Multifactorial Designs Research Articles

“Quantifying the Impacts of Multiple Stressors” (QIMS)—a new experimental platform for robust multifactorial experiments in benthic ecosystems

AbstractTo predict the ecological consequences of expected global change, we need to understand the independent and combined effects of multiple stressors. Multiple experimental treatments are required to simultaneously test for effects of multiple stressors at different levels of intensity, independently and combined, and at different levels of biological organization. Most marine multiple stressors studies to date are conducted on assembled communities in mesocosms with a low number of treatments or low replication of treatments or both. These limitations prevent (1) robust data analyses, (2) characterization of single and combined effects of multiple stressors, and (3) identification of mechanisms underpinning biological responses. We present a new mesocosm‐based experimental platform for benthic communities: Quantifying the Impacts of Multiple Stressors (QIMS). Here, 96 independent mesocosms facilitate multifactorial and multilevel experimental designs with the high replication required for robust tests of multiple stressors and biological interactions. For example, three distinct pH levels are achieved by manipulating CO2 concentrations in the air supply, and three water temperature levels are provided by a cooling system in a fully crossed design that is required to identify all potential interactions, from which all combinations can be replicated 10 times (i.e., 90 experimental units). We demonstrate clearly how different levels of temperature and pCO2/pH can be manipulated precisely and maintained for at least 7 weeks. QIMS complements the limited number of permanently installed marine mesocosm facilities worldwide that simulate ocean warming and/or acidification and expedites multiple stressor research by providing an unprecedented level of replication for statistical robustness.

Charting the Trajectory of Corpus Translation Studies: Exploring Future Avenues for Advancement

Abstract This article delves into the trajectory of corpus translation studies (CTS) over the past two decades, summarizing key areas of existing research and identifying potential gaps and challenges within the field. The review encompasses various research areas, including translation universals, translator style, translation norms, and translation pedagogy. It acknowledges the valuable contributions made in these areas while also highlighting potential areas for improvement, such as the need to incorporate functional aspects in translator style research and align translation training programs with professional requirements. The review introduces (De Sutter, Gert, and Marie-Aude Lefer. 2020. “On the Need for a New Research Agenda for Corpus-Based Translation Studies: A Multi-Methodological, Multifactorial and Interdisciplinary Approach.” Perspectives 28 (1): 1–23) new research agenda for CTS, which advocates for multifactorial designs, methodological pluralism, and interdisciplinarity. This agenda facilitates two analysis modes: one utilizing corpus methods to examine translation products, and the other employing diverse methods to investigate products, processes, participants, and contexts in corpus-assisted translation practices. It is argued that these two analysis modes offer valuable guidance for future corpus-assisted translation studies.

Learner corpus research: a critical appraisal and roadmap for contributing (more) to SLA research agendas

Abstract Over the past decade, learner corpora have gained recognition as valuable data sources in Second Language Acquisition (SLA) research. This development can be attributed to significant progress in Learner Corpus Research (LCR). However, there is still substantial work to be done. This article highlights key issues essential for sustaining the relevance of learner corpora in SLA. More particularly, I focus on the need for more diverse types of learner corpora, stress the importance of detailed metadata, and advocate for multifactorial study designs. I then revisit ongoing debates regarding the role of the native speaker in LCR and propose a practical solution to address this thorny issue. Finally, I also readdress the need for improvement in the quantitative methods and statistics, arguing that the importance of robust quantitative analysis cannot be overstated. In conclusion, I envision an ambitious learner corpus compilation project that adheres to the FAIR principles, with the goal of further elevating study quality in LCR.

Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM

Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior information and can have poor statistical properties in small samples. We show how L-RM-ANOVA can be used with Bayesian estimation to resolve the aforementioned issues. We demonstrate how to place informative priors on model parameters that constitute main and interaction effects. We further show how to place weakly informative priors on standardized parameters which can be used when no prior information is available. We conclude that Bayesian estimation can lower Type 1 error and bias, and increase power and efficiency when priors are chosen adequately. We demonstrate the approach using a real empirical example and guide the readers through specification of the model. We argue that ANOVA tables and incomplete descriptive statistics are not sufficient information to specify informative priors, and we identify which parameter estimates should be reported in future research; thereby promoting cumulative research.

