Preliminary Evaluation of a Prolonged Grief Questionnaire for Adolescents

2016 ◽  
Vol 74 (1) ◽  
pp. 80-95 ◽  
Author(s):  
Johanna Unterhitzenberger ◽  
Rita Rosner
Keyword(s):  
Principal Component Analysis ◽  
Small Sample Size ◽  
Principal Component ◽  
Small Sample ◽  
Preliminary Evaluation ◽  
Prolonged Grief ◽  
Promising Tool ◽  
Parentally Bereaved ◽  
Separation Distress ◽  
Two Factors

Currently, there is no established measure to assess prolonged grief in adolescents. A new measure was designed based on the Extended Grief Inventory, the Inventory of Complicated Grief—Revised for Children, and the Inventory of Prolonged Grief for Children/Adolescents. We investigated the psychometric properties of the Prolonged Grief Questionnaire for Adolescents in a sample of 69 14- to 18-year-old parentally bereaved adolescents living in rural Rwanda. Additionally, we obtained sociodemographic information and assessed loss experiences and depressive symptoms. A principal component analysis revealed item loadings on two factors, which we named separation distress and secondary emotions. Internal consistency in this first evaluation was high (α = .94), and the criterion validity was satisfying. A sensitivity of 85.3% and a specificity of 85.9% were found. The small sample size is a major limitation. However, the Prolonged Grief Questionnaire for Adolescents may be a promising tool for assessing prolonged grief symptoms in adolescents.

Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

The classification of supplier selection criteria with respect to lean or agile manufacturing strategies

2017 ◽  
Vol 28 (2) ◽  
pp. 232-249 ◽  
Author(s):  
Mohamed El Mokadem
Keyword(s):  
Supplier Selection ◽  
Selection Criteria ◽  
Small Sample Size ◽  
Principal Component ◽  
Agile Manufacturing ◽  
Small Sample ◽  
Underlying Structure ◽  
Principal Component Factor Analysis ◽  
Content Type

Purpose The purpose of this paper is to investigate how manufacturing strategies could affect the supplier selection criteria used by business firms. Design/methodology/approach Survey strategy was used to empirically understand the research argument. First, principal component factor analysis was employed to validate the underlying structure of the supplier selection criteria. Then, simple regression analysis was employed to test the research hypotheses. Findings Organizations pursuing lean strategies will emphasize factors that improve their efficiency when selecting their suppliers, while organizations pursuing agile strategies will assert factors that improve their ability to respond to customer unique requirements when selecting their suppliers. Research limitations/implications This study provides new insight for researchers to understand the effect of manufacturing strategies on the classification of supplier selection criteria. However, the small sample size might limit the ability to generalize research findings. Further research is required to confirm the findings using a wider sample. Practical implications This research provides practitioners with useful insights into how to select their suppliers based on their manufacturing strategies. Originality/value This paper classifies supplier selection criteria into three distinct groups: lean, agile, and common capabilities. Lean capability refers to the list of criteria that are highly relevant in selecting suppliers for lean implementers. Agile capability refers to the list of criteria that are highly relevant in selecting suppliers for agile implementers. Finally, common capability refers to the list of criteria that have comparable relevance in selecting suppliers for both lean and agile implementers.

Inclusive growth in Africa: are Chinese investment and local industry participation compatible?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emmanuel T. Kodzi Jr
Keyword(s):  
Small Sample Size ◽  
Value Added ◽  
Principal Component ◽  
Small Sample ◽  
Necessary Condition ◽  
Inclusive Growth ◽  
Host Countries ◽  
African Countries ◽  
Content Type ◽  
Chinese Investment

