An SPSS R-Menu for Ordinal Factor Analysis
30 Jan 2012-Journal of Statistical Software (Foundation for Open Access Statistics)-Vol. 46, Iss: 1, pp 1-29
TL;DR: This paper offers 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.
Abstract: 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.
Citations
More filters
TL;DR: The 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.
Abstract: 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
738 citations
TL;DR: Five major decisions made in conducting factor analysis are focused on, including establishing how large the sample needs to be, choosing between factor analysis and principal components analysis, determining the number of factors to retain, selecting a method of data extraction, and deciding upon the methods of factor rotation.
378 citations
TL;DR: How to program IBM SPSS Statistics software (SPSS) to conveniently perform five modern techniques designed to estimate the number of factors to retain to be able to more judiciously model data.
Abstract: 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.
265 citations
Cites background or methods from "An SPSS R-Menu for Ordinal Factor A..."
...…despite limitations of simulated performance, it is widely recognized as the best approach to determining the real-world practicability of such procedures (Zwick & Velicer, 1986; Garrido, Abad, & Ponsoda, 2011; Garrido, Abad, & Ponsoda, 2012; Basto & Pereira, 2012; Ruscio & Roche, 2012)....
[...]
...In addition, many simulation studies have been carried out to evaluate the comparative efficiency of these methods (Zwick & Velicer, 1986; Garrido, Abad, & Ponsoda, 2011; Garrido, Abad, & Ponsoda, 2012; Basto & Pereira, 2012; Ruscio & Roche, 2012)....
[...]
...Second, Basto and Pereira (2012a) have built the CD procedure into version 2.0....
[...]
TL;DR: In this article, the authors describe the application of methods recommended to get the most out of Exploratory Factor Analysis (EFA) using FACTOR (http://psico.fcep.urv.es/utilitats/factor/, Lorenzo-Seva & Ferrando, 2006).
Abstract: 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.
188 citations
TL;DR: In this article, a review of 64 articles published since the year 2000, a strong association between self-efficacy and student learning outcomes was apparent, indicating that university student selfefficacy is higher under certain conditions than others, and that it can be improved.
Abstract: 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.
156 citations
Cites methods from "An SPSS R-Menu for Ordinal Factor A..."
...In order to analyse these data appropriately, it may be necessary to use more advanced statistical software such as MPlus or R, or find add-ons to expand the capabilities of standard statistics packages such as SPSS (e.g. Basto and Pereira 2012)....
[...]
References
More filters
Journal Article•
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
"An SPSS R-Menu for Ordinal Factor A..." refers methods in this paper
...The SPSS dialog is written in the R programming language (R Development Core Team 2011), and requires SPSS 19 (IBM Corporation 2010a), the R plug-in 2.10 (IBM Corporation 2010b) and the following R packages: polycor (Fox 2009), psych (Revelle 2011), GPArotation (Bernaards and Jennrich 2005), nFactors (Raiche and Magis 2011), corpcor (Schaefer, Opgen-Rhein, Zuber, Duarte Silva, and Strimmer 2011) and ICS (Nordhausen, Oja, and Tyler 2008)....
[...]
...The SPSS dialog is written in the R programming language (R Development Core Team 2011), and requires SPSS 19 (IBM Corporation 2010a), the R plug-in 2.10 (IBM Corporation 2010b) and the following R packages: polycor (Fox 2009), psych (Revelle 2011), GPArotation (Bernaards and Jennrich 2005),…...
[...]
TL;DR: The Scree Test for the Number Of Factors this paper was first proposed in 1966 and has been used extensively in the field of behavioral analysis since then, e.g., in this paper.
Abstract: (1966). The Scree Test For The Number Of Factors. Multivariate Behavioral Research: Vol. 1, No. 2, pp. 245-276.
12,228 citations
Journal Article•
TL;DR: In this paper, the authors collect, in one article, information that will allow researchers and practitioners to understand the various choices available through popular software packages, and to make decisions about "best practices" in exploratory factor analysis.
Abstract: Exploratory factor analysis (EFA) is a complex, multi-step process. The goal of this paper is to collect, in one article, information that will allow researchers and practitioners to understand the various choices available through popular software packages, and to make decisions about ”best practices” in exploratory factor analysis. In particular, this paper provides practical information on making decisions regarding (a) extraction, (b) rotation, (c) the number of factors to interpret, and (d) sample size.
7,865 citations
TL;DR: This paper reviewed the major design and analytical decisions that must be made when conducting exploratory factor analysis and notes that each of these decisions has important consequences for the obtained results, and the implications of these practices for psychological research are discussed.
Abstract: Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed.
7,590 citations
TL;DR: Practical information on making decisions regarding (a) extraction, (b) rotation, (c) the number of factors to interpret, and (d) sample size is provided.
6,726 citations
"An SPSS R-Menu for Ordinal Factor A..." refers background or result in this paper
...If the best factorial solution involves factors uncorrelated, orthogonal and oblique rotations produce nearly identical results (Costello and Osborne 2005; Fabrigar, Wegener, MacCallum, and Strahan 1999)....
[...]
...The Kaiser criterion may overestimate or underestimate the true number of factors, but it usually overestimates the true number of factors (Costello and Osborne 2005; Lance, Butts, and Michels 2006; Zwick and Velicer 1986)....
[...]