An SPSS R-Menu for Ordinal Factor Analysis

Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calcula- tions. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It o ers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper o ers an SPSS dialog written in the R programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

Effect of filler content on morphology and physical-chemical characteristics of poly(vinylidene fluoride)/NaY-zeolite filled membranes

Poly(vinylidene fluoride) electrospun membranes have been prepared with different NaY zeolite contents up to 32%wt. Inclusion of zeolites induces an increase of average fiber size from ~200 nm in the pure polymer up to ~500 nm in the composite with 16%wt zeolite content. For higher filler contents, a wider distribution of fibers occurs leading to a broader size distributions between the previous fiber size values. Hydrophobicity of the membranes increases from ~115º water contact angle to ~128º with the addition of the filler and is independent on filler content, indicating a wrapping of the zeolite by the polymer. The water contact angle further increases with fiber alignment up to ~137º. Electrospun membranes are formed with ~80 % of the polymer crystalline phase in the electroactive  phase, independently on the electrospinning processing conditions or filler content. Viability of MC3T3-E1 cells on the composite membranes after 72 h of cell culture indicates the suitability of the membranes for tissue engineering applications.

Application of Gage R&R methodology for improving the training and evaluation of automatic optical inspection machine operators

The current level of demand by customers in the electronics industry requires the production of parts with an extremely high level of reliability and quality to ensure complete confidence on the end customer. Automatic Optical Inspection (AOI) machines have an important role in the monitoring and detection of errors during the manufacturing process for printed circuit boards. These machines present images of products with probable assembly mistakes to an operator and him decide whether the product has a real defect or if in turn this was an automated false detection. Operator training is an important aspect for obtaining a lower rate of evaluation failure by the operator and consequently a lower rate of actual defects that slip through to the following processes. The Gage R&R methodology for attributes is part of a Six Sigma strategy to examine the repeatability and reproducibility of an evaluation system, thus giving important feedback on the suitability of each operator in classifying defects. This methodology was already applied in several industry sectors and services at different processes, with excellent results in the evaluation of subjective parameters. An application for training operators of AOI machines was developed, in order to be able to check their fitness and improve future evaluation performance. This application will provide a better understanding of the specific training needs for each operator, and also to accompany the evolution of the training program for new components which in turn present additional new difficulties for the operator evaluation. The use of this application will contribute to reduce the number of defects misclassified by the operators that are passed on to the following steps in the productive process. This defect reduction will also contribute to the continuous improvement of the operator evaluation performance, which is seen as a quality management goal.