PurposeThis paper aims to explore whether increasing Chinese foreign direct investment (FDI) is associated with rising contributions of local industry in African countries connected to the Belt and Road Initiative (BRI). The existence of cooperative industry linkages between Chinese investments and local businesses is a necessary condition for achieving the mutual benefits asserted by the BRI.Design/methodology/approachUnder growing FDI, the authors framed increasing local industry contribution as indicative of existing industry linkages. Using principal component analysis and multiple regression on collated country-level data, the authors examined relationships between key industry output variables and several independent variables representing Chinese investment and economic activity in a contiguous three-country region, over two investment periods.FindingsIncreasing Chinese FDI was associated with positive economic outcomes including decreasing unemployment; however, it did not appear to support local industry participation. The authors identified a “China effect” that hampered industry contribution to gross domestic product. The authors found that attempting to counterbalance this effect through direct exports to China was not strategically sound. Similarly, export-focused clusters in special zones may not foster industry linkages if they result in isolationism. Rather, host countries have an opportunity to enhance local industry contribution through leveraging interconnectivity factors under increasing FDI.Research limitations/implicationsSmall sample size of the study has implications for the predictive power of the model and for the complete explanation all the emerging findings. However, the authors presented compelling arguments for selecting the specific three countries. By conducting robustness checks on a separate region, findings of this study were substantially corroborated.Practical implicationsInstead of exporting directly to China as a way to mitigate local industry contraction, host countries need to thoughtfully pursue opportunities that present the greatest value-added export advantages. Proposed Chinese-funded infrastructure projects must be negotiated with a goal to strategically reduce interconnectivity barriers and achieve broader logistics improvements in the host countries.Social implicationsThe study provides a tool for proponents of local industry growth to present clearer frameworks in their advocacy. The social tensions around Chinese dominance in the host countries can be reduced by understanding and pursuing levers that enhance industry contribution in those contexts.Originality/valueThis study takes a different approach to examining the professed win-win proposition of the BRI in Africa. It uncovers important effects of increasing Chinese FDI and addresses viable host country responses, including a clear pathway for forging the cooperative industry linkages needed for inclusive growth and sustainable development.

scholarly journals Motor Capacities in Boys with High Functioning Autism: Which Evaluations to Choose?

2019 ◽  
Vol 8 (10) ◽  
pp. 1521
Author(s):  
Véronique-Aurélie BRICOUT ◽  
Marion PACE ◽  
Léa DUMORTIER ◽  
Sahal MIGANEH ◽  
Yohan MAHISTRE ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Autism Spectrum ◽  
Small Sample ◽  
Ensemble Classification ◽  
Control Group ◽  
Children With Asd ◽  
Agglomerative Hierarchical Cluster Analysis ◽  
Motor Tests

The difficulties with motor skills in children with autism spectrum disorders (ASD) has become a major focus of interest. Our objectives were to provide an overall profile of motor capacities in children with ASD compared to neurotypically developed children through specific tests, and to identify which motor tests best discriminate children with or without ASD. Twenty-two male children with ASD (ASD—10.7 ± 1.3 years) and twenty controls (CONT—10.0 ± 1.6 years) completed an evaluation with 42 motor tests from European Physical Fitness Test Battery (EUROFIT), the Physical and Neurological Exam for Subtle Signs (PANESS) and the Movement Assessment Battery for Children ( M-ABC). However, it was challenging to design a single global classifier to integrate all these features for effective classification due to the issue of small sample size. To this end, we proposed a hierarchical ensemble classification method to combine multilevel classifiers by gradually integrating a large number of features from different motor assessments. In the ASD group, flexibility, explosive power and strength scores (p < 0.01) were significantly lower compared to the control group. Our results also showed significant difficulties in children with ASD for dexterity and ball skills (p < 0.001). The principal component analysis and agglomerative hierarchical cluster analysis allowed for the classification of children based on motor tests, correctly distinguishing clusters between children with and without motor impairments.

scholarly journals Electronic Tongue Combined with Chemometrics to Provenance Discrimination for a Green Tea (Anji-White Tea)

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Si-Min Yan ◽  
Zi-Feng Hu ◽  
Cheng-Xin Wu ◽  
Lu Jin ◽  
Gong Chen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Green Tea ◽  
Electronic Tongue ◽  
Principal Component ◽  
Small Sample ◽  
Classification Model ◽  
Instrumental Method ◽  
Provenance Analysis ◽  
White Tea

