Multigroup Context Research Articles

Correcting for Extreme Response Style: Model Choice Matters.

Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model ERS and correct for it. Comparisons of these models are however rare in the literature, especially in the context of cross-cultural comparisons, where ERS is even more relevant due to cultural differences between groups. To remedy this issue, the current article examines two frequently used IRT models that can be estimated using standard software: a multidimensional nominal response model (MNRM) and a IRTree model. Studying conceptual differences between these models reveals that they differ substantially in their conceptualization of ERS. These differences result in different category probabilities between the models. To evaluate the impact of these differences in a multigroup context, a simulation study is conducted. Our results show that when the groups differ in their average ERS, the IRTree model and MNRM can drastically differ in their conclusions about the size and presence of differences in the substantive trait between these groups. An empirical example is given and implications for the future use of both models and the conceptualization of ERS are discussed.

Measuring the occupational segregation of males and females in Pakistan in a multigroup context

This study measures the occupational segregation of male and female workers in Pakistan using the microdata obtained from the labour force survey of Pakistan for the period 2013–18. The local segregation method is used to study occupational segregation by gender and for several subgroups based on individual and labour market characteristics. Results show that female segregation explains a substantially large proportion of the overall gender segregation, even though the demographic weight of the female labour force is low compared to the male labour force. The analysis of different age groups shows that occupational segregation is significantly higher among elderly males than males in other age groups. Similarly, for females, occupational segregation is significantly higher among elderly females compared to females in other age groups and males in any age group. Furthermore, it is also found that human capital characteristics such as higher education do not contribute to reducing occupational segregation in the labour market, as both males and females with higher levels of education are more segregated than those with low education. Finally, the study concludes that the devaluation and compensating differentials theories partially explain gender segregation in Pakistan.

On Measuring Segregation in a Multigroup Context: Standardized Versus Unstandardized Indices

There has been little discussion about the consequences of using standardized, rather than unstandardized, segregation measures when comparing societies with different demographic compositions. This paper explores standardization in a multigroup setting through an analytical framework that offers a clear distinction between the measurement of overall and local segregation, embeds existing indices within this framework, and addresses gaps in previous research. The local approach developed here allows us to focus on the principle of transfers used in the measurement of overall segregation from a new angle and brings analytical support to the interpretation of the components of standardized overall measures as the segregation levels of the groups involved. This approach also helps clarify the debate around the measurement of school segregation since the distinction between local and overall measures, together with standardization, is key to understanding the different proposals that have been used in empirical studies. This research also gives formal support to empirical strategies that compare the distribution of a minority group with that of the remaining population since they can be viewed as standardized local segregation measures satisfying basic properties.

Teasing apart trauma: neural oscillations differentiate individual cases of mild traumatic brain injury from post-traumatic stress disorder even when symptoms overlap

Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are highly prevalent and closely related disorders. Affected individuals often exhibit substantially overlapping symptomatology – a major challenge for differential diagnosis in both military and civilian contexts. According to our symptom assessment, the PTSD group exhibited comparable levels of concussion symptoms and severity to the mTBI group. An objective and reliable system to uncover the key neural signatures differentiating these disorders would be an important step towards translational and applied clinical use. Here we explore use of MEG (magnetoencephalography)-multivariate statistical learning analysis in identifying the neural features for differential PTSD/mTBI characterisation. Resting state MEG-derived regional neural activity and coherence (or functional connectivity) across seven canonical neural oscillation frequencies (delta to high gamma) were used. The selected features were consistent and largely confirmatory with previously established neurophysiological markers for the two disorders. For regional power from theta, alpha and high gamma bands, the amygdala, hippocampus and temporal areas were identified. In line with regional activity, additional connections within the occipital, parietal and temporal regions were selected across a number of frequency bands. This study is the first to employ MEG-derived neural features to reliably and differentially stratify the two disorders in a multi-group context. The features from alpha and beta bands exhibited the best classification performance, even in cases where distinction by concussion symptom profiles alone were extremely difficult. We demonstrate the potential of using ‘invisible’ neural indices of brain functioning to understand and differentiate these debilitating conditions.

The Impact of Item Parceling on Structural Parameter Invariance in Multi-group Structural Equation Modeling

ABSTRACT Item parcels are the indicators of a factor which are created by summing or averaging scores of individual items. Item parceling has been widely applied in structural equation modeling (SEM) but has been less discussed in the multi-group context, especially for structural parameter invariance. The present study investigated the effect of item parceling on parameter bias and invariance tests at the structural level (i.e., latent means and a structural path) in multi-group SEM using a Monte Carlo simulation study. The results show that under a correctly specified measurement invariance model, indicator type would not introduce bias in structural parameter estimates. The estimation and statistical decision would depend on the assumption for measurement (non-)invariance and testing method, rather than the indicator type. When falsely assuming measurement invariance, latent mean or structural path differences are likely to be biased and to conclude incorrect statistical decisions in testing.

