The Quest for Comparability: Studying the Invariance of the Teachers’ Sense of Self-Efficacy (TSES) Measure across Countries

Teachers’ self-efficacy is an important motivational construct that is positively related to a variety of outcomes for both the teachers and their students. This study addresses challenges associated with the commonly used ‘Teachers’ Sense of Self-Efficacy (TSES)’ measure across countries and provides a synergism between substantive research on teachers’ self-efficacy and the novel methodological approach of exploratory structural equation modeling (ESEM). These challenges include adequately representing the conceptual overlap between the facets of self-efficacy in a measurement model (cross-loadings) and comparing means and factor structures across countries (measurement invariance). On the basis of the OECD Teaching and Learning International Survey (TALIS) 2013 data set comprising 32 countries (N = 164,687), we investigate the effects of cross-loadings in the TSES measurement model on the results of measurement invariance testing and the estimation of relations to external constructs (i.e., working experience, job satisfaction). To further test the robustness of our results, we replicate the 32-countries analyses for three selected sub-groups of countries (i.e., Nordic, East and South-East Asian, and Anglo-Saxon country clusters). For each of the TALIS 2013 participating countries, we found that the factor structure of the self-efficacy measure is better represented by ESEM than by confirmatory factor analysis (CFA) models that do not allow for cross-loadings. For both ESEM and CFA, only metric invariance could be achieved. Nevertheless, invariance levels beyond metric invariance are better achieved with ESEM within selected country clusters. Moreover, the existence of cross-loadings did not affect the relations between the dimensions of teachers’ self-efficacy and external constructs. Overall, this study shows that a conceptual overlap between the facets of self-efficacy exists and can be well-represented by ESEM. We further argue for the cross-cultural generalizability of the corresponding measurement model.     

FindFoci: A Focus Detection Algorithm with Automated Parameter Training That Closely Matches Human Assignments,Reduces Human Inconsistencies and Increases Speed of Analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.     

Microbial analysis of a phototrophic sludge producing hydrogen from acidified wastewater

Based on DNA-cloning analysis, the microbial community of a phototrophic sludge producing H₂ from an acidified wastewater was composed of 81% of a species resembling Rhodobacter capsulatus (with 99.2% similarity) and two unidentified species of the Bacillus/Clostridium group. The sludge produced a biogas comprising 82 ± 2% H₂, 13 ± 2% CO₂, 4.5 ± 0.5% N₂, and 0.5 ± 0.2% H₂S.     

GROWTH OF COLPOMENIA PEREGRINA (PHAEOPHYCEAE) IN CULTURE: EFFECTS OF SALINITY,TEMPERATURE AND DAYLENGTH1

Increases in size of Colpomenia peregrina (Sauv.) Hamel microthalli were determined at six different combinations of salinity and temperature under laboratory conditions. Microthalli grown at low salinity (15%) did not have a growth rate significantly different from microthalli grown at high salinity (30%). However, more macrothalli were produced in higher salinity media. Fewer macrothalli were produced at lower temperatures. Growth of C. peregrina microthalli was also determined at six different combinations of daylength and temperature. Microthalli under 15:9 h (LD cycle) did not have a growth rate significantly different from microthalli under 9:15 h LD. However, more macrothalli were produced under long day conditions. Of the three different temperatures utilized (5, 13 and 20° C) only 5° C produced significantly different growth rates. Low temperature reduced growth to such an extent that macrothalli of a size that could be recognized in the field took approximately two months to produce in culture. These results may explain the seasonal presence/absence of the delophycean phase of C. peregrina in the field. Zoospore survivorship and macrothallus production of C. peregrina in culture indicated that both PES and an artificial medium were suitable for the laboratory cultivation of this plant.