Old trees are perceived as a valuable element of the municipal forest landscape

Urban trees are important to maintain biodiversity and, therefore, need public acceptance. Few studies, however, have addressed the topic of social acceptability of old trees. The aim of this research was to examine city residents’ perception of old trees, including hollow-bearing ones, mainly in the aspect of safety and aesthetics. A total of 448 Warsaw municipal forest’ users expressed their opinions by completing an online questionnaire. Several methods were used to analyse the results of the study: the Chi-square test of independence, the Kruskal–Wallis H test, the Mann–Whitney U test and the Quartimax method of factor rotation analysis. The results revealed a correlation between the frequency of forest visits and the level of sensitivity toward old trees, which translates to less radical notion of danger and less radical decisions about cutting such trees down. Age of the respondents (56+) was a factor contributing to higher willingness to protect and care for old trees. The results also indicated that outdoor activity in the urban forest may increase ancient trees acceptance by developing emotional connection with them, and eventually contribute to their protection.

The use of principal component analysis to measure fundamental cognitive processes in neuropsychological data

For years, dissociation studies on neurological single cases were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value and even misleading. Still, principal component analysis (PCA), an associational method for dimensionality reduction, recently became popular for the identification of fundamental functions. The current study evaluated the ability of PCA to identify the fundamental variables underlying a battery of measures. Synthetic data were simulated to resemble typical neuropsychological data, including varying dissociation patterns. In most experiments, PCA succeeded to measure the underlying target variables with high up to almost perfect precision. However, this success relied on additional factor rotation. Unroated PCA struggled with the dependence of data and often failed. On the other hand, the performance of rotated factor solutions required single measures that anchored the rotation. When no test scores existed that primarily and precisely measured each underlying target variable, rotated solutions also failed their intended purpose. Further, the dimensionality of the simulated data was consistently underestimated. Commonly used strategies to estimate the number of meaningful factors appear to be inappropriate for neuropsychological data. Finally, simulations suggested a high potential of PCA to denoise data, with factor rotation providing an additional filter function. This can be invaluable in neuropsychology, where measures are often inherently noisy, and PCA can be superior to common compound measures - such as the arithmetic mean - in the measurement of variables with high reliability. In summary, PCA appears to be a powerful tool in neuropsychology that is well capable to infer fundamental cognitive functions with high precision, but the typical structure of neuropsychological data places clear limitations and a risk of a complete methodological failure on the method.

Cluster and Factorial Analysis Applications in Statistical Methods

Cluster analysis is a mathematical technique in Multivariate Data Analysis which indicates the proper guidelines in grouping the data into clusters. We can understand the concept with illustrated notations of cluster Analysis and various Clustering Techniques in this Research paper. Similarity and Dissimilarity measures and Dendogram Analysis will be computed as required measures for Analysis. Factor analysis technique is useful for understanding the underlying hidden factors for the correlations among the variables. Identification and isolation of such facts is sometimes important in several statistical methods in various fields. We can understand the importance of the Factor Analysis and major concept with illustrated Factor Analysis approaches. We can estimated the Basic Factor Modeling and Factor Loadings, and also Factor Rotation process. Provides the complete application process and approaches of Principal Factor M.L.Factor and PCA comparison of Factor Analysis in this Research paper

On the Detection of the Correct Number of Factors in Two-Facet Models by Means of Parallel Analysis

Methods for optimal factor rotation of two-facet loading matrices have recently been proposed. However, the problem of the correct number of factors to retain for rotation of two-facet loading matrices has rarely been addressed in the context of exploratory factor analysis. Most previous studies were based on the observation that two-facet loading matrices may be rank deficient when the salient loadings of each factor have the same sign. It was shown here that full-rank two-facet loading matrices are, in principle, possible, when some factors have positive and negative salient loadings. Accordingly, the current simulation study on the number of factors to extract for two-facet models was based on rank-deficient and full-rank two-facet population models. The number of factors to extract was estimated from traditional parallel analysis based on the mean of the unreduced eigenvalues as well as from nine other rather traditional versions of parallel analysis (based on the 95th percentile of eigenvalues, based on reduced eigenvalues, based on eigenvalue differences). Parallel analysis based on the mean eigenvalues of the correlation matrix with the squared multiple correlations of each variable with the remaining variables inserted in the main diagonal had the highest detection rates for most of the two-facet factor models. Recommendations for the identification of the correct number of factors are based on the simulation results, on the results of an empirical example data set, and on the conditions for approximately rank-deficient and full-rank two-facet models.

