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38647 results in Cambridge Textbooks

Page 1325 of 1933

  • 15 - Speaking

  • from PART III - PROCESS AND PRESENTATION
  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 250-261

  • Summary

    Having surveyed the craft of writing, we turn to the craft of speaking, the oral form of language. Good speaking is somewhat different from good writing. Of course, one can simply read a prepared text, in which case the difference virtually disappears. But this does not generally qualify as good speaking in an academic or professional context – unless, that is, it is carefully honed to appear as if it is extemporaneous.

    People expect a “live” performance. Your presence should be real, unmediated. Naturally, it may be mediated by various technologies and it may be pre-recorded; but it should feel as if it is happening right here and right now. This is the dynamic quality of public speaking. It is a special quality that is probably hard-wired in our brains and therefore carries a resonance that cannot be simulated with prose.

    Public speaking is inextricably linked to comportment, i.e., how you carry yourself. Every time you make a public intervention you convey a vision of yourself. It is this persona that people tend to remember. Thus, when we say “speech” in this chapter we intend to include all the visual cues that accompany speech – dress, gaze, posture, gestures, and so forth.

    In ancient times, speech was the preeminent art of persuasion. Rhetoric meant speech, and only secondarily prose or poetry. That prioritization is easy to understand in the context of a predominantly oral culture.

    Nowadays, the craft of public speaking has fallen into desuetude (though some writing courses also include a component devoted to public speaking). People still talk, but speech is no longer cultivated as a professional activity, with a few exceptions such as moot courts in law school. This is unfortunate because speech is no less important today than it was a century ago. Perhaps it is destined to become more important over time as online lectures and YouTube videos replace written texts, and video calls and video conferences replace email. The spoken word may turn out to be mightier than the written word.

    In any case, whether you are a good speaker or a poor speaker is likely to affect how successfully you can get your ideas across – not to mention getting good grades, landing a job, and succeeding in your chosen profession. To be sure, college courses generally don't allocate much credit for participation.

  • Chapter 3 - Race/Ethnicity

  • from PART I - RE-ASSESSING THE THREE PILLARS: MODERN AND POSTMODERN SOCIOLOGIES OF EDUCATION
  • Gordon Tait, Queensland University of Technology
  • Book:
    Schooling and Society
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 64-92

  • Summary

    Of all the ways humans have chosen to divide themselves, none has a history as arbitrary, as spurious, and as terrible as the idea of race. This concept has significant implications for almost every aspect of contemporary human conduct, irrespective of what ‘race’ we are deemed to belong to, and whether we realise it or not. This is particularly so for the field of education.

    In order to describe the complex issues within this important area, a wide range of interrelated terms are used, often in plural ways. Probably the most important of these terms is the underpinning notion of ‘othering’ – i.e. the process of deciding who's in any given social group, who's out, and why – and arguably two of the most pernicious examples of this are racism and ethnic discrimination.

    Myth #1 Humanity is naturally divided into races. ‘Just as there are different breeds of dog, or species of beetle, so too are there different races of people. It's just part of nature: yellow people are naturally clever, black people are naturally good at sport, and white people are naturally good at being in charge’. None of this is true. Race is a relatively recent invention; it is one that has found a way of blaming nature for a series of socially constructed forms of discrimination. Unfortunately, though this concept is without foundation, it still has real effects.

    Myth #2 We no longer discriminate on the basis of race or ethnicity. ‘Sure, people who aren't white once had a rough time, but most of us now have friends with every skin-colour imaginable. No-one cares anymore’. While hopefully there's some truth here, the evidence suggests that prejudice is still alive and well, and that discrimination extends beyond the simple and the overt, and is often at its most pervasive in its institutional and cultural forms.

    Myth #3 Educational outcomes are unaffected by race or ethnicity. ‘Maybe there's still some discrimination around, but the education system educates all kids equally. In the end, it just matters how smart you are’. Whether by deliberate discrimination or otherwise, some racial and ethnic minorities often still find themselves at the losing end of the education system. That said, it is also important to take into account intersectional variations in educational outcomes, within such groups, based upon social class and gender.

