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.

Psychometric Properties of a New Scale Measuring Neglect and Abuse of Older Adults in the Community: Implications for Social Activity

2020 ◽  
Vol 41 (2) ◽  
pp. 163-172
Author(s):  
Nestor Asiamah ◽  
Kyriakos Kouveliotis ◽  
Richard Eduafo ◽  
Richard Borkey
Keyword(s):  
Older Adults ◽  
Principal Component Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Psychometric Properties ◽  
Social Activity ◽  
Principal Component ◽  
Varimax Rotation ◽  
Factor Solution ◽  
Confirmatory Factor

Background Neglect and abuse of older adults are still prevalent in communities of developing countries, a situation that could discourage active and healthy behaviors in community-dwelling older adults. Methods This study used the cross-sectional and correlational approaches to construct a scale measuring neglect and abuse in older adults living in the community. The study population was all (3,211) older adults who were resident in Accra and were part of the database of the Social Security and National Insurance Trust. Participants were 515 individuals who met some inclusion criteria. Three steps were followed to determine an initial set of 11 items, and principal component analysis with varimax rotation and confirmatory factor analysis were used to select relevant items and assess the psychometric properties of the final scale. Results Principal component analysis with varimax rotation yielded a two-factor solution on all 11 items. The first factor extracted was “discrimination and exploitation,” which accounted for a variance of 53.9% out of total variance of 70.8%. The scale had a good internal consistency (Cronbach’s α = .90, factor loading ≥0.50). Confirmatory factor analysis confirmed the two-factor solution and produced satisfactory convergent and discriminant validity. Conclusion It is concluded that 11 items that make up 2 factors are potential indicators of neglect and abuse of older adults at the community level. Finally, neglect and abuse of older people in the community can contribute to an increase or decrease in social activity.

scholarly journals Exploring Factor Structures Of A Thinking Questionnaire

2020 ◽  
Vol 31 ◽  
pp. 35-53
Author(s):  
Hazlina Abdullah
Keyword(s):  
Principal Component Analysis ◽  
Monte Carlo ◽  
Factor Analysis ◽  
Undergraduate Students ◽  
Principal Component ◽  
Parallel Analysis ◽  
Factor Solution ◽  
Self Reflection ◽  
Five Factors ◽  
Strongly Agree

The purpose of this study was to investigate the construct validity of a thinking questionnaire through Factor Analysis over a Malaysian university students’ sample. The questionnaire is a 5 point Likert scale survey ranging from Strongly Agree, Agree, Neutral, Disagree and Strongly Disagree, intended to measure students’ self-reflection on their thinking upon their Invention Project. A total of 350 undergraduate students from various faculties participated in this study. Principal Component Analysis was used because the primary purpose was to identify and compute composite thinking scores for the factors underlying the Thinking questionnaire. The five factor solution, which explained 52.4% of the variance was chosen because of the ‘levelling off’ of eigenvalues on the scree plot after five factors, and was also confirmed by the Monte Carlo Parallel Analysis which indicated smaller values of the 5 factors compared to the eigenvalues displayed in the SPSS.

scholarly journals Principal Components Analysis Utility in the Livestock Field

2016 ◽  
Vol 73 (2) ◽  
pp. 251
Author(s):  
Ancuta Simona Rotaru ◽  
Ioana Pop ◽  
Anamaria Vatca ◽  
Luisa Andronie
Keyword(s):  
Factor Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Latent Variables ◽  
Principal Component ◽  
The Other ◽  
Data Sets ◽  
Method Factor ◽  
Two Factors ◽  
Components Analysis

Principal Component Analysis is a method factor - factor analysis - and is used to reduce data complexity by replacingmassive data sets by smaller sets. It is also used to highlight the way in which the variables are correlated with eachother and to determining the (less)latent variableswhich are behind the (more)measured variables. These latent variables are called factors, hence the name of the methodi.e. factor analysis. Our paper shows the applicability of Principal Components Analysis (PCA) in livestock area of study by carrying out a researchon some physiological characteristics in the case of tencow breeds.By using PCA only two factors have been preserved, concentrating over 80% of their information from the four variables in question, one factor concentrating weight and height and the other factor concentrating trunk circumference and weight at calving, respectively.

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.

scholarly journals Metacognitive Rating Scale: A Study Applying a Korean Translation to Individuals with Schizophrenia

2021 ◽  
Vol 18 (13) ◽  
pp. 6853
Author(s):  
Mihwa Han ◽  
Kyunghee Lee ◽  
Mijung Kim ◽  
Youngjin Heo ◽  
Hyunseok Choi
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Mental Status ◽  
Rating Scale ◽  
Anger Expression ◽  
Principal Component ◽  
Negative Beliefs ◽  
Five Factors ◽  
Metacognitive Beliefs

Metacognition is a higher-level cognition of identifying one’s own mental status, beliefs, and intentions. This research comprised a survey of 184 people with schizophrenia to verify the reliability of the metacognitive rating scale (MCRS) with the revised and supplemented metacognitions questionnaire (MCQ) to measure the dysfunctional metacognitive beliefs of people with schizophrenia by adding the concepts of anger and anxiety. This study analyzed the data using principal component analysis and the varimax method for exploratory factor analysis. To examine the reliability of the extracted factors, Cronbach’s α was used. According to the results, reliability was ensured for five factors: positive beliefs about worry, negative beliefs about uncontrollability and danger of worry, cognitive confidence, need for control, and cognitive self-consciousness. The negative beliefs about uncontrollability and danger of worry and the need for control on anger expression, which were both added in this research, exhibited the highest correlation (r = 0.727). The results suggest that the MCRS is a reliable tool to measure the metacognition of people with schizophrenia.

scholarly journals Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Added Value ◽  
Time Activity ◽  
Positron Emission ◽  
Activity Curve ◽  
The Mean ◽  
Mean Time ◽  
Met Pet

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

Use of the Perceived Physical Competence Scale with Adolescents with Disabilities

1995 ◽  
Vol 80 (2) ◽  
pp. 571-577 ◽  
Author(s):  
Taru Lintunen ◽  
Pilvikki Heikinaro-Johansson ◽  
Claudine Sherrill
Keyword(s):  
Gender Differences ◽  
Factor Analysis ◽  
Principal Component ◽  
Principal Component Factor Analysis ◽  
Validity And Reliability ◽  
Varimax Rotation ◽  
Physical Competence ◽  
Related Literature ◽  
Competence Scale ◽  
Two Factors

The construct validity and reliability of the 1987 Perceived Physical Competence Scale of Lintunen were examined to assess the applicability of the instrument for use with adolescents with disabilities. Subjects were 51 girls and 34 boys ( M age = 15.1 yr.) from several schools in central Finland. Principal component factor analysis with varimax rotation yielded the same two factors for adolescents with disabilities as reported for nondisabled adolescents in the related literature. Cronbach alphas for the two factors were .89 and .56. It was concluded that the scale is an appropriate measure for adolescents with disabilities. Statistical analysis indicated no gender differences for adolescents with disabilities. When compared with nondisabled groups in the related literature, these adolescents had perceived fitness similar to nondisabled peers but significantly lower than that of athletes without disabilities.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

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