Isolation and characterization of Candida tropicalis B: a promising yeast strain for biodegradation of petroleum oil in marine environments

The increasing interest in environmental protection laws has compelled companies to regulate the disposal of waste organic materials. Despite efforts to explore alternative energy sources, the world remains heavily dependent on crude petroleum oil and its derivatives. The expansion of the petroleum industry has significant implications for human and environmental well-being. Bioremediation, employing living microorganisms, presents a promising approach to mitigate the harmful effects of organic hydrocarbons derived from petroleum. This study aimed to isolate and purify local yeast strains from oil-contaminated marine water samples capable of aerobically degrading crude petroleum oils and utilizing them as sole carbon and energy sources. One yeast strain (isolate B) identified as Candida tropicalis demonstrated high potential for biodegrading petroleum oil in seawater. Physiological characterization revealed the strain’s ability to thrive across a wide pH range (4–11) with optimal growth at pH 4, as well as tolerate salt concentrations ranging from 1 to 12%. The presence of glucose and yeast extract in the growth medium significantly enhanced the strain's biomass formation and biodegradation capacity. Scanning electron microscopy indicated that the yeast cell diameter varied based on the medium composition, further emphasizing the importance of organic nitrogenous sources for initial growth. Furthermore, the yeast strain exhibited remarkable capabilities in degrading various aliphatic and aromatic hydrocarbons, with a notable preference for naphthalene and phenol at 500 and 1000 mg/l, naphthalene removal reached 97.4% and 98.6%, and phenol removal reached 79.48% and 52.79%, respectively. Optimization experiments using multi-factorial sequential designs highlighted the influential role of oil concentration on the bioremediation efficiency of Candida tropicalis strain B. Moreover, immobilized yeast cells on thin wood chips demonstrated enhanced crude oil degradation compared to thick wood chips, likely due to increased surface area for cell attachment. These findings contribute to our understanding of the potential of Candida tropicalis for petroleum oil bioremediation in marine environments, paving the way for sustainable approaches to address oil pollution.

Maximizing Bioethanol Production from Eucalyptus globulus Using Steam Explosion Pretreatment: A Multifactorial Design and Fermenter Development for High Solid Loads

Steam explosion pretreatment is suitable for bioethanol production from Eucalyptus globulus wood. Multifactorial experiment designs were used to find the optimal temperature and residence time required to obtain the best glucose yield from the enzymatic hydrolysis of pretreated materials. The chemical composition, crystallinity index, morphology and polymerization degree of the pretreated materials were correlated with enzymatic accessibility. Simultaneous saccharification and fermentation (SSF) using a fed-batch strategy was applied to three different laboratory-scale fermenters. The optimization of the pretreatment was obtained at 208 °C and 11 min. However, the enzymatic hydrolysis performance did not show significant differences from the material obtained at 196 °C and 9.5 min, which was determined to be the real optimum, owing to its lower energy requirement. The vertical fermenter with type “G” blades and the horizontal fermenter with helical blades were both highly efficient for reaching ethanol yields close to 90% based on dry wood, and ethanol concentrations close to 9.0% v/v.

Ecotoxicological response surface analysis of salt and pH in textile effluent on Bacillus subtilis and Lactuca sativa.

Textile effluents, although their composition can vary considerably, typically contain high levels of dissolved salts and exhibit wide variations in pH. Ecotoxicological studies regarding the effects of these parameters, however, have been limited owing to the need for sensitive and easy-to-handle bioindicators that require low amounts of sampling, are cost-effective, time-efficient, and ethically endorsed. This kind of study, additionally, demands robust multi-factorial statistical designs that can accurately characterize the individual and combined relationship between variables. In this research, Response Surface Methodology (RSM) was used to calculate the individual and interaction effects of NaCl concentration and pH value of a Simulated Textile Effluent (STE) on the development rate (DR) of the bioindicators: Bacillus subtilis bacteria and Lactuca sativa lettuce. The results demonstrated that the bioindicators were sensitive to both NaCl and pH factors, where the relative sensitivity relationship was B. subtilis > L. sativa. The quadratic equations generated in the experiments indicated that increased concentrations of 50-250 mg L-1 of NaCl caused a perturbance of 1.40%-34.40% on the DR of B. subtilis and 0.50%-12.30% on L. sativa. The pH factor at values of 3-11 caused an alteration of 27.00%-64.78% on the DR of the B. subtilis and 51.37%-37.37% on the L. sativa. These findings suggest that the selected bioindicators could serve as effective tools to assess the ecotoxicological effects of textile effluents on different ecological systems, and the RSM was an excellent tool to consider the ecotoxicological effects of the parameters and to describe the behavior of the results. In conclusion, the NaCl and pH factors may be responsible for disrupting different ecosystems, causing imbalances in their biodiversity and biomass. Before discharge or reuse, it is suggested to remove salts and neutralize pH from textile effluents and, mostly, develop novel, eco-friendlier textile processing techniques.

