Methodological challenges to research among Chinese : using mixed methods to investigate older persons with depression in Macau

The article offers an example of using a mixed methods design that was implemented when investigating depression among Chinese older persons in Macau. A challenge in designing the study was that in light of literature on Chinese culture and somatization theory, the acceptability of the methodology was uncertain. Participants (n = 31) were purposively selected and quantitative data were collected using multiple standardized measures. Questions raised by the quantitative approach were then reflected through in-depth interviews. Four dominant categories emerged: (a) negative thinking, (b) physical limitations and complaints, (c) present living conditions and social support, and (d) the lives participants have lived. There was a high level of congruence between quantitative scores and narratives. The research approach was acceptable to the participants, and the findings contributed to an understanding of depression and therapeutic interventions for that population within Chinese culture.

"Flows and catchments" : a mixed method study utilising nvivo to facilitate cross-disciplinary collaboration incorporating practice-based research

This paper responds to the problem of how Practice-Based Research (PBR) can be usefully incorporated into a cross-disciplinary, Mixed Method project design, by considering a non-conventional use of the NVivo qualitative analysis software package. NVivo also proves valuable in maximising the creative-arts practice interdisciplinary potential within PBR itself. The ensuing discussion centres around Deakin University’s ‘Flows and Catchments’ research and teaching initiative.

The use of mixed methods in drug discovery: integrating qualitative methods into clinical trials

Contemporary methods in clinical trials are pivoted around hypothesis confirmation, not generation. This is a problem for new drug discovery, since the pharmacokinetic or receptor profile of most novel agents do not link to pathophysiology, which is very poorly understood. Therefore, it is difficult to impute the therapeutic potential of a candidate agent. Most psychotropic agents were discovered serendipitously, either through careful clinical observation or by researchers finding unexpected associations in datasets. Methods that increase the ability to detect latent signals in data are needed. These include mixed methods that incorporate qualitative methods into randomized controlled trials.

This chapter proposes a methodology for the integration of mixed methods in clinical trials, fusing qualitative and quantitative methods, and presents an exemplar using this approach.

Mixed methods show potential for signal detection, hypothesis generation, and associations that may be otherwise undetected in traditional clinical trials.

Bayesian longitudinal data analysis with mixed models and thick-tailed distributions using MCMC

Linear mixed effects models are frequently used to analyse longitudinal data, due to their flexibility in modelling the covariance structure between and within observations. Further, it is easy to deal with unbalanced data, either with respect to the number of observations per subject or per time period, and with varying time intervals between observations. In most applications of mixed models to biological sciences, a normal distribution is assumed both for the random effects and for the residuals. This, however, makes inferences vulnerable to the presence of outliers. Here, linear mixed models employing thick-tailed distributions for robust inferences in longitudinal data analysis are described. Specific distributions discussed include the Student-t, the slash and the contaminated normal. A Bayesian framework is adopted, and the Gibbs sampler and the Metropolis-Hastings algorithms are used to carry out the posterior analyses. An example with data on orthodontic distance growth in children is discussed to illustrate the methodology. Analyses based on either the Student-t distribution or on the usual Gaussian assumption are contrasted. The thick-tailed distributions provide an appealing robust alternative to the Gaussian process for modelling distributions of the random effects and of residuals in linear mixed models, and the MCMC implementation allows the computations to be performed in a flexible manner.

Selection of alfalfa cultivars adapted for tropical environments with repeated measures using PROC MIXED of SAS (R) System

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Methodology analysis of a computational tool used in electronic circuit design

This paper considers the importance of using a top-down methodology and suitable CAD tools in the development of electronic circuits. The paper presents an evaluation of the methodology used in a computational tool created to support the synthesis of digital to analog converter models by translating between different tools used in a wide variety of applications. This tool is named MS 2SV and works directly with the following two commercial tools: MATLAB/Simulink and SystemVision. Model translation of an electronic circuit is achieved by translating a mixed-signal block diagram developed in Simulink into a lower level of abstraction in VHDL-AMS and the simulation project support structure in SystemVision. The method validation was performed by analyzing the power spectral of the signal obtained by the discrete Fourier transform of a digital to analog converter simulation model. © 2011 IEEE.

