The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data.</p

How Do We Assess Civic Attitudes Toward Equal Rights? Data and Methodology

This open access thematic report identifies factors and conditions that can help schools and education systems promote tolerance in a globalized world. The IEA’s International Civic and Citizenship Study (ICCS) is a comparative research program designed to investigate the ways in which young people are prepared to undertake their roles as citizens, and provides a wealth of data permitting not only comparison between countries but also comparisons between schools within countries, and students within countries. Advanced analytical methods provide insights into relationships between students’ attitudes towards cultural diversity and the characteristics of the students themselves, their families, their teachers and school principals. The rich diversity of educational and cultural contexts in the 38 countries who participated in ICCS 2009 are also acknowledged and addressed. Readers interested in civic education and adolescents’ attitudes towards cultural diversity will find the theoretical perspectives explored engaging. For readers interested in methodology, the advanced analytical methods employed present textbook examples of how to address cross-cultural comparability of measurement instruments and multilevel data structures in international large-scale assessments (ILSA). Meanwhile, those interested in educational policy should find the identification and comparison of malleable factors across education systems that contribute to positive student attitudes towards cultural diversity a useful and thought-provoking resource

Who wants to move? The role of neighbourhood change

This is the author accepted manuscript. The final version is available from SAGE via http://dx.doi.org/10.1177/0308518X15615367 There is growing interest in how, when and where neighbourhoods affect individual behaviours and outcomes. In Britain, falling levels of owner-occupation and the growth of ethnic minority populations have sparked a debate about how neighbourhood characteristics and neighbourhood change intersect with the decision to move. In this paper we investigate how mobility preferences vary with neighbourhood characteristics and neighbourhood change. We use multilevel logistic regression models to test whether this is configured by personal attributes or attachment to one's neighbourhood and perceived similarity to one's neighbours. The results show that neighbourhood deprivation, changes in neighbourhood ethnic composition and changes in tenure mix are associated with preferring to move. Importantly, we show that a feeling of belonging to the neighbourhood or feeling similar to others in the neighbourhood significantly reduces the desire to move. </jats:p

Area and individual differences in personal crime victimization incidence: The role of individual, lifestyle/routine activities and contextual predictors

This article examines how personal crime differences between areas and between individuals are predicted by area and population heterogeneity and their synergies. It draws on lifestyle/routine activities and social disorganization theories to model the number of personal victimization incidents over individuals including routine activities and area characteristics, respectively, as well as their (cross-cluster) interactions. The methodology employs multilevel or hierarchical negative binomial regression with extra binomial variation using data from the British Crime Survey and the UK Census. Personal crime rates differ substantially across areas, reflecting to a large degree the clustering of individuals with measured vulnerability factors in the same areas. Most factors suggested by theory and previous research are conducive to frequent personal victimization except the following new results. Pensioners living alone in densely populated areas face disproportionally high numbers of personal crimes. Frequent club and pub visits are associated with more personal crimes only for males and adults living with young children, respectively. Ethnic minority individuals experience fewer personal crimes than whites. The findings suggest integrating social disorganization and lifestyle theories and prioritizing resources to the most vulnerable, rather than all, residents of poor and densely populated areas to prevent personal crimes

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

The comparative effectiveness and efficiency of cognitive behaviour therapy and generic counselling in the treatment of depression: evidence from the 2(nd) UK National Audit of psychological therapies.

BACKGROUND: Cognitive Behaviour Therapy (CBT) is the front-line psychological intervention for step 3 within UK psychological therapy services. Counselling is recommended only when other interventions have failed and its effectiveness has been questioned. METHOD: A secondary data analysis was conducted of data collected from 33,243 patients across 103 Improving Access to Psychological Therapies (IAPT) services as part of the second round of the National Audit of Psychological Therapies (NAPT). Initial analysis considered levels of pre-post therapy effect sizes (ESs) and reliable improvement (RI) and reliable and clinically significant improvement (RCSI). Multilevel modelling was used to model predictors of outcome, namely patient pre-post change on PHQ-9 scores at last therapy session. RESULTS: Counselling received more referrals from patients experiencing moderate to severe depression than CBT. For patients scoring above the clinical cut-off on the PHQ-9 at intake, the pre-post ES (95% CI) for CBT was 1.59 (1.58, 1.62) with 46.6% making RCSI criteria and for counselling the pre-post ES was 1.55 (1.52, 1.59) with 44.3% of patients meeting RCSI criteria. Multilevel modelling revealed a significant site effect of 1.8%, while therapy type was not a predictor of outcome. A significant interaction was found between the number of sessions attended and therapy type, with patients attending fewer sessions on average for counselling [M = 7.5 (5.54) sessions and a median (IQR) of 6 (3-10)] than CBT [M = 8.9 (6.34) sessions and a median (IQR) of 7 (4-12)]. Only where patients had 18 or 20 sessions was CBT significantly more effective than counselling, with recovery rates (95% CIs) of 62.2% (57.1, 66.9) and 62.4% (56.5, 68.0) respectively, compared with 44.4% (32.7, 56.6) and 42.6% (30.0, 55.9) for counselling. Counselling was significantly more effective at two sessions with a recovery rate of 34.9% (31.9, 37.9) compared with 22.2% (20.5, 24.0) for CBT. CONCLUSIONS: Outcomes for counselling and CBT in the treatment of depression were comparable. Research efforts should focus on factors other than therapy type that may influence outcomes, namely the inherent variability between services, and adopt multilevel modelling as the given analytic approach in order to capture the naturally nested nature of the implementation and delivery of psychological therapies. It is of concern that half of all patients, regardless of type of intervention, did not show reliable improvement

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Null Models of Economic Networks: The Case of the World Trade Web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.Comment: 39 pages, 46 figures, 2 table