A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares

BackgroundAdequately handling unbalanced groups remains one of the major challenges for the analysis of multivariate data collected from multifactorial experimental designs. While partial least squares-based methods, such as analysis of variance multiblock orthogonal partial least squares (AMOPLS), can offer better discrimination between factor levels, they can be more heavily affected by this issue, and unbalanced designs of experiments may lead to a substantial confusion of the effects. Even state-of-the-art analysis of variance (ANOVA) decomposition methodologies using general linear models (GLM) lack the ability to efficiently disentangle these sources of variation when combined with AMOPLS. ResultsA versatile solution developed as an extension of a prior rebalancing strategy is proposed for the first decomposition step based on ANOVA. This approach has the advantage of yielding an unbiased estimation of the parameters and retaining the within-group variation in the rebalanced design, while preserving the orthogonality of effect matrices, even in presence of unequal group sizes. This property is of utmost importance for model interpretation because it avoids mixing sources of variation related to the different effects in the design. A real case study involving metabolomic data from in vitro toxicological experiments was used to demonstrate the potential of this strategy to handle unequal group sizes using a supervised approach. Primary 3D rat neural cell cultures were exposed to trimethyltin following a multifactorial design of experiments involving three fixed effect factors. Significance and NoveltyThe rebalancing strategy was demonstrated as a novel and potent solution to handle unbalanced experimental designs by offering unbiased parameter estimators and orthogonal submatrices, thus avoiding confusion of the effects and facilitating model interpretation. Moreover, it can be combined with any multivariate method used for the analysis of high-dimensional data collected from multifactorial designs.

Statistical improvement of protease production from a new isolate Bacillus thuringiensis strain-MA8 and its application in the production of enzyme-modified cheese

Enzyme-modified cheese (EMC) is a concentrated cheese flavor that is produced enzymatically from dairy substrates to provide an intense source of cheese flavor with broad applications. In this study, EMC was produced by enzymatic biotransformation from a new bacterial isolate described and molecularly identified as Bacillus thuringiensis strain-MA8. Optimization of protease production conditions using one-variable-at-a-time followed by multi-factorial (Plackett-Burman and Box-Behnken) designs increased production by 7-fold. Protease was used at different concentrations (300 and 900 U/100 g curd) as a cost-effective source of concentrated cheese flavor in the EMC preparation. Sensorial evaluation of EMC revealed that the overall acceptability, flavor, and texture were improved from the 2nd day compared to the control, and then decreased on the 4th day without any apparent bitterness. The chemical characteristics of EMC showed that the addition of protease extracts increased the total volatile fatty acids, water-soluble nitrogen, and acidity of EMC significantly (p≤0.05) compared to the control. The amino acids profile revealed that EMC1 which was treated with (300 U/100 g curd) protease had the highest essential amino acids (EAA) and EAA/total amino acids ratio. Nutritional parameters including protein efficiency ratio, biological value, and chemical score of EMC were higher than control based on Val, Met + Cys, Ile, Leu, and Phe + Tyr amino acids. Also, Scanning Electron Microscopy showed significant changes in EMC compared to the control. In conclusion, the addition of (300 U/100g curd) of protease revealed good EMC characteristics without any apparent defect.

Speaking in the first-person singular or plural

Abstract Conventionally, the professional interpreter speaks in the first-person singular. Research in corpus-based discourse analysis has reported shifts from this norm towards first-person plural becoming the most frequent pronoun shift in political institutional interpreting, possibly signalling interpreter ‘alignment with the institution’. Nonetheless, few studies have teased apart the simultaneous constraints of social, cognitive, and linguistic factors on institutional interpreters’ preference for the plural. The present research adopts the usage-based theory to consider the three types of explanations together. It extends recent multivariate methodologies based on this theory to analyse 2,438 first-person cases in parallel interpreting and comparable speech corpora. Following robust context analyses and cross-linguistic prosodic transcription, this study weighs the strengths of 33 associates regarding the three explanations through regression analyses. The results show that first-person shifts are better explained by chunking effects when interpreters process complex forms and referents in the source and target speeches, and when they process zero-subject source inputs. The institutional alignment explanation fails to account for the extensive grammaticalisation of plural constructions in the interpreted speech. When all the interactive and additive effects are considered together, institutional alignment or monofactorial paradigms have little explanatory power. This study concludes by highlighting the relevance of usage-based multifactorial designs to interpreting research.

