s pathogenic relational experiences and behavioral outcomes. Specifically, we intended to explore emotion differentiation and regulation in reaction to bullying perpetration and victimization and in terms of positive (proud, confident, good) and negative (ashamed, excited, guilty), Passive (sad, embarrassed, humiliated) and Reactive (angry, scared) emotions and how it impacted and predicted positive and negative outcomes.

A stratified convenient sample of 494 Italian students aged 13–19 years (M = 15.27, SD = 1.23) was selected to represent all different school types in Italy and the students were administered a self-report questionnaire on school bullying involvement. General Linear Models, ANOVA, and T-tests were employed to explore gender differences, the relationships between variables, and their contribution to the predictive model. A two-step Cluster analysis was used to profile adolescents based on patterns of resilience, health outcomes, and cyberbullying involvement.

Results showed significant gender differences, with females using internet and Facebook more than males and being more resilient, positive, and prosocial, but also responding to victimization with higher levels of alienation, anger, humiliation, and psychosomatic and emotional symptoms. Males perpetrated peer violence more than females, were less likely to be victimized, and were generally less emotionally impacted by it. Victimization rates (63.7%, n = 296) were higher than perpetration rates (51.7%, n = 233) and bully-victimization was prevalent (47.1%). Victims prevalently experienced passive emotions (sadness, humiliation, embarrassment) while perpetrators experienced negative ones (guilt and shame). Cluster analysis evidenced different pathways and trajectories of resilience and cyberbullying involvement: Resilient victims (RV), Healthy uninvolved (HU), Healthy Bullies (HB), Alienated Bully-Victims (ABV), and Resilient Bully-Victims (RBV). RV, HU, and HB resulted all well-adjusted, despite the different involvement in cyberbullying, and also RBV and despite the double involvement in cyberbullying, ABV were the only maladjusted and at-risk group in our sample characterized by very low Positivity, very low Resilience, and extremely high Alienation.

This study proposes a comprehensive, developmental, ecological, relational, and self-regulatory resilience approach to cyberbullying, which represents an innovative and advanced contribution to the literature with significant implication for research and practice. Fully understanding and measuring the emotional impact of cyber peer violence and resilience following cyberbullying victimization and perpetration can help in developing targeted interventions for both victims and bullies. This study highlighted the need for a self-regulatory model of resilience for modulating emotions, arousal, and behaviors across contexts, relationships, and difficulties. It also evidenced that moderate levels of resilience and positivity are sufficient to buffer youth from involvement in cyberbullying and to predict healthy adjustment and less pathological outcomes.

By profiling adolescents based on resilience levels, health outcomes, and cyberbullying involvement, we evidenced five distinct trajectories of risk evaluation for cyberbullying beyond participating roles. Our results confirmed the fundamental importance of assessing resilience and emotion regulatory resources together with peer violence involvement in identifying and targeting adolescents at risk.

Most multi-level studies are cross-sectional and focus on a certain point in time, though various changes within levels may occur over time. This chapter presents a statistical method for assessing whether the degree of interdependency within a group has changed over time, using the intraclass correlation coefficient (ICC) as an indicator of the degree of homogeneity within the groups. It then shows how to apply this method using the SAS MIXED procedure. The problem was motivated by a study in which 120 subjects were divided into 40 groups of three. In a portion of the study, collective efficacy was the dependent variable measured for each subject under four different conditions (two levels of task interdependence at two points in time). ICC was used as a measure of group homogeneity with respect to collective efficacy, and the problem was how to compare the dependent ICCs associated with the different conditions.

