Awareness is bliss: How acquiescence affects exploratory factor analysis

Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurement of individuals' latent psychological constructs. This entails evaluating the number of measured constructs and determining which construct is measured by which item. Exploratory factor analysis (EFA) is the most-used method to evaluate these psychometric properties, where the number of measured constructs (i.e., factors) is assessed, and, afterwards, rotational freedom is resolved to interpret these factors. This study assessed the effects of an acquiescence response style (ARS) on EFA for unidimensional and multidimensional (un)balanced scales. Specifically, we evaluated (i) whether ARS is captured as an additional factor, (ii) the effect of different rotation approaches on the recovery of the content and ARS factors, and (iii) the effect of extracting the additional ARS factor on the recovery of factor loadings. ARS was often captured as an additional factor in balanced scales when it was strong. For these scales, ignoring (i.e., not extracting) this additional ARS factor, or rotating to simple structure when extracting it, harmed the recovery of the original MM by introducing bias in loadings and cross-loadings. These issues were avoided by using informed rotation approaches (i.e., target rotation), where (part of) the MM is specified a priori. Not extracting the additional ARS factor did not affect the loading recovery in unbalanced scales. Researchers should consider the potential presence of an additional ARS factor when assessing the psychometric properties of balanced scales, and use informed rotation approaches when suspecting that an additional factor is an ARS factor.

Visual Object Multimodality Tracking Based on Correlation Filters for Edge Computing

In recent years, visual object tracking has become a very active research field which is mainly divided into the correlation filter-based tracking and deep learning (e.g., deep convolutional neural network and Siamese neural network) based tracking. For target tracking algorithms based on deep learning, a large amount of computation is required, usually deployed on expensive graphics cards. However, for the rich monitoring devices in the Internet of Things, it is difficult to capture all the moving targets in each device in real time, so it is necessary to perform hierarchical processing and use tracking based on correlation filtering in insensitive areas to alleviate the local computing pressure. In sensitive areas, upload the video stream to a cloud computing platform with a faster computing speed to perform an algorithm based on deep features. In this paper, we mainly focus on the correlation filter-based tracking. In the correlation filter-based tracking, the discriminative scale space tracker (DSST) is one of the most popular and typical ones which is successfully applied to many application fields. However, there are still some improvements that need to be further studied for DSST. One is that the algorithms do not consider the target rotation on purpose. The other is that it is a very heavy computational load to extract the histogram of oriented gradient (HOG) features from too many patches centered at the target position in order to ensure the scale estimation accuracy. To address these two problems, we introduce the alterable patch number for target scale tracking and the space searching for target rotation tracking into the standard DSST tracking method and propose a visual object multimodality tracker based on correlation filters (MTCF) to simultaneously cope with translation, scale, and rotation in plane for the tracked target and to obtain the target information of position, scale, and attitude angle at the same time. Finally, in Visual Tracker Benchmark data set, the experiments are performed on the proposed algorithms to show their effectiveness in multimodality tracking.

ISAR Image Matching and Three-Dimensional Scattering Imaging Based on Extracted Dominant Scatterers

This paper studies inverse synthetic aperture radar (ISAR) image matching and three-dimensional (3D) scattering imaging based on extracted dominant scatterers. In the condition of a long baseline between two radars, it is easy for obvious rotation, scale, distortion, and shift to occur between two-dimensional (2D) radar images. These problems lead to the difficulty of radar-image matching, which cannot be resolved by motion compensation and cross-correlation. What is more, due to the anisotropy, existing image-matching algorithms, such as scale invariant feature transform (SIFT), do not adapt to ISAR images very well. In addition, the angle between the target rotation axis and the radar line of sight (LOS) cannot be neglected. If so, the calibration result will be smaller than the real projection size. Furthermore, this angle cannot be estimated by monostatic radar. Therefore, instead of matching image by image, this paper proposes a novel ISAR imaging matching and 3D imaging based on extracted scatterers to deal with these issues. First, taking advantage of ISAR image sparsity, radar images are converted into scattering point sets. Then, a coarse scatterer matching based on the random sampling consistency algorithm (RANSAC) is performed. The scatterer height and accurate affine transformation parameters are estimated iteratively. Based on matched scatterers, information such as the angle and 3D image can be obtained. Finally, experiments based on the electromagnetic simulation software CADFEKO have been conducted to demonstrate the effectiveness of the proposed algorithm.

