Random-effects Procedures Research Articles

Beyond subsistence? The role of rural cooperatives in driving smallholder commercialization in Ethiopia

AbstractThe transition from subsistence to market‐oriented agriculture holds the potential to boost rural economic progress and improve the well‐being of the rural poor in developing countries. Despite this potential, there is limited understanding of the key drivers for smallholder commercialization. In this study, we utilize comprehensive three‐wave panel data collected from three major maize‐producing regions in Ethiopia to examine the role of agricultural cooperatives in promoting smallholder commercialization. Employing a double‐hurdle model, we smallholder commercialization as a two‐step decision process involving market participation and the extent of participation (sales quantity), conditional on market participation. Unobserved heterogeneity is accounted for using a correlated random effect procedure, and the potential endogeneity of cooperative membership is addressed through a control function approach. The findings reveal that cooperative membership increases the probability of market participation and the intensity of sales. However, further analysis demonstrates a heterogeneous relationship between cooperative membership and commercialization, underscoring the importance of targeted interventions to boost the contribution of cooperatives to rural development through commercialization. The main findings highlight that promoting smallholder commercialization through farmer groups necessitates implementing policies to increase cooperative membership, coupled with allocating resources that enhance the contribution of agricultural cooperatives [EconLit Citations: Q12, Q13, Q18].

Sustainability transitions of contaminated sites: A global meta‐analysis on economic effects of remediation behaviour

AbstractThe worldwide diversity of contaminated sites, coupled with a scarcity of available land presents a challenge for urban spatial planning and, has led to an increasing political significance for brownfield conservation and reuse to achieve land resource sustainability. In this study, economic or the so‐called ‘rebound effects’ of land regeneration are studied via a global meta‐analysis on value fluctuation of surrounding property. To this end, a total of 91 observations from 28 HPM (hedonic pricing model) studies were synthesized to conduct a meta‐analysis following a conditional random effects procedure. The empirical results indicate that, in line with expectations, the conservation and recycling of land resource indeed generate significant rebound in the implicit price of residential houses, especially for those located within 2 km of contaminated sites. Before land remediation and reuse, dwellings closest in distance to contaminated sites experience the greatest value loss. On average, the depreciation in property values within the first 1 km distance from a contaminated site is about 8.18%, significantly at the 1% level, while the corresponding adverse impact from 1 to 2 km distance is a 4.8% price premium significantly at the 5% level. The significance of the stigma or rebound effects depends on 12 attributes, in which, house age, location, floor area ratio (FAR), and central business district (CBD) variables have the largest impact of −37.38% to 37.5%. From a practical perspective, the findings of this meta‐analysis: (1) help refine contributing parameters in HPM studies to evaluate environmental economics; and (2) provide meaningful decision‐making support for cost‐effective remediation and benefit maximization.

Exploring alterations in sensory pathways in migraine

BackgroundMigraine is a neurological disorder characterized by intense, debilitating headaches, often coupled with nausea, vomiting and sensitivity to light and sound. Whilst changes in sensory processes during a migraine attack have been well-described, there is growing evidence that even between migraine attacks, sensory abilities are disrupted in migraine. Brain imaging studies have investigated altered coupling between areas of the descending pain modulatory pathway but coupling between somatosensory processing regions between migraine attacks has not been properly studied. The aim of this study was to determine if ongoing functional connectivity between visual, auditory, olfactory, gustatory and somatosensory cortices are altered during the interictal phase of migraine.MethodsTo explore the neural mechanisms underpinning interictal changes in sensory processing, we used functional magnetic resonance imaging to compare resting brain activity patterns and connectivity in migraineurs between migraine attacks (n = 32) and in healthy controls (n = 71). Significant differences between groups were determined using two-sample random effects procedures (p < 0.05, corrected for multiple comparisons, minimum cluster size 10 contiguous voxels, age and gender included as nuisance variables).ResultsIn the migraine group, increases in infra-slow oscillatory activity were detected in the right primary visual cortex (V1), secondary visual cortex (V2) and third visual complex (V3), and left V3. In addition, resting connectivity analysis revealed that migraineurs displayed significantly enhanced connectivity between V1 and V2 with other sensory cortices including the auditory, gustatory, motor and somatosensory cortices.ConclusionsThese data provide evidence for a dysfunctional sensory network in pain-free migraine patients which may be underlying altered sensory processing between migraine attacks.

