Conducting statistical tests of hypotheses : five common misconceptions found in transportation research

Most statistical methods use hypothesis testing. Analysis of variance, regression, discrete choice models, contingency tables, and other analysis methods commonly used in transportation research share hypothesis testing as the means of making inferences about the population of interest. Despite the fact that hypothesis testing has been a cornerstone of empirical research for many years, various aspects of hypothesis tests commonly are incorrectly applied, misinterpreted, and ignored—by novices and expert researchers alike. On initial glance, hypothesis testing appears straightforward: develop the null and alternative hypotheses, compute the test statistic to compare to a standard distribution, estimate the probability of rejecting the null hypothesis, and then make claims about the importance of the finding. This is an oversimplification of the process of hypothesis testing. Hypothesis testing as applied in empirical research is examined here. The reader is assumed to have a basic knowledge of the role of hypothesis testing in various statistical methods. Through the use of an example, the mechanics of hypothesis testing is first reviewed. Then, five precautions surrounding the use and interpretation of hypothesis tests are developed; examples of each are provided to demonstrate how errors are made, and solutions are identified so similar errors can be avoided. Remedies are provided for common errors, and conclusions are drawn on how to use the results of this paper to improve the conduct of empirical research in transportation.

Analysis of alternative water sources for use in the manufacture of concrete

In Australia and many other countries worldwide, water used in the manufacture of concrete must be potable. At present, it is currently thought that concrete properties are highly influenced by the water type used and its proportion in the concrete mix, but actually there is little knowledge of the effects of different, alternative water sources used in concrete mix design. Therefore, the identification of the level and nature of contamination in available water sources and their subsequent influence on concrete properties is becoming increasingly important. Of most interest, is the recycled washout water currently used by batch plants as mixing water for concrete. Recycled washout water is the water used onsite for a variety of purposes, including washing of truck agitator bowls, wetting down of aggregate and run off. This report presents current information on the quality of concrete mixing water in terms of mandatory limits and guidelines on impurities as well as investigating the impact of recycled washout water on concrete performance. It also explores new sources of recycled water in terms of their quality and suitability for use in concrete production. The complete recycling of washout water has been considered for use in concrete mixing plants because of the great benefit in terms of reducing the cost of waste disposal cost and environmental conservation. The objective of this study was to investigate the effects of using washout water on the properties of fresh and hardened concrete. This was carried out by utilizing a 10 week sampling program from three representative sites across South East Queensland. The sample sites chosen represented a cross-section of plant recycling methods, from most effective to least effective. The washout water samples collected from each site were then analysed in accordance with Standards Association of Australia AS/NZS 5667.1 :1998. These tests revealed that, compared with tap water, the washout water was higher in alkalinity, pH, and total dissolved solids content. However, washout water with a total dissolved solids content of less than 6% could be used in the production of concrete with acceptable strength and durability. These results were then interpreted using chemometric techniques of Principal Component Analysis, SIMCA and the Multi-Criteria Decision Making methods PROMETHEE and GAIA were used to rank the samples from cleanest to unclean. It was found that even the simplest purifying processes provided water suitable for the manufacture of concrete form wash out water. These results were compared to a series of alternative water sources. The water sources included treated effluent, sea water and dam water and were subject to the same testing parameters as the reference set. Analysis of these results also found that despite having higher levels of both organic and inorganic properties, the waters complied with the parameter thresholds given in the American Standard Test Method (ASTM) C913-08. All of the alternative sources were found to be suitable sources of water for the manufacture of plain concrete.

Investigating the hydrodynamic performance of a gross pollutant trap

Field studies show that the internal screens in a gross pollutant trap (GPT) are often clogged with organic matter, due to infrequent cleaning. The hydrodynamic performance of a GPT with fully blocked screens was comprehensively investigated under a typical range of onsite operating conditions. Using an acoustic Doppler velocimeter (ADV), velocity profiles across three critical sections of the GPT were measured and integrated to examine the net fluid flow at each section. The data revealed that when the screens are fully blocked, the flow structure within the GPT radically changes. Consequently, the capture/retention performance of the device rapidly deteriorates. Good agreement was achieved between the experimental and the previous 2D computational fluid dynamics (CFD) velocity profiles for the lower GPT inlet flow conditions.

Invention and business performance in the tissue-engineering industry

Tissue engineering is a young and interdisciplinary scientific discipline but it offers exciting opportunities to improve the quality of health care for hundreds of thousands of patients. Lured by its potential, several start-up companies, pharmaceutical corporations, and medical device enterprises alike are investing heavily in this sector. Invention is a key driver of competition in this sector. In this study, we aim to explain the variation in inventive output across the different firms in the sector. Our major premise is that firms that forge alliances will be able to tap into the expertise of their partners and thus improve their chances of inventive output. We further argue that alliances that enable technology acquisition or learning will enhance the inventive output of firms more than other kinds of alliances. We measure the inventive output of a company by the number of patents filed. On the basis of a preliminary analysis of seven companies, we find support for the hypotheses. We also argue that, to achieve commercial success, firms need to manage time to market (through alliances or otherwise), have a global outlook, nurture their financial resources, and attain critical mass through mergers.

On the endogeneity of the pay-performance relationship in professional soccer

In this reply we show that the Nüesch (2009) comment paper to our initial contribution (Torgler and Schmidt 2007) has several shortcomings. He suggests that professional soccer wages seem to buy talent rather than motivation. We therefore provide a larger set of talent proxies and estimations to check whether this assertion is correct. Our results indicate that his conclusion is problematic. We still observe a strong motivational effect, and in some cases the effect is even larger than the talent effect. A further key problem in Nüesch’s contribution is the fact that he neglects to consider the relevance of the relative salary situation.

New criteria for evaluating methods of identifying hot spots

Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria—namely false positives and false negatives—are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.