A mixed-integer LP model for the reconfiguration of radial electric distribution systems considering distributed generation

The problem of reconfiguration of distribution systems considering the presence of distributed generation is modeled as a mixed-integer linear programming (MILP) problem in this paper. The demands of the electric distribution system are modeled through linear approximations in terms of real and imaginary parts of the voltage, taking into account typical operating conditions of the electric distribution system. The use of an MILP formulation has the following benefits: (a) a robust mathematical model that is equivalent to the mixed-integer non-linear programming model; (b) an efficient computational behavior with exiting MILP solvers; and (c) guarantees convergence to optimality using classical optimization techniques. Results from one test system and two real systems show the excellent performance of the proposed methodology compared with conventional methods. © 2012 Published by Elsevier B.V. All rights reserved.

Analysis Methodology for Flow-level Evaluation of a Hybrid Mobile-Sensor Network

Our society uses a large diversity of co-existing wired and wireless networks in order to satisfy its communication needs. A cooper- ation between these networks can benefit performance, service availabil- ity and deployment ease, and leads to the emergence of hybrid networks. This position paper focuses on a hybrid mobile-sensor network identify- ing potential advantages and challenges of its use and defining feasible applications. The main value of the paper, however, is in the proposed analysis approach to evaluate the performance at the mobile network side given the mixed mobile-sensor traffic. The approach combines packet- level analysis with modelling of flow-level behaviour and can be applied for the study of various application scenarios. In this paper we consider two applications with distinct traffic models namely multimedia traffic and best-effort traffic.

On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

Evaluation of a Bayesian MCMC Random Effects Inference Methodology for Capture-Mark-Recapture Data

Monte Carlo simulation was used to evaluate properties of a simple Bayesian MCMC analysis of the random effects model for single group Cormack-Jolly-Seber capture-recapture data. The MCMC method is applied to the model via a logit link, so parameters p, S are on a logit scale, where logit(S) is assumed to have, and is generated from, a normal distribution with mean μ and variance σ2 . Marginal prior distributions on logit(p) and μ were independent normal with mean zero and standard deviation 1.75 for logit(p) and 100 for μ ; hence minimally informative. Marginal prior distribution on σ2 was placed on τ2=1/σ2 as a gamma distribution with α=β=0.001 . The study design has 432 points spread over 5 factors: occasions (t) , new releases per occasion (u), p, μ , and σ . At each design point 100 independent trials were completed (hence 43,200 trials in total), each with sample size n=10,000 from the parameter posterior distribution. At 128 of these design points comparisons are made to previously reported results from a method of moments procedure. We looked at properties of point and interval inference on μ , and σ based on the posterior mean, median, and mode and equal-tailed 95% credibility interval. Bayesian inference did very well for the parameter μ , but under the conditions used here, MCMC inference performance for σ was mixed: poor for sparse data (i.e., only 7 occasions) or σ=0 , but good when there were sufficient data and not small σ .

Mixed effects model in negative exponential curve

Despite many researches on development in education and psychology, not often is the methodology tested with real data. A major barrier to test the growth model is that the design of study includes repeated observations and the nature of the growth is nonlinear. The repeat measurements on a nonlinear model require sophisticated statistical methods. In this study, we present mixed effects model in a negative exponential curve to describe the development of children's reading skills. This model can describe the nature of the growth on children's reading skills and account for intra-individual and inter-individual variation. We also apply simple techniques including cross-validation, regression, and graphical methods to determine the most appropriate curve for data, to find efficient initial values of parameters, and to select potential covariates. We illustrate with an example that motivated this research: a longitudinal study of academic skills from grade 1 to grade 12 in Connecticut public schools. ^