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. This revised version was published online in June 2006 with corrections to the Cover Date.     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.     

Longitudinal Meta-analysis

The goal of meta-analysis is to integrate the research results of a number of studies on a specific topic. Characteristic for meta-analysis is that in general only the summary statistics of the studies are used and not the original data. When the published research results to be integrated are longitudinal, multilevel analysis can be used for the meta-analysis. We will demonstrate this with an example of longitudinal data on the mental development of infants. We distinguish four levels in the data. The highest level (4) is the publication, in which the results of one or more studies are published. The third level consists of the separate studies. At this level we have knowledge about the degree of prematurity of the group of infants in the specific study. The second level are the repeated measures. We have data about the test age, the mental development, the corresponding standard deviations, and the sample sizes. The lowest level is needed for the specification of the meta-analysis model. Both the way in which the multilevel model has to be specified (the Mln-program is used) as the results will be presented and interpreted.     

Day tour itineraries: Searching for the balance between commercial needs and experiential desires

Little research has been conducted examining the factors that influence the development of day tour itineraries. While the literature suggests that a combination of commercial, operational, political and experiential factors contribute to itinerary development, this article argues that it is the business model chosen that affects it the most. Essentially two business models predominate: commission-based and noncommission-based. This study examines the impact of the business model on the selection of day tour itineraries in the Macau Special Administrative Region of China. It finds that experiential values are traded off almost entirely in the itineraries of commission-based tours, while a better balance between experiential and operational considerations drives itinerary choices for noncommission-based tours.     

The sanitization of colonial history: authenticity,heritage interpretation and the case of Macau's tour guides

Macau was founded by the Portuguese in the mid-sixteenth century. The centuries of colonial rule that preceded its recent return to China gave it a distinctive character unique in China. The tangible and intangible imprint of the colonial history of Macau is central to its attractiveness as a cultural tourism destination. A literature review reveals worldwide examples of the manipulation of history for both political and tourism purposes. This research uses in-depth thematic interviews with 14 local tour guides to show how Macau's colonial heritage is presented and interpreted to tourist groups by the local tour guides, as regards both what they are shown and what they are told. It is found that the guides adjust their interpretation to the geographical origin of the tourists in a significant way; in particular, they tend to almost totally eschew any reference to its colonial history when addressing a Chinese audience, in contrast to the interpretation presented to non-Chinese visitors. The use of Hall's three-fold interpretative message framework (dominant – hegemonic/negotiated/oppositional) is discussed, as is Langer's concept of mindless and mindful tourists. Some consequences for the sustainability of heritage tourism in Macau are also explored.