We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The current context of regressive border regimes challenges critical theory’s commitments. Can we still take recent legal and political practices as starting points for reimagining political norms and institutions based on a reconstruction of hidden emancipatory potentials? The chapter argues that critical border theory could benefit from recentering the idea of political representation, and especially from building on insights of the recent constructivist turn in representation theory. Understanding political representation as shape-shifting and constituency-mobilizing changes long-held assumptions about the spaces, subjects, and demands articulated in border politics. While this representative perspective has diagnostic advantages, it is unable to criticize the legitimacy of existing border regimes owing to its thin normative assumptions. Reconstructive approaches to border politics should therefore use the diagnostic tools of the recent representation scholarship without committing to their limited critical potential
Multivariate selection can be represented as a linear transformation in a geometric framework. This approach has led to considerable simplification in the study of the effects of selection on factor analysis. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation.
Bayesian least squares techniques are adapted to estimation of stimulus-response curves, rather broadly conceived. Illustrative examples deal with estimation of person characteristic curves and item characteristic curves in the context of mental testing, and estimation of a stimulus-response curve using data from a psychophysical experiment.
There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain.
This paper discusses least squares methods for fitting a reformulation of the general Euclidean model for the external analysis of preference data. The reformulated subject weights refer to a common set of reference vectors for all subjects and hence are comparable across subjects. If the rotation of the stimulus space is fixed, the subject weight estimates in the model are uniquely determined. Weight estimates can be guaranteed nonnegative. While the reformulation is a metric model for single stimulus data, the paper briefly discusses extensions to nonmetric, pairwise, and logistic models. The reformulated model is less general than Carroll's earlier formulation.
A summary and interpretation of the recent literature on the indeterminacy of factor scores is given in simple terms. A good index of factor score determinacy is the squared multiple correlation of the factor with the observed variables.
For analyses with missing data, some popular procedures delete cases with missing values, perform analysis with “missing value” correlation or covariance matrices, or estimate missing values by sample means. There are objections to each of these procedures. Several procedures are outlined here for replacing missing values by regression values obtained in various ways, and for adjusting coefficients (such as factor score coefficients) when data are missing. None of the procedures are complex or expensive.
Structural equation models with latent variables are sometimes estimated using an intuitive three-step approach, here denoted factor score regression. Consider a structural equation model composed of an explanatory latent variable and a response latent variable related by a structural parameter of scientific interest. In this simple example estimation of the structural parameter proceeds as follows: First, common factor models areseparately estimated for each latent variable. Second, factor scores areseparately assigned to each latent variable, based on the estimates. Third, ordinary linear regression analysis is performed among the factor scores producing an estimate for the structural parameter. We investigate the asymptotic and finite sample performance of different factor score regression methods for structural equation models with latent variables. It is demonstrated that the conventional approach to factor score regression performs very badly. Revised factor score regression, using Regression factor scores for the explanatory latent variables and Bartlett scores for the response latent variables, produces consistent estimators for all parameters.
In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.
In the course of the medical program at the University of Limburg, students complete a total of 24 progress tests, consisting of items drawn from a constant itembank. A model is presented for the growth of knowledge reflected by these results. The Rasch model is used as a starting point, but both ability and difficulty parameters are taken to be random, and moreover the logistic distribution is replaced by the normal. Both individual and group abilities are estimated and explained through simple linear regression. Application to real data shows that the model fits very well.
This chapter empirically analyzes how portfolios of external finance impact aid agreements. The chapter integrates data on external debt and foreign aid to establish a comprehensive picture of developing countries' portfolios of external finance, demonstrating that these have become less reliant on traditional donors over time. The analysis tests if a greater share of finance from Chinese or private sources is associated with favorable terms from traditional donors, using measures of aid volume, infrastructure project share, and conditions attached to World Bank projects. The findings indicate that as countries draw a greater share of their external finance from nontraditional sources, they are more likely to receive aid on preferred terms. The relationship is stronger for countries of strategic significance to donors and, especially, those with higher donor trust.
Suppose you are running a company that provides proofreading services to publishers. You employ people who sit in front of screens, correcting written text. Spelling errors are the most frequent problem, so you are motivated to hire proofreaders who are excellent spellers. Therefore, you decide to give your job applicants a spelling test. It isn’t hard: throw together 25 words, and score everyone on a scale of 0–25. You are now a social scientist, a specialist called a psychometrician, measuring “spelling ability.”
The reader should be officially informed that in this chapter I take leave of the widely accepted consensus about nature–nurture. This is not a textbook, and everything that I have said up to now has been very much my own take on things, but for the most part I have not strayed far from what most scientists would say about the intellectual history of nature and nurture. Not everyone perhaps, but most people agree that Galton was a racist, eugenics a moral and scientific failure, heritability of behavioral differences nearly universal, heritability a less than useful explanatory concept, twin studies an interesting but ultimately limited research paradigm, and linkage and candidate gene analysis of human behavior decisive failures.