Efficiently handling high‐dimensional data from multifactorial designs with unequal group sizes using Rebalanced ASCA (RASCA)

AbstractA novel chemometric approach is proposed to analyze high‐dimensional data from unbalanced designs of experiments. It combines a rebalancing strategy based on averages with the ASCA method under the name Rebalanced ASCA (RASCA). The ability of RASCA to handle unbalanced designs was compared with standard ASCA, as well as state‐of‐the‐art methods, such as ASCA+ and WE‐ASCA. For that purpose, a controlled framework was designed to provide a systematic comparison of the various approaches. It included two real datasets obtained from initially balanced designs, which were gradually unbalanced by removing observations belonging to specific combinations of factor levels. The results illustrate that all methods considered led to identical solutions when the initial balanced design was kept. Nevertheless, increasing differences appeared when the design was gradually unbalanced. The proposed benchmark showed that RASCA and ASCA+ provided overall similar results for all effects with high agreement with the balanced solutions in comparison to classical ASCA and WE‐ASCA. RASCA was found to be a suitable chemometric tool to tackle unbalanced designs by ensuring unbiased parameter estimators with the added benefit of producing rigorously orthogonal effect matrices, thus facilitating interpretation.

Encouraging bystander helping behaviour in a violent incident: a virtual reality study using reinforcement learning

Virtual reality (VR) affords the study of the behaviour of people in social situations that would be logistically difficult or ethically problematic in reality. The laboratory-controlled setup makes it straightforward to collect multi-modal data and compare the responses across different experimental conditions. However, the scenario is typically fixed and the resulting data are usually analysed only once the VR experience has ended. Here we describe a method that allows adaptation of the environment to the behaviours of participants and where data is collected and processed during the experience. The goal was to examine the extent to which helping behaviour of participants towards the victim of a violent aggression might be encouraged, with the use of reinforcement learning (RL). In the scenario, a virtual human character represented as a supporter of the Arsenal Football Club, was attacked by another with the aggression escalating over time. (In some countries football is referred to as ‘soccer’, but we will use ‘football’ throughout). Each participant, a bystander in the scene, might intervene to help the victim or do nothing. By varying the extent to which some actions of the virtual characters during the scenario were determined by the RL we were able to examine whether the RL resulted in a greater number of helping interventions. Forty five participants took part in the study divided into three groups: with no RL, a medium level of RL, or full operation of the RL. The results show that the greater extent to which the RL operated the greater the number of interventions. We suggest that this methodology could be an alternative to full multi-factorial experimental designs, and more importantly as a way to produce adaptive VR scenarios that encourage participants towards a particular line of action.

A tutorial on the analysis of multifactorial designs from one or more data sources using AComDim

AbstractTo analyze data acquired from experiments based on multifactorial designs, approaches able to decompose and model the main sources of variation in the measurements are particularly useful. Although methods combining analysis of variance (ANOVA) with principal components analysis (PCA) or simultaneous components analysis (SCA), namely, ANOVA‐PCA (APCA) and ANOVA‐SCA (ASCA), have proven to be efficient to tackle such data structures, they require the construction and interpretation of a model for each experimental factor and interaction. Other methods like parallel factor analysis‐SCA (PARAFASCA), ANOVA multiblock orthogonal partial least squares (AMOPLS) and ANOVA‐Common Dimensions (AComDim) offer the possibility to have an overall picture of all the sources of variation by means of a single model. Moreover, acquiring data from one or multiple data sources through the same experimental design is becoming more common, and approaches able to cope with such complex data structures are needed. In this tutorial, an explanation of the theory and software implementation of AComDim as well as its usage for the analysis of data acquired from experimental designs is provided. Relying on the multiblock nature of AComDim, an extension to cases where data from multiple sources are acquired on the same samples is proposed.

