Use Of Partial Least Squares Research Articles

Consistent Partial Least Squares Path Modeling

This paper resumes the discussion in information systems research on the use of partial least squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for estimates when PLS is applied to reflective constructs: The path coefficients, inter-construct correlations, and indicator loadings become consistent. The outcome of a Monte Carlo simulation reveals that the bias of PLSc parameter estimates is comparable to that of covariance-based structural equation modeling. Moreover, the outcome shows that PLSc has advantages when using non-normally distributed data. We discuss the implications for IS research and provide guidelines for choosing among structural equation modeling techniques.

Use of partial least squares for identifying the effect of through process variables on some of the magnetic properties (permeability) of grain oriented electrical steel

As there are a large number of process variables involved in manufacturing grain oriented electrical steel, partial least squares (PLS) was used to study the influence of these process parameters on some magnetic properties and in particular on permeability. It was found that a model containing just two PLS components provided an adequate best fit to the process data with the most important variables being some of the alloying elements contained within the delivery of steel from the IJmuiden plant and the chemistry changes at the decarburisation line. Also of importance was the temperature in the early zones in the decarburising furnace and the latter zones in the annealing pickling line furnace.

Degree of deacetylation of chitosan by infrared spectroscopy and partial least squares

The determination of the degree of deacetylation of highly deacetylated chitosan by infrared (IR) spectroscopy was significantly improved with the use of partial least squares (PLS). The IR spectral region from 1500 to 1800cm−1 was taken as the dataset. Different PLS models resulting from various data pre-treatments were evaluated and compared. The PLS model that gave excellent internal and external validation performance came from the data that were corrected for the baseline and that was normalized relative to the maximum corrected absorbance. Analysis of the PLS loadings plot showed that the important variables in the spectral region came from the absorption maxima related to the amide bands at 1660 and 1550cm−1 and amine band at 1600cm−1. IR-PLS results were comparable to the results obtained by potentiometric titration. IR-PLS results were found to be more precise and rugged compared to the usual IR absorbance ratio method. This is consistent with the fact that IR spectral resolution is not really high and that the absorption at a single wavelength is influenced by other factors like hydrogen bonding and the presence of water.

Synthesizing complex data on functional decline in older people using partial least squares (PLS) analysis.

Declining capacity to function safely and independently in the community is a manifestation of ageing. Multiple measures are used to define and predict functional decline. This paper explores the use of partial least squares (PLS) analysis to understand the interaction between overtime measures of functional decline. Comprehensive information was captured on individuals aged 65+ who presented at a large metropolitan Australian hospital Emergency Department (ED) for a complaint, which did not result in a hospital admission. They were followed-up by telephone 1 and 3 months, post-ED discharge. Information was collected each time on home situations (living alone, using community services, using a gait aid and having a formal carer), recent falls, hospitalizations and instrumental activities of daily living (IADLs). PLS analysis was applied to identify overtime relationships between measures. Valid information was provided by 147 individuals at all three time points. The eight individual IADL items clustered clearly around physical, mental or combined mental and physical IADLs. These clusters were strongly related to increased use of community supports, gait aid and carer, and living alone. The relationship was less convincingly for hospitalizations, and falls were not explained well. PLS analysis offers a novel and comprehensive way of analysing complex health data, which allows sense to be made of relationships over a 3-month period. This analysis provides a better understanding of declining function over time, than could be provided by current health modelling methods.

Environmental strategies and organizational competitiveness in the hotel industry: The role of learning and innovation as determinants of environmental success

This paper examines the links between proactive environmental strategies, organizational capabilities and competitiveness. A model is proposed and tested using a sample of 232 Spanish hotels. An orientation for learning and innovation are conceived not only as drivers for adopting pro-environmental policies, but also as determinants of competitiveness. Data are analyzed through the use of partial least squares. The findings confirm that a proactive environmental strategy and innovation favor organizational competitiveness. However, a learning orientation does not directly predict organizational competitiveness. The paper discusses both conceptual and practical implications for the development of successful hotel operations and management.

Forecasting EU Economic Activity Using Summary Indicators

This paper analyses the forecasting ability of economic summary indicators in EU economies. We employ the use of Partial Least Squares and Bayesian Shrinkage Regression methods and we predict the growth rates of quarterly GDP and Consumption and monthly Industrial Production. We find evidence that these methods result in better short term forecasts.

