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We provide reporting guidelines for multilevel factor analysis (MFA) and use these guidelines to systematically review 72 MFA applications in journals across a range of disciplines (e.g., education, health/nursing, management, and...
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We provide reporting guidelines for multilevel factor analysis (MFA) and use these guidelines to systematically review 72 MFA applications in journals across a range of disciplines (e.g., education, health/nursing, management, and psychology) published between 1994 and 2014. Results are organized in terms of the (a) characteristics of the MFA application (e.g., construct measured), (b) purpose (e.g., measurement validation), (c) data source (e.g., number of cases at Level 1 and Level 2), (d) statistical approach (e.g., maximum likelihood), and (e) results reported (e.g., intraclass correlations for indicators and latent variables, standardized factor loadings, fit indices). Results from this review have implications for applied researchers interested in expanding their approaches to psychometric analyses and construct validation within a multilevel framework and for methodologists using Monte Carlo methods to explore technical and methodological issues grounded in realistic research design conditions.
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The term "multilevel meta-analysis" is encountered not only in applied research studies, but in multilevel resources comparing traditional meta-analysis to multilevel meta-analysis. In this tutorial, we argue that the term "multil...
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The term "multilevel meta-analysis" is encountered not only in applied research studies, but in multilevel resources comparing traditional meta-analysis to multilevel meta-analysis. In this tutorial, we argue that the term "multilevel meta-analysis" is redundant since all meta-analysis can be formulated as a special kind of multilevel model. To clarify the multilevel nature of meta-analysis the four standard meta-analytic models are presented using multilevel equations and fit to an example data set using four software programs: two specific to meta-analysis (metafor in R and SPSS macros) and two specific to multilevel modeling (PROC MIXED in SAS and HLM). The same parameter estimates are obtained across programs underscoring that all meta-analyses are multilevel in nature. Despite the equivalent results, not all software programs are alike and differences are noted in the output provided and estimators available. This tutorial also recasts distinctions made in the literature between traditional and multilevel meta-analysis as differences between meta-analytic choices, not between meta-analytic models, and provides guidance to inform choices in estimators, significance tests, moderator analyses, and modeling sequence. The extent to which the software programs allow flexibility with respect to these decisions is noted, with metafor emerging as the most favorable program reviewed.
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We consider the application of multilevel Monte Carlo methods to elliptic PDEs with random coefficients. We focus on models of the random coefficient that lack uniform ellipticity and boundedness with respect to the random paramet...
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We consider the application of multilevel Monte Carlo methods to elliptic PDEs with random coefficients. We focus on models of the random coefficient that lack uniform ellipticity and boundedness with respect to the random parameter, and that only have limited spatial regularity. We extend the finite element error analysis for this type of equation, carried out in Charrier et al. (SIAM J Numer Anal, 2013), to more difficult problems, posed on non-smooth domains and with discontinuities in the coefficient. For this wider class of model problem, we prove convergence of the multilevel Monte Carlo algorithm for estimating any bounded, linear functional and any continuously Fréchet differentiable non-linear functional of the solution. We further improve the performance of the multilevel estimator by introducing level dependent truncations of the Karhunen-Loève expansion of the random coefficient. Numerical results complete the paper.
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Apositive relationship between themental health consultant (MHC) and early care and education staff is considered important for achieving positive early childhood mental health consultation outcomes, but little is known about the ...
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Apositive relationship between themental health consultant (MHC) and early care and education staff is considered important for achieving positive early childhood mental health consultation outcomes, but little is known about the attributes of MHCs that contribute to relationships with staff and to staff-reported child outcomes. This study was a secondary analysis of 57 MHCs and 407 Head Start staff who responded to the Head StartMental Health Services Survey. Hierarchical linear models examined the relationship between five attributes of MHCs and staff reports of improved child outcomes and a positive relationship with the MHC. The results suggest that MHC reports of positive relationships with staff, positive relationships with families, and high levels of supervision and support are positively associated with staff reports of positive relationships with the MHC (p < .05). None of the MHC-reported attributes were associated with staff-reported child outcomes.
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We propose to use multilevel discrete-time hazard models to assess the impact of societal and individual level covariates on the timing and occurrence of third births. We focus mainly on the impact of educational attainment on thi...