Vegetarian diet and depression scores: A meta-analysis

BackgroundSeveral studies have suggested an association between vegetarian diet and higher depression scores. However, some studies have also shown an effect in the opposite direction, indicating lower depression scores in vegetarians. Given this discrepancy in the literature, this meta-analysis was aimed to determine whether there is a significant association between vegetarian diet and depression scores across different published studies. MethodsA keyword search in major databases was conducted. Studies reporting depression scores in vegetarians and a non-vegetarian control group were included. Meta-analysis following a conditional random-effects procedure was conducted in R. ResultsAfter duplicates were removed and studies were analyzed for inclusion criteria, k=13 studies with an overall n of 49889 participants (8057 vegetarians and 41832 non-vegetarian controls) were included in the analysis. Random-effects meta-analysis revealed a significant difference between vegetarians and non-vegetarians, with vegetarians showing higher depression scores than non-vegetarians. LimitationsThe heterogeneity between studies was high and geographical variation in study location was low, limiting cross-cultural insights. ConclusionsVegetarians show higher depression scores than non-vegetarians. However, due to high heterogeneity of published studies, more empirical research is needed before any final conclusions can be drawn. Also, empirical studies from a higher number of different countries would be desirable.

Effect of off‐farm income on smallholder commercialization: panel evidence from rural households in Ethiopia

AbstractThis article investigates how off‐farm income affects crop output market participation decisions and marketed surplus of smallholder farmers in Ethiopia. A double‐hurdle model is estimated using three waves of panel data from the Ethiopian Rural Household Survey. Unobserved heterogeneity is controlled for using a correlated random effect procedure and potential endogeneity of explanatory variables using a control function approach. The results show that off‐farm income has no significant influence on household crop output market participation. However, conditional on market participation, additional off‐farm earnings negatively affect the marketed surplus. This indicates that farmers use off‐farm earnings for consumption rather than for investment in agricultural production. Policy measures that promote rural investment may help increase returns to labor for land‐poor households participating in off‐farm work in the process of agricultural commercialization.

Site-Condition Proxies, Ground Motion Variability, and Data-Driven GMPEs: Insights from the NGA-West2 and RESORCE Data Sets

We compare the ability of various site-condition proxies (SCPs) to reduce the aleatory variability of ground motion prediction equations (GMPEs). Three SCPs (measured V S30, inferred V S30, local topographic slope) and two accelerometric databases (RESORCE and NGA-West2) are considered. An artificial neural network (ANN) approach including a random-effect procedure is used to derive GMPEs setting the relationship between peak ground acceleration ( PGA), peak ground velocity ( PGV), pseudo-spectral acceleration [ PSA( T)], and explanatory variables ( M w, R JB, and V S30 or Slope). The analysis is performed using both discrete site classes and continuous proxy values. All “non-measured” SCPs exhibit a rather poor performance in reducing aleatory variability, compared to the better performance of measured V S30. A new, fully data-driven GMPE based on the NGA-West2 is then derived, with an aleatory variability value depending on the quality of the SCP. It proves very consistent with previous GMPEs built on the same data set. Measuring V S30 allows for benefit from an aleatory variability reduction up to 15%.

Study of behavioural and neural bases of visuo-spatial working memory with an fMRI paradigm based on an n-back task.

The goal of this study was to propose a new functional magnetic resonance imaging (fMRI) paradigm using a language-free adaptation of a 2-back working memory task to avoid cultural and educational bias. We additionally provide an index of the validity of the proposed paradigm and test whether the experimental task discriminates the behavioural performances of healthy participants from those of individuals with working memory deficits. Ten healthy participants and nine patients presenting working memory (WM) deficits due to acquired brain injury (ABI) performed the developed task. To inspect whether the paradigm activates brain areas typically involved in visual working memory (VWM), brain activation of the healthy participants was assessed with fMRIs. To examine the task's capacity to discriminate behavioural data, performances of the healthy participants in the task were compared with those of ABI patients. Data were analysed with GLM-based random effects procedures and t-tests. We found an increase of the BOLD signal in the specialized areas of VWM. Concerning behavioural performances, healthy participants showed the predicted pattern of more hits, less omissions and a tendency for fewer false alarms, more self-corrected responses, and faster reaction times, when compared with subjects presenting WM impairments. The results suggest that this task activates brain areas involved in VWM and discriminates behavioural performances of clinical and non-clinical groups. It can thus be used as a research methodology for behavioural and neuroimaging studies of VWM in block-design paradigms.

