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Low front-end cost and rapid accrual make Web-based surveys and enrolment in studies attractive, but participants are often self-selected with little reference to a well-defined study base. Of course, high quality studies must be internally valid (validity of inferences for the sample at hand), but Web-based enrolment reactivates discussion of external validity (generalization of within-study inferences to a target population or context) in epidemiology and clinical trials. Survey research relies on a representative sample produced by a sampling frame, prespecified sampling process and weighting that maps results to an intended population. In contrast, recent analytical epidemiology has shifted the focus away from survey-type representativity to internal validity in the sample. Against this background, it is a good time for statisticians to take stock of our role and position regarding surveys, observational research in epidemiology and clinical studies. The central issue is whether conditional effects in the sample (the study population) may be transported to desired target populations. Success depends on compatibility of causal structures in study and target populations, and will require subject matter considerations in each concrete case. Statisticians, epidemiologists and survey researchers should work together to increase understanding of these challenges and to develop improved tools to handle them.

Single-case experimental designs (SCEDs) have a long history in clinical and educational disciplines. One underdeveloped area in advancing SCED design and analysis is understanding the process of how internal validity threats and operational concerns are avoided or mitigated. Two strategies to ameliorate such issues in SCED involve replication and randomization. Although replication and randomization are indispensable tools in improving the internal validity of SCEDs, little attention has been paid to (a) why this is the case; or (b) the ways in which these design features are not immune from internal validity threats and operational concerns. In the current paper, we describe the use of directed acyclic graphs (DAGs) to better understand, discover, and mitigate internal validity threats and operational concerns in SCEDs. DAGs are a tool for visualizing causal relations among variables and can help researchers identify both causal and noncausal relations among their variables according to specific algorithms. We introduce the use of DAGs in SCEDs to prompt applied researchers to conceptualize internal validity threats and operational concerns, even when an SCED includes replication and randomization in the design structure. We discuss the general principles of causal inference in conventional “group” designs and in SCEDs, the unique factors impacting SCEDs, and how DAGs can be incorporated into SCEDs. We also discuss the limitations of DAGs applied to SCEDs, as well as future directions for this area of work.

An appropriate optimal number of market segments (ONS) estimation is essential for an enterprise to achieve successful market segmentation, but at present, there is a serious lack of attention to this issue in market segmentation. In our study, an independent adaptive ONS estimation method BWCON-NSDK-means++ is proposed by integrating a new internal validity index (IVI) Between-Within-Connectivity (BWCON) and a new stable clustering algorithm Natural-SDK-means++ (NSDK-means++) in a novel way. First, to complete the evaluation dimensions of the existing IVIs, we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating the connectivity with other two commonly considered measures of compactness and separation. Then, considering the stability, number of parameters and clustering performance, we proposed the NSDK-means++to participate in the integration where the natural neighbor was used to optimize the initial cluster centers (ICCs) determination strategy in the SDK-means++. At last, to ensure the objectivity of the estimated ONS, we designed a BWCON-based ONS estimation framework that does not require the user to set any parameters in advance and integrated the NSDK-means++ into this framework forming a practical ONS estimation tool BWCON-NSDK-means++. The final experimental results show that the proposed BWCON and NSDK-means++ are significantly more suitable than their respective existing models to participate in the integration for determining the ONS, and the proposed BWCON-NSDK-means++ is demonstrably superior to the BWCON-KMA, BWCONMBK, BWCON-KM++, BWCON-RKM++, BWCON-SDKM++, BWCON-Single linkage, BWCON-Complete linkage, BWCON-Average linkage and BWCON-Ward linkage in terms of the ONS estimation. Moreover, as an independent market segmentation tool, the BWCON-NSDK-means++ also outperforms the existing models with respect to the inter-market differentiation and sub-market size.

To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications ( n  = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae.

The primary claim of the Richiardi et al. (2015) Science article is that a measure of correlated gene expression, significant strength fraction (SSF), is related to resting state fMRI (rsfMRI) networks. However, there is still debate about this claim and whether spatial proximity, in the form of contiguous clusters, accounts entirely, or only partially, for SSF (Pantazatos and Li, 2017; Richiardi et al., 2017). Here, 13 distributed networks were simulated by combining 34 contiguous clusters randomly placed throughout cortex, with resulting edge distance distributions similar to rsfMRI networks. Cluster size was modulated (6-15 mm radius) to test its influence on SSF false positive rate (SSF-FPR) among the simulated \"noise\" networks. The contribution of rsfMRI networks on SSF-FPR was examined by comparing simulated networks whose clusters were sampled from: (1) all 1,777 cortical tissue samples, (2) all samples, but with non-rsfMRI cluster centers, and (3) only 1,276 non-rsfMRI samples. Results show that SSF-FPR is influenced only by cluster size ( > 0.9, < 0.001), not by rsfMRI samples. Simulations using 14 mm radius clusters most resembled rsfMRI networks. When thresholding at < 10 , the SSF-FPR was 0.47. Genes that maximize SF have high spatial autocorrelation. In conclusion, SSF is unrelated to rsfMRI networks. The main conclusion of Richiardi et al. (2015) is based on a finding that is ∼50% likely to be a false positive, not <0.01% as originally reported in the article (Richiardi et al., 2015). We discuss why distance corrections alone and external face validity are insufficient to establish a trustworthy relationship between correlated gene expression measures and rsfMRI networks, and propose more rigorous approaches to preclude common pitfalls in related studies.

The risk attitude plays an important role in analyzing decision making under uncertainty. It is essential to confirm whether the risk aversion parameter in a certain situation, called \"domain,\" can be applied to other situations. Using a dataset on hospitalization insurance policies in Japan, this study tests whether individuals' risk preferences remain consistent across domains. Based on the assumption of expected utility maximizer, we derive a plausible distribution of the degree of risk aversion. We find that degree of risk aversion is consistent between hospitalization benefits and additional insurance for specific diseases. Contrarily, the degree of risk aversion from hospitalization benefits has a negative relationship with that based on a survey question on the self-assessment of general preferences. This result indicates that the imputation of risk aversion from the literature would distort research results markedly if characteristics of the domains targeted by both previous research and this study differ.

A broad consensus exists that the use of appropriate methods are important in the Scholarship of Teaching and Learning. However, methodological controversies arise around what constitutes acceptable evidence, if one needs a control group, how generalizable results must be, and other similar issues. Much SoTL work, I argue, asks questions about how much a particular treatment (innovation) caused an effect (student learning), and how the results found in one particular context can be extended outside that context (generalizability). These concepts, known as internal validity and external validity, respectively, provide a common point of departure for much scholarship on teaching and learning. This paper addresses these concepts and demonstrates how they can unite much of what divides us within the methodological realm of SoTL.

Data clustering is a basic technique to show the structure of a data set. K-means clustering is a widely acceptable method of data clustering, which follow a partitioned approach for dividing the given data set into non-overlapping groups. Unfortunately, it has the pitfall of randomly choosing the initial cluster centers. Due to its gradient nature, this algorithm is highly sensitive to the initial seed value. In this paper, we propose a kernel density-based method to compute an initial seed value for the k-means algorithm. The idea is to select an initial point from the denser region because they truly reflect the property of the overall data set. Subsequently, we are avoiding the selection of outliers as an initial seed value. We have verified the proposed method on real data sets with the help of different internal and external validity measures. The experimental analysis illustrates that the proposed method has better performance over the k-means, k-means++ algorithm, and other recent initialization methods.

Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But howcan marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss howtext can be a powerful tool both for prediction and for understanding (i.e., insights).Then, the authorsoverview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss howresearchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.