Use of partial least squares analysis in concrete technology

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

Partial Least Square and Hierarchical Clustering in ADMET Modeling: Prediction of Blood – Brain Barrier Permeation of α-Adrenergic and Imidazoline Receptor Ligands

PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 α-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property Relationship (QSPR) study. METHODS. Experimentally determined chromatographic retention data (logKw at pH 4.4, slope (S) at pH 4.4, logKw at pH 7.4, slope (S) at pH 7.4, logKw at pH 9.1, and slope (S) at pH 9.1) and capillary electrophoresis migration parameters (μeff at pH 4.4, μeff at pH 7.4, and μeff at pH 9.1), together with calculated molecular descriptors, were used as independent variables in the QSPR study by use of partial least square (PLS) methodology. RESULTS. Predictive potential of the formed QSPR models, QSPR(logPS), QSPR(logPS-brain), QSPR(logBB), was confirmed by cross- and external validation. Hydrophilicity (Hy) and H-indices (H7m) were selected as significant parameters negatively correlated with both logPS and logPS-brain, while topological polar surface area (TPSA(NO)) was chosen as molecular descriptor negatively correlated with both logPS and logBB. The principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to cluster examined drugs based on their chromatographic, electrophoretic and molecular properties. Significant positive correlations were obtained between the slope (S) at pH 7.4 and logBB in A/B cluster and between the logKw at pH 9.1 and logPS in C/D cluster. CONCLUSIONS. Results of the QSPR, clustering and correlation studies could be used as novel tool for evaluation of blood-brain barrier permeation of related α-adrenergic/imidazoline receptor ligands.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 α-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property Relationship (QSPR) study. METHODS. Experimentally determined chromatographic retention data (logKw at pH 4.4, slope (S) at pH 4.4, logKw at pH 7.4, slope (S) at pH 7.4, logKw at pH 9.1, and slope (S) at pH 9.1) and capillary electrophoresis migration parameters (μeff at pH 4.4, μeff at pH 7.4, and μeff at pH 9.1), together with calculated molecular descriptors, were used as independent variables in the QSPR study by use of partial least square (PLS) methodology. RESULTS. Predictive potential of the formed QSPR models, QSPR(logPS), QSPR(logPS-brain), QSPR(logBB), was confirmed by cross- and external validation. Hydrophilicity (Hy) and H-indices (H7m) were selected as significant parameters negatively correlated with both logPS and logPS-brain, while topological polar surface area (TPSA(NO)) was chosen as molecular descriptor negatively correlated with both logPS and logBB. The principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to cluster examined drugs based on their chromatographic, electrophoretic and molecular properties. Significant positive correlations were obtained between the slope (S) at pH 7.4 and logBB in A/B cluster and between the logKw at pH 9.1 and logPS in C/D cluster. CONCLUSIONS. Results of the QSPR, clustering and correlation studies could be used as novel tool for evaluation of blood-brain barrier permeation of related α-adrenergic/imidazoline receptor ligands. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

PP33 A N Age-Period-Cohort Analysis for Trends in Body Mass Index in the Republic of Ireland in 18,016 Participants in Slan (Surveys of Lifestyle, Attitudes and Nutrition) 1998-2007

BackgroundObesity has increased in prevalence worldwide in the last 20 years and is associated with a variety of adverse health outcomes. It is valuable to examine these trends in relation...

The Use of Partial Least Square (PLS) to Explore the Importance of Sperm Characteristics in the Prediction of Bull Fertility

The Use of Partial Least Square (PLS) to Explore the Importance of Sperm Characteristics in the Prediction of Bull Fertility

A discussion on the use of prediction uncertainty estimation of NIR data in partial least squares for quantitative pharmaceutical tablet assay methods

Out-of-scope sample detection is a key element in successful pharmaceutical quality monitoring by multivariate models and spectroscopic measurements. It is of importance that the spectral data for new measurements are spectrally equivalent to that used in developing the calibration set to ensure that the predicted values will be accurately expressed. In the present work we evaluated prediction uncertainty approaches as applied to partial least squares (PLS) models that were applied to a pharmaceutical tablet assay method incorporating near infrared (NIR) spectroscopy. During the method implementation with TQ Analyst software, the algorithm was compared with the Unscrambler software, the SIMCA-P+ software, Error-in-Variable (EIV) approach, and verified by the use of PLS_Toolbox software. It is found that the uncertainty values are very close between TQ Analyst and the Unscrambler though the algorithms are quite different. The magnitude of the uncertainty can be sensitive to variations in instrument performance and tablet composition. Confidence limit setting for the prediction uncertainty is discussed with consideration of confidence limits used in Hotelling-T2 and Q-residual statistics and multiple linear regression.

