To Reverse Item Orientation or Not to Reverse Item Orientation, That Is the Question

To investigate the effect of using negatively oriented items, we wrote semantic reversals of the items in the Rosenberg Self Esteem Scale, UCLA Loneliness Scale, and the General Belongingness Scale and used them to create four experimental conditions. Participants ( N = 2,019) were recruited through Amazon’s Mechanical Turk. Data were assessed for dimensionality, item functioning, instrument properties, and associations with other variables. Regarding dimensionality, although a two-factor model (positively vs. negatively oriented factors) exhibits better fit than a unidimensional model across all conditions, bifactor indices were used to argue that a unidimensional interpretation of the data can be employed. With respect to item functioning, factor loadings were found to be nearly invariant across conditions, but thresholds were not. Concerning instrument properties, inclusion of negatively oriented items results in lower mean scores and higher score variances. Instruments with both positively and negatively oriented items demonstrated lower reliability estimates than those with only one orientation. For associations with other variables, path coefficients in a model where loneliness mediates the effects of belongingness on life satisfaction and self-esteem were found to vary across conditions. Findings suggest that negatively oriented items have minor impact on instrument quality, but influence measurement model and path coefficients.

Image Artefacts in Industrial Computed Tomography

Computed tomography is a method that has been used for many years in medicine and material analysis, and recently it has also been introduced in dimensional measurements. The method has a lot of advantages compared to other 3D measurement methods, with the largest one being the possibility to perform a non-destructive measurement of an object’s inner geometry. However, it is a complex method with a large number of parameters that influence measurement results. Some of these parameters are image artefacts that occur in the scanning and reconstruction process. An artefact is any artificial feature which appears on the CT image, but does not correspond to the physical feature of an object. In order to achieve metrological traceability, it is necessary to eliminate and minimize the influence of image artefacts on measurement results. This paper presents and explains image artefacts in industrial computed tomography as the consequences of different influence parameters in the CT system.

Weighted synthetical influence of degree and H-index in link prediction of complex networks

Link prediction based on traditional models have attracted many interests recently. Among all models, the ones based on topological similarity have achieved great success. However, researchers pay more attention to links, but less to endpoint influence. After profound investigation, we find that the synthesis of degree and H-index plays an important role in modeling endpoint influence. So, in this paper, we propose link prediction models based on weighted synthetical influence, exploring the role of H-index and degree in endpoint influence measurement. Experiments on 12 real-world networks show that the proposed models can provide higher accuracy.

Exploring reliability heterogeneity with multiverse analyses: Data processing decisions unpredictably influence measurement reliability

Analytic flexibility is known to influence the results of statistical tests, e.g. effect sizes and p-values. Yet, the degree to which flexibility in data-processing decisions influences the reliability of our measures is unknown. In this paper I attempt to address this question using a series of reliability multiverse analyses. The methods section incorporates a brief tutorial for readers interested in implementing multiverse analyses reported in this manuscript; all functions are contained in the R package splithalf. I report six multiverse analyses of data-processing specifications, including accuracy and response time cutoffs. I used data from a Stroop task and Flanker task at two time points. This allowed for an internal consistency reliability multiverse at time 1 and 2, and a test-retest reliability multiverse between time 1 and 2. Largely arbitrary decisions in data-processing led to differences between the highest and lowest reliability estimate of at least 0.2. Importantly, there was no consistent pattern in the data-processing specifications that led to greater reliability, across time as well as tasks. Together, data-processing decisions are highly influential, and largely unpredictable, on measure reliability. I discuss actions researchers could take to mitigate some of the influence of reliability heterogeneity, including adopting hierarchical modelling approaches. Yet, there are no approaches that can completely save us from measurement error. Measurement matters and I call on readers to help us move from what could be a measurement crisis towards a measurement revolution.

Research on User Influence Model Integrating Personality Traits under Strong Connection

User influence has always been a major topic in the field of social networking. At present, most of the research focuses on three aspects: topological structure, social-behavioral dimension, and topic dimension and most of them ignore the difference between the audience. These models do not consider the impact of personality differences on user influences. To meet this need, this paper introduces the personality traits factor and proposes a user influence model which integrates personality traits (IPUIM) under a strong connection. The user influence measurement is constructed through the information dimension, structural dimension, and user behavioral dimension. The personality report of the user group is obtained by means of NEO-PI-R (The big five personality inventory, Chinese edition) and machine learning method, and it is integrated into the user influence model. The experiment proves that the model proposed in this paper has good accuracy and applicability in measuring user influence, and can effectively identify the key opinion leaders of different personality trait clusters.