Repeated measures /tests

Tumor growth rate, as assessed by repeated measure of tumor burden, is analyzed using a repeated measures test (32). 

Kennedy, R.S., Turnage, J. J., and Wilkes, R.L. 1993. Effects of graded doses of alcohol on nine computerized repeated-measures tests. Ergonomics 36 1195. 

Russell et al. (17) used a repeated measures /-test to determine the effectiveness of animations showing synchronous depictions of chemical reactions using the macroscopic, microscopic, and symbolic representations on students conceptual chemistry knowledge. Students in two sections of college introductory chemistry were given a pre-test, then received instruction using these animations, and answered similar questions on a post-test. A comparison of pre- and post-test scores (presumably done using difference scores) showed a... 

For one-way ANOVAs, chemical education researchers can compare mean scores for several groups of students (n > 2) based on differences in one independent variable. Repeated (dependent) measures ANOVAs are used when data for the different treatment methods were collected from the same group of students (similar to repeated measures /-tests). Repeated measures... 

Independent measures /-test One-way ANOVA Multi-way ANOVA Repeated measures /-test Repeated measures ANOVA Repeated measures ANOVA One-way ANOVA Multi-way ANOVA One-way ANOVA Multi-way ANOVA ... 

The gradients may be caloulated from surface fluid densities, or may be directly measured by downhole pressure measurements using the repeat formation testing tool (RFT). The interfaces predicted can be used to confirm wireline measurements of fluid contact. 

Example 2 Calculation of Error with Doubled Sample Weight Repeated measurements from a lot of anhydrous alumina for loss on ignition established test standard error of 0.15 percent for sample weight of 500 grams, noting V is the square of s.e. Calculation of variance V and s.e. for a 1000 gram sample is... 

Identifying the minimum number of specific measurements containing the most information such that the model parameters are uniquely estimated requires that the model and parameter estimates be known in advance. Repeated unit tests and model building exercises will ultimately lead to the appropriate measurements. However, for the first unit test in absence of a model, the identification of the minimum number of measurements is not possible. 

Using dilatometry in parallel with cyclic voltammetry (CV) measurements in lmolL 1 LiC104 EC-l,2-dimethoxy-ethane (DME), Besenhard et al. [87] found that over the voltage range of about 0.8-0.3 V (vs. Li/Li+), the HOPG crystal expands by up to 150 percent. Some of this expansion seems to be reversible, as up to 50 percent contraction due to partial deintercalation of solvated lithium cations was observed on the return step of the CV. It was concluded [87] that film formation occurs via chemical reduction of a solvated graphite intercalation compound (GIC) and that the permselective film (SEI) in fact penetrates into the bulk of the HOPG. It is important to repeat the tests conducted by Besenhard et al. [87] in other EC-based electrolytes in order to determine the severity of this phenomenon. 

Narrow limits any statement based on a statistical test would be wrong very often, a fact which would certainly not augment the analyst s credibility. Alternatively, the statement would rest on such a large number of repeat measurements that the result would be extremely expensive and perhaps out of date. 

Interpretation The model can only be improved upon if the residual standard deviation remains significantly larger (F-test ) than the experimental repeatability (standard deviation over many repeat measurements under constant conditions, which usually implies within a short period of time ). Goodness of fit can also be judged by glancing along the horizontal (residual = 0) and looking for systematic curvature. 

In working with PCP and polydrug abusers who have impaired everyday functioning, counselors must be ultimately concerned with the individual s ability to recover. Objective prediction of recovery of function, using NP tests, involves a baseline evaluation and at least one repeated assessment. Patterns of NP strengths and deficits in the two or more assessments may then be compared. Clinical inferences can be made, describing skills that show relative permanence of deficit (with no change on repeated measurement), or... 

One study has reported effects on neurobehavioral function in lead-exposed workers at mean PbB levels of 50 pg/dL (Williamson and Teo 1986). Neurobehavioral function was measured using tests that are based on information processing theory in 59 lead workers and 59 controls matched for age, type of job, time on the job, education level, smoking history, and alcohol consumption. Statistically significant decreases in the lead-exposed workers were seen for critical flicker fusion reaction, simple reaction time, tracking speeds, hand steadiness tests, and sensory store memory. Sensory store memory speed showed a low but statistically significant correlation with PbB concentrations. Measurements of neurobehavioral function seemed well chosen, and repeated measures with associated appropriate statistics were used. 

The coefficients a,- are estimated from the results of experiments carried out according to a design matrix such as Table 5.9 which shows a 23 plan matrix. The significance of the several factors are tested by comparing the coefficients with the experimental error, to be exact, by testing whether the confidence intervals Aai include 0 or not. The experimental error can be estimated by repeated measurements of each experiment or - as it is done frequently in a more effective way - by replications at the centre of the plan (so-called zero replications ), see Fig. 5.2. 

This value for the standard deviation was accepted as the best available approximation to the population value for a. The next step was to take several different aliquots from a large sample (a different sample than used previously) and collect multiple readings from each of them. Six aliquots were placed in each of six flasks, and six repeat measurements were made on each of these six flasks. Each aliquot consisted of 10 g of test sample/lOOml water. The results are shown in Table 9-2. 

These biosensors were tested for glucose and lactate measurements in sera, and for lactate measurements in whey solutions. Good agreements were obtained between the present method and reference methods. For glucose analysis in serum, the coefficient of variation for 53 repeated measurements performed over a 10 h period was 4.8% while for lactate analysis, 80 assays performed over a 15 h period gave a coefficient of variation of 6.7%. Thus,... 

Precision is the closeness of agreement between independent test results obtained under stipulated conditions. Precision depends only on the distribution of random errors and does not relate to the true value. It is calculated by determining the standard deviation of the test results from repeat measurements. In numerical terms, a large number for the precision indicates that the results are scattered, i.e. the precision is poor. Quantitative measures of precision depend critically on the stipulated conditions. Repeatability and reproducibility are the two extreme conditions. 

Precision is the closeness of agreement between independent test results obtained under stipulated conditions. The precision tells us by how much we can expect the results of repeated measurements to vary. The precision of a set of measurement results will depend on the magnitude of the random errors affecting the measurement process. Precision is normally expressed as a standard deviation or relative standard deviation (see Section 6.1.3). 

There is a statistical test to check the homogeneity of variances. We repeatedly measure the highest and the lowest standard samples (10 times each) and calculate the variances for both data sets. The F-test gives us an answer on the question, whether they are significantly different or not. 

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