Use of partial least squares within the control relevant identification for buildings

Climate changes, diminishing world supplies of non-renewable fuels, as well as economic aspects are probably the most significant driving factors of the current effort to save energy. As buildings account for about 40% of global final energy use, efficient building climate control can significantly contribute to the saving effort. Predictive building automation can be used to operate buildings in an energy and cost effective manner with minimum retrofitting requirements. In such a predictive control approach, dynamic building models are of crucial importance for a good control performance. An algorithm which has not been used in building modeling yet, namely a combination of minimization of multi-step ahead prediction errors and partial least squares will be investigated. Subsequently, two case studies are presented: the first is an artificial model of a building constructed in Trnsys environment, while the second is a real-life case study. The proposed identification algorithm is then validated and tested.

Antecedents and outcomes of human resource information system (HRIS) use

PurposeThe purpose of this paper is to test some antecedents and outcomes of human resource information system (HRIS) use in Malaysia.Design/methodology/approachA research model based on a questionnaire gleaned from the literature was used to collect data through a purposive sampling technique, whereby only those companies in Penang, Malaysia which are using HRIS were targeted. The data collected were analyzed using structural equation modelling technique with the use of partial least squares approach.FindingsResults show that four out of the five antecedents are significant predictors of extent of use and that extent of use is significantly related to all five outcome variables. This paper provides further evidence on the appropriateness of using Roger's innovation attributes to measure different dimensions of attitude towards extent of HRIS use and Remenyi's and Zuboff's IT framework to measure the outcomes from the extent of HRIS use.Research limitations/implicationsFirst, this study cannot be generalized to all other individuals as the respondents are confined to HR professional employees in local services companies and multinational companies. Therefore, the results cannot be generalized to individuals working in other industries. Second, the study's is carried out using a particular type of technological innovation, which is the HRIS system in this case. As such, the research needs to be replicated to examine the robustness of the findings across a wider range of technologies solution and samples.Practical implicationsThe findings suggest that information technology (IT) appears to serve as an empowering function for HR professionals, providing a medium in which HR professionals can provide increased value in their work. The paper will help organizations in the implementation or deployment of new information technology products, systems or processes. Organization can now ensure a smoother implementation plan by addressing the attributes of the innovation that will most likely concern its employees.Originality/valueThis paper adds to the existing literature of HRIS antecedents and outcomes in a developing country setting. Although there has been some research on the same issue, the number is very small in comparison to the literature appearing from the developed countries.

Satisfaction and trust on customer loyalty: a PLS approach

PurposeThe purpose of this paper is to test some antecedents and outcomes of satisfaction in the banking sector in Malaysia.Design/methodology/approachA research model based on the customer satisfaction index (CSI) was developed and tested using structural equation modeling technique with the use of partial least squares (PLS) approach. Data was analyzed using 239 bank customer responses.FindingsThe results showed that satisfaction has a positive effect on trust and this trust leads to loyalty to the bank. Good complaint handling by banks will also elevate satisfaction, trust and loyalty.Research limitations/implicationsThis work contributes to the existing literature on CSI models by introducing customer satisfaction as an antecedent of trust. The findings are limited by the constraints of the number of measures and the participation of banks in this study.Practical implicationsBanks should focus on building credibility trust and benevolence trust with their customers. Commercial service is of the utmost importance, and hence banks should develop strategies and train officers in delivering efficient communication and administrative service, as this will elevate customer satisfaction.Social implicationsThis work contributes to the understanding of customer behavior, needs and preferences, which can help banks to improve customer satisfaction, trust and loyalty.Originality/valueThis paper adds to the existing literature on service satisfaction antecedents and outcomes in a developing country setting. Although there has been some research works on the same issue, their number has been very small in comparison to the literature appearing based on developed countries.

Using partial least squares in operations management research: A practical guideline and summary of past research

The partial least squares (PLS) approach to structural equation modeling (SEM) has been widely adopted in business research fields such as information systems, consumer behavior, and marketing. The use of PLS in the field of operations management is also growing. However, questions still exist among some operations management researchers regarding whether and how PLS should be used. To address these questions, our study provides a practical guideline for using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied. In addition, our study reviews and summarizes the use of PLS in the recent operations management literature according to our guideline. The main contribution of this study is to present a practical guideline for evaluating and using PLS that is tailored to the operations management field.

Complementary Use of Partial Least-Squares and Artificial Neural Networks for the Annual Electricity Consumption Forecast

A model for predicting annual electricity consumption based on the combination of neural network and partial least square method was proposed. The factors affecting the annual electricity consumption are analyzed by means of partial least square method to extract the most important components so that not only the problem of multi-correlation among variables can be solves but also the amount of input dimensions of the neural network can be reduced. Besides, the application of neural network helps to solve the problem of non-linearity of the model. The application example shows that the proposed model has high precision.

