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

Capturing Community Context of Human Response to Forest Disturbance by Insects: A Multi-Method Assessment

The socioeconomic and environmental features of local places (community context) influence the relationship between humans and their physical environment. In times of environmental disturbance, this community context is expected to influence human perceptual and behavioral responses. Residents from nine Colorado communities experiencing a large outbreak of mountain pine beetles (Dendroctonus ponderosae) were surveyed in 2007. Multiple analytic methods including ordinary least squares regression and multilevel modeling techniques were used to evaluate a community-context conceptual model of factors influencing individual actions in response to forest disturbance by beetles. Results indicated that community biophysical and socioeconomic characteristics had important impacts on participation in beetle-related actions and influenced the relationships of individual-level variables in the conceptual model with beetle-related activities. Our findings have implications for natural resource management and policy related to forest disturbances, and for developing a methodology appropriate to measure the general community context of human-environment interactions

Screening for data clustering in multicenter studies: the residual intraclass correlation

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When Art Moves the Eyes: A Behavioral and Eye-Tracking Study

The aim of this study was to investigate, using eye-tracking technique, the influence of bottom-up and top-down processes on visual behavior while subjects, na \u308\u131ve to art criticism, were presented with representational paintings. Forty-two subjects viewed color and black and white paintings (Color) categorized as dynamic or static (Dynamism) (bottom-up processes). Half of the images represented natural environments and half human subjects (Content); all stimuli were displayed under aesthetic and movement judgment conditions (Task) (top-down processes). Results on gazing behavior showed that content-related top-down processes prevailed over low-level visually-driven bottom-up processes when a human subject is represented in the painting. On the contrary, bottom-up processes, mediated by low-level visual features, particularly affected gazing behavior when looking at nature-content images. We discuss our results proposing a reconsideration of the definition of content-related top-down processes in accordance with the concept of embodied simulation in art perception

Effectiveness of a smartphone app in increasing physical activity amongst male adults: a randomised controlled trial.

BACKGROUND: Smartphones are ideal for promoting physical activity in those with little intrinsic motivation for exercise. This study tested three hypotheses: H1 - receipt of social feedback generates higher step-counts than receipt of no feedback; H2 - receipt of social feedback generates higher step-counts than only receiving feedback on one's own walking; H3 - receipt of feedback on one's own walking generates higher step-counts than no feedback (H3). METHODS: A parallel group randomised controlled trial measured the impact of feedback on steps-counts. Healthy male participants (n = 165) aged 18-40 were given phones pre-installed with an app that recorded steps continuously, without the need for user activation. Participants carried these with them as their main phones for a two-week run-in and six-week trial. Randomisation was to three groups: no feedback (control); personal feedback on step-counts; group feedback comparing step-counts against those taken by others in their group. The primary outcome measure, steps per day, was assessed using longitudinal multilevel regression analysis. Control variables included attitude to physical activity and perceived barriers to physical activity. RESULTS: Fifty-five participants were allocated to each group; 152 completed the study and were included in the analysis: n = 49, no feedback; n = 53, individual feedback; n = 50, individual and social feedback. The study provided support for H1 and H3 but not H2. Receipt of either form of feedback explained 7.7 % of between-subject variability in step-count (F = 6.626, p < 0.0005). Compared to the control, the expected step-count for the individual feedback group was 60 % higher (effect on log step-count = 0.474, 95 % CI = 0.166-0.782) and that for the social feedback group, 69 % higher (effect on log step-count = 0.526, 95 % CI = 0.212-0.840). The difference between the two feedback groups (individual vs social feedback) was not statistically significant. CONCLUSIONS: Always-on smartphone apps that provide step-counts can increase physical activity in young to early-middle-aged men but the provision of social feedback has no apparent incremental impact. This approach may be particularly suitable for inactive people with low levels of physical activity; it should now be tested with this population

Multilevel analysis in CSCL Research

Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2011). Multilevel analysis in CSCL research. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 187-205). New York: Springer. doi:10.1007/978-1-4419-7710-6_9CSCL researchers are often interested in the processes that unfold between learners in online learning environments and the outcomes that stem from these interactions. However, studying collaborative learning processes is not an easy task. Researchers have to make quite a few methodological decisions such as how to study the collaborative process itself (e.g., develop a coding scheme or a questionnaire), on the appropriate unit of analysis (e.g., the individual or the group), and which statistical technique to use (e.g., descriptive statistics, analysis of variance, correlation analysis). Recently, several researchers have turned to multilevel analysis (MLA) to answer their research questions (e.g., Cress, 2008; De Wever, Van Keer, Schellens, & Valcke, 2007; Dewiyanti, Brand-Gruwel, Jochems, & Broers, 2007; Schellens, Van Keer, & Valcke, 2005; Strijbos, Martens, Jochems, & Broers, 2004; Stylianou-Georgiou, Papanastasiou, & Puntambekar, chapter #). However, CSCL studies that apply MLA analysis still remain relatively scarce. Instead, many CSCL researchers continue to use ‘traditional’ statistical techniques (e.g., analysis of variance, regression analysis), although these techniques may not be appropriate for what is being studied. An important aim of this chapter is therefore to explain why MLA is often necessary to correctly answer the questions CSCL researchers address. Furthermore, we wish to highlight the consequences of failing to use MLA when this is called for, using data from our own studies

