Exploratory factor analysis of the Brazilian OHIP for edentulous subjects

P>The use of seven domains for the Oral Health Impact Profile (OHIP)-EDENT was not supported for its Brazilian version, making data interpretation in clinical settings difficult. Thus, the aim of this study was to assess patients` responses for the translated OHIP-EDENT in a group of edentulous subjects and to develop factor scales for application in future studies. Data from 103 conventional and implant-retained complete denture wearers (36 men, mean age of 69 center dot 1 +/- 10 center dot 3 years) were assessed using the Brazilian version of the OHIP-EDENT. Oral health-related quality of life domains were identified by factor analysis using principal component analysis as the extraction method, followed by varimax rotation. Factor analysis identified four factors that accounted for 63% of the 19 items total variance, named masticatory discomfort and disability (four items), psychological discomfort and disability (five items), social disability (five items) and oral pain and discomfort (five items). Four factors/domains of the Brazilian OHIP-EDENT version represent patient-important aspects of oral health-related quality of life.

Testing the factor structure of the behavioral-intentions battery: an empirical study of the Australian banking industry

The five-factor ‘Behavioural-Intentions Battery’ was developed by Zeithaml, Berry and Parasuraman (1996), to measure customer behavioural and attitudinal intentions. The structure of this model was re-examined by Bloomer, de Ruyter and Wetzels (1999) across different service industries. They concluded that service loyalty is a multi dimensional construct consisting of four, not five, distinct dimensions. To date, neither model has been tested within a banking environment. This research independently tested the ‘goodness of fit’ of both the four and five-factor models, to data collected from branch bank customers. Data were collected via questionnaire with a sample of 348 banking customers. A confirmatory factor analysis was conducted upon the two opposing factor structures, revealing that the five-factor structure has a superior model fit; however, the fit is ‘marginal’.

The factor structure of the strengths and difficulties questionnaire

The Strengths and Difficulties Questionnaire (SDQ) has been widely adopted as a measure of child and adolescent mental health and behavioral problems. However, despite an impressive number of studies demonstrating its psychometric properties, appropriate analyses of the instrument's underlying structure have not been reported. We conducted confirmatory factor analyses on matched data obtained from a large community sample of 7-17-year-olds, their parents, and their teachers. The analyses indicated that the reputed factor structure was not supported, with none of the subscales being unidimensional. Further, each informant group appeared to respond differently to the questionnaire. Considering the findings in relation to the stringency of the tests used to evaluate the SDQ, its utility, and previous research on its clinical validity, it is suggested that the SDQ be used with caution and in conjunction with other forms of assessment.

The effects of clustered data on standard error estimates in covariance structure analysis : a field data application

Purpose – The purpose of this article is to present an empirical analysis of complex sample data with regard to the biasing effect of non-independence of observations on standard error parameter estimates. Using field data structured in the form of repeated measurements it is to be shown, in a two-factor confirmatory factor analysis model, how the bias in SE can be derived when the non-independence is ignored.

Design/methodology/approach – Three estimation procedures are compared: normal asymptotic theory (maximum likelihood); non-parametric standard error estimation (naïve bootstrap); and sandwich (robust covariance matrix) estimation (pseudo-maximum likelihood).

Findings – The study reveals that, when using either normal asymptotic theory or non-parametric standard error estimation, the SE bias produced by the non-independence of observations can be noteworthy.

Research limitations/implications – Considering the methodological constraints in employing field data, the three analyses examined must be interpreted independently and as a result taxonomic generalisations are limited. However, the study still provides “case study” evidence suggesting the existence of the relationship between non-independence of observations and standard error bias estimates.

Originality/value – Given the increasing popularity of structural equation models in the social sciences and in particular in the marketing discipline, the paper provides a theoretical and practical insight into how to treat repeated measures and clustered data in general, adding to previous methodological research. Some conclusions and suggestions for researchers who make use of partial least squares modelling are also drawn.

Analysis of satisfaction at Gallipoli on ANZAC day, 2007 : results of a survey of event participants

This paper explores the critical success factors of special events, in this case the Anzac Day commemorations at Gallipoli, Turkey. This event has become increasingly popular in recent years with crowd numbers growing to around 20,000 people traveling to attend the 2005 Dawn Service at historic Anzac Cove on Anzac day. The aim of this research is to investigate the success factors associated with Anzac Day commemorations at Gallipoli and to assess how these influence visitor satisfaction. Data was collected from 331 attendees of Anzac Day commemorations at Gallipoli while they were in transit from Gallipoli to Istanbul on 25 April 2007. The analysis of this data was undertaken using exploratory and confirmatory factor analysis as a basis to the development of a model of satisfaction using structural equation modeling. In this case constructs relating to amenities, transport, ceremonies and recommending behavior were found to be significant predictors of event satisfaction. These issues during the event were found to be of equal importance for both genders and all age groups.