Identification of Facet Models by Means of Factor Rotation: A Simulation Study and Data Analysis of a Test for the Berlin Model of Intelligence Structure

We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor analysis with subsequent Varimax, Equamax, Parsimax, Factor Parsimony, Tandem I, Tandem II, Infomax, and McCammon’s minimum entropy rotation. Samples drawn from oblique populations were submitted to factor analysis with subsequent Geomin rotation and a Promax-based Tandem II rotation. As a benchmark, we investigated a target rotation of the sample loadings toward the corresponding faceted population loadings. The three conditions were sample size ( n = 400, 1,000), number of factors ( q = 4-12), and main loading size ( l = .40, .50, .60). For less than six orthogonal factors Infomax and McCammon’s minimum entropy rotation and for six and more factors Tandem II rotation yielded the highest congruence of sample loading matrices with faceted population loading matrices. For six and more oblique factors Geomin rotation and a Promax-based Tandem II rotation yielded the highest congruence with faceted population loadings. Analysis of data of 393 participants that performed a test for the Berlin Model of Intelligence Structure revealed that the faceted structure of this model could be identified by means of a Promax-based Tandem II rotation of task aggregates corresponding to the cross-products of the facets. Implications for the identification of faceted models by means of factor rotation are discussed.

Identification of facet models by means of factor rotation: A simulation study and data analysis of a test for the Berlin Model of Intelligence Structure

Until now there has been no successful exploration of a priori unknown faceted structure by means of exploratory factor analysis (EFA) of the measured variables (items or tasks). For this reason, we investigate by means of a simulation study how well methods for factor rotation can identify a two-facet orthogonal simple structure. Samples were generated from orthogonal two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet) and submitted to factor analysis with subsequent Varimax, Equamax, Parsimax, Factor Parsimony, Tandem I, Tandem II, Infomax, and McCammon’s Minimum Entropy rotation. As a benchmark, orthogonal target rotation of the sample loadings towards the corresponding faceted population loadings was also investigated. The conditions were sample size (n = 400, 1,000), number of factors (q = 4-12), and main loading size (l = .40, .50, .60). Mean congruence coefficients of the sample loading matrices with the corresponding population loading matrices and the root mean squared error between sample loading matrices and corresponding population loading matrices were used as dependent measures. For less than six factors Infomax and McCammon’s Minimum Entropy rotation and for six and more factors Tandem II rotation yielded the highest similarity of sample loading matrices with faceted population loading matrices. Analysis of data of 393 participants that performed a test for the Berlin Model of Intelligence Structure revealed that the faceted structure of this model could be found by means of target rotation of task aggregates corresponding to the cross-products of the facets. Moreover, McCammon’s Minimum Entropy rotation resulted in a loading pattern corresponding to the model, although the factor for figural intelligence was only weakly represented. Implications for the identification of faceted models by means of factor rotation are discussed.

Hough Transform-Based Large Dynamic Reflection Coefficient Micro-Motion Target Detection in SAR

Special phase modulation of SAR echoes resulted from target rotation or vibration, is a phenomenon called the micro-Doppler (m-D) effect. Such an effect offers favorable information for micro-motion (MM) target detection, thereby improving the performance of the synthetic aperture radar (SAR) system. However, when there are MM targets with large differences in reflection coefficient, the weak reflection components will be difficult to be detected. To find a solution to this problem, we propose a novel algorithm. First, we extract and detect the strongest reflection component. By removing the strongest reflection component from the original azimuth echo one by one, we realize the detection of reflection components sequentially, from the strongest to the weakest. Our algorithm applies to detecting MM targets with different reflection coefficients and has high precision of parameter estimation. The results of simulation and field experiments verify the advantages of the algorithm.