Does Time Since Immigration Modify Neighborhood Deprivation Gradients in Preterm Birth? A Multilevel Analysis

Immigrants’ health is jointly influenced by their pre- and post-migration exposures, but how these two influences operate with increasing duration of residence has not been well-researched. We aimed to examine how the influence of maternal country of birth and neighborhood deprivation effects, if any, change over time since migration and how neighborhood effects among immigrants compare with those observed in the Canadian-born population. Birth data from Ontario hospital records (2002–2007) were linked with an official Canadian immigration database (1985–2000). The outcome measure was preterm birth. Neighborhoods were ranked according to a neighborhood deprivation index developed for Canadian urban areas and collapsed into tertiles of approximately equal size. Time since immigration was measured from the date of arrival to Canada to the date of delivery, ranging from 1 to 22 years. We used cross-classified random effect models to simultaneously account for the membership of births (N = 83,233) to urban neighborhoods (N = 1,801) and maternal countries of birth (N = 168). There were no differences in preterm birth between neighborhood deprivation tertiles among immigrants with less than 15 years of residence. Among immigrants with 15 years of stay or more, the adjusted absolute risk difference (ARD%, 95% confidence interval) between high-deprived (tertile 3) and low-deprived (tertile 1) neighborhoods was 1.86 (0.68, 2.98), while the ARD% observed among the Canadian-born (N = 314,237) was 1.34 (1.11, 1.57). Time since migration modifies the neighborhood deprivation gradient in preterm birth among immigrants living in Ontario cities. Immigrants reached the level of inequalities in preterm birth observed at the neighborhood level among the Canadian-born after 14 years of stay, but neighborhoods did not influence preterm birth among more recent immigrants, for whom the maternal country of birth was more predictive of preterm birth

When One Size Does Not Fit All: A Simple Statistical Method to Deal with Across-Individual Variations of Effects

In science, it is a common experience to discover that although the investigated effect is very clear in some individuals, statistical tests are not significant because the effect is null or even opposite in other individuals. Indeed, t-tests, Anovas and linear regressions compare the average effect with respect to its inter-individual variability, so that they can fail to evidence a factor that has a high effect in many individuals (with respect to the intra-individual variability). In such paradoxical situations, statistical tools are at odds with the researcher’s aim to uncover any factor that affects individual behavior, and not only those with stereotypical effects. In order to go beyond the reductive and sometimes illusory description of the average behavior, we propose a simple statistical method: applying a Kolmogorov-Smirnov test to assess whether the distribution of p-values provided by individual tests is significantly biased towards zero. Using Monte-Carlo studies, we assess the power of this two-step procedure with respect to RM Anova and multilevel mixed-effect analyses, and probe its robustness when individual data violate the assumption of normality and homoscedasticity. We find that the method is powerful and robust even with small sample sizes for which multilevel methods reach their limits. In contrast to existing methods for combining p-values, the Kolmogorov-Smirnov test has unique resistance to outlier individuals: it cannot yield significance based on a high effect in one or two exceptional individuals, which allows drawing valid population inferences. The simplicity and ease of use of our method facilitates the identification of factors that would otherwise be overlooked because they affect individual behavior in significant but variable ways, and its power and reliability with small sample sizes (<30–50 individuals) suggest it as a tool of choice in exploratory studies

The Relationship Between Therapist Effects and Therapy Delivery Factors: Therapy Modality, Dosage, and Non-completion.

To consider the relationships between, therapist variability, therapy modality, therapeutic dose and therapy ending type and assess their effects on the variability of patient outcomes. Multilevel modeling was used to analyse a large sample of routinely collected data. Model residuals identified more and less effective therapists, controlling for case-mix. After controlling for case mix, 5.8 % of the variance in outcome was due to therapists. More sessions generally improved outcomes, by about half a point on the PHQ-9 for each additional session, while non-completion of therapy reduced the amount of pre-post change by six points. Therapy modality had little effect on outcome. Patient and service outcomes may be improved by greater focus on the variability between therapists and in keeping patients in therapy to completion