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Does staffing contribute to organizational effectiveness and sustained competitive advantage, or are many of staffing’s implications merely cross-level fallacies? This article provides a critical examination of staffing research and practice, and proposes a multilevel model of staffing that ties together micro (e.g. personnel selection), meso (e.g. team staffing), and macro (e.g. organizational strategy, Human Resources practices) theory, research, and practice. The model is both integrative and prescriptive, providing a basic organizing structure for examining staffing research within and across levels. The article begins with a review of multilevel theory, followed by a review and critique of the dominant staffing paradigms from a multilevel perspective. It is shown these single level paradigms cannot answer many of the primary questions of interest to staffing specialists. In contrast, the multilevel staffing model not only addresses these limitations, but also prompts a variety of new predictions that oftentimes run counter to prevailing wisdom. Staffing specialists are challenged to show how our science and practice contribute to better functioning organizations.

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 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.

The aim of this paper is to review the structure of road accident data, the database framework within which it is stored, and the potential for exploiting a hierarchical structure using multilevel statistical models. Most national accident databases regard accidents as the primary units of observation, with other characteristics stored as attributes. But it is more natural to picture the network, accidents, and other variables as a collection of related objects within a hierarchical system, which can be achieved using object-oriented database technology within a Geographical Information System (GIS) framework. This would permit more efficient data capture and storage, facilitate analysis of accident frequencies as a function of road layout, and facilitate the development of multilevel statistical models.

Purpose – In this chapter, we examine individual- and country-level differences in perceived job insecurity in the 27 European Union countries (EU27) within a multilevel framework.

Design/methodology/approach – We primarily focus on cross-national differences in perceived job insecurity in the EU27 and consider several possible explanations of it, including flexible employment practices, economic conditions, labor market structure, and political institutions. We examine both individual- and country-level determinants using multilevel partial proportional odds models based on individual-level data from the 2006 Eurobarometer 65.3 and country-level data from a variety of sources.

Findings – We find that European workers feel most insecure in countries with high unemployment, low union density, low levels of part-time and temporary employment, relatively little social spending on unemployment benefits, and in post-socialist countries.

Research limitations/implications – The findings from this study suggest that flexible employment practices do not necessarily cause workers to feel insecure in their jobs. This is likely due to the different nature of part-time and temporary employment in different institutional contexts.

Originality/value – This study is one of the most comprehensive accounts of perceived job insecurity in Europe given the focus on a larger number of countries and macro-level explanations for perceived job insecurity.

The purpose of this chapter is to offer a discussion of the key issues in mixed-level, multi-theoretical research in strategic management. Mixed-level issues are grouped into two categories: (1) measurement of constructs, with discussion of situations in which the level of theory, level of measurement, and level of analysis differ; and (2) relationships among constructs, including cross-level and multilevel models. Key theories and views found in the strategic management literature are discussed briefly to illustrate the basic arguments of each, its focal unit of analysis, and the implicit or explicit incorporation of mixed-level perspectives.

Recently, the institutional performance model has been used to explain the increased distrust of health care system by arguing that distrust is a function of individuals’ perceptions on the quality of life in neighborhood and social institutions. We examined (1) whether individuals assess two dimensions of distrust consistently, (2) if the multilevel institutional performance model explains the variation of distrust across neighborhoods, and (3) how distrust patterns affect preventive health care behaviors.

Using data from 9,497 respondents in 914 census tracts (neighborhoods) in Philadelphia, we examined the patterns of how individuals evaluate the competence and values distrust using the Multilevel Latent Class Analysis (MLCA), and then investigated how neighborhood environment factors are associated with distrust patterns. Finally, we used regression to examine the relationships between distrust patterns and preventive health care.

The MLCA identified four distrust patterns: Believers, Doubters, Competence Skeptics, and Values Skeptics. We found that 55 percent of the individuals evaluated competence and values distrust coherently, with Believers reporting low levels and Doubters having high levels of distrust. Competence and Values Skeptics assessed distrust inconsistently. Believers were the least likely to reside in socioeconomically disadvantaged and racially segregated neighborhoods among these patterns. In contrast to Doubters, Believers were more likely to use preventive health care, even after controlling for other socioeconomic factors including insurance coverage.

Our findings suggest that distrust patterns are a function of neighborhood conditions and distrust patterns are associated with preventive health care. This study provides important policy implications for health care and future interventions.