In the past Dylan Molenaar has collaborated on articles with Dora Matzke. One of their most recent publications is The power to detect sex differences in IQ test scores using Multi-Group Covariance and Means Structure Analyses☆. Which was published in journal Intelligence.

More information about Dylan Molenaar research including statistics on their citations can be found on their Copernicus Academic profile page.

Dylan Molenaar's Articles: (3)

The power to detect sex differences in IQ test scores using Multi-Group Covariance and Means Structure Analyses☆

AbstractResearch into sex differences in general intelligence, g, has resulted in two opposite views. In the first view, a g-difference is nonexistent, while in the second view, g is associated with a male advantage. Past research using Multi-Group Covariance and Mean Structure Analysis (MG-CMSA) found no sex difference in g. This failure raised the question whether the g-difference is truly absent or whether MG-CMSA lacked statistical power to detect it. The present study used the likelihood ratio test to investigate the power to detect a g-difference in the WAIS-III factor structure with MG-CMSA. Various situations were examined including those reported in the literature. Results showed that power varies greatly among different scenarios. The scenarios based on previous results were associated with power coefficients of about 0.5–0.6. Implications of these findings are discussed in the light of research into sex differences in IQ.

The issue of power in the identification of “g” with lower-order factors

AbstractIn higher order factor models, general intelligence (g) is often found to correlate perfectly with lower-order common factors, suggesting that g and some well-defined cognitive ability, such as working memory, may be identical. However, the results of studies that addressed the equivalence of g and lower-order factors are inconsistent. We suggest that this inconsistency may partly be attributable to the lack of statistical power to detect the distinctiveness of the two factors. The present study therefore investigated the power to reject the hypothesis that g and a lower-order factor are perfectly correlated using artificial datasets, based on realistic parameter values and on the results of selected publications. The results of the power analyses indicated that power was substantially influenced by the effect size and the number and the reliability of the indicators. The examination of published studies revealed that most case studies that reported a perfect correlation between g and a lower-order factor were underpowered, with power coefficients rarely exceeding 0.30. We conclude the paper by emphasizing the importance of considering power in the context of identifying g with lower-order factors.

On the distortion of model fit in comparing the bifactor model and the higher-order factor model☆

Highlights•The distortion of fit may appear perfectly correlated in the setting of Gignac (2016), the relation is in essence nonlinear.•A pattern of distortion of model fit as found by Gignac is generally expected and not unique to the bifactor versus higher-order factor model comparison.

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