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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.

Multilevel approaches are generally well suited to group communication because what people say and do in groups is a function of intra- and trans-individual mechanisms. This chapter first provides a brief overview of group research as a multilevel problem and then describes more modern approaches to modeling nested data using latent variable models, including multilevel structural equation modeling and latent class analysis. The chapter concludes by addressing conceptual opportunities provided by multilevel latent modeling approaches to group communication.

To review and address current approaches and limitations to modeling change over time in social entrepreneurship research.

The article provides a narrative review of different practices used to assess change over time. It also shows how different research questions require different methodologies for assessing changes over time. Finally, it presents worked examples for modeling these changes.

Our review suggests that there is a lack of research in social entrepreneurship that takes into account the many different considerations for addressing how time influences outcomes.

This chapter introduces an analytic technique to social entrepreneurship that effectively models changes in predictors and outcomes even when data are non-normal or nested across time or levels of analysis.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

The purpose of this paper is to address the statistical issues associated with the hierarchically structured data in previous studies that focused on servant leadership. To resolve these issues, multilevel modeling methods were applied to re-visit the construct validity of the servant leadership questionnaire developed by Barbuto and Wheeler (2006) and investigate the relationship between servant leadership and job satisfaction under a multilevel framework.

The survey data was obtained from a sample of 2,089 teachers from 117 primary and secondary schools in Hong Kong. The analyses were conducted using multilevel confirmatory factor analysis (MLCFA) and multilevel structural equation modeling (MLSEM).

The results revealed the significant and non-trivial variances that were explained at the organization level in the items measuring servant leadership, which justified the use of MLCFA and MLSEM. The results of MLCFA provided empirical support for the multidimensional construct as well as the second-order factorial structure of servant leadership measures at both the individual and organization levels. In addition, the positive relationships between servant leadership and the followers’ job satisfaction were found to vary at different levels.

This study reiterates the importance of using appropriate methods to capture a solid definition of the construct of servant leadership and provides new insights into the conceptual framework of servant leadership as well as the effects of servant leadership on individual and organizational outcomes.

The purpose of this paper is twofold. The first is to review the extant literature on hospitality management by tracking past research patterns and critically reviewing the use of multilevel theory and techniques in this stream of research. The second is to suggest potential research opportunities to stimulate a leap forward in the current multilevel research.

To answer the four main research questions raised by the current review, the author performed a critical analysis of a total of 149 selected articles published between 2011 and 2021 in seven leading hospitality management journals.

Overall, the number of multilevel studies has increased significantly since 2017. However, some deficiencies remain: a lack of fit between the level of theory and the level of measurement, the revelation of insufficient information, misspecification of the multilevel model and small sample sizes at higher levels. Furthermore, several interesting and understudied topics are also identified as ripe for future investigation.

In addition to encourage the scholars in hospitality management to assess the possibility of using the multilevel research design for their research topics, the current article also provides recommendations and opportunities for the future multilevel research.

This article is a pioneer in providing a critical synthesis of multilevel research in the field of hospitality management. Although reviews of the issues involved in multilevel research are available in the existing literature, none of them focuses on the situation and needs of hospitality management. As multilevel research increases in popularity, this review offers a snapshot of the introductory phase and outlines important issue in conducting such research.

This chapter describes how to use popular software programs (Hierarchical Linear Modeling, LISREL) to analyze multiwave panel data. We review prevailing methods for panel data analyzes in strategic management research and identify their limitations. Then, we explain how multilevel and latent growth modeling provide more rigorous methodologies for studying dynamic phenomena. We present an example illustrating how firm performance can initiate temporal change in the human and social capital of members of Board of Directors, using hierarchical linear modeling. With the same data set, we replicate this test with first-order factor latent growth modeling (LGM). Next, we explain how to use second-order factor LGM with panel data on employee cognitions. Finally, we review the relative advantages and disadvantages of these new data-analytical approaches.

Our study was designed to investigate the longitudinal trajectories of student leader development capacities in a sample of students enrolled in multiple leadership-focused courses across several semesters. Our goal was to assess the degree to which course enrollment was associated with growth over the time that students engage as undergraduates in academic leadership programs, and if so, to assess the shape and speed of capacity change.

We utilized a multilevel intra-individual modeling approach assessing students’ motivation to lead, leader self-efficacy, and leadership skills across multiple data collection points for students in a campus major or minor focused on leadership studies. We compared an unconditional model, a fixed effect model, a random intercept model, a random slope model, and a random slope and intercept model to determine the shape of score trajectories. Our approach was not to collect traditional pre-test and post-test data – choosing to collect data only at the beginning of each semester – to reduce time cues typically inherent within pre-test and post-test collections.

Our results strongly suggested that individual students differ greatly in the degree to which they report the capacity to lead when initially enrolling in their first class. Surprisingly, the various models were unable to predict a pattern of longitudinal leader development through repeated course enrollment in our sample.

Our investigation employed statistical methods that are not often utilized in leadership education quantitative research, and also included a data collection effort designed to avoid a linear pre-test/post-test score comparison.

The purpose of this paper is to develop the concept of a high performance alliance macro-culture as a multilevel construct reflective of resilient collaborative systems of exchange within strategic alliances and explores the distinct capabilities of this multilevel approach in predicting alliance outcomes.

The hypotheses developed in this study are tested using primary data collected from 650 members of 15 non-profit organizations in two multi-organizational collaborative networks. Considering the multilevel nature of the study the structural hypotheses are tested using a multilevel confirmatory factor analysis and the predictive hypotheses are tested using multilevel structural equation modeling.

All but one structural hypothesis are supported and all predictive hypotheses are supported suggesting that a multilevel macro-cultural conceptualization is effective in exploring the relationship between collaborative exchange systems and their outcomes.

Limitations stem from the generalizability of the data collected as the alliances formed by non-profit firms may not be wholly reflective of the alliance structures and goals of other firm types.

This study primarily contributes to multilevel study of strategic alliances and the study of collaborative norms and structures of allied groupings. The results of this study lend support to the importance of taking a network governance perspective and illustrate the limitations of traditional single-level approaches when studying interfirm collaborative networks and structural resilience therein.