There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Why most published research findings are false

Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM) have long histories in clinical research. Although CFA has largely superseded EFA, CFAs of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors in support of discriminant validity. Part of the problem is undue reliance on overly restrictive CFAs in which each item loads on only one factor. Exploratory SEM (ESEM), an overarching integration of the best aspects of CFA/SEM and traditional EFA, provides confirmatory tests of a priori factor structures, relations between latent factors and multigroup/multioccasion tests of full (mean structure) measurement invariance. It incorporates all combinations of CFA factors, ESEM factors, covariates, grouping/multiple-indicator multiple-cause (MIMIC) variables, latent growth, and complex structures that typically have required CFA/SEM. ESEM has broad applicability to clinical studies that are not appropriately addressed either by traditional EFA or CFA/SEM.

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Exploratory Structural Equation Modeling: An Integration of the Best Features of Exploratory and Confirmatory Factor Analysis

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.

Influence of connected and autonomous vehicles on traffic flow stability and throughput

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Multiple factor analysis.

Researchers must make numerous choices when conducting factor analyses, each of which can have significant ramifications on the model results. They must decide on an appropriate sample size to achieve accurate parameter estimates and adequate power, a factor model and estimation method, a method for determining the number of factors and evaluating model fit, and a rotation criterion. Unfortunately, researchers continue to use outdated methods in each of these areas. The present article provides a current overview of these areas in an effort to provide researchers with up-to-date methods and considerations in both exploratory and confirmatory factor analysis. A demonstration was provided to illustrate current approaches. Choosing between confirmatory and exploratory methods is also discussed, as researchers often make incorrect assumptions about the application of each.

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Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis

A majority (95%) of crashes can be attributed to humans, with the highest cause category (41%) involving errors of recognition (i.e., inattention, distraction, inadequate surveillance) [1]. Driving safety research often claims that as much as 90% of the information that drivers use is visual. However, these claims have been hampered by a lack of numerical measurement systems [2]. Presently, we develop an ordinal visual driving scene complexity measurement based on human judgments and eye behavior. Mimicking the rebuilding of situation awareness in take-over conditions we presented 60 randomly ordered video clips (3 s duration), varying complexity factors of traffic density, road curvature, and miscellaneous visual features. Eyes of 15 participants were recorded while viewing the clips, and participants rated “how much effort for you to take control and drive within that segment?” on a 100 point scale. Effort ratings showed a monotonic increase with the number of complexity factors present. A statistically significant increase was also found for saccade amplitude, whereas a statistically significant decrease was found for fixation duration. Pupil size also showed a significant increase but only between 2 complexity levels and at a relatively less convincing strength. In conclusion, the present complexity factor coding scheme apparently corresponds to subjective effort. Further consideration should be given to relating eye tracking measures to visual driving scene components and task demands. In real-time driving systems, both human occupant(s) and computerized processes may observe the same scene at the same time, and matching the machine quantification of the situation to intuitive human judgments is expected to aid in the adherence to advisories and acceptance of automated aids.

What makes driving difficult? : Perceived effort and eye measures follow visible semantic complexity factors

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Effects of automated driving on traffic flow efficiency: Challenges and recent developments

Traffic accidents are a major public health concern with 1.2 million fatalities occurring every year and with millions more individuals getting injured [1]. More than 90% of the accidents are, at least partially, caused by the driver [2]. Figure 1 shows the overrepresentation of younger and older drivers among these crashes. The overrepresentation of younger drivers [1] can be explained by an increased willingness to take risk, poor anticipation of hazards, and insufficiently learned lateral and longitudinal vehicle control [3]. Figure 1 also shows the overrepresentation of elderly drivers which is caused by decline of cognitive and physical abilities [4] such as visual impairment (e.g., glaucoma).

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Can We Discriminate Safe and Unsafe Visual Scanning in Multitask Driving Conditions

Riding performance on a conventional bicycle and an e-bike during three low-speed exercises: Comparing middle-aged with older cyclists

International Cycling Safety Conference 2014 18-19 November 2014, Göteborg, Sweden

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J.C.F. de Winter

Papers

What makes driving difficult? : Perceived effort and eye measures follow visible semantic complexity factors

Effects of automated driving on traffic flow efficiency: Challenges and recent developments

Can We Discriminate Safe and Unsafe Visual Scanning in Multitask Driving Conditions

Riding performance on a conventional bicycle and an e-bike during three low-speed exercises: Comparing middle-aged with older cyclists

Factors contributing to pedelec crashes in the Netherlands (poster)