A simulation study to assess statistical methods for binary repeated measures data

Binary repeated measures data are commonly encountered in both experimental and observational veterinary studies. Among the wide range of statistical methods and software applicable to such data one major distinction is between marginal and random effects procedures. The objective of the study was to review and assess the performance of marginal and random effects estimation procedures for the analysis of binary repeated measures data. Two simulation studies were carried out, using relatively small, balanced, two-level (time within subjects) datasets. The first study was based on data generated from a marginal model with first order autocorrelation, the second on a random effects model with autocorrelated random effects within subjects. Three versions of the models were considered in which a dichotomous treatment was modelled additively, either between or within subjects, or modelled by a time interaction. Among the studied statistical procedures were: generalized estimating equations (GEE), Marginal Quasi Likelihood, likelihood based on numerical integration, penalized quasi-likelihood, restricted pseudo likelihood and Bayesian Markov Chain Monte Carlo. Results for data generated by the marginal model showed autoregressive GEE to be highly efficient when treatment was within subjects, even with strongly correlated responses. For treatment between subjects, random effects procedures also performed well in some situations; however, a relatively small number of subjects with a short time series proved a challenge for both marginal and random effects procedures. Results for data generated by the random effects model showed bias in estimates from random effects procedures when autocorrelation was present in the data, while the marginal procedures generally gave estimates close to the marginal parameters.

Annual Age-Grouping and Athlete Development

Annual age-grouping is a common organizational strategy in sport. However, such a strategy appears to promote relative age effects (RAEs). RAEs refer both to the immediate participation and long-term attainment constraints in sport, occurring as a result of chronological age and associated physical (e.g. height) differences as well as selection practices in annual age-grouped cohorts. This article represents the first meta-analytical review of RAEs, aimed to collectively determine (i) the overall prevalence and strength of RAEs across and within sports, and (ii) identify moderator variables. A total of 38 studies, spanning 1984-2007, containing 253 independent samples across 14 sports and 16 countries were re-examined and included in a single analysis using odds ratios and random effects procedures for combining study estimates. Overall results identified consistent prevalence of RAEs, but with small effect sizes. Effect size increased linearly with relative age differences. Follow-up analyses identified age category, skill level and sport context as moderators of RAE magnitude. Sports context involving adolescent (aged 15-18 years) males, at the representative (i.e. regional and national) level in highly popular sports appear most at risk to RAE inequalities. Researchers need to understand the mechanisms by which RAEs magnify and subside, as well as confirm whether RAEs exist in female and more culturally diverse contexts. To reduce and eliminate this social inequality from influencing athletes' experiences, especially within developmental periods, direct policy, organizational and practitioner intervention is required.

An Illustration of the Consequences of Meta-Analysis Model Choice

Fixed- and random-effects models represent two different approaches to analyzing and understanding data with meta-analysis. The current article describes the results of a two-part study to illustrate the effect of choice of meta-analytic model on study conclusions. Part 1 illustrates the effect of model choice by analyzing data simulated to conform to either fixed- or random-effects scenarios with both fixed- and random-effects methods of data analysis. Part 2 uses two published meta-analyses to show that methodological choices, in this case mainly the choice of fixed- or random-effects models, affect estimates both of mean effect size and of the random-effects variance component (REVC). Overall, results suggest that random-effects procedures represent the best initial choice when conducting a meta-analysis.

Markov Models for Repeated Ordinal Data

To demonstrate the application of Markov models, especially for ordinal outcomes, within the context of regression models for correlated data. A brief review of regression methods for correlated data is given. A proportional odds model and a continuation ratio model is applied to repeated measurements of macular pigment density, obtained in an intervention study on the supplementation of macular carotenoids. The correlation between repeated assessments is assumed to follow a first-order Markov model. The models are implemented with standard statistical software. Both models, though not directly comparable, provide a similar conclusion. The application of these models with standard statistical software is straightforward. Markov models can be valuable alternatives to random effects modes or procedures based on generalized estimation equations.