Assessing indicant directionality of a media consumption construct using confirmatory tetrad analysis

When assessing the psychometric properties of measures and estimate relations among latent variables, many studies in the social sciences (including marketing) often fail to comprehensively appraise the directionality of indicants. Such failures can lead to model misspecification and inaccurate parameter estimates (Jarvis et al. 2003). In order to further assess the correct directionality of a ‘media consumption’ construct’s indicants, this paper employs confirmatory tetrad analysis (CTA). Previous studies advocate this construct being best viewed as formative. However, our CTA suggests it could be modelled using a reflective orientation. We then conclude the paper drawing recommendations for future studies advocating that when assessing item directionality researchers should implement pre and post hoc tests.

A Rasch analysis of the teachers music confidence scale

This article presents a new measure of teachers’ confidence to conduct musical activities with young children; Teachers Music Confidence Scale (TMCS). The TMCS was developed using a sample of 284 in-service and pre-service early childhood teachers in Hong Kong Special Administrative Region (HKSAR). The TMCS consisted of 10 musical activities. Teachers rated their confidence levels to conduct each activity on a scale from 1 (Not confident at all) to 5 (Very confident). An exploratory factor analysis retained a 10-item single factor that was replicated using confirmatory factor analysis procedures. All items of the TMCS fitted the Rasch model adequately. In-service teachers showed higher confidence levels to conduct several musical activities with young children than pre-service teachers. Implications of these findings for measuring teachers’ confidence to conduct musical activities with young children were discussed.

Channel estimation of dual-hop MIMO relay system via parallel factor analysis

The optimal source precoding matrix and relay amplifying matrix have been developed in recent works on multiple-input multiple-output (MIMO) relay communication systems assuming that the instantaneous channel state information (CSI) is available. However, in practical relay communication systems, the instantaneous CSI is unknown, and therefore, has to be estimated at the destination node. In this paper, we develop a novel channel estimation algorithm for two-hop MIMO relay systems using the parallel factor (PARAFAC) analysis. The proposed algorithm provides the destination node with full knowledge of all channel matrices involved in the communication. Compared with existing approaches, the proposed algorithm requires less number of training data blocks, yields smaller channel estimation error, and is applicable for both one-way and two-way MIMO relay systems with single or multiple relay nodes. Numerical examples demonstrate the effectiveness of the PARAFAC-based channel estimation algorithm.

Underdetermined blind source separation by parallel factor analysis in time-frequency domain

This paper presents a new time-frequency approach to the underdetermined blind source separation using the parallel factor decomposition of third-order tensors. Without any constraint on the number of active sources at an auto-term time-frequency point, this approach can directly separate the sources as long as the uniqueness condition of parallel factor decomposition is satisfied. Compared with the existing two-stage methods where the mixing matrix should be estimated at first and then used to recover the sources, our approach yields better source separation performance in the presence of noise. Moreover, the mixing matrix can be estimated at the same time of the source separation process. Numerical simulations are presented to show the superior performance of the proposed approach to some of the existing two-stage blind source separation methods that use the time-frequency representation as well.

The factor structure of the Chinese family assessment instrument adapted for parent report

This article reports on a confirmatory factor analytic study of an adapted version of an instrument designed to assess family functioning of Chinese families. The Chinese Family Assessment Instrument, originally designed for completion by adolescents, was adapted for completion by parents. A sample of 700 parent dyads of elementary school children (382 girls and 318 boys) completed the adapted questionnaire. Initial factor analyses showed that the existing five-factor structure used for adolescents’ responses was not a good fit for these data. Instead, a four-factor solution emerged where the factors were positive family functioning, negative family functioning, tolerance for family members, and parental understanding. This structure was the same for both mothers and fathers. Further studies of the Chinese Family Assessment Instrument parent adaptation are required to test the factor structure that emerged. Following such studies, validation studies will be required.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

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

Exploratory factor analysis (hereafter, factor analysis) is a complex statistical method that is integral to many fields of research. Using factor analysis requires researchers to make several decisions, each of which affects the solutions generated. In this paper, we focus on five major decisions that are made in conducting factor analysis: (i) establishing how large the sample needs to be, (ii) choosing between factor analysis and principal components analysis, (iii) determining the number of factors to retain, (iv) selecting a method of data extraction, and (v) deciding upon the methods of factor rotation. The purpose of this paper is threefold: (i) to review the literature with respect to these five decisions, (ii) to assess current practices in nursing research, and (iii) to offer recommendations for future use. The literature reviews illustrate that factor analysis remains a dynamic field of study, with recent research having practical implications for those who use this statistical method. The assessment was conducted on 54 factor analysis (and principal components analysis) solutions presented in the results sections of 28 papers published in the 2012 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. The main findings from the assessment were that researchers commonly used (a) participants-to-items ratios for determining sample sizes (used for 43% of solutions), (b) principal components analysis (61%) rather than factor analysis (39%), (c) the eigenvalues greater than one rule and screen tests to decide upon the numbers of factors/components to retain (61% and 46%, respectively), (d) principal components analysis and unweighted least squares as methods of data extraction (61% and 19%, respectively), and (e) the Varimax method of rotation (44%). In general, well-established, but out-dated, heuristics and practices informed decision making with respect to the performance of factor analysis in nursing studies. Based on the findings from factor analysis research, it seems likely that the use of such methods may have had a material, adverse effect on the solutions generated. We offer recommendations for future practice with respect to each of the five decisions discussed in this paper.