QSAR and QSPR Model Interpretation Using Partial Least Squares (PLS) Analysis

Carefully developed quantitative structure-activity and structure-property relationship models contain detailed information regarding how differences in the molecular structure of compounds correlate with differences in the observed biological or other physicochemical properties of those compounds. The ability to understand the behavior of existing molecules and to design new molecules is facilitated by using an objective method to extract and explain the details of the underlying structure-activity or structure-property relationship. Furthermore, a clear understanding of how and why compounds behave as they do can lead to new innovations through model-directed selection of compounds to be used in complex mixtures such as laundry detergents, fabric softeners, and shampoos. Such a method has been developed based on partial least-squares (PLS) regression analysis that allows for the identification of specific structural trends that relate to differences in observed properties. But the analysis of the completed model is only the last step of the process. The model development process itself affects the ability to extract a clear interpretation of the model. Everything from the selection of initial pool of molecular descriptors to evaluate to data set and model optimization impacts the ability to derive detailed molecular design information. This review describes the method details and examples of the use of PLS for model interpretation and also outlines suggestions regarding model development and model and data set optimization that enable the interpretation process.

Simultaneous Spectrophotometric Kinetic Determination of Four Flavor Enhancers in Foods with the Aid of Chemometrics

A sensitive kinetic spectrophotometric method was developed for the determination of four flavor enhancers--maltol, ethyl maltol, vanillin, and ethyl vanillin--in food samples. The method was based on the reduction of iron(III) by the four analytes in a sulfuric acid medium (0.012 mol/L), and the subsequent interaction of iron(II) with hexacyanoferrate(III) to form the strongly colored Prussian blue complex, which exhibited an absorption maximum at 800 nm. The optimized method had linear calibrations over the concentration ranges of 0.2-2.8 mg/L for maltol, ethyl maltol, and vanillin, as well as 0.2-1.8 mg/L for ethyl vanillin; the corresponding detection limits were 0.07, 0.07, 0.06, and 0.06 mg/L, respectively. Calibration models were constructed from the original and first-derivative spectral data with the use of partial least-squares (PLS) and principal component regression chemometrics methods. Ultimately, the proposed analytical procedure was successively applied for the determination of the four compounds in commercial food samples with the use of a PLS calibration based on the first-derivative spectral data. The results were comparable with those from a reference HPLC method.

Why don't all maths teachers use dynamic geometry software in their classrooms?

<span>In this exploratory study, we sought to examine the influence of mathematics teachers' beliefs on their intended and actual usage of dynamic mathematics software in their classrooms. The theory of planned behaviour (TPB), the technology acceptance model (TAM) and the innovation diffusion theory (IDT) were used to examine the influence of teachers' attitudes, subjective norms and perceived behavioural control on their intention to use dynamic mathematics software in their classrooms. The study adopted the co-relational research design, with both correlation statistics and regression analysis used to analyse the data. By using stepwise regression analysis, it was possible to identify the most important belief predictors and their weights for the different constructs. The results were verified by the use of partial least squares. This study found that beliefs about the perceived usefulness and beliefs about their level of technological proficiency are the most important predictors of teachers' intended and actual usage of the software. In this preliminary study the suggested simplified model sufficiently explains 15 (83.3%) of the 18 teachers adaption and use of dynamic mathematics software in their classrooms.</span>

A cross country explanation of performance of heath care systems: The consumer point of view using the Euro Health Consumer Index

This paper performs an empirical analysis based on a published assessment of several health care systems. This assessment, performed by the think tank Health Consumer Powerhouse, reports outcomes, and ranks accordingly several countries. In turn, this paper explores the ability of national data at explaining the differences observed in that ranking. The innovative features of this paper are mainly two. First, it shows how the Euro Health Consumer Index – which is comprised from 6 indicators – can be used as a single-outcome latent variable. This feature expands the possibilities of incorporating EHCI in future research. Secondly, this paper also serves as an example of the use of Partial Least Squares (PLS) and its characteristics. The analytical technique is PLS. The empirical analysis uses a set of 22 European countries (20 from European Union plus Norway and Switzerland) and departs from a model that relates four latent constructs, one representing National Performance (the aggregated measure of the EHCI) and the others representing financial investment in health care and human and physical resources. The final estimates provide very interesting results. The main result, or a judgment from the point of view of political implications of this research, is that a successful health care system needs mainly financial investment. Secondly, it also benefits – to a lesser extent – from physical capabilities. Finally, the way resources – namely human resources – are organized is possibly critical to the overall performance, measured from a consumer point of view.