This methodological paper examines current conceptions of reliability in chemistry education research (CER) and provides recommendations for moving beyond the current reliance on reporting coefficient alpha (α) as reliability evidence without regard to its appropriateness for the research context. To help foster a better understanding of reliability and the assumptions that underlie reliability coefficients, reliability is first described from a conceptual framework, drawing on examples from measurement in the physical sciences; then classical test theory is used to frame a discussion of how reliability evidence for psychometric measurements is commonly examined in CER, primarily in the form of single-administration reliability coefficients. Following this more conceptual introduction to reliability, the paper transitions to a more mathematical treatment of reliability using a factor analysis framework with emphasis on the assumptions underlying coefficient alpha and other single-administration reliability coefficients, such as omega (ω) and coefficient H, which are recommended as successors to alpha in CER due to their more broad applicability to a variety of factor models. The factor analysis-based reliability discussion is accompanied by R code that demonstrates the mathematical relations underlying single-administration reliability coefficients and provides interested readers the opportunity to compute coefficients beyond alpha for their own data.
The Cronbach's alpha (α) statistic is regularly reported in science education studies. However, recent reviews have noted that it is not well-understood. Therefore, this commentary provides additional clarity regarding the language used when describing and interpreting alpha and other estimates of reliability.
In an attempt to address some student misconceptions in electrochemistry, this guided-inquiry laboratory was devised to give students an opportunity to use a manipulative that simulates the particulate-level activity within an electrochemical cell, in addition to using an actual electrochemical cell. Students are led through a review of expected prior knowledge relating to oxidation and reduction half-reactions. Then, the students examine the macroscopic level by constructing and using an electrochemical cell. Finally, students use the manipulative and make connections between the two levels through class discussion. The misconceptions involve the movement of electrons and ions through solution and the salt bridge, the resulting charges of the half-cells, and the charge sign given to the anode and cathode on electrochemical and electrolytic cells. Additionally, the activity covers oxidation and reduction reactions in electrochemical cells and provides practice drawing and labeling parts of an electrochemical cell. Results, pre- and post-testing and student comments, indicate that this laboratory facilitates students’ understanding of electrochemical cells.
Evaluating the effect of a pedagogical innovation is often done by looking for a significant difference in a content measure using a pre–post design. While this approach provides valuable information regarding the presence or absence of an effect, it is limited in providing details about the nature of the effect. A measure of the magnitude of the pre–post change, commonly called learning gain, could provide this additional information to chemical education researchers. In this paper, we compare two methods of measuring learning gains using data from large-scale administrations of the Chemical Concepts Inventory at four universities. The intent of this study is to compare various measures of learning gain, not to contrast the teaching effectiveness at the four universities. In this gain analysis, we introduce a method based on Rasch modeling and discuss the advantages offered by this type of analysis over more commonly used measures of learning gain.
maximum activity at optimal wo values ranging from 10 to 15. At the same time, adjusting a reversed micellar solution of high wo to the optimal range by adding surfactant may not improve catalytic efficiency due to adverse proteinsurfactant interactions.% The ability to adjust wo through hydrate formation (keeping surfactant concentration constant) may thus have implications to the design of protein-containing reversed micellar systems.
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