Developing a Questionnaire to Assess Opinions Regarding the Importance of Bathing and Washing Without Water

Bathing is one of the most performed activities among nurses. Although care recipients experience bathing as an important activity in daily living, nurses often undervalue this care task. We developed a questionnaire to measure nurses’ opinions regarding 1) the importance of the bed bath, and 2) a bathing innovation known as Washing Without Water. Construction of the questionnaire items was based on literature and interviews with nursing home residents (n=8), their family (n=5) and nurses (n=6). After items construction, nurses and nursing students (n=124) completed the questionnaire to assess the questionnaire’s internal consistency (IC) and construct validity. Cronbach’s alpha coefficients were analyzed as an indicator for IC and items were deleted if this resulted in improved IC. To analyze the construct validity, a Principal Component Analyses (PCA) with Direct Oblimin Factor rotation was performed. The final scale consists of two subscales. The first subscale measures nurses’ opinions about the importance of the bed bath and consists of 12 items. The second subscale consists of 17 items and aims to inventory nurses’ opinions about the Washing Without Water innovation. The Cronbach’s alpha coefficients are high (.81 for the first and .89 for the second subscale). The PCA results show a one factor loading for both subscales, explaining 33,20% and 37,08% of the variance for the first and second subscale respectively. Results indicate a reliable and valid questionnaire to measure nurses’ opinions related to the bed bath, which can support health care institutions in evaluating the bed bathing process.

A Test of the Reproducibility of the Clustering of Cultural Variables

Cultural variables from many different cross-cultural studies can be divided into two clusters of variables that are strongly correlated within each cluster. This is reflected in two factors that are found to be reproduced by independent sets of cultural variables and also reflected in several different cross-cultural studies. The first factor, called superfactor, reflects the combined effects of development and modernization, together with social-psychological effects such as collectivism, conservatism, regality, and tightness. The second factor, called East Asian factor, combines several effects related to East Asian cultures, and possibly also differences in response style. These two factors can be found in several previously published cultural maps, but rotated differently. The common practice of factor rotation has obscured similarities between many different cross-cultural studies. Many previously published cultural factors with different names are in fact differently rotated solutions reflecting the same or closely related underlying cultural differences.

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.

Efficiency of the electricity sector in Mexico 2008-2015

Purpose The purpose of this paper is to determine the level of efficiency in the Mexico electricity industry during the 2008-2015 period. Design/methodology/approach A data envelopment analysis (DEA) network model is proposed, where technical efficiency is calculated. A factorial analysis using the principal components method was carried out first. Later, latent dimensions were calculated through the variance criterion and sedimentation graph, where four components were presented. After performing factor rotation, the nodes were grouped: generation, transmission, distribution and sales. It proceeded later to structure a DEA network model. Findings From the calculations made, the most efficient node was the transmission, while the North Gulf and East Center divisions were the only efficient. Research limitations/implications The limitations presented in this study were data collection. Practical implications The implications that were observed were that through the results obtained, proposals can be made to the Mexican electricity sector to improve each of the nodes, and have a better operation and reduce energy losses. Social implications The social impact of this type of study is that based on the results obtained, they present the basis for improving energy policy and users can have a better service that has better quality and coverage. Originality/value The originality of this study consists in the use of two methodologies, factor analysis methodology and DEA network model.

Process of forming a regional recreation system in the conditions of a border region

Significant heterogeneity of the level of development of the Russian border, including in the field of recreation, imposes requirements for differentiation in the regional policy. Definition of the types of territories helps to solve applied management tasks more effectively. In this context, the factors by which these types were formed are important. To solve this problem, the authors conducted a factor analysis through the principal component method using oblique factor rotation. Three blocks of variables were analyzed that characterize the subjects of the Russian Federation that have land borders on the mainland (including river and lake borders) and sea borders with neighboring countries located on the map clockwise from Norway to the United States (border regions of Russia) for the period from 2010 to 2018. As a result, five factors were identified: the factor of the demand for the services of the recreational system, the factor of the development of the infrastructure of the recreational system in climatic conditions, the environmental safety factor, the factor of investment in the development of the recreational system infrastructure, the factor of the location at the border. The results of the study can be used as a practical tool for developing recommendations in the field of regional policy aimed at development of a recreational system, taking into account the factors determined for each identified group. The results of the study were obtained in the framework of the state task of the IE KarRC RAS on the topic “Institutions and social inequality in the face of global challenges and regional restrictions”.

Is it possible to improve bank performance? Evidence from banks in Bosnia and Herzegovina

This research was conducted to identify variables that affect the efficiency of banks in Bosnia and Herzegovina. The required data were collected from 30 respondents (banks directors and CEOs) and a targeted set of 20 questions. For the purposes of data analysis, the statistical technique of factor analysis was used with the help of principal components. In the process of implementing this technique, the general applicability of the model and each variable was tested in order to identify key indicators that affect the efficiency of bank operations. Therefore, the main objective of this research is to identify the factors that most affect the efficiency of banks in Bosnia and Herzegovina. The results of the research showed that the value of the Kaiser-Meyer-Olkin (KMO) is greater than 0.50, which certainly confirms the application of factor analysis, that is, the significance of certain variables for the efficiency and effectiveness of banks in Bosnia and Herzegovina. Also, the factor rotation matrix indicates that the following variables have the greatest impact on the efficiency and effectiveness of banks operations: the bank provides fast service (q8), communication between the bank and clients is good (q9), the higher amount of funds held by clients in bank influences banks to meet the needs of clients when approving loans (q19), banks provide different types of loans (q14) and banks offer moderate interest rates on credit placements (q15).