Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores

ABSTRACT Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Likert-type items, and the aim of the analysis is to estimate participants' scores on the corresponding latent traits. We propose a new approach to deal with missing responses in such a situation that is based on (1) multiple imputation of non-responses and (2) simultaneous rotation of the imputed datasets. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses, and a simulation study based on artificial datasets. The results show that our approach (specifically, Hot-Deck multiple imputation followed of Consensus Promin rotation) was able to successfully compute factor score estimates even for participants that have missing data.

Application of exploratory factor analysis to assess fish consumption in a university community

The objective of this research was to use the technique of Exploratory Factor Analysis (EFA) for the adequacy of a tool for the assessment of fish consumption and the characteristics involved in this process. Data were collected during a campaign to encourage fish consumption in Brazil with the voluntarily participation of members of a university community. An assessment instrument consisting of multiple-choice questions and a five-point Likert scale was designed and used to measure the importance of certain attributes that influence the choice and consumption of fish. This study sample was composed of of 224 individuals, the majority were women (65.6%). With regard to the frequency of fish consumption, 37.67% of the volunteers interviewed said they consume the product two or three times a month, and 29.6% once a week. The Exploratory Factor Analysis (EFA) was used to group the variables; the extraction was made using the principal components and the rotation using the Quartimax method. The results show clusters in two main constructs, quality and consumption with Cronbach Alpha coefficients of 0.75 and 0.69, respectively, indicating good internal consistency.

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Endogenous expression of Müllerian inhibiting substance in early postnatal rat Sertoli cells requires multiple steroidogenic factor-1 and GATA-4-binding sites

Müllerian inhibiting substance (MIS) is a key element required to complete mammalian male sex differentiation. The expression pattern of MIS is tightly regulated in fetal, neonatal, and prepubertal testes and adult ovaries and is well conserved among mammalian species. Although several factors have been shown to be essential to MIS expression, its regulatory mechanisms are not fully understood. We have examined MIS promoter activity in 2-day postnatal primary cultures of rat Sertoli cells that continue to express endogenous MIS mRNA. Using this system, we found that the region between human MIS−269 and −192 is necessary for full MIS promoter activity. We identified by DNase I footprint and electrophoretic mobility-shift analyses a distal steroidogenic factor-1 (SF-1)-binding site that is essential for full promoter activity. Mutational analysis of this new distal SF-1 site and the previously identified proximal SF-1 site showed that both are necessary for transcriptional activation. Moreover, the proximal promoter also contains multiple GATA-4-binding sites that are essential for functional promoter activity. Thus multiple SF-1- and GATA-4-binding sites in the MIS promoter are required for normal tissue-specific and developmental expression of MIS.

Evaluating the properties of a stage-specific self-efficacy scale for physical activity using classical test theory, confirmatory factor analysis and item response modeling

The purpose of this paper was to evaluate the psychometric properties of a stage-specific selfefficacy scale for physical activity with classical test theory (CTT), confirmatory factor analysis (CFA) and item response modeling (IRM). Women who enrolled in the Women On The Move study completed a 20-item stage-specific self-efficacy scale developed for this study [n = 226, 51.1% African-American and 48.9% Hispanic women, mean age = 49.2 (67.0) years, mean body mass index = 29.7 (66.4)]. Three analyses were conducted: (i) a CTT item analysis, (ii) a CFA to validate the factor structure and (iii) an IRM analysis. The CTT item analysis and the CFA results showed that the scale had high internal consistency (ranging from 0.76 to 0.93) and a strong factor structure. Results also showed that the scale could be improved by modifying or eliminating some of the existing items without significantly altering the content of the scale. The IRM results also showed that the scale had few items that targeted high self-efficacy and the stage-specific assumption underlying the scale was rejected. In addition, the IRM analyses found that the five-point response format functioned more like a four-point response format. Overall, employing multiple methods to assess the psychometric properties of the stage-specific self-efficacy scale demonstrated the complimentary nature of these methods and it highlighted the strengths and weaknesses of this scale.

Data analysis methods in optometry Part 8: Principal components analysis (PCA) and factor analysis (FA)

PCA/FA is a method of analyzing complex data sets in which there are no clearly defined X or Y variables. It has multiple uses including the study of the pattern of variation between individual entities such as patients with particular disorders and the detailed study of descriptive variables. In most applications, variables are related to a smaller number of ‘factors’ or PCs that account for the maximum variance in the data and hence, may explain important trends among the variables. An increasingly important application of the method is in the ‘validation’ of questionnaires that attempt to relate subjective aspects of a patients experience with more objective measures of vision.

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

Long-term social recognition memory in adult male rats: factor analysis of the social and non-social behaviors

A modified version of the intruder-resident paradigm was used to investigate if social recognition memory lasts at least 24 h. One hundred and forty-six adult male Wistar rats were used. Independent groups of rats were exposed to an intruder for 0.083, 0.5, 2, 24, or 168 h and tested 24 h after the first encounter with the familiar or a different conspecific. Factor analysis was employed to identify associations between behaviors and treatments. Resident rats exhibited a 24-h social recognition memory, as indicated by a 3- to 5-fold decrease in social behaviors in the second encounter with the same conspecific compared to those observed for a different conspecific, when the duration of the first encounter was 2 h or longer. It was possible to distinguish between two different categories of social behaviors and their expression depended on the duration of the first encounter. Sniffing the anogenital area (49.9% of the social behaviors), sniffing the body (17.9%), sniffing the head (3%), and following the conspecific (3.1%), exhibited mostly by resident rats, characterized social investigation and revealed long-term social recognition memory. However, dominance (23.8%) and mild aggression (2.3%), exhibited by both resident and intruders, characterized social agonistic behaviors and were not affected by memory. Differently, sniffing the environment (76.8% of the non-social behaviors) and rearing (14.3%), both exhibited mostly by adult intruder rats, characterized non-social behaviors. Together, these results show that social recognition memory in rats may last at least 24 h after a 2-h or longer exposure to the conspecific.

Assessing food dietary intakes in Japanese-Brazilians using factor analysis

We describe the use of factor analysis for assessing food habits in Japanese-Brazilians. Dietary data from 1,283 participants of a cross-sectional study were used. Besides statistical criteria, we also used the conceptual meaning of identified profiles to obtain scores for dietary patterns (Japanese or Western profile). Paired Student t test, linear regression and Poisson models were used to verify the existence of relationship between these scores and generation, body mass index (BMI), waist circumference and presence of metabolic syndrome, respectively. First generation subjects had higher mean Japanese profile scores and lower Western profile scores than those of second generation. The Western dietary pattern was associated with BMI (p = 0.001), waist circumference (p = 0.023) and metabolic syndrome (p < 0.05). We concluded that these scores were able to discriminate subjects who maintained their traditional Japanese lifestyle or otherwise, and that the incorporation of a Western lifestyle is associated to high values of BMI, waist circumference and presence of metabolic syndrome.

Assessing alternative models of individualism and collectivism: A confirmatory factor analysis

Six alternative structural models of individualism-collectivism are reviewed and empirically compared in a confirmatory factor analysis of questionnaire data from an Australian student sample (N=340). Central to the debate about the structure of this broad social attitude are the issues of (I) polarity (are individualism and collectivism bipolar opposites, or orthogonal factors?) and (2) dimensionality (are individualism and collectivism themselves higher-order constructs subsuming several more specific factors and, if so, what are they?). The data from this Australian sample support a model that represents individualism and collectivism as a higher-order bipolar factor hierarchically subsuming several bipolar reference-group-specific individualisms and collectivisms. Copyright (C) 2001 John Wiley & Sons, Ltd.

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.