In this study we focus on math anxiety, comparing its dimensions, levels, and relationship with mathematics achievement across samples of 6th-grade students from China, Taiwan, and the United States. The results of confirmatory factor analyses supported the theoretical distinction between affective and cognitive dimensions of math anxiety in all 3 national samples. The analyses of structural equation models provided evidence for the differential predictive validity of the 2 dimensions of math anxiety. Specifically, across the 3 national samples, the affective factor of math anxiety was significantly related to mathematics achievement in the negative direction. Gender by nation interactions were also found to be significant for both affective and cognitive math anxiety.

This study presents a genetic algorithm approach to detecting financial statement fraud. The study uses a sample comprising a target class of 51 companies accused by the Securities and Exchange Commission of improperly recognizing revenue and a peer class of 339 companies matched on industry and size (revenue). Variables include 76 comparative metrics, based on specific financial metrics and ratios that capture company performance in the context of historical and industry performance, and nine company characteristics. Time‐based patterns detected by the genetic algorithm accurately classify 63% of the target class companies and 95% of the peer class companies. Copyright © 2007 John Wiley & Sons, Ltd.

What is item mapping and how does it aid test score interpretation? Which item mapping technique produces the most consistent results and most closely matches expert opinion?

Background: The relationship between the external load lifted and movement velocity can be modeled by a simple linear regression, and the variables derived from the load-velocity (L-V) relationship were recently used to estimate the maximal neuromuscular capacities during 2 variants of the back-squat exercise. Hypothesis: The L-V relationship variables will be highly reliable and will be highly associated with the traditional tests commonly used to evaluate the maximal force and power. Study Design: Twenty-four male wrestlers performed 5 testing sessions (a 1-repetition maximum [1RM] session, and 4 experimental sessions [2 with the concentric-only back-squat and 2 with the eccentric-concentric back-squat]). Each experimental session consisted of performing 3 repetitions against 5 loads (45%-55%-65%-75%-85% of the 1RM), followed by single 1RM attempts. Level of Evidence: Level 3. Methods: Individual L-V relationships were modeled from the mean velocity collected under all loading conditions from which the following 3 variables were calculated: load-axis intercept ( L0), velocity-axis intercept ( v0), and area under the line ( Aline = L0· v0/2). The back-squat 1RM strength and the maximum power determined as the apex of the power-velocity relationship ( Pmax) were also determined as traditional measures of maximal force and power capacities, respectively. Results: The between-session reliability was high for the Aline (coefficient of variation [CV] range = 2.58%-4.37%; intraclass correlation coefficient [ICC] range = 0.98-0.99) and generally acceptable for L0 and v0 (CV range = 5.08%-9.01%; ICC range = 0.45-0.96). Regarding the concurrent validity, the correlations were very large between L0 and the 1RM strength ( rrange = 0.87-0.88) and nearly perfect between Aline and Pmax ( r = 0.98-0.99). Conclusion: The load-velocity relationship variables can be obtained with a high reliability ( L0, v0, and Aline) and validity ( L0 and Aline) during the back-squat exercise. Clinical Relevance: The load-velocity relationship modeling represents a quick and simple procedure to estimate the maximal neuromuscular capacities of lower-body muscles.

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