Has it always been the case that living people must struggle with the moral failings of their dead ancestors, or is that a special burden that has been placed on the shoulders of citizens and scientists living in contemporary Europe and North America? Recently, the culture feels as though it is being torn apart by this question. I was taught in grade school that the United States is the greatest country in the world, the land of the free and the home of the brave, where anyone could be a millionaire or president if they put in the effort. It is hardly radical to recognize that this is less than true today and isn’t even close to true historically, especially if one is not white, Christian, and male.
Notwithstanding Galton’s admonition to count everything, counting is just a tool; it is no more science than hammering is architecture. One hundred years after Galton, Robert Hutchins remarked, contemptuously, that a social scientist is a person who counts telephone poles. The obvious way to turn counting into science is by conducting experiments, that is by manipulating nature and observing what the consequences are for whatever one is counting. Gregor Mendel, for example, was certainly a counter – he counted the mixtures of smooth and wrinkled peas in the progeny of the pea plants he intentionally crossed. What made Mendel’s work science was the intentional crossing of the plants, not the counting itself. It would have been much more difficult – perhaps impossible – to observe the segregation and independent assortment of traits by counting smooth and wrinkled peas in the wild.
Why is divorce heritable? It’s clear that it is heritable, in the rMZ > rDZ sense. I hope I have convinced you that the heritability of divorce doesn’t mean that there are “divorce genes,” or that divorce is passed down genetically from parents to children, but seriously: how does something like that happen? I am aware that my constant minimizing of the implications of heritability can seem as though I am keeping my finger in the dike against an inevitable onslaught of scientifically based genetic determinism, the final Plominesque realization that our genes make us who we are, the apotheosis of Galton’s proclamation in 1869: “I propose to show … that a man’s natural abilities are derived by inheritance, under exactly the same limitations as are the form and physical features of the whole organic world” (Hereditary Genius, p. 1).
Robert Plomin, whose name has come up a few times already, is unquestionably the most important psychological geneticist of our time. Trained in social and personality psychology at the University of Texas at Austin in the 1970s (my graduate alma mater, though we didn’t overlap), he went on to faculty positions at the University of Colorado and the Pennsylvania State University (both major American centers for behavior genetics) before moving to London to take a position at the Institute of Psychiatry. Plomin’s career has embodied the integration of behavioral genetics into mainstream social science and psychology. Everywhere Plomin has been, he has initiated twin and adoption studies, many of which continue to make contributions today. Although genetics has always played a central role in Plomin’s research, you would never mistake his work for that of a biologist or quantitative geneticist: he (like me) has always been first and foremost a psychologist.
The Second World War marked a turning point for what was considered acceptable in genetics and its implications for eugenic and racially motivated social policies. To be sure, the change in attitude was not quick or decisive. Tens of thousands of Americans were sterilized involuntarily after the war. Anti-black racism, antisemitism, and anti-immigrant sentiment, needless to say, persisted for a long while and have not yet been eliminated; interracial marriage was still illegal in much of the country during my lifetime. But – and despite the foot-dragging, I think this needs to be recognized as an advance – it slowly became less and less acceptable to adopt openly eugenic or racist opinions in public or to justify them based on science. Retrograde attitudes about such things persist to this day, but they have mostly been relegated to the fringes of scientific discourse.
Many people outside of psychology and biology come to the subject of nature–nurture because of an interest in race. That is unfortunate, but I get it. People, especially in the United States, are obsessed with race, for obvious reasons: American history is indelibly steeped in racial categories. The two foundational failures of the American experience – genocide of Indigenous Americans and enslavement of Africans – happened because of race and racism. Even today in the United States, people of all persuasions think about race all the time, whether as hereditarian racists convinced that there are essential biological differences among ancestral groups, progressives fascinated by personal identity and the degradations that non-white people still experience, or the dozens of racial and ethnic categories obsessively collected by the U.S. census.
Let’s summarize where the nature–nurture debate stood as the twentieth century drew to a close. When the century began, thinkers were faced for the first time with the hard evolutionary fact that human beings were not fundamentally different biologically than other evolved organisms. Galton and his eugenic followers concluded that even those parts of human experience that seemed to be unique – social, class, and cultural differences; abilities, attitudes, and personal struggles – were likewise subsumed by evolution and the mammalian biology it produced. People and societies could therefore be treated like herds of animals, rated on their superior and inferior qualities, bred to maintain them, treated to fix them, and culled as necessary for the good of the herd. Not every mid-century moral disaster that followed resulted from their misinterpretation of human evolution, but it played a role. Society has been trying to recover from biologically justified racism, eugenics, and genocide ever since.