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Advances in nanotechnology have drawn attention to nanotechnology education. The aim of this study is to determine awareness, exposure, and knowledge levels of science (physics, chemistry and biology) teachers about nanoscience and nanotechnology. Teachers (N=624) from secondary schools in Turkey comprised the working group. Descriptive methods were used in data analyses. Awareness and exposure subscales were adopted from the Nanotechnology Awareness Instrument developed by Dyehouse et al. (2008), and the knowledge subscale, developed by the authors, was added to the Turkish version. Differences among or between the teachers' awareness, exposure, and knowledge levels regarding nanoscience and nanotechnology were determined. No significant differences were found in levels of awareness, exposure, and knowledge of teachers in terms of subject specialisation and educational levels; however, some significant differences were found in gender, tenure, grade and type of school, in-service training participation, following scientific publications, documentary watching frequency and school localisation variables. In general, nanoscience and nanotechnology awareness, exposure, and knowledge levels of the teachers were at a \"neutral\" level. This study shows needs for further training of science teachers in nanoscience and nanotechnology, to increase their level of awareness and knowledge and to ensure their preparedness for teaching this topic.

The UCLA (University of California, Los Angeles) Loneliness Scale is frequently employed instrument for assessing the structure of loneliness in psychology studies. Although many studies have tested the scale’s factor structure, the literature has produced inconsistent findings or different results. Some studies support a unidimensional structure, while other studies support a multidimensional structure including two-factor, three-factor, five-factor models. Moreover, these dimensions are denoted differently across studies. Hence, this study employs meta-analytical structural equation modeling (MASEM) to investigate the factor structure of the UCLA Loneliness Scale, MASEM provides more precise parameter estimations than conventional structural equation modeling and allows for the synthesis of conflicting findings concerning factor structure. Furthermore, MASEM provides an examination of the factor structure by utilizing correlation matrices from studies in which the scale has been employed but without a specific examination of its factor structures. Consequently, this study analyzed the correlation matrices from 52 studies encompassing 52 correlation matrices. The results of the meta-analysis revealed that the two-factor and second-order three-factor models provided the best fit for the pooled correlation matrix. Even when considering subgroup analyses based on sample size and sample age, which are possible variables that can explain heterogeneity, the three-factor structure of the scale remained consistent. This suggests that different variables may account for the observed heterogeneity.

In this study, misuses of KR-20 and Cronbach's alpha reliability coefficients, used asinternal consistency estimates, are illustrated. Two real data sets were used, dichotomouslyscored, KR-20, data set and polytomously scored, alpha, data set, to see variations in internalconsistency coefficients when the unidimensionality assumption is violated. It is shown that avery high, but misleading internal consistency coefficient, KR-20 or alpha, can be obtained evenwhen the unidimensionality assumption is violated. It is also shown that using the standarderror of measurement as an indicator of reliability results in similar problems. Finally, misusesand the ways of avoiding those misuses of reporting internal consistency of a scale are suggestedand briefly illustrated.

The main focus of this study is to determine whether or not using an item discrimination estimate as a weight of the item effects the alpha reliability coefficient for the Likert-type data. Different calibration procedures along with test length and violation of the model assumptions for the real and simulated data sets were used within the contexts of the comparisons. These four scoring methods are as follows: (1) summated rating method (SRM), (2) weighting by the item-reminder correlation (WIRC), (3) weighting by principal component analysis (WPCA), (4) weighting by graded response model (WGRM). The results showed that the estimated total scores yielded by the four types of scoring procedures were highly correlated. However, when all scores were transformed to a theoretical scale, significant differences were found between the mean of SRM and the means of the three modified scoring procedures for all of the data sets. The intercorrelations between the scale values of the items yielded by the three modified scoring procedures were high. A Perfect correlation was found between WIRC and WPCA for all of the data sets. All of the presented alternative scoring procedures of the summated rating method yielded higher alpha reliability coefficients than the summated rating method for both the homogenous and the heterogenous subsets of the real and simulated data sets. As conclusion, by using an item discrimination power estimate to weight Likert-type items, some of the measurement error will be removed from the total scores whether the types of measurement error is a fixed, systematic, or random. Therefore, the internal consistency of the total scores will be increased as well as the content validity of a Likert-type scale. Using any of the three modified scoring procedures can cut the cost of developing items and reduce the length of the test and maintain high standards of reliability. The results indicated that in the scale construction process, a principal component analysis or factor analysis could be replaced by an item-reminder correlation procedure to select items for including in the final measurement tool.