This paper aims to provide a stable instrumental method for provenance discrimination of Anji-White tea by its distinctive taste. 180 authentic and 60 counterfeit white tea samples were collected for specific geographical origins detection; all of them were measured by electronic tongue coupled with 7 independent sensors. Therefore, chemometrics methods, principal component analysis (PCA), and partial least squares discriminant analysis (PLSDA) were performed in classification. The PCA distribution shows that, in provenance analysis, PCA is a simple and reliable tool for small sample sets, but for sets with large objects, PCA seems powerless in classification. Therefore, PLSDA was applied to develop a classification model. The prediction sensitivity and specificity of PLSDA, respectively, reached 0.917 and 0.950. This study demonstrates the potential of combining electronic tongue system and chemometrics as an effective tool for specific geographical origins detection in Anji-White tea.

scholarly journals Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network

2021 ◽  
Vol 13 (12) ◽  
pp. 2253
Author(s):  
Yanling Han ◽  
Xi Shi ◽  
Shuhu Yang ◽  
Yun Zhang ◽  
Zhonghua Hong ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Sea Ice ◽  
Image Classification ◽  
Spatial Information ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Small Sample ◽  
Bohai Bay ◽  
Hybrid Strategy

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time.

scholarly journals Principal Component Analysis (PCA) Combined with Naturally Occurring Crystallization Inhibitors: An Integrated Strategy for a more Sustainable Control of Salt Decay in Built Heritage

Heritage ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 220-229 ◽  
Author(s):  
Francesca Cardinali ◽  
Maria Paola Bracciale ◽  
Maria Laura Santarelli ◽  
Assunta Marrocchi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Solubility In Water ◽  
Built Heritage ◽  
Promising Tool ◽  
Naturally Occurring ◽  
New Directions ◽  
Experimental Errors

Salt inhibitors have been receiving increasing attention as potential innovative systems to counteract salt damage by preventing crystallisation of the salts within the natural stone structure—and related disruptive action—of built heritage. Especially, we focus on biomass-derived inhibitor systems featuring complete solubility in water or alcohol and intrinsic non-toxicity. Moving from the promising results obtained, the present study aims to develop research concerning the possibility of rationalizing the collected data sets and making them amenable to statistical analysis. This paper reports on an exploratory application of one of the most powerful methods in chemometrics, i.e., principal component analysis (PCA), in this area. It will be seen that this method is a promising tool to extract information from a series of tests to optimize them and to reduce the level of “noise” present in the data collected, i.e., unnecessary information or experimental errors, and to suggest new directions.

scholarly journals Description of facial symmetry in patients with unilateral cleft lip and palate using geometric morphometry and computerized tomography

2017 ◽  
Vol 22 (1) ◽  
pp. 20-25
Author(s):  
Sthepanie Ruiz ◽  
Anderson Silva ◽  
Mayra Celis ◽  
Rocio Ruales ◽  
Francined Pardo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Thin Plate ◽  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
Principal Component ◽  
Component Analysis ◽  
Small Sample ◽  
Geometric Methods ◽  
Affected Side ◽  
Significant Difference

Objective: To describe the maxillary asymmetry in patients with single cleft lip and palate by using morphometrics geometric methods. Materials and Methods: Applied morphometrics geometric methods to analyze images captured from 3D reconstructions of CT scans of 9 patients with unilateral cleft lip and palate, mean age of 13.7 years was used. Tps Dig2 software was used to digitalize 6 maxillary landmarks shaping both the affected and the sides unaffected. TpsPower and TpsPLS to a small sample for relative warps and consensus for superimposition. Thin plate function and asymmetry was used applying ASI-CLIC® package, and the principal component analysis was performed with the PAST software version 2.17.0. Results: There is a statistically significant difference (p<0.05) between the conformation of the maxilla on the affected side compared to unaffected. The study of asymmetry indicated different degrees and differences in the nature of the asymmetry that characterizes different deformities of unilateral cleft lip and palate. The principal component analysis demonstrates both inter-group variability and recognizes two principal components, 39.4%, to the first component and 27.5% to the second component. There is a high correlation between the formation of the unaffected side and affected side conformation r= 0.93847. The thin plate deformation is uniform. The allometry study indicated that there is no association between the shape and size. Conclusions: Morphometry Geometric method is a useful tool for assessing preoperative maxillary conformations in patients with unilateral cleft lip and palate. The side without the cleft is also affected, and is associated with the formation on the side of the cleft. The frontonasal suture is also affected, in a greater proportion than the fronto zigomatic.

