Search

Norms are inherently social entities. The link between collectively shared social norms and individual well-being is an important juncture for social scientists as well as for applied social researchers. In this regard it is oftentimes contended that in a modern society the internalized norms of masculinity tend to form behavioral models that frequently contribute to young men's forming an apathetic attitude toward their social responsibility, as well as to dissociation and indecision, and can worsen their emotional states and, eventually, lead to poorer well-being. The goals of the present research were twofold and involved exploring the contents of the cultural norms of masculinity and evaluating their effect on mental well-being of young college males (based on a sample from National University of KyivMohyla Academy (N = 215)). Grounded in the operationalization of Pearson & Marr conceptual framework, a formalized instrument was designed to measure the degree of consensuality of different norms of masculinity and to investigate their relationships with the mental well-being of the study participants. The study conducted in Kyiv in Spring 2024 collected self-reported cognitive data by means of an online form. The data analysis employed techniques of multidimensional analysis operating on multi-item scales. The findings show that a more pronounced expression of the normative traits that are marked as masculine serves as a predictor of higher mental well-being levels, more salient feeling of social belonging, more positive outlook on life and perceiving life events in a more constructive way. Granted that individual correspondence to sociocultural normativity in the domain of masculinity also positively affected psychological condition and the experience of social belonging of male participants, improving their well-being, it can be surmised that the destructive criticism of masculinity perse, radical reshaping of agreed-upon norms regulating masculinity and exposing the young men to the mixed signals of "what a man should be" can have harmful consequences for social adaptation and psychological well-being of male students. On the other hand, it is important to contextualize the criticism of masculinity within collectively meaningful axiological networks of a society and their contingencies. Specifically, congruence with socially desirable repertoires can confer a greater sense of responsibility, understanding one's role in the group and developing one's decision-making skills. [ABSTRACT FROM AUTHOR]

Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to ad-hoc methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which ad-hoc procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts ad-hoc methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of ad-hoc procedures may be muted when missing data percentages are modest.

Recent trends in strategic management and the strategy-as-practice stream of research have led to a proliferation of studies on open strategy. However, there is a general lack of research focused on valid and reliable measures of open strategy. In this paper, we developed and validated the open strategy scale to measure open strategy constructs derived from two dimensions—transparency and inclusion. We used the mixed methods composed in the multi-phase model of scale development. As a result, we have proposed a multi-item scale to measure the strategy openness. Our results demonstrate the validity and reliability of the scale proposed. The main implication of this research is that the scale may serve as both—an integrated tool for assessment of the overall level of open strategy development and an instrument for more detailed analysis of constructs to reveal the room for improvement or investigate the effect brought by managerial decisions. [ABSTRACT FROM AUTHOR]

Health-science researchers often measure psychological constructs using multi-item scales and encounter missing items on some participants. Multiple imputation (MI) has emerged as an alternative to ad-hoc methods (e.g., mean substitution) for handling incomplete data on multi-item scales, appealingly reflecting available information while accounting for uncertainty due to missing values in a unified inferential framework. However, MI can be implemented in a variety of ways. When the number of variables to impute gets large, some strategies yield unstable estimates of quantities of interest while others are not technically feasible to implement. These considerations raise pragmatic questions about the extent to which ad-hoc procedures would yield statistical properties that are competitive with theoretically motivated methods. Drawing on an HIV study where depression and anxiety symptoms are measured with multi-item scales, this empirical investigation contrasts ad-hoc methods for handling missing items with various MI implementations that differ as to whether imputation is at the item-level or scale-level and how auxiliary variables are incorporated. While the findings are consistent with previous reports favoring item-level imputation when feasible to implement, we found only subtle differences in statistical properties across procedures, suggesting that weaknesses of ad-hoc procedures may be muted when missing data percentages are modest. [ABSTRACT FROM AUTHOR]

