Urbano Lorenzo-Seva

FACTOR: A computer program to fit the exploratory factor analysis model

Exploratory factor analysis (EFA) is one of the most widely used statistical procedures in psychological research. It is a classic technique, but statistical research into EFA is still quite active, and various new developments and methods have been presented in recent years. The authors of the most popular statistical packages, however, do not seem very interested in incorporating these new advances. We present the program FACTOR, which was designed as a general, user-friendly program for computing EFA. It implements traditional procedures and indices and incorporates the benefits of some more recent developments. Two of the traditional procedures implemented are polychoric correlations and parallel analysis, the latter of which is considered to be one of the best methods for determining the number of factors or components to be retained. Good examples of the most recent developments implemented in our program are (1) minimum rank factor analysis, which is the only factor method that allows one to compute the proportion of variance explained by each factor, and (2) the simplimax rotation method, which has proved to be the most powerful rotation method available. Of these methods, only polychoric correlations are available in some commercial programs. A copy of the software, a demo, and a short manual can be obtained free of charge from the first author.

Tucker's Congruence Coefficient as a Meaningful Index of Factor Similarity

When Tuckers congruence coefficient is used to assess the similarity of factor interpretations, it is desirable to have a critical congruence level less than unity that can be regarded as indicative of identity of the factors. The literature only reports rules of thumb. The present article repeats and broadens the approach used in the study by Haven and ten Berge (1977). It aims to find a critical congruence level on the basis of judgments of factor similarity by practitioners of factor analysis. Our results suggest that a value in the range .85-.94 corresponds to a fair similarity, while a value higher than .95 implies that the two factors or components compared can be considered equal.

Dimensionality Assessment of Ordered Polytomous Items With Parallel Analysis

Parallel analysis (PA) is an often-recommended approach for assessment of the dimensionality of a variable set. PA is known in different variants, which may yield different dimensionality indications. In this article, the authors considered the most appropriate PA procedure to assess the number of common factors underlying ordered polytomously scored variables. They proposed minimum rank factor analysis (MRFA) as an extraction method, rather than the currently applied principal component analysis (PCA) and principal axes factoring. A simulation study, based on data with major and minor factors, showed that all procedures consistently point at the number of major common factors. A polychoric-based PA slightly outperformed a Pearson-based PA, but convergence problems may hamper its empirical application. In empirical practice, PA-MRFA with a 95% threshold based on polychoric correlations or, in case of nonconvergence, Pearson correlations with mean thresholds appear to be a good choice for identification of the number of common factors. PA-MRFA is a common-factor-based method and performed best in the simulation experiment. PA based on PCA with a 95% threshold is second best, as this method showed good performances in the empirically relevant conditions of the simulation experiment.

The Hull Method for Selecting the Number of Common Factors

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an extensive simulation study in which the simulated data are based on major and minor factors. The study compares the method with four other methods such as parallel analysis and the minimum average partial test, which were selected because they have been proven to perform well and/or they are frequently used in applied research. The Hull method outperformed all four methods at recovering the correct number of major factors. Its usefulness was further illustrated by its assessment of the dimensionality of the Five-Factor Personality Inventory (Hendriks, Hofstee, & De Raad, 1999). This inventory has 100 items, and the typical methods for assessing dimensionality prove to be useless: the large number of factors they suggest has no theoretical justification. The Hull method, however, suggested retaining the number of factors that the theoretical background to the inventory actually proposes.

Promin: A Method for Oblique Factor Rotation.

A usual practice in factor analysis is the oblique rotation of the retained factors. For this purpose, different rotation procedures have been proposed. The present article discusses Promin, an alternative to Promaj (Trendafilov, 1994). It is easier to use than Simplimax (Kiers, 1994) and the rotated solution obtained by Promin is very similar to the one obtained by Simplimax. Promin also seems to perform better than other well-known procedures.

A factor simplicity index.

We propose an index for assessing the degree of factor simplicity in the context of principal components and exploratory factor analysis. The new index, which is called Loading Simplicity, is based on the idea that the communality of each variable should be related to few components, or factors, so that the loadings in each variable are either zero or as far from zero as possible. This index does not depend on the scale of the factors, and its maximum and minimum are only related to the degree of simplicity in the loading matrix. The aim of the index is to enable the degree of simplicity in loading matrices to be compared.

Two SPSS programs for interpreting multiple regression results.

When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http:// brm.psychonomic-journals.org/content/supplemental.

Spanish adaptation of Dickman's impulsivity inventory: its relationship to Eysenck's personality questionnaire

Abstract We used exploratory factor analysis to determine the factorial structure of the Spanish adaptation of Dickmans impulsivity inventory in a sample of 355 university students. Our results showed the two impulsivity factors, functional and dysfunctional, described by Dickman (1990). We applied consensus direct oblimin rotation to the Spanish, American and Dutch versions of the inventory and obtained a high congruence between the three factorial solutions which seems to suggest that they are quite stable across languages and populations. Both kinds of impulsivity showed relationships to the extraversion and psychoticism dimensions of the EPQ-R although extraversion was more related to functional impulsivity and psychoticism was more related to dysfunctional impulsivity.

Self-Assessment of Individual Differences in Language Switching

Language switching is omnipresent in bilingual individuals. In fact, the ability to switch languages (code switching) is a very fast, efficient, and flexible process that seems to be a fundamental aspect of bilingual language processing. In this study, we aimed to characterize psychometrically self-perceived individual differences in language switching and to create a reliable measure of this behavioral pattern by introducing a bilingual switching questionnaire. As a working hypothesis based on the previous literature about code switching, we decomposed language switching into four constructs: (i) L1 switching tendencies (the tendency to switch to L1; L1-switch); (ii) L2 switching tendencies (L2-switch); (iii) contextual switch, which indexes the frequency of switches usually triggered by a particular situation, topic, or environment; and (iv) unintended switch, which measures the lack of intention and awareness of the language switches. A total of 582 Spanish–Catalan bilingual university students were studied. Twelve items were selected (three for each construct). The correlation matrix was factor-analyzed using minimum rank factor analysis followed by oblique direct oblimin rotation. The overall proportion of common variance explained by the four extracted factors was 0.86. Finally, to assess the external validity of the individual differences scored with the new questionnaire, we evaluated the correlations between these measures and several psychometric (language proficiency) and behavioral measures related to cognitive and attentional control. The present study highlights the importance of evaluating individual differences in language switching using self-assessment instruments when studying the interface between cognitive control and bilingualism.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.