Vertical distribution patterns of ichthyoplankton in temperate waters of New Zealand

Multifactorial sampling designs were used to determine the vertical distribution of ichthyoplankton at multiple temporal and spatial scales in New Zealand. Hypotheses concerning the vertical distribution of fish larvae were tested in the following: depth strata, surface, near-surface, mid-depth and deep, and near the substratum. The consistency of abundance patterns was examined at three sites separated by 2–20 km over 2 months. We also tested for differences in shallow water columns of two depths (20 and 40 m) and both day and night. Although peak abundance of total larval fish was found at upper and lower strata, regardless of total depth of the water column, consistent taxa specific patterns of vertical distribution were also found. Some taxa were most abundant at the surface (e.g. mullids, hemiramphids, and kyphosids), whereas others were found at multiple depths below the surface and throughout the water column, regardless of site, time or depth of water column (e.g. carangids, engraulids, clupeids, scombrids, sparids and pleuronectids). Some taxa were most abundant in shallow water columns (e.g. mullids, tripterygiids and gobiids). Rank abundance by depth stratum for non-surface dwelling species varied among sites and times. Diel vertical movements were detected, some taxa (e.g. clupeids, scombrids and bothids) that were most abundant at the surface at night whereas for other taxa this pattern was more variable (e.g. carangids). We conclude that diel depth-related patterns in shallow water columns will influence interactions among taxa and the importance of different transport mechanisms for larval transport.

Micro-Clustering and Rank-Learning Profiling of a Small Water-Quality Multi-Index Dataset to Improve a Recycling Process

The efficiency improvement of wastewater recycling has been prioritized by ‘Goal 6’ of the United Nations Sustainable Development initiative. A methodology is developed to synchronously profile multiple water-quality indices of a wastewater electrodialysis (ED) process. The non-linear multifactorial screener is exclusively synthesized by assembling proper R-based statistical freeware routines. In sync with current trends, the new methodology promotes convenient, open and rapid implementation. The new proposal unites the ‘small-and-fast’ data-sampling features of the fractional multifactorial designs to the downsizing, by microclustering, of the multiple water quality indices—using optimized silhouette-based classification. The non-linear multifactorial profiling process is catalyzed by the ‘ordinalization’ of the regular nominal nature of the resulting optimum clusters. A bump chart screening virtually eliminates weak performances. A follow-up application of the ordinal regression succeeds in assigning statistical significance to the resultant factorial potency. The rank-learning aptitude of the new profiler is tested and confirmed on recently published wastewater ED-datasets. The small ED-datasets attest to the usefulness to convert limited data in real world applications, wherever there is a necessity to improve the quality status of water for agricultural irrigation in arid areas. The predictions have been compared with other techniques and found to be agreeable.

Analysis of similarities (ANOSIM) for 3‐way designs

AbstractAnalysis of similarities (ANOSIM) is a robust non‐parametric hypothesis‐testing framework for differences in resemblances among groups of samples. To date, the generalisation and use of ANOSIM to analyse various 2‐way nested and crossed designs with unordered or ordered factors has been described. This paper describes how the 2‐way tests may be extended and modified for the analysis of 3‐way designs, including the introduction of a different type of constrained permutation procedure for a design in which one factor is nested in another and crossed with a third. The construction of 3‐way tests using the generalised statistic in various nested and crossed designs, with or without ordered factors, and with or without replication, is described. Applications of the new tests to ecological data are demonstrated using three marine examples. They are as follows: a study of changes in fish diet for fish of increasing size sampled in different locations at different times (a 3‐way fully crossed design with ordered factors); a hierarchical spatial study of the fauna inhabiting kelp holdfasts (a 3‐way fully nested design with unordered factors); and a study of infaunal macrobenthos in which sites within areas were resampled over a long time series (a design in which sites are nested in areas but crossed with years, both latter factors potentially being ordered). The magnitudes of the ANOSIM statistics provide information about relative effect sizes (accounting for other factors), which is often a focus for multifactorial designs. Though the described ANOSIM tests do not provide parallels for all the range of 3‐way mixed‐factor designs possible in ANOVA (and its multivariate semi‐parametric counterpart PERMANOVA), it is seen that for nested factors these ANOSIM tests parallel the matching PERMANOVA random‐effects models, and not their fixed‐effects counterparts, thus allowing the same broader inference about the space from which these random factor levels are drawn.

