Journal Articles

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    Cross-Cultural Adaptation and Application of the One-Parameter Item Response Model to the Santa Clara Brief Compassion Scale (SCBCS)
    (Springer Science+Business Media, LLC, part of Springer Nature, 2025-09-22) Adu P; Popoola T; Iqbal N; Roemer A; Collings S; Aspin C; Medvedev ON; Simpson CR; Medvedev ON; Krägeloh CU
    Objectives International research has consistently demonstrated the positive impact of compassion towards others on both physical and mental well-being, with significant implications for mindfulness practice. Based on this evidence, we aimed to adapt the Santa Clara Brief Compassion Scale (SCBCS) into German while simultaneously conducting a cross-cultural validation and enhancing its measurement precision using Rasch methodology across samples from Germany, Ghana, India, and New Zealand. Method We applied the unrestricted Partial Credit Model to analyze data from a randomly selected subsample of 500 participants, drawn from a total convenience sample of 1822 individuals recruited from the general populations of Germany, Ghana, India, and New Zealand. Results Our initial analysis of the SCBCS showed significant misfit to the Rasch model (χ2(30) = 58.48, p < 0.001), which was successfully addressed by testlet creation resulting in satisfactory model fit (χ2(24) = 24.80, p = 0.09). This included strict unidimensionality, strong reliability (Person Separation Index = 0.81), and invariance across personal factors, such as country, educational levels, sex, and age. We then developed an algorithm for transforming ordinal scores to interval-level data to enhance the accuracy of the SCBCS. The scale demonstrated sound divergent and convergent validity. Conclusions Our study has validated both the German and English versions of the SCBCS using Rasch methodology. The precision of measuring compassion towards others using the two versions of the SCBCS can be further enhanced by applying the ordinal-to-interval transformation tables developed in this paper.
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    Validating Three-dimensional Model of Ethical Competencies’ Education in Accounting Program
    (Allameh Tabataba'i University Press, 2021-07-01) Babajani J; Saghafi A; Ghorbanizade V; Rastegar Moghadam H
    It is expected that accountants provide information which increases the ability of decision making and professional judgement. In this regard, the rendered information should entail characteristics such as ethical principles. In other words, professional accountants should comply not only with the accounting standards and technical norms, but also with ethics which is a preventive factor in financial fraud and corruption. However, the question is how to equip accounting graduates with ethical competencies. Increasing attention to this issue in recent years has resulted in creation of some models for educating ethical competencies. In the present research, the validity of three-dimensional model of ethical competencies’ education has been analyzed. Three-dimensional model of ethical competencies’ education emphasizes on education of eleven competencies as a separate course with combinational teaching method. The statistical population has consisted of bachelor students majoring accounting at the universities in Tehran. Moreover, two-stage sampling technique has been used as a sampling method. The research has been implemented using Pretest-posttest control group design and the data have been analyzed by univariate analysis of covariance. The results have shown that the mentioned ethical education has developed the students’ moral judgements. So, it is expected that by using the model in academia, accounting graduates will become familiarized with necessary competencies to make ethical decisions.
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    Validation of prediction equations to estimate the nutritive value of broiler chicken diets based on their chemical composition
    (Elsevier BV, Netherlands, 2025-02-18) Thiruchchenthuran S; Zaefarian F; Abdollahi MR; Wester TJ; Morel PCH
    An experiment was conducted to validate the accuracy of previously published prediction equations developed to estimate the coefficient of apparent ileal digestibility (CAID) and ileal digestible content (IDC) of nitrogen (N), crude fat, starch, calcium (Ca), phosphorus (P), energy, and dry matter (DM) in broilers using the chemical composition of diets. Twenty new diets were formulated to have a wide range of chemical characteristics relevant to commercial diets. The CAID of N, crude fat, starch, Ca, P, energy, and DM of the diets were determined in broiler growers fed ad libitum from 15 to 22 days post-hatch. The chemical composition and in vivo digestibility values were used to validate the prediction equations developed from a previous study. Comparison between the determined values and predicted values was used to assess the accuracy of prediction equations using the coefficient of determination (R2), root mean square error of prediction, concordance correlation coefficient (CCC), and mean bias (MB). The most accurate prediction was achieved in terms of R2 and CCC for CAID of energy and DM (R2 = 0.57 and 0.66, CCC = 0.45 and 0.47, respectively) as well as for IDC of N, starch, energy, and DM (R2 = 0.90, 1.00, 0.65, and 0.66, CCC = 0.48, 0.97, 0.51, and 0.47, respectively). The R2 and CCC values obtained for CAID of N, crude fat, starch, Ca, and P and IDC of Ca and P were not consistent with the expectation of predictive performance. The R2 for IDC of crude fat was high (0.94), however, CCC was moderate (0.43). The determined MB values showed that some equations underpredicted (CAID and IDC of N, crude fat, starch, energy, and DM) and some overpredicted (CAID of Ca and P and IDC of P) the observed values of in vivo study. In conclusion, the equations obtained for CAID of energy and DM as well as IDC of N, starch, energy, and DM could be considered the best fit according to R2 and CCC. Moreover, this study highlights the importance of validation with external data before applying each prediction equation to practical situations.
