Marc A. Tomiuk

Italian ethnic identity and its relative impact on the consumption of convenience and traditional foods

Ethnic identity or the retention or loss of the attitudes, values and behaviours of one’s culture of origin is presented as a multidimensional construct. It is further suggested that acculturation or the acquisition of traits of the dominant/host culture constitutes a separate yet correlated process. Initial exploratory and confirmatory factor analyses were conducted on responses from Italian‐Canadians to various items designed to tap Italian ethnic identity. These analyses revealed that ethnic identity did indeed constitute a multidimensional process. Specifically, three dimensions were identified: Italian Social Interaction and Participation, Italian Language Use with Family Members, and Catholicism. A final CFA model incorporated the three ethnic identity dimensions and two dimensions of acculturation. Consistent with previous findings, LISREL VIII estimation resulted in significant negative correlations between some ethnic identity and acculturation dimensions. The discriminant validity of ethnic identity was also more thoroughly established vis‐a‐vis acculturation by computing 95 per cent confidence intervals for the parameter estimates. Finally, subsequent stepwise regression analyses showed that the three ethnic identity dimensions along with the two acculturation dimensions and three socioeconomic factors had differential impacts on the consumption of various convenience and traditional foods. A hypothesis holding that ethnic identity was negatively related to the consumption of convenience foods was partly confirmed. Another holding that it was positively related to the consumption of traditional Italian foods was better established.

A comparative study on parameter recovery of three approaches to structural equation modeling

Traditionally, two approaches have been employed for structural equation modeling: covariance structure analysis and partial least squares. A third alternative, generalized structured component analysis, was introduced recently in the psychometric literature. The authors conduct a simulation study to evaluate the relative performance of these three approaches in terms of parameter recovery under different experimental conditions of sample size, data distribution, and model specification. In this study, model specification is the only meaningful condition in differentiating the performance of the three approaches in parameter recovery. Specifically, when the model is correctly specified, covariance structure analysis tends to recover parameters better than the other two approaches. Conversely, when the model is misspecified, generalized structured component analysis tends to recover parameters better. Finally, partial least squares exhibits inferior performance in parameter recovery compared with the other approaches. In particular, this tendency is salient when the model involves cross-loadings. Thus, generalized structured component analysis may be a good alternative to partial least squares for structural equation modeling and is recommended over covariance structure analysis unless correct model specification is ensured.

Test of a Nonlinear Relationship between Linguistic Acculturation and Ethnic Identification

Following a review of the literature concerning the relationship between acculturation (acquisition of a dominant culture) and ethnic identification (retention of culture of origin), the authors propose that linguistic acculturation is nonlinearly related to ethnic identity. An empirical study was conducted to test this relationship between linguistic acculturation and ethnic identification using data gathered from multiple studies investigating four different ethnic groups in Eastern Canada over a period of 7 years. Regression analysis results indicate that the two underlying dimensions of ethnicity are related and that the postulated function exhibits an excellent fit, thus providing support for the proposition.

A measure of acculturation for Italian Canadians: scale development and construct validation

Abstract Conceptualizations of the immigrant adaptation process are diverse and often incongruous. There is also a lack of agreement in the interpretation and measurement of constructs underlying the notion of changing ethnicity. In an effort to resolve these differences, this study initially presents a conceptual framework for changing ethnicity by delineating and then integrating its key underlying aspects. This is followed by the development and validation of a multidimensional measure of acculturation for Italian Canadians—one aspect of their ethnic change. Construct validation is based on confirmatory factor analyses of the multitrait-multimethod (MTMM) data. Once validated, the measure is used for an investigation of the relationships between acculturation and some aspects of gender-role perceptions of Italian-Canadian spouses. The paper is concluded with a brief discussion on the possibility of extending the application of our measure to other ethnic groups in North America.

Fuzzy clusterwise quasi-likelihood generalized linear models

The quasi-likelihood method has emerged as a useful approach to the parameter estimation of generalized linear models (GLM) in circumstances where there is insufficient distributional information to construct a likelihood function. Despite its flexibility, the quasi-likelihood approach to GLM is currently designed for an aggregate-sample analysis based on the assumption that the entire sample of observations is taken from a single homogenous population. Thus, this approach may not be suitable when heterogeneous subgroups exist in the population, which involve qualitatively distinct effects of covariates on the response variable. In this paper, the quasi-likelihood GLM approach is generalized to a fuzzy clustering framework which explicitly accounts for such cluster-level heterogeneity. A simple iterative estimation algorithm is presented to optimize the regularized fuzzy clustering criterion of the proposed method. The performance of the proposed method in recovering parameters is investigated based on a Monte Carlo analysis involving synthetic data. Finally, the empirical usefulness of the proposed method is illustrated through an application to actual data on the coupon usage behaviour of a sample of consumers.

An Item Response Theory Analysis of Rosenberg’s Self Esteem Scale

Traditional scale purification methods and an Item Response Theory (IRT) approach (Testgraf) are applied to a 5 item unidimensional composite, the abridged Rosenberg Self-Esteem scale. Traditional methods indicate that the scale is highly reliable in its English and Chinese language versions. On the other hand, IRT analysis identifies anomalies with individual scale items. In particular, items tend not to discriminate well between individuals who score highly on self-esteem and those whose score is lower. Moreover, Differential Item Functioning (DIF) appears for some items in comparisons between Chinese and English versions of the scale. It is argued that IRT analysis capabilities augment the current measure development paradigm.