  • 7 - Experimental Designs

  • from PART II - CAUSALITY
  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 101-117

  • Summary

    Recall (from Chapter 6) that estimating a causal effect involves comparing a factual (that which actually happened) to a counterfactual (that which might have happened). Returning to our perennial example, let us say that we wish to estimate the effect of a job-training program on the earnings of unemployed people after they have completed the program. Those who participate in the program are members of the treatment group. Non-participants are members of the control group.

    Let us say that we know the earnings of participants and non-participants after completing the program. These are the factuals. What we do not know is what their earnings would have been if their roles had been reversed. These are the counterfactuals.

    Although the only sure way to compare factuals with counterfactuals is to employ a time-machine, a well-designed experiment comes close to the mythical time-machine insofar as the control group exemplifies the (unobserved) counterfactual for the treatment group, and the treatment group exemplifies the (unobserved) counterfactual for the control group. Under certain conditions, this is a plausible scenario.

    For present purposes, the defining criterion of an experiment is that the treatment is randomly assigned (“randomized”) across subjects. We do not care who controls the experiment – the researcher conducting the study or someone else. (Sometimes, an experiment that unfolds naturally, without intervention by a researcher, is referred to as a natural experiment, as discussed below.)

    We begin this chapter with a review of the problem of confounding as it applies to experimental research. We proceed to introduce various approaches to experimental research. The final section offers a series of examples of experiments conducted on a variety of diverse topics. Together, these sections should give the reader a sense of the opportunities for, and limitations of, experimental research in the social sciences.

    Experiments With and Without Confounding

    In its simplest version, a single treatment (e.g., a worker-training program) is randomly assigned to members drawn from a known population (e.g., unemployed people of a given age). That is, some are chosen to participate in the program (the treatment group) and others are chosen to become part of the control group. The causal effect of the program is measured by comparing annual earnings of program participants with annual earnings of non-participants one year after the completion of the program.

  • Preface

  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp xxv-xxx

  • Summary

    Once upon a time, the practice of social science could be understood as the application of commonsense and intuition – something you might develop in the course of growing up. This is no longer true, or only partly true. Although commonsense and intuition are still useful, the social science disciplines have moved well beyond what can be understood without specialized training.

    If you want to become an artist, musician, engineer – or pretty much anything, these days – developing your technique in these highly specialized areas is essential. It takes great dedication, countless hours of concentrated work, and professional guidance. The same may be said for social science in the contemporary era. One may mourn the death of the amateur social scientist. But one might as well reconcile oneself to the fact.

    In response, methods courses have proliferated at both the undergraduate and graduate level. Likewise, methodological skills are in high demand in the social sciences and their cognate professions. Successful careers in government, communications, education, social work, business, law, and all of the policy fields require a solid grounding in methodology. Whether one is applying for graduate programs or for a job, the material covered in this book should stand one in good stead.

    Indeed, a working knowledge of social science tools of analysis may prove more crucial for one's career than whatever substantive knowledge one acquires in the course of a college education. What one knows is less important than what one can do, and what one can do depends on a working knowledge of methodology.

    These developments may be viewed as part of a broader sea-change, driven by the rise of computers and the Internet. With sophisticated IT tools at our disposal, factual knowledge about a subject is no longer at a premium and can usually be obtained from a Google search or from a specially designed database in milliseconds. Likewise, any repetitive procedure can be programmed as a set of algorithms on a computer. This means that the value of an education is no longer in the facts or established protocols you might learn. This sort of knowledge can be produced by machines in a more timely and accurate fashion than by the human brain.