Medical research often involves using multi‐item scales to assess individual characteristics, disease severity, and other health‐related outcomes. It is common to observe missing data in the scale scores, due to missing data in one or more items that make up that score. Multiple imputation (MI) is a popular method for handling missing data. However, it is not clear how best to use MI in the context of scale scores, particularly when they are assessed at multiple waves of data collection resulting in large numbers of items. The aim of this article is to provide practical advice on how to impute missing values in a repeatedly measured multi‐item scale using MI when inference on the scale score is of interest. We evaluated the performance of five MI strategies for imputing missing data at either the item or scale level using simulated data and a case study based on four waves of the Longitudinal Study of Australian Children (LSAC). MI was implemented using both multivariate normal imputation and fully conditional specification, with two rules for calculating the scale score. A complete case analysis was also performed for comparison. Based on our results, we caution against the use of a MI strategy that does not include the scale score in the imputation model(s) when the scale score is required for analysis. [ABSTRACT FROM AUTHOR]

This article elaborates on the methodological aspects of social innovation research, with a focus on an examination of the validity of quantitative scales in the identification of socially innovative non-profits (n = 850 NGOs, 2015). This elaboration is necessary due to the methodological shortcomings of existing metrics of social and general, i.e., those relating to technological and business innovations. Based on theoretical conceptualization and derived demarcation lines of the concept, a multi-item scale was generated. It was applied along with a self-assessment scale, which replicates the general innovation measurement. Both scales of an NGO's social innovativeness resulted in appropriate validity coefficients. However, scales capture different and only partly intersecting shares of socially innovative NGOs. We propose that theoretical and self-assessment scales could be administered simultaneously to eliminate self-assessment biases and to provide empirical data on the scope and size of a sub-sector of socially innovative NGOs. [ABSTRACT FROM AUTHOR]

Background: Missing data in a large scale survey presents major challenges. We focus on performing multiple imputation by chained equations when data contain multiple incomplete multi-item scales. Recent authors have proposed imputing such data at the level of the individual item, but this can lead to infeasibly large imputation models. Methods: We use data gathered from a large multinational survey, where analysis uses separate logistic regression models in each of nine country-specific data sets. In these data, applying multiple imputation by chained equations to the individual scale items is computationally infeasible. We propose an adaptation of multiple imputation by chained equations which imputes the individual scale items but reduces the number of variables in the imputation models by replacing most scale items with scale summary scores. We evaluate the feasibility of the proposed approach and compare it with a complete case analysis. We perform a simulation study to compare the proposed method with alternative approaches: we do this in a simplified setting to allow comparison with the full imputation model. Results: For the case study, the proposed approach reduces the size of the prediction models from 134 predictors to a maximum of 72 and makes multiple imputation by chained equations computationally feasible. Distributions of imputed data are seen to be consistent with observed data. Results from the regression analysis with multiple imputation are similar to, but more precise than, results for complete case analysis; for the same regression models a 39 % reduction in the standard error is observed. The simulation shows that our proposed method can perform comparably against the alternatives. Conclusions: By substantially reducing imputation model sizes, our adaptation makes multiple imputation feasible for large scale survey data with multiple multi-item scales. For the data considered, analysis of the multiply imputed data shows greater power and efficiency than complete case analysis. The adaptation of multiple imputation makes better use of available data and can yield substantively different results from simpler techniques. [ABSTRACT FROM AUTHOR]

The most popular index of agreement has been rWG(J); more recently, the ADM(J) index also has been used. This study addresses two problems: first, how to test the statistical significance of rWG(J) and ADM(J) and, second, how to infer from the indices that were evaluated for each group about the agreement of the ensemble of groups. The authors extend the inference based on either rWG(J) or ADM(J) by focusing on multiple-item scales and on the whole ensemble of groups. Their method is based on simulations, as was done by Dunlap, Burke, and Smith-Crowe (2003) and by Cohen, Doveh, and Eick (2001). The tests are illustrated on the data of Bliese, Halverson, and Schriesheim (2002) pertaining to a sample of 2,042 U.S Army soldiers in 49 U.S. Army companies. Software for our procedures is available both as a SAS code and in the Multilevel Modeling in R package (Bliese, 2006). [ABSTRACT FROM AUTHOR]