Search results

It is important to remain creative when conducting segmentation research, as many different ways to segment a market can exist. Five main bases are discussed: geographic, demographic, psychographic, behaviouristic and image. This is followed by an overview of the main techniques used to establish and verify segments, including automatic interaction detector, conjoint analysis, multidimensional scaling and canonical analysis.

This paper aims to learn a decision-maker’s (DM’s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets.

The study includes emerging preference learning algorithms.

Learning of a DM’s attitudinal choice model.

Preferences-based learning of a DM’s attitudinal decision model.

Laws geared toward regulating the employment relationship cling to traditional definitions of workplaces, neglecting the domain of the home and those who work there. Domestic workers, a population of largely immigrant women of color, have performed labor inside of New York City's homes for centuries and yet have consistently been denied coverage under labor law protections at both the state and federal level. This article traces out the exclusions of domestic workers historically and then turn to a particular piece of legislation – the 2010 New York Domestic Worker Bill of Rights – which was the first law of its kind to regulate the household as a site of labor, therefore disrupting that long-standing pattern. However, the law falls short in granting basic worker protections to this particular group. Drawing from 52 in-depth interviews and analysis of legislative documents, The author argues that the problematics of the law can be understood by recognizing its embeddedness, or rather the broader political, legal, historical, and social ecology within which the law is embedded, which inhibited in a number of important ways the law's ability to work. This article shows how this plays out through the law obscuring the specificity of where this labor is performed – the home – as well as the demographic makeup of the immigrant women of color – the whom – performing it. Using the case study of domestic workers' recent inclusion into labor law coverage, this article urges a closer scrutiny of and attention to the changing nature of inequality, race, and gender present in employment relationships within the private household as well as found more generally throughout the low-wage sector.

Principal components analysis (PCA) is one of the foremost multivariate methods utilized in marketing and business research for data reduction, latent variable modeling, multicollinearity resolution, etc. However, while its optimal properties make PCA solutions unique, interpreting the results of such analyses can be problematic. A plethora of rotation methods are available for such interpretive uses, but there is no theory as to which rotation method should be applied in any given social science problem. In addition, different rotational procedures typically render different interpretive results. The paper aims to introduce restricted PCA (RPCA), which attempts to optimally derive latent components whose coefficients are integer‐constrained (e.g.: {−1,0,1}, {0,1}, etc.).

The paper presents two algorithms for deriving efficient solutions for RPCA: an augmented branch and bound algorithm for sequential extraction, and a combinatorial optimization procedure for simultaneous extraction of these constrained components. The paper then contrasts the traditional PCA‐derived solution with those obtained from both proposed RPCA procedures with respect to a published data set of psychographic variables collected from potential buyers of the Dodge Viper sports car.

This constraint results in solutions which are easily interpretable with no need for rotation. In addition, the proposed procedure can enhance data reduction efforts since fewer raw variables define each derived component.

The paper provides two algorithms for estimating RPCA solutions from empirical data.

The purpose of this paper is twofold: to explore how preservice teachers in a young adult literature course critically conceptualize discussions in school spaces about race and police/community relations; and to understand the constraints and affordances of using the young adult (YA) novel, All American Boys, as a critical literacy tool for discussing race and police/community relations.

This qualitative exploratory case study (Stake, 1995) investigated 24 pre-service teachers in two university YA literature courses as they read and discussed All American Boys. Thematic analysis consisted of open coding through the theoretical lenses of critical literacy and critical race theory.

Pre-service English language arts teachers largely thought that while race and police relations was important and the YA book was powerful, it was too political. Their fears about what might happen lead to privileging the role of neutrality as the desired goal for teachers when tackling difficult conversations about racial injustice in America. Although students made some shifts in terms of moving from neutral to more critical stances, three sub-themes of neutrality were predominant: a need for both sides of the story, the view that all beliefs are valid and the belief that we are all humans therefore all lives matter equally.

A search at the time of this study yielded few research tackling racial injustice and community/police relations through YA literature in the classroom. This study is important as stories of police brutality and racism are all too common and adolescents are too often the victims.