PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 16817517

FACTOR: a computer program to fit the exploratory factor analysis model.

Urbano Lorenzo-Seva·Pere J Ferrando
View original source ↗Export metadata ↓

Abstract

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.

Explore related subjects

Behavioral ResearchFactor Analysis, StatisticalHumansSoftwareJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Urbano Lorenzo-Seva, Pere J Ferrando. 2006. FACTOR: a computer program to fit the exploratory factor analysis model.. https://doi.org/10.3758/bf03192753

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Network analysis in public health: history, methods, and applications.

Network analysis is an approach to research that is uniquely suited to describing, exploring, and understanding structural and relational aspects of health. It is both a methodological tool and a theoretical paradigm that allows us to pose and answer important ecological questions in public health. In this review we trace the history of network analysis, provide a methodological overview of network techniques, and discuss where and how network analysis has been used in public health. We show how network analysis has its roots in mathematics, statistics, sociology, anthropology, psychology, biology, physics, and computer science. In public health, network analysis has been used to study primarily disease transmission, especially for HIV/AIDS and other sexually transmitted diseases; information transmission, particularly for diffusion of innovations; the role of social support and social capital; the influence of personal and social networks on health behavior; and the interorganizational structure of health systems. We conclude with future directions for network analysis in public health.

Behavioral Research↗
PubMed2006

Circadian rhythms in cognitive performance: methodological constraints, protocols, theoretical underpinnings.

The investigation of time-of-day effects on cognitive performance began in the early days of psychophysiological performance assessments. Since then, standardised, highly controlled protocols (constant routine and forced desynchrony) and a standard performance task (psychomotor vigilance task) have been developed to quantify sleep-wake homeostatic and internal circadian time-dependent effects on human cognitive performance. However, performance assessment in this field depends on a plethora of factors. The roles of task difficulty, task duration and complexity, the performance measure per se, practice effects, inter-individual differences, and ageing are all relevant aspects. Therefore, well-defined theoretical approaches and standard procedures are needed for tasks pinpointing higher cortical functions along with more information about time-dependent changes in the neural basis of task performance. This promises a fascinating challenge for future research on sleep-wake related and circadian aspects of different cognitive domains.

Behavioral Research↗
PubMed2006

Understanding successful behaviour change: the role of intentions, attitudes to the target and motivations and the example of diet.

Although many attempts to change health behaviour fail, some individuals do show successful behaviour change. This study assessed the role of behavioural intentions, motivations and attitudes to the target in explaining successful changes in diet with a particular focus on positive and negative intentions and positive and negative attitudes. Participants (n=282) completed a questionnaire describing a recent change in eating behaviour (becoming a vegetarian, cutting out a food group, eating fewer calories), their intentions, their attitudes to the food being avoided, a range of motivations and their degree of success. The results showed that the three behaviour change groups differed in terms of their cognitions with those trying to eat fewer calories reporting less success in changing their behaviour. Successful vegetarianism was associated with a lower positive attitude; successfully cutting out a food group was related to ethical motivations, a lower positive attitude and greater positive and negative intentions, and reducing calorie intake was associated with greater positive intentions and a lower positive attitude. Therefore, success was associated with different cognitions depending upon the type of change being made, although cognitions such as 'I will eat more vegetables' and 'I no longer find high fat foods palatable' were consistently most predictive of success. Suggestions for the development of more effective interventions to change health behaviours are made.

Behavioral Research↗