Anatomical correlates of recovery in apraxia: A longitudinal lesion-mapping study in stroke patients

ObjectiveThis study investigates the clinical course of recovery of apraxia after left-hemisphere stroke and the underlying neuroanatomical correlates for persisting or recovering deficits in relation to the major processing streams in the network for motor cognition. Methods90 patients were examined during the acute (4.74 ± 2.73 days) and chronic (14.3 ± 15.39 months) stage after left-hemisphere stroke for deficits in meaningless imitation, as well as production and conceptual errors in tool use pantomime. Lesion correlates for persisting or recovering deficits were analyzed with an extension of the non-parametric Brunner–Munzel rank-order test for multi-factorial designs (two-way repeated-measures ANOVA) using acute images. ResultsMeaningless imitation and tool use production deficits persisted into the chronic stage. Conceptual errors in tool use pantomime showed an almost complete recovery. Imitation errors persisted after occipitotemporal and superior temporal lesions in the dorso-dorsal stream. Chronic pantomime production errors were related to the supramarginal gyrus, the key structure of the ventro-dorsal stream. More anterior lesions in the ventro-dorsal stream (ventral premotor cortex) were additionally associated with poor recovery of production errors in pantomime. Conceptual errors in pantomime after temporal and supramarginal gyrus lesions persisted into the chronic stage. However, they resolved completely when related to angular gyrus or insular lesions. ConclusionThe diverging courses of recovery in different apraxia tasks can be related to different mechanisms. Critical lesions to key structures of the network or entrance areas of the processing streams lead to persisting deficits in the corresponding tasks. Contrary, lesions located outside the core network but inducing a temporary network dysfunction allow good recovery e.g., of conceptual errors in pantomime. The identification of lesion correlates for different long-term recovery patterns in apraxia might also allow early clinical prediction of the course of recovery.

Embracing the complexity of plant-microbe-insect interactions under a changing climate for sustainable agriculture.

Using beneficial soil bacteria to promote plant growth and reduce pests is a promising direction for sustainable agriculture. However, we need to understand the ecological basis of these interactions in order to identify those with the greatest potential to have an impact in the field. To do this, we need to embrace the complexity of multifactorial experiments to observe the strength of benefits across variable environments. I briefly review the recent literature on plant-microbe-insect interactions across changing environments, focusing on those using multiple factors. I finish by exploring ecological research approaches and multifactorial experimental designs that can be used to simplify the study of plant-microbe-insect interactions.

Markers of Cardiovascular Health in Older Adults with and Without Chronic Low Back and Radicular Leg Pain: A Comparative Analysis

Abstract Objectives There is considerable overlap in risk profiles between chronic low back pain with radiculopathy (CLBPR) and cardiovascular health among older adults; obesity and smoking are related to both conditions and may largely drive the potential relationship. We sought to explore the impact of CLBPR on cardiovascular health outcomes, independent of body mass index (BMI) and current smoking status. Methods Age- and sex-matched older adults (60–85 years of age) with (n = 21) and without (n = 21) CLBPR were recruited. Current smokers were excluded. Blood samples were collected to measure cholesterol levels and pro-inflammatory markers (i.e., C-reactive protein and interleukin-6). Vascular endothelial function, a marker of cardiovascular health, was evaluated by measuring brachial artery flow-mediated dilation (FMD). General linear models with multifactorial designs were evaluated; group membership, BMI, education, and their respective two-way interaction terms were included as independent variables. Results Older adults with CLBPR had significantly higher BMIs (P = 0.004) and lower educational levels (P = 0.013) than did those without pain. There was a significant group-by-education interaction effect (P = 0.049) for endothelial function. Older adults without pain who were highly educated had higher FMD values, indicating better endothelial function (9.2%), whereas the following combinations all had lower FMD values: no pain plus low education, CLBPR plus high education, and CLBPR plus low education (5.9%, 6.1%, and 6.6%, respectively). Conclusions Among older adults, CLBPR is linked with worse endothelial function, regardless of educational level and independent of BMI and smoking. These findings suggest that older adults with CLBPR may be at a higher risk of cardiovascular disease.

The Impact of Complexity on Methods and Findings in Psychological Science

The study of human behavior is severely hampered by logistical problems, ethical and legal constraints, and funding shortfalls. However, the biggest difficulty of conducting social and behavioral research is the extraordinary complexity of the study phenomena. In this article, we review the impact of complexity on research design, hypothesis testing, measurement, data analyses, reproducibility, and the communication of findings in psychological science. The systematic investigation of the world often requires different approaches because of the variability in complexity. Confirmatory testing, multi-factorial designs, survey methods, large samples, and modeling are frequently needed to study complex social and behavioral topics. Complexity impedes the measurement of general constructs, the reproducibility of results and scientific reporting, and the general rigor of research. Many of the benchmarks established by classic work in physical science are not attainable in studies of more complex phenomena. Consequently, the standards used to evaluate scientific research should be tethered to the complexity of the study topic.