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We propose to use multilevel discrete-time hazard models to assess the impact of societal and individual level covariates on the timing and occurrence of third births. We focus mainly on the impact of educational attainment on third births across 15 European countries. From the analysis in this paper, the effect of education on the propensity to have a third child is found to be negative. This education effect is not significantly weakened by the Nordic countries, but living in Scandinavia does increase the hazard for a third birth.
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Purpose 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 s...
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Purpose 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.Design/methodology/approach 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.Findings 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.Research limitations/implications 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.Originality/value 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.
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Multilevel multiprocess models are simultaneous equation systems that include multilevel hazard equations with correlated random effects. Demographers routinely use these models to adjust estimates for endogeneity and sample selec...
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Multilevel multiprocess models are simultaneous equation systems that include multilevel hazard equations with correlated random effects. Demographers routinely use these models to adjust estimates for endogeneity and sample selection. In this article, I demonstrate how multilevel multiprocess models can be fit with the gsem command. I distinguish between two classes of multilevel multiprocess models: nonrecursive systems of hazard equations without observed endogenous variables and recursive systems that include a hazard equation with observed endogenous qualitative variables. I illustrate the estimation of both classes of models using sample datasets shipped with the statistical software aML. I pay special attention to identifying structural coefficients in nonrecursive simultaneous systems.
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Information systems (IS) research usually investigates phenomena at one level of analysis at a time. However, complex IS phenomena may be difficult to address from such a single-level perspective. A multilevel perspective offers a...
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Information systems (IS) research usually investigates phenomena at one level of analysis at a time. However, complex IS phenomena may be difficult to address from such a single-level perspective. A multilevel perspective offers an alternative means to examine phenomena by simultaneously accounting for multiple levels of analysis. Although useful guidelines for theory development are widely available, they give little specific attention to developing theory that is conceptualized and analyzed at multiple levels. Multilevel theorizing or developing theory from a multilevel perspective is more complex and involves unique challenges. To promote multilevel theorizing in the IS discipline, we focus on addressing challenges involved in multilevel theorizing and propose a holistic framework for systematically developing theory from a multilevel perspective. Drawing from the organization science and IS literature, the proposed framework harmonizes and synthesizes previous guidelines, providing a practical basis for conceptualizing and studying multilevel phenomena.
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Meta-analyses are well known and widely implemented in almost every domain of research in management as well as the social, medical, and behavioral sciences. While this technique is useful for determining validity coefficients (i....
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Meta-analyses are well known and widely implemented in almost every domain of research in management as well as the social, medical, and behavioral sciences. While this technique is useful for determining validity coefficients (i.e., effect sizes), meta-analyses are predicated on the assumption of independence of primary effect sizes, which might be routinely violated in the organizational sciences. Here, we discuss the implications of violating the independence assumption and demonstrate how meta-analysis could be cast as a multilevel, variance known (Vknown) model to account for such dependency in primary studies’ effect sizes. We illustrate such techniques for meta-analytic data via the HLM 7.0 software as it remains the most widely used multilevel analyses software in management. In so doing, we draw on examples in educational psychology (where such techniques were first developed), organizational sciences, and a Monte Carlo simulation (Appendix). We conclude with a discussion of implications, caveats, and future extensions. Our Appendix details features of a newly developed application that is free (based on R), user-friendly, and provides an alternative to the HLM program.
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This paper investigates whetherand how regional social contexts influencefertility decisions of women living in westernGermany during the 1980s and 1990s. It isargued that regional opportunity structures aswell as local patterns o...
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This paper investigates whetherand how regional social contexts influencefertility decisions of women living in westernGermany during the 1980s and 1990s. It isargued that regional opportunity structures aswell as local patterns of social interactionand culture may translate into parameters thatdirectly affect individual behaviour. Data fromthe German Socio-Economic Panel (GSOEP) arelinked with a set of regional indicators toestimate multilevel discrete-time logit modelsfor the transition to the first and secondchild. The empirical analysis provides noevidence that fertility differentials observedat the regional level are due to autonomouscontextual effects. Rather, it is suggestedthat most of the observed regional variationresults from differences in the spatialdistribution of individual characteristics.
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