Neural Mechanisms Underlying Probabilistic Category Learning in Normal Aging

Probabilistic category learning engages neural circuitry that includes the prefrontal cortex and caudate nucleus, two regions that show prominent changes with normal aging. However, the specific contributions of these brain regions are uncertain, and the effects of normal aging have not been examined previously in probabilistic category learning. In the present study, using a blood oxygenation level-dependent functional magnetic resonance imaging block design, 18 healthy young adults (mean age, 25.5 +/- 2.6 years) and 15 older adults (mean age, 67.1 +/- 5.3 years) were assessed on the probabilistic category learning "weather prediction" test. Whole-brain functional images acquired using a 1.5T scanner (General Electric, Milwaukee, WI) with gradient echo, echo planar imaging (3/1 mm; repetition time, 3000 ms; echo time, 50 ms) were analyzed using second-level random-effects procedures [SPM99 (Statistical Parametric Mapping)]. Young and older adults displayed equivalent probabilistic category learning curves, used similar strategies, and activated analogous neural networks, including the prefrontal and parietal cortices and the caudate nucleus. However, the extent of caudate and prefrontal activation was less and parietal activation was greater in older participants. The percentage correct and reaction time were mainly positively correlated with caudate and prefrontal activation in young individuals but positively correlated with prefrontal and parietal cortices in older individuals. Differential activation within a circumscribed neural network in the context of equivalent learning suggests that some brain regions, such as the parietal cortices, may provide a compensatory mechanism for healthy older adults in the context of deficient prefrontal cortex and caudate nuclei responses.

Altered Functional Connectivity Related to White Matter Changes inside the Working Memory Network at the Very Early Stage of MS

Functional magnetic resonance imaging (fMRI) using paced auditory serial addition test (PASAT) as paradigm was used to study the functional connectivity in 18 patients at the very early stage of multiple sclerosis (MS) compared with 18 controls, to determine the existence of circuitry disturbance inside the working memory network and its relationship with white matter abnormalities assessed by conventional MRI and magnetization transfer ratio (MTR) imaging. The left BA 45/46 was selected as the seed region to compute correlation maps with other brain regions. After obtaining the correlation map for each subject, between-group comparisons were performed using random effect procedure. Compared with controls, patients did not show any greater functional connectivity between left BA 45/46 and other regions during PASAT. In contrast, decrease in functional connectivity was observed in patients between left BA 45/46 and left BA 9, right BA 3, and the anterior cingulate cortex (BA 24). In patients, no correlations were found between altered functional connectivity and clinical data. However, functional connectivity observed between left BA 45/46 and BA 24 in patients was correlated with the MTR of normal appearing white matter, and with brain T(2) lesion load. Altered functional connectivity is present inside the working memory network of patients at the very early stage of MS and is related to the extent of diffuse white matter changes.

Is the P300 wave an endophenotype for schizophrenia? A meta-analysis and a family study

Introduction: We assessed the usefulness of the P300 wave as endophenotype for schizophrenia by means of a meta-analysis of the literature as well as our own family study. Method: Meta-analysis: We conducted a systematic search for articles published between 1983 and 2003 that reported P300 measures in non-psychotic relatives of schizophrenic patients and in healthy controls. Meta-regression analyses were performed using a random effects procedure. The pooled standardized effect size (PSES) was calculated as the difference between the means of the two groups divided by the common standard deviation. Local study: We examined the P300 wave with a standard two-tone oddball paradigm in 30 patients with schizophrenia, 40 non-psychotic relatives, and 40 controls using linear mixed models. Results: Meta-analysis: We pooled 472 relatives and 513 controls. The P300 amplitude was significantly reduced in relatives (PSES = 0.61; 95% CI: 0.30 to 0.91; P < 0.001). The P300 latency was significantly delayed in relatives (PSES of −0.50; 95% CI: −0.88 to −0.13; P = 0.009]. Local study: The patients showed a trend for amplitude reductions ( P = 0.06) and significant latency delays ( P < 0.01). The relatives displayed normal amplitude but had significant latency delays ( P = 0.01). The P300 amplitude and especially the P300 latency are promising alternative phenotypes for genetic research into schizophrenia.

