Statistical analysis of preliminary data for the safe performance curriculum driving knowledge test. Interim report.

DeKalb County School System, Decatur, Ga.

Field test evaluation of rear lighting systems. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Results of automotive test equipment manufacturer interviews. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Site report: Lexington, Kentucky - field test of combined speed, alcohol, and safety belt enforcement strategies. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Development and test of selected model pedestrian safety regulations. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Cotton fiber and processing test results.

Description based on: Crop of 1959 (Aug. 3, 1959).

Results of the 1996 Race and Ethnic Targeted Test /

"May 1997."

Simulating aggregate and distributional effects of various plans for modifying the retirement earnings test /

"July 1990."

Salt Lake City Ogden, UT wages and benefits construction industry test survey, November 1998.

"June 1999."

Test-retest reliability of four physical activity measures used in population surveys

Accurate monitoring of prevalence and trends in population levels of physical activity (PA) is a fundamental public health need. Test-retest reliability (repeatability) was assessed in population samples for four self-report PA measures: the Active Australia survey (AA, N=356), the short International Physical Activity Questionnaire (IPAQ, N=104), the physical activity items in the Behavioral Risk Factor Surveillance System (BRFSS, N=127) and in the Australian National Health Survey (NHS, N=122). Percent agreement and Kappa statistics were used to assess reliability of classification of activity status as 'active', 'insufficiently active' or 'sedentary'. Intraclass correlations (ICCs) were used to assess agreement on minutes of activity reported for each item of each survey and for total minutes. Percent agreement scores for activity status were very good on all four instruments, ranging from 60% for the NHS to 79% for the IPAQ. Corresponding Kappa statistics ranged from 0.40 (NHS) to 0.52 (AA). For individual items, ICCs were highest for walking (0.45 to 0.78) and vigorous activity (0.22 to 0.64) and lowest for the moderate questions (0.16 to 0.44). All four measures provide acceptable levels of test-retest reliability for assessing both activity status and sedentariness, and moderate reliability for assessing total minutes of activity.

A time-domain test for some types of nonlinearity

The bispectrum and third-order moment can be viewed as equivalent tools for testing for the presence of nonlinearity in stationary time series. This is because the bispectrum is the Fourier transform of the third-order moment. An advantage of the bispectrum is that its estimator comprises terms that are asymptotically independent at distinct bifrequencies under the null hypothesis of linearity. An advantage of the third-order moment is that its values in any subset of joint lags can be used in the test, whereas when using the bispectrum the entire (or truncated) third-order moment is required to construct the Fourier transform. In this paper, we propose a test for nonlinearity based upon the estimated third-order moment. We use the phase scrambling bootstrap method to give a nonparametric estimate of the variance of our test statistic under the null hypothesis. Using a simulation study, we demonstrate that the test obtains its target significance level, with large power, when compared to an existing standard parametric test that uses the bispectrum. Further we show how the proposed test can be used to identify the source of nonlinearity due to interactions at specific frequencies. We also investigate implications for heuristic diagnosis of nonstationarity.

Test-retest reliability and practice effects of a rapid screen of mild traumatic brain injury

Test-retest reliabilities and practice affects of measures from the Rapid Screen of Concussion (RSC), in addition to the Digit Symbol Substitution Test (Digit Symbol), were examined. Twenty five male participants were tested three times; each testing session scheduled a week apart. The test-retest reliability estimates for most measures were reasonably good, ranging from .79 to .97. An exception was the delayed word recall test, which has had a reliability estimate of .66 for the first retest, and .59 for the second retest. Practice effects were evident from Times 1 to 2 on the sentence comprehension and delayed recall subtests of the RSC, Digit Symbol and a composite score. There was also a practice effect of the same magnitude found from Time 2 to Time 3 on Digit Symbol, delayed recall and the composite score. Statistics on measures for both the first and second retest intervals, with associated practice affects, are presented to enable the calculation of reliable change indices (RCI). The RCI may be used to assess any improvement in cognitive functioning after mild Traumatic Brain Injury.

A Score Test for Zero-Inflation in Correlated Count Data

To account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero-inflated Poisson mixed regression models is applicable for accommodating the within-cluster dependence. In this paper, a score test for zero-inflation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are evaluated by simulation studies. The results show that the test statistic performs satisfactorily under a wide range of conditions. The test procedure is further illustrated using a data set on recurrent urinary tract infections. Copyright (c) 2005 John Wiley & Sons, Ltd.

Non-normally distributed data and non-parametric statistics

Different types of numerical data can be collected in a scientific investigation and the choice of statistical analysis will often depend on the distribution of the data. A basic distinction between variables is whether they are ‘parametric’ or ‘non-parametric’. When a variable is parametric, the data come from a symmetrically shaped distribution known as the ‘Gaussian’ or ‘normal distribution’ whereas non-parametric variables may have a distribution which deviates markedly in shape from normal. This article describes several aspects of the problem of non-normality including: (1) how to test for two common types of deviation from a normal distribution, viz., ‘skew’ and ‘kurtosis’, (2) how to fit the normal distribution to a sample of data, (3) the transformation of non-normally distributed data and scores, and (4) commonly used ‘non-parametric’ statistics which can be used in a variety of circumstances.

A Model of a Diagnostic Test for Controlling the Results of a Computer Based Education in Applied Mathematics at the University of Ruse

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015