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’.

Latent variable models of need for uniqueness

The theory of uniqueness has been invoked to explain attitudinal and behavioral nonconformity with respect to peer-group, social-cultural, and statistical norms, as well as the development of a distinctive view of self via seeking novelty goods, adopting new products, acquiring scarce commodities, and amassing material possessions. Present research endeavors in psychology and consumer behavior are inhibited by uncertainty regarding the psychometric properties of the Need for Uniqueness Scale, the primary instrument for measuring individual differences in uniqueness motivation. In an important step toward facilitating research on uniqueness motivation, we used confirmatory factor analysis to evaluate three a priori latent variable models of responses to the Need for Uniqueness Scale. Among the a priori models, an oblique three-factor model best accounted for commonality among items. Exploratory factor analysis followed by estimation of unrestricted three- and four-factor models revealed that a model with a complex pattern of loadings on four modestly correlated factors may best explain the latent structure of the Need for Uniqueness Scale. Additional analyses evaluated the associations among the three a priori factors and an array of individual differences. Results of those analyses indicated the need to distinguish among facets of the uniqueness motive in behavioral research.

On the estimation and inference in factor-augmented panel regressions with correlated loadings

In an influential paper, Pesaran [Pesaran, M.H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012] proposes a very simple estimator of factor-augmented regressions that has since then become very popular. In this note we demonstrate how the presence of correlated factor loadings can render this estimator inconsistent.

A factor analytical approach to price discovery

Existing econometric approaches for studying price discovery presume that the number of markets are small, and their properties become suspect when this restriction is not met. They also require making identifying restrictions and are in many cases not suitable for statistical inference. The current paper takes these shortcomings as a starting point to develop a factor analytical approach that makes use of the cross-sectional variation of the data, yet is very user-friendly in that it does not involve any identifying restrictions or obstacles to inference.

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, such that their cumulative loadings rise proportionally to the number of cross-sectional units, which of course need not be the case in practice. Motivated by this, the current paper offers an indepth analysis of the effect of non-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).

Measuring chronic condition self-management in an Australian community: factor structure of the revised Partners in Health (PIH) scale

Purpose To evaluate the factor structure of the revised Partners in Health (PIH) scale for measuring chronic condition self-management in a representative sample from the Australian community.

Methods A series of consultations between clinical groups underpinned the revision of the PIH. The factors in the revised instrument were proposed to be: knowledge of illness and treatment, patient–health professional partnership, recognition and management of symptoms and coping with chronic illness. Participants (N = 904) reporting having a chronic illness completed the revised 12-item scale. Two a priori models, the 4-factor and bi-factor models were then evaluated using Bayesian confirmatory factor analysis (BCFA). Final model selection was established on model complexity, posterior predictive p values and deviance information criterion.

Results Both 4-factor and bi-factor BCFA models with small informative priors for cross-loadings provided an acceptable fit with the data. The 4-factor model was shown to provide a better and more parsimonious fit with the observed data in terms of substantive theory. McDonald’s omega coefficients indicated that the reliability of subscale raw scores was mostly in the acceptable range.

Conclusion The findings showed that the PIH scale is a relevant and structurally valid instrument for measuring chronic condition self-management in an Australian community. The PIH scale may help health professionals to introduce the concept of self-management to their patients and provide assessment of areas of self-management. A limitation is the narrow range of validated PIH measurement properties to date. Further research is needed to evaluate other important properties such as test–retest reliability, responsiveness over time and content validity.

Distributions of caregiving tasks among family members: the place of gender and availability.

To extend family-oriented approaches to caregiving, participants in 2 studies were asked to distribute tasks among a set of adult children, first with information only about gender and then with systematically varied information about commitments to paid work, marriage, and/or parenting. Making the distributions, using a computer-based program, were 2 groups of older adults (ages 60 to 90 years). In Study 1, gender composition was kept constant (2 sons and 2 daughters). In Study 2, it was varied. The results showed several ways in which people combine attention to gender and to availability. The results also pointed to the need to consider both the number and type of tasks allocated. The results are discussed in terms of implications for the way caregiving is regarded, the development of multiple-factor models for variations among family members, and the possible replications and extensions to other circumstances and populations.

The factorial validity of emotional intelligence : an unresolved issue

An unresolved but pertinent issue in the field of emotional intelligence (EI) is factorial validity. Numerous studies have investigated this issue (Gignac, 2005; Mayer, Salovey, Caruso, & Sitarenios, 2003; Petrides & Furnham, 2000; Saklofske, Austin, & Minski, 2003), but most are based on correlations among subscale scores from relevant measures, making the implicit assumption that subscale scores are unidimensional, rather than questioning the structure of subscales themselves. Accordingly, the present study adopts the Anderson and Gerbing (1988) two-step strategy of first considering the structure within subscales before examining the relationship between subscales. An evaluation was undertaken using the Emotional Intelligence Scale (EIS, Schutte et al., 1998), the Work Profile Questionnaire – Emotional Intelligence Version (WQPei, Cameron, 1999) and the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT V.2., Mayer, Salovey, & Caruso, 1999b). Results were characterised by instability, heterogeneity and inconsistency. Specifically, the EIS was not found to form the homogenous structure postulated by authors. Similarly, support was not found for the seven factor model of the WPQei. Large discrepancies exist between the one, two and four factor models described by Mayer et al. (2003) for the MSCEIT V.2. and the 21 components revealed at the primary level in the current analyses. Additionally, reliability statistics for the MSCEIT V.2. were less than optimal. Questions remain regarding the clarity, reliability and validity of the instruments examined.