Functional MRI study of PASAT in normal subjects

The paced auditory serial addition test (PASAT) is routinely used to evaluate the cognitive part of the multiple sclerosis functional composite (MSFC) score, the new reference index of patient disability. PASAT is sensitive to subtle cognitive impairment related to MS, although the cognitive components of this test still remain unclear. In order to better characterize brain systems involved during this complex task, functional magnetic resonance imaging (fMRI) experiments were conducted during PASAT in a population of ten normal subjects. The paradigm consisted of a series of 61 single-digit numbers delivered every 3 s. After each number, subjects were asked to overt vocalize the result of the addition of the two last numbers heard. A control task consisting of the repetition of the same series of single-digit numbers was used. Statistical group analysis was performed using the random effect procedure (SPM 99). Cortical activation was observed in the left prefrontal cortex, the supplementary motor area, the lateral premotor cortex, the cingulate gyrus, the left parietal lobe, the left superior temporal gyrus, the left temporal pole, and visual associative areas. fMRI activations underlying PASAT were consistent with an involvement of verbal working memory and the semantic memory retrieval network which could be related to arithmetic fact retrieval. This study on normal subjects could provide a base for the understanding of the potential abnormal cortical activation in MS patients performing this test for a cognitive evaluation.

Effects of Participation on Performance and Satisfaction: Additional Meta-Analytic Evidence

Stimulated by recent debate, this study investigated whether prior research supports the statement that different forms of participation have different effects on performance and satisfaction in the workplace. Using a collection of 75 correlations drawn from published analyses, a meta-analysis using random effects procedures indicated that relationships between participation and performance reported in the research literature are similar in size and direction across different types of participation. Meta-analytic results also indicated similarity in the size and direction of relationships between participation and satisfaction across different forms of participation and suggested that effect size statistics published in research on participation and performance are generally similar to those reported in studies of participation and satisfaction. These findings differ from the results of another recent meta-analysis and from those of several previous literature reviews but support the primary conclusions of an earlier meta-analytic assessment.

Fixed- and random-effects models in meta-analysis.

There are 2 families of statistical procedures in meta-analysis: fixed- and randomeffects procedures. They were developed for somewhat different inference goals: making inferences about the effect parameters in the studies that have been observed versus making inferences about the distribution of effect parameters in a population of studies from a random sample of studies. The authors evaluate the performance of confidence intervals and hypothesis tests when each type of statistical procedure is used for each type of inference and confirm that each procedure is best for making the kind of inference for which it was designed. Conditionally random-effects procedures (a hybrid type) are shown to have properties in between those of fixed- and random-effects procedures. The use of quantitative methods to summarize the results of several empirical research studies, or metaanalysis, is now widely used in psychology, medicine, and the social sciences. Meta-analysis usually involves describing the results of each study by means of a numerical index (an estimate of effect size, such as a correlation coefficient, a standardized mean difference, or an odds ratio) and then combining these estimates across studies to obtain a summary. Two somewhat different statistical models have been developed for inference about average effect size from a collection of studies, called the fixed-effects and random-effects models. (A third alternative, the mixedeffects model, arises in conjunction with analyses involving study-level covariates or moderator variables, which we do not consider in this article; see Hedges, 1992.) Fixed-effects models treat the effect-size parameters as fixed but unknown constants to be estimated and usually (but not necessarily) are used in conjunction with assumptions about the homogeneity of effect parameters (see, e.g., Hedges, 1982; Rosenthal & Rubin, 1982). Random-effects models treat the effectsize parameters as if they were a random sample from

A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials

A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials

Comparison of general multivariate and random-effects models for growth curve analysis: incomplete-data smallsample situations

This study investigates the application of general multivariate and the random-effects models under incomplete-data small-sample growth curve situations. The primary interest is testing hypotheses of parallelism and identity of linear growth curves for subjects in two groups. The null distributions of Wald statistics for the random-effects model are examined for appropriateness of asymptotic chi-square and recommendations are made for controlling type I error rates. Monte Carlo techniques are used to find an approximation of the null distribution. Simulations used each combination of one of three covariance structures, one of three missing data patterns (1/5, 1/3, and 2/5 proportion missing), one of two hypotheses (parallelism and identity), and one of four nominal significance levels (.05, .025,.01, and .005). This leads to a recommendation of an approximation of the null distribution for the random-effects procedure. Monte Carlo techniques are also used to compare the power performance of the procedure ...