Development and Validation of a Theory-Based Questionnaire to Measure Different Types of Cognitive Load

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Abstract

According to cognitive load theory, learning can only be successful when instructional materials and procedures are designed in accordance with human cognitive architecture. In this context, one of the biggest challenges is the accurate measurement of the different cognitive load types as these are associated with various activities during learning. Building on psychometric limitations of currently available questionnaires, a new instrument for measuring the three types of cognitive load—intrinsic, extraneous, and germane cognitive load—is developed and validated relying on a set of five empirical studies. In Study 1, a principal component analysis revealed a three-component model which was subsequently confirmed using a confirmatory factor analysis (Study 2). Finally, across three experiments (Studies 3–5), the questionnaire was shown to be sensitive to changes in cognitive load supporting its predictive validity. The quality of the cognitive load questionnaire was underlined by satisfactory internal consistencies across all studies. In sum, the proposed questionnaire can be used in experimental settings to measure the different types of cognitive load in a valid and reliable manner. The construction and validation process of the questionnaire has also shown that the construct germane cognitive load remains controversial concerning its measurement and theoretical embedding in cognitive load theory.

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Interrater reliability: the kappa statistic

Mary L McHugh (2012)

The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.

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