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Factor analysis, first developed over a century ago (Spearman 1904, 1927), attempts to explain the covariation and variance of a set of observed variables using a more parsimonious number of latent variables. Many observed variables in the social sciences, such as responses to items, are correlated. The correlation that these variables share can be explained using a theoretical latent variable, sometimes referred to as a factor. For example, suppose you develop a test comprised of constructed-response items purported to measure algebra skills. After you administer the test to a large sample of eighth graders, you notice that many of the items are correlated, indicating that students who have better algebra skills tended to score higher on each item compared to those who have lesser algebra skills. This correlation among the items is an indication that the items may be associated with a common latent variable represented by math proficiency in algebra. In other words, the observed variables, referred to as indicators, are correlated because they are influenced by a common factor, which is unobservable (such as math proficiency, anxiety). Factor analysis was developed to capture this common relationship shared among a set of observed variables.
Confirmatory factor analysis (CFA) provides a convenient framework for evaluating measurement invariance. In CFA we specify a measurement model that defines the factorial structure underlying the observed data (e.g., item responses). The factorial structure includes the number of latent variables (i.e., factors) and the pattern of factor loadings (see Chapter 5 for a more detailed description of CFA). If the CFA model adequately captures the factorial structure, then group membership will not provide any additional information about observed variables above and beyond that explained by the latent variable. In fact, DIF has been framed as a dimensionality issue (Ackerman, 1992) and CFA makes the role of dimensionality in the analyses more explicit.
The sixth session of the United Nations (UN) open-ended intergovernmental working group (IGWG) tasked with ‘elaborating an international legally binding instrument to regulate, in international human rights law, the activities of transnational corporations and other business enterprises’1 (BHR Treaty) took place virtually during 26–30 October 2020.2
We offer a preliminary examination of whether national and organizational level contexts amplify or reduce the effects of de-globalization on the performance of MNCs. Theoretically, we borrow ideas from both event system theory and institutional fit to propose a model explicating key dimensions of the relationship between de-globalization, national and organizational context, and MNC performance. We then test our ideas using data assembled from 283 MNCs in 20 countries. We find that while de-globalization has a negative effect on MNC performance, national and organizational level contextual endowments do moderate this relationship. We discuss some implications of our findings and highlight attendant limitations.
In the waning days of the indelible 2020, China and the European Union (EU) clinched an ‘in-principle’ deal on their Comprehensive Agreement on Investment (CAI).1 After seven years of negotiation, the treaty attracted widespread public scrutiny, partly due to the significant investment flows between China and the EU, although wider considerations are also at play. China’s 2001 accession to the World Trade Organization laid a major building block in the global order. Its economic rise and export growth have provided cheaper electronics, appliances and clothes for global consumers, but also prompted concerns about the environment and implications for jobs and working conditions in deindustrializing countries. This economic reconfiguration has created complex, often tense, relations between China and established powers such as the EU and the United States (US), to which multilateral arrangements have offered only partial responses.2
In the nineteenth century, the Liverpool Cotton Brokers' Association (CBA) coordinated the dramatic growth of Liverpool's raw cotton market. This article shows how the CBA achieved this through the development of a private-order institutional framework that improved information flows, introduced standardization and contracting regimes, and regulated market exchange platforms. These developments corresponded with significantly improved market coordination, which facilitated the growth of the largest raw cotton market in the world. The article's findings demonstrate and quantify the importance of nonstate actors in creating institutions of global exchange central to the first wave of globalization.
On 24 May 2020, Rio Tinto detonated an area of the Juukan Gorge in the Pilbara in Western Australia as part of its iron ore mining operations, damaging two ancient rock shelters with profound cultural significance to the Puutu Kunti Kurrama and Pinikura (PKKP) People.1 The incident has brought international attention to the importance of Indigenous cultural heritage within broader environmental, social and governance (ESG) considerations.