Coupled Anti Multigroups: Some Properties

Motivated by [1], the authors in [2] extended the notion of anti fuzzy groups to the multigroup context and studied some of their properties. In this paper we extend the work in a new direction termed coupled multigroup and obtain some new properties in this context. A conjecture concludes the paper.

On Commutators of Fuzzy Multigroups

Fuzzy multigroup is an application of fuzzy multiset to group theory. Although, a lots have been done on the theory of fuzzy multigroups, some group's theoretic notions could still be investigated in fuzzy multigroup context. Certainly, the idea of commutator is one of such group's theoretic notions yet to be studied in the environment of fuzzy multigroups. Hence, the aim of this article is to establish the notion of commutator in fuzzy multigroup setting. A number of some related results are obtained and characterized. Among several results that are obtained, it is established that, if $A$ and $B$ are fuzzy submultigroups of a fuzzy multigroup $C$, then $[A, B]\subseteq A\cup B$ holds. Some homomorphic properties of commutator in fuzzy multigroup context are discussed. The notion of admissible fuzzy submultisets $A$ and $B$ of $C\in FMG(X)$ under an operator domain $\mathcal{D}$ is explicated, and it is shown that $(A,B)$ and $[A,B]$ are $\mathcal{D}$-admissible.

Simultaneous Sparse Estimation of Canonical Vectors in the p ≫ N Setting

ABSTRACTThis article considers the problem of sparse estimation of canonical vectors in linear discriminant analysis when p ≫ N. Several methods have been proposed in the literature that estimate one canonical vector in the two-group case. However, G − 1 canonical vectors can be considered if the number of groups is G. In the multi-group context, it is common to estimate canonical vectors in a sequential fashion. Moreover, separate prior estimation of the covariance structure is often required. We propose a novel methodology for direct estimation of canonical vectors. In contrast to existing techniques, the proposed method estimates all canonical vectors at once, performs variable selection across all the vectors and comes with theoretical guarantees on the variable selection and classification consistency. First, we highlight the fact that in the N > p setting the canonical vectors can be expressed in a closed form up to an orthogonal transformation. Secondly, we propose an extension of this form to the p ≫ N setting and achieve feature selection by using a group penalty. The resulting optimization problem is convex and can be solved using a block-coordinate descent algorithm. The practical performance of the method is evaluated through simulation studies as well as real data applications. Supplementary materials for this article are available online.

Racial diversity and change in metropolitan neighborhoods

This study investigates the changing racial diversity and structure of metropolitan neighborhoods. We consider three alternative perspectives about localized racial change: that neighborhoods are bifurcating along a white/nonwhite color line, fragmenting into homogeneous enclaves, or integrating white, black, Latino, and Asian residents into diverse residential environments. To assess hypotheses drawn from these perspectives, we develop a hybrid methodology (incorporating the entropy index and majority-rule criteria) that offers advantages over previous typological efforts. Our analysis of 1990–2000 census tract data for the 100 largest US metropolitan areas finds that most neighborhoods are becoming more diverse and that members of all groups have experienced increasing exposure to neighborhood diversity. However, white populations tend to diminish rapidly in the presence of multiple minority groups and there has been concomitant white growth in low-diversity neighborhoods. Latino population dynamics have emerged as a primary force driving neighborhood change in a multi-group context.

Local versus overall segregation measures

This paper proposes a theoretical framework in which to study the segregation of a target group in a multigroup context, which can be labeled as local segregation. In doing so, this paper first presents an axiomatic set-up within which local segregation measures can be evaluated and it defines local segregation curves. Next, a class of additive local segregation indexes that is related to the generalized entropy family and consistent with the above curves is characterized. Finally, this paper offers a bridge between local and overall segregation indexes by using population weights.

Explaining intention to consume a new fish product: A cross-generational and cross-cultural comparison

The general purpose of this study is to describe and explain attitudes and intention to consume a new fish product by using the general framework from the theory of planned behaviour (TPB) in a multi-group context. The field experiments were performed in Norway with 110 adolescents and young adults and 149 of their parents, and in Spain with 175 adolescents and young adults (termed “young consumers”). The product is a fish burger produced by a Norwegian firm for the Norwegian market, and was tested in-home in both countries. All constructs are analysed by multi-group analysis in LISREL for reliability, validity and invariance across groups. Results from this study indicate that the constructs within the TPB are valid and similar across cultures and generations. The parents showed a significantly higher motivation to consume and more positive attitude toward the fish burger compared with their kids. The Spanish consumers expressed a significantly more negative attitude towards the product compared with the two Norwegian groups. Perceived behavioural control is more important than attitudes and norms in explaining intention to consume the fish burger across cultures (Spain and Norway) and across generations (parents and their kids). This indicates that the TPB can be used not only in explaining intention to consume common food products, but also in the area of testing new products.