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    Evaluation of equations for predicting ileal nutrient digestibility and digestible nutrient content of broiler diets based on their gross chemical composition
    (Elsevier B V, 2024-06) Thiruchchenthuran S; Lopez-Villalobos N; Zaefarian F; Abdollahi MR; Wester TJ; Pedersen NB; Storm AC; Cowieson AJ; Morel PCH
    The coefficient of apparent ileal digestibility (CAID) and ileal digestible contents (IDC) of nutrients of 56 diets using 10 feed ingredients were measured in broilers (21–24 d post-hatch). Diets contained varying inclusion levels of traditional and non-traditional ingredients and differed widely in chemical composition. The chemical composition and in vivo digestibility values were used to establish prediction equations for CAID and IDC of nutrients using stepwise multiple regression. The strength and accuracy of the developed equations were evaluated by root mean square error (RMSE), coefficient of determination (R2), adjusted R2 (adj. R2), and Akaikie's Information Criteria (AIC). The bootstrap method was used to validate the choice of variables by stepwise selection method in the original equation based on their frequencies of selection. Selection of variables was validated if the variables that appear in the original stepwise model were selected in more than 30% of the 1000 bootstrap samples. A close agreement between the original equations and bootstrap resampling was observed for CAID of nitrogen (N) and energy and IDC of energy, starch, and calcium (Ca). Additionally, the original data was subjected to another run of stepwise regression analysis using the selected variables by bootstrapping. The initial regression showed that the CAID of N and energy was highly dependent on crude fibre (CF) and energy contents of the diets. The CAID of energy can be predicted (R2 = 0.89 and RMSE = 0.035) by CF, gross energy (GE), CF2, and starch-to-CF ratio (starch:CF). Calcium content had a positive influence, while phosphorus (P) content had a negative influence on the prediction of CAID of fat. The main variable to predict CAID and IDC of most nutrients was the dietary CF content. Based on the lowest RMSE and AIC, the best predictors for IDC of N were ash, N, fat, CF, CF2, and starch:CF, while the best predictors for IDC of energy were CF, GE, CF2, and starch:CF. The results of the original stepwise regression models and the stepwise regression with the selected variables from the bootstrap results for CAID of N, energy, fat, and DM, as well as IDC of energy, starch, and Ca, were the same with no differences in R2, Adj. R2, RMSE, and AIC. This method can be useful for developing stable and reproducible models using stepwise regression. However, an external validation is needed to confirm the use of these equations in commercial settings.
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    Validation of low-cost air quality monitoring platforms using model-based control charts
    (Elsevier Ltd, 2024-04-01) Boulic M; Phipps R; Wang Y; Vignes M; Adegoke NA
    The SARS COVID-19 pandemic highlighted the importance of routine indoor air quality (IAQ) monitoring. Recent advances in IAQ sensors and remote logging technologies offer opportunities to use low-cost platforms to monitor indoor air. The sensor's accuracy and stability are critical for reliable monitoring and health protection. Data from our low-cost IAQ platform (SKOMOBO) was validated against a commercial platform for carbon dioxide, temperature, and relative humidity measurements to test the reliability of the low-cost instrument. The traditional statistical method to test the variability between two data sets is the coefficient of determination method. We identified that this traditional method did not detect drifts in measurements, when comparing data from two platforms, in a controlled and uncontrolled environment. In our paper, we propose two complementary methods to detect potential drifts in measurements (a modified Shewhart method and a cumulative sum control chart method). The traditional coefficient of determination method indicated strong consistency (between 0.70 and 0.99) in the measurements between SKOMOBO and the reference platforms for both tested environments. Our more sensitive methods detected 100 % data matching for the controlled environment between the SKOMOBO and the reference platform but detected some drifts for the uncontrolled environment (between 81 % and 100 % data matching). It was expected that the uncontrolled environment would create more drifts in measurements than the controlled environment. Our new statistical methods achieved two important results; namely it advanced the validation process and proved the reliability of our low-cost platform for IAQ monitoring and assurance.