Two types of squalor: findings from a factor analysis of the Environmental Cleanliness and Clutter Scale (ECCS)

2013 ◽  
Vol 25 (7) ◽  
pp. 1191-1198 ◽  
Author(s):  
John Snowdon ◽  
Graeme Halliday ◽  
Glenn E. Hunt
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Latent Variables ◽  
Principal Component ◽  
Living Conditions ◽  
Project Team ◽  
Factor Solution ◽  
Hoarding Disorder ◽  
Two Factors ◽  
The Mean

ABSTRACTBackground: Most people who collect and hoard, and then have difficulty discarding items, do not live in squalor, even though accumulation of hoarded items can make cleaning very difficult. Commonly, people living in squalor accumulate garbage, but relatively few fulfill proposed criteria for “hoarding disorder.” We examined the overlap between hoarding and squalor among people referred because of unacceptable living conditions.Methods: Ongoing collection of data by a Squalor Project team, including ratings on the Environmental Cleanliness and Clutter Scale (ECCS), allowed (1) description of characteristics of cases and (2) examination of ratings of uncleanliness, and of the effect of accumulation of items or material on access within dwellings. Principal component analysis was used to examine latent variables underlying the ECCS.Results: The mean age of the referred occupants (108 male, 95 female) was 61.9 years. The mean ECCS score in 186 rated cases was 18.5. Factor analysis of ECCS data showed a two-factor solution as the most plausible. Factor 1, comprising seven squalor items, accounted for 33.7% of the variance. Factor 2 comprised reduced accessibility and accumulation of items of little value (variance 17.6%). Accumulation of garbage loaded equally on the two factors. High levels of squalor and/or accumulation were recorded in 105 (56%) of the 186 dwellings. One-third scored high on accumulation/hoarding, while 38% scored high on squalor; 15% scored high on both squalor and accumulation. A quarter of those scoring high on squalor scored low on hoarding/accumulation.Conclusions: The ECCS is useful when describing whether referred cases show high levels of squalor, hoarding, or both.

The use of ordination techniques in the preliminary evaluation of Stylosanthes accessions

1973 ◽  
Vol 24 (5) ◽  
pp. 715 ◽  
Author(s):  
WT Williams ◽  
LA Edye ◽  
RL Burt ◽  
B Grof
Keyword(s):  
Principal Component Analysis ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Component Analysis ◽  
Preliminary Evaluation ◽  
Ordination Techniques ◽  
Three Components ◽  
The Way

It is suggested that ordination is preferable to classification if two sets of data are to be rigorously compared. Ordination techniques have been applied to data from 154 Stylosanthes accessions grown at two contrasted sites. Eleven agronomic attributes were recorded for each site. Each set of data was submitted separately to a principal component analysis, and the first three components in each case are described and discussed. Techniques are explained as well as the way in which the components can be used for predicting the most promising accessions. The two sets of components were compared by canonical procedures, and although the between-attributes-between-sites correlation coefficients are everywhere low, the canonical coefficients are extremely high. There is, therefore, a robust attribute- pattern, common to the two sites, but exemplified by different species in each instance. If a desirable performance can be defined in terms of the canonical components, it follows that accessions can always be found that will meet it, but different species will be required at different sites. The results strongly suggest that, among the set of accessions tested, there may be forms potentially capable of out-yielding even the best of the existing cultivars.

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