Quantifying Neighborhood Racial and Ethnic Transition Clusters in Multiethnic Cities

Dynamic racial and ethnic transitions are a critically important aspect of urban neighborhood social geography and demography, which receive little attention from urban analysts compared to static racial and ethnic patterns. To determine whether cluster analysis could bring analytic power to the study of neighborhood racial and ethnic change in multigroup context, we performed a case study of Los Angeles County during the period 1990 to 2000, using the prediction strength technique to determine empirically, rather than arbitrarily, how many clusters fit the data. Clustering identifies which combinations of the direction and magnitude of groups' local changes drive local trends across the region and, equally important, those that do not. The case study supports the conclusion that cluster analysis serves as a powerful data-mining technique for local racial and ethnic trends, and will yield satisfactory results for any region at any scale. [Key words: Neighborhood transition, succession, human ecology, racial/ ethnic change.]

The Role of Leadership Responsibility and Social Identity on Intra- and Intergroup Leadership Favorability Ratings

Building on recent work using Social Identity Theory as a conceptual framework for analyzing leadership dynamics (Hogg, 2001; van Knippenberg & van Knippenberg, 2003; Haslam & Platow, 2001; Lord & Hall, 2003; Kramer, 2003), we conducted two studies to examine how information about leaders' responsibility for a negative world event and group members' social identification affect leader favorability ratings. In contrast to traditional leadership research, which often focuses on intragroup leadership, we adopted an intergroup leadership dynamics perspective - which studies leadership in a multi-group context (Pittinsky, 2004) - and examined favorability ratings for both domestic and foreign leaders. In Study 1, conducted in the Hong Kong Special Administrative Region of the People's Republic of China, we hypothesized that participants would be more favorable toward their own leadership when another nation's leaders were largely responsible for a negative event. Data did not support this hypothesis, likely due to contextual political factors. A second hypothesis - that outgroup leaders would be rated significantly less favorably when responsible for the negative event - was supported. Study 2 replicated the first study in the United States, but this time strength of participants' national identification was measured. Study 2 found that participants who strongly identify with their nation feel significantly more favorable towards their leaders when another nation's leadership is responsible for a negative event. Participants who weakly identify do not show this effect. As predicted, regardless of strength of identification, participants view outgroup leadership more negatively when it is responsible for the negative event. Implications of these findings for leadership studies through the lens of self and identity are discussed.

Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons

Treating Likert rating scale data as continuous outcomes in confirmatory factor analysis violates the assumption of multivariate normality. Given certain requirements pertaining to the number of categories, skewness, size of the factor loadings, and so forth, it seems nevertheless possible to recover true parameter values if the data stem from a single homogeneous population. It is shown that, in a multigroup context, an analysis of Likert data under the assumption of multivariate normality may distort the factor structure differently across groups. In that case, investigations of measurement invariance (MI), which are necessary for meaningful group comparisons, are problematic. Analyzing subscale scores computed from Likert items does not seem to solve the problem.

Measures of Geographically Uneven Subpopulation Group Change and Local Group Transitions: Examples from Los Angeles

The author notes the shortcomings of quantitative tools available for the analysis of local area transitions between subpopulation groups, particularly when compared to the power of methodologies available for the study of static segregation geographies. The author derives simple statistics for examining single‐group, two‐group, and multigroup subpopulation change and provides empirical examples of the properties and uses of these statistics. These measures of group change and transition are suitable for the continuous measurement and modeling of ecological outcomes in multi‐group context. Statistics are also derived to measure aggregate regional unevenness in local subpopulation group change.

Measures of Geographically Uneven Subpopulation Group Change and Local Group Transitions: Examples from Los Angeles

The author notes the shortcomings of quantitative tools available for the analysis of local area transitions between subpopulation groups, particularly when compared to the power of methodologies available for the study of static segregation geographies. The author derives simple statistics for examining single-group, two-group, and multigroup subpopulation change and provides empirical examples of the properties and uses of these statistics. These measures of group change and transition are suitable for the continuous measurement and modeling of ecological outcomes in multigroup context. Statistics are also derived to measure aggregate regional unevenness in local subpopulation group change.

The Constraints on Minority Housing Choices, New York City 1978-1987

This study uses a multivariate analysis to evaluate the housing and neighborhood characteristics that predict the residential choices made by non-Hispanic white, non-Hispanic black, Puerto Rican, and other Hispanic mover households in New York City from 1978 to 1987. This study evaluates minority housing choices in a multigroup context, rather than in a two-group context that implicitly assumes that minority groups compete for housing with only whites. After the influence of household characteristics is controlled, the results show that indicators of low-quality housing and poor neighborhood conditions significantly increase the likelihood that the household that moves in is a minority one, most likely Puerto Rican. Moreover, location in an area dominated by a particular minority group significantly increases the chance that the housing unit is occupied by a household belonging to that same group. By constraining minority housing choices, housing market segmentation effectively predetermines the types of housing units and neighborhoods that are available to different groups, and thus maintains racially and ethnically separate neighborhoods.