This paper focuses on the role of manufacturer brands for resellers within retail channels. This topic is important because of the strategic value of manufacturer brands and the increasing influence of resellers within channels of distribution. Much of the branding research emphasizes a customer-brand knowledge perspective; however, emerging perspectives suggest that brands are also relevant to other stakeholders including resellers. In contrast, channels research recognizes the manufacturer sources of market power, but does not consider the impact of manufacturer “push and pull” strategies within channels. Existing theoretical frameworks, therefore, do not address the reseller perspective of the brand. As a result, the research approach is a multi-method design, consisting of two phases. The first phase involves in-depth interviews, allowing the development of a conceptual framework. In the second phase, a survey of supermarket buyers on brands in several product categories tests this framework. Structural equation modeling analyzes the survey responses and tests the hypotheses. The structural model shows very good fit to the data with good construct validity, reliability, and stability. The findings show that manufacturer support, brand equity, and customer demand reflect the manufacturer brand benefits to resellers. A key contribution of this research is the development of a validated scale on manufacturer brand benefits from the point of view of a reseller. This research shows that the resources that relate to the brand, not just the brand name itself, create value for resellers in channel relationships.

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

Purpose — The research was designed to explore people's willingness/ability to understand complex road user charges. However, the results raise issues about respondent engagement and ecological validity and so have important implications for questionnaire practice.

Methodology — Computer-based experiments administered in the United Kingdom and Germany gathered respondents' estimates of road user charges along with their response latencies, personal characteristics, acceptance of road charging, assessments of task complexity and attitudes to analytical tasks.

Findings — The results demonstrate questionnaire learning effects and show the effect of personal characteristics on the accuracy and speed of questionnaire completion. The tendency of males, younger people and students to complete the task more quickly is interesting as is the fact that fewer and smaller errors were made by participants who claimed to gain satisfaction from completing a task which has involved mental effort. Engagement was seen to vary with personal characteristics, attitudes to decision making, task complexity and acceptance of the policy being tested. A key finding is that disengagement was more evident among participants who were broadly supportive of road charging than among those who were not.

Implications — The findings have important implications for the design of data collection exercises and for the interpretation of resulting data. It is concluded that repeated choice experiments are an inappropriate source of data on responses to unfamiliar circumstances. The collection of data on response latencies and the inclusion of questions on respondents' attitudes to task completion is a strongly recommended addition to standard questionnaire practice. The extent to which disengagement in an experimental context is, or is not, indicative of real-world behaviour is an important and urgent subject for further research.

Purpose: In the current work, we provide a portrait of heavy alcohol use, cigarette smoking, mental health, and suicide ideation by sexual orientation among a large sample of US adults aged 25 years and older.

Design/methodology/approach: We produce a repository of information on sexual orientation, substance use, mental well-being, and suicide ideation for adults aged 25 years and older using Behavioral Risk Factor Surveillance System (BRFSS) surveys for nine US states from 2011 to 2018. We establish baseline differences on these outcomes for gay, lesbian, and bisexual (GLB), relative to heterosexual, adults and then use regression techniques to adjust the estimates for important sociodemographic, socioeconomic, and relationship status variables.

Findings: Disparities by sexual orientation across substance use, mental health, and suicide ideation are concerning, some alarmingly so. Bisexuals, particularly women, face pronounced challenges across outcomes. Sexual minority men and women report significantly more poor mental health days and much higher odds of suicide ideation. To illustrate, gay men, lesbians, and bisexual men and women, relative to their heterosexual counterparts, have odds of seriously contemplating taking their own lives that are two to four times higher even after adjusting for relevant controls.

Originality/value: Existing knowledge connecting GLB identity and mental well-being has focused largely on adolescent and young adults. We provide a representative study on older adult differences across four different behavioral health outcomes by sexual orientation. The scale of the disparities we report here, and their implications for overall well-being across groups, deserves national attention and action.

Multivariate latent growth modeling (multivariate LGM) provides a flexible data analytic framework for representing and assessing cross-domain (i.e., between-constructs) relationships in intraindividual changes over time, which also allows incorporation of multiple levels of analysis. Using the chapter by Cortina, Pant, and Smith-Darden (this volume) as a point of departure, this chapter discusses important preliminary data analysis and interpretation issues prior to performing multivariate LGM analyses.