An investigation on asset allocation and performance measurement for unit trust funds in Malaysia using multifactor model : a post crisis period analysis

This study examines the pattern of asset allocation and the performance of unit trust in Malaysia over the post crisis period by using risk-adjusted performance measures and multi-factor model from the year 2000 to 2004. Evidence from the statistics suggests that an active asset allocation strategy had been observed among Malaysian fund managers during the post Asian financial crisis. It is also suggested that investment allocation in equity remained a dominant vehicle for investment and asset allocation. Findings from multifactor model suggest that all funds of different objectives registered positive alphas except for income funds, with growth funds being among the top. While balanced funds registered highest diversification effectively, diversifying away about 70%-80% of unsystematic risk, the momentum factor is not among the important elements to explain unit trust performance in Malaysia.

A nonparametric Bayesian Poisson Gamma model for count data

We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models.

Validation of patient health questionnaire (PHQ) for major depression in Chinese outpatients with multiple somatic symptoms: A multicenter cross-sectional study

Background: Despite the high co-morbidity of depressive symptoms in patients with multiple somatic symptoms, the validity of the 9-item Patient Health Questionnaire (PHQ-9) has not yet been investigated in Chinese patients with multiple somatic symptoms. Methods: The multicenter cross-sectional study was conducted in ten outpatient departments located in four cities in China. The psychometric properties of the PHQ-9 were examined by confirmative factor analysis (CFA). Criterion validation was undertaken by comparing results with depression diagnoses obtained from the Mini International Neuropsychiatric Interview (MINI) as the gold standard. Results: Overall, 491 patients were recruited of whom 237 had multiple somatic symptoms (SOM+ group, PHQ-15 ≥ 10). Cronbach's α of the PHQ-9 was 0.87, 0.87, and 0.90 for SOM+ patients, SOM- patients, and total sample respectively. All items and the total score were moderately correlated. The factor models of PHQ-9 tested by CFA yielded similar diagnostic performance when compared to sum score estimation. Multi-group confirmatory factor analysis based on unidimensional model showed similar psychometric properties over the groups with low and high somatic symptom burden. The optimal cut-off point to detect depression in Chinese outpatients was 10 for PHQ-9 (sensitivity=0.77, specificity=0.76) and 3 for PHQ-2 (sensitivity=0.77, specificity=0.74). Limitations: Potential selection bias and nonresponse bias with applied sampling method. Conclusions: PHQ-9 (cut-off point=10) and PHQ-2 (cut-off point=3) were reliable and valid to detect major depression in Chinese patients with multiple somatic symptoms.

On the determination of the number of factors using information criteria with data-driven penalty

As is well known, when using an information criterion to select the number of common factors in factor models the appropriate penalty is generally indetermine in the sense that it can be scaled by an arbitrary constant, c say, without affecting consistency. In an influential paper, Hallin and Liška (J Am Stat Assoc102:603–617, 2007) proposes a data-driven procedure for selecting the appropriate value of c. However, by removing one source of indeterminacy, the new procedure simultaneously creates several new ones, which make for rather complicated implementation, a problem that has been largely overlooked in the literature. By providing an extensive analysis using both simulated and real data, the current paper fills this gap.

Cross-sectional averages versus principal components

In spite of the increased use of factor-augmented regressions in recent years, little is known regarding the relative merits of the two main approaches to estimation and inference, namely, the cross-sectional average and principal component estimators. By providing a formal comparison of the approaches, the current paper fills this gap in the literature.

A factor analytical method to interactive effects dynamic panel models with or without unit root

In a recent study, Bai (Fixed-Effects Dynamic Panel Models, A Factor Analytical Method. Econometrica 81, 285-314, 2013a) proposes a new factor analytic (FA) method to the estimation of dynamic panel data models, which has the unique and very useful property that it is completely bias-free. However, while certainly appealing, it is restricted to fixed effects models without a unit root. In many situations of practical relevance this is a rather restrictive consideration. The purpose of the current study is therefore to extend the FA approach to cover models with multiple interactive effects and a possible unit root.

Operationalising the balanced scorecard : an initial analysis of factor structure

To date there has been very little empirical analysis of the Balanced Scorecard (BSC) within the marketing literature. With measuring performance being a central issue in marketing and the BSC being one of the most utilised approaches, this paper investigates the BSC and its factor structure. This research tested independently the “goodness-of-fit” of both the traditional four-factor model and a later five-factor model, which included an “employee/human resource” dimension. Data were collected from a sample of medium-tolarge Australian businesses. Factor analysis was conducted on the two alternative factor structures, revealing that the five-factor model fits the observed data as well as does the fourfactor model, supporting the inclusion of an “employee/human resource” perspective in future BSC models.