Exploratory Factor analysis is one of the techniques used in the development, validation and adaptation of psychological measurement instruments Its use spread during the 1960s and has been growing exponentially thanks to the advancement of information technology The criteria used, of course, have also evolved But the applied researchers, who use this technique as a routine, remain often ignorant of all this In the last few decades numerous studies have denounced this situation There is an urgent need to update the classic criteria The incorporation of the most suitable criteria will improve the quality of our research In this work we review the classic criteria and, depending on the case, we also propose current criteria to replace or complement the former Our objective is to offer the interested applied researcher updated guidance on how to perform an Exploratory Item Factor Analysis, according to the “post-Little Jiffy” psychometrics This review and the guide with the corresponding recommendations have been articulated in four large blocks: 1) the data type and the matrix of association, 2) the method of factor estimation, 3) the number of factors to be retained, and 4) the method of rotation and allocation of items An abridged version of the complete guide is provided at the end of the article

https://scielo.isciii.es/pdf/ap/v30n3/metodologia1.pdf

El Análisis Factorial Exploratorio de los Ítems: una guía práctica, revisada y actualizada

On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use

Exploratory factor analysis (EFA) is a common technique utilized in the development of assessment instruments. The key question when performing this procedure is how to best estimate the number of factors to retain. This is especially important as underor over-extraction may lead to erroneous conclusions. Although recent advancements have been made to answer the number of factors question, popular statistical packages do not come standard with these modern techniques. This paper details how to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain. By utilizing the five empirically-supported techniques illustrated in this article, researchers will be able to more judiciously model data.

/pdf/determining-the-number-of-factors-to-retain-in-efa-using-the-v7p3gsccdv.pdf

Determining the Number of Factors to Retain in EFA: Using the SPSS R-Menu v2 0 to Make More Judicious Estimations

Exploratory factor analysis (EFA) methods are used extensively in the field of assessment and evaluation. Due to EFA's widespread use, common methods and practices have come under close scrutiny. A substantial body of literature has been compiled highlighting problems with many of the methods and practices used in EFA, and, in response, many guidelines have been proposed with the aim to improve application. Unfortunately, implementing recommended EFA practices has been restricted by the range of options available in commercial statistical packages and, perhaps, due to an absence of clear, practical 'how-to' demonstrations. Consequently, this article describes the application of methods recommended to get the most out of your EFA. The article focuses on dealing with the common situation of analysing ordinal data as derived from Likert-type scales. These methods are demonstrated using the free, stand-alone, easy-to-use and powerful EFA package FACTOR (http://psico.fcep.urv.es/utilitats/factor/, Lorenzo-Seva & Ferrando, 2006). The demonstration applies the recommended techniques using an accompanying dataset, based on the Big 5 personality test. The outcomes obtained by the EFA using the recommended procedures through FACTOR are compared to the default techniques currently available in SPSS.

/pdf/improving-your-exploratory-factor-analysis-for-ordinal-data-4bmmoo6150.pdf

Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR.

In this review of 64 articles published since the year 2000, a strong association between self-efficacy and student learning outcomes was apparent. Self-efficacy is also related to other factors such as value, self-regulation and metacognition, locus of control, intrinsic motivation, and strategy learning use. The review revealed that university student self-efficacy is higher under certain conditions than others, and that it can be improved. Examples of teaching strategies that may be used to improve self-efficacy are outlined. In screening articles for inclusion in the review, several conflicting definitions of self-efficacy arose. Clarification on the meaning and scope of the self-efficacy term is provided. The interpretation of the results of some studies reviewed was limited by design or analysis issues. Suggestions for addressing these issues in future research and evaluation work is given.

The study, evaluation, and improvement of university student self-efficacy

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 calculations. 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 oers 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 oers 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.

/pdf/an-spss-r-menu-for-ordinal-factor-analysis-3czh5s2q0a.pdf

Manuel Pereira

Papers

An SPSS R-Menu for Ordinal Factor Analysis