  • 20 - Statistical Inference

  • from PART IV - STATISTICS
  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 302-312

  • Summary

    Statistics can be more than a tool for describing data. In the social sciences we have hypotheses that move us beyond simple descriptions of populations to relationships between two or more variables. To analyze these relationships we often rely in practice on statistical inference. That is, we need to make decisions based on data collected on a small group (sample) for the larger group that we want to study (population). In Chapter 4 we introduced the concept of sampling and provided a description of the three general objectives pertaining to sampling: representativeness, size, and level of analysis. Here we move to a more focused explanation of how sampling helps researchers overcome practical limitations and what implications this has for quantitative analyses.

    Sampling

    As we noted earlier, when it comes to sampling, larger is generally better, the aforementioned issues aside. So why sample at all? Why not collect data on the full population? In all types of research we find the same two answers: time and money. Research occurs in the real world and collecting the ideal data is often limited by how much time and money the researcher can direct to the project. Thus every research design must take into account the practical circumstances. In doing so, researchers necessarily restrict their investigation into a sample or subset of the population that they would like to study.

    In general, when we make comparisons we would like to talk about more than just the observations in the sample. We would like to talk about the larger group of interest in our research, the population. How we do this is the objective of statistical inference. More specifically, statistical inference helps researchers provide statements of confidence in our ability to generalize or infer from the sample to the population. The ability to offer an estimate of relative precision is another reason why quantitative empirical research is so useful and popular.

    Consider the ANES dataset that we used for the examples in Chapter 18. How many cases or individuals do we have in our sample? Obviously, the nearly 6,000 individuals in that sample is not anywhere near the size of the full voting age population of the United States, yet we would like to use this sample of data to describe that population.

  • PART I - BUILDING BLOCKS

  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 1-2

  • Summary

    This part of the book is focused on fundamental elements of social science, elements that form building blocks for everything else. In Chapter 1, we lay out the rationale for a unified approach to our subject, social science methodology. In Chapter 2, we discuss social science arguments, with primary attention to descriptive and causal arguments. In Chapter 3, we turn to the topic of conceptualization and measurement. In Chapter 4, we discuss the generic features of empirical analysis.

  • 17 - Data Management

  • from PART IV - STATISTICS
  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 271-281

  • Summary

    The first step toward analysis is to get your data into a format that is convenient for the sort of analysis you wish to pursue. This, in turn, probably depends on the sort of data you are collecting. Here, we shall distinguish among four data types: qualitative, medium-N, large-N, and textual (though one can have data that is a combination of these; e.g., medium-N and qualitative, large-N and textual … etc.). Methods for handling these data types are continually invented and reinvented, so the reader may wish to consult other sources to obtain the most up-to-date information on these subjects. Our intent is to provide a useful overview, in any case, not to delve into the details.

    Qualitative Data

    Suppose that you are trying to integrate data drawn from a limited number of units with a diversity of evidence. The evidence may have been gathered with any of the techniques (or combination of techniques) discussed in Chapter 13. It might include text, photos, maps, and other media. The nature of the material might be variegated – a combination of what informants said and did, the researcher's own observations and theories, multi-media artifacts, locations, relevant articles from academic journals and/or popular media, feedback from colleagues, and so forth. Some of the evidence may apply across all studied units in the sample while some is specific to certain units. But one doesn't have systematic observations for a limited set of variables across all units in the sample. It may not even be clear what the variables are, what the sample is, or what the population of the study is. There may be – at least initially – no specific hypothesis but rather a general research question that awaits further refinement. In other words, the investigation may be more exploratory (to discover a theory or hypothesis) than confirmatory (to test a theory or hypothesis).

    This setting exemplifies a good deal of work often described as qualitative, so we shall refer to it as qualitative data (as defined in Chapter 4).

  • Chapter 5 - Subjectivity

  • from PART II - THE FOUNDATIONS OF AN ALTERNATIVE APPROACH: EDUCATION AND GOVERNANCE
  • Gordon Tait, Queensland University of Technology
  • Book:
    Schooling and Society
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 117-136

  • Summary

    This chapter argues that our subjective experiences – how we experience the world, and understand ourselves within it – are just as closely governed as our objective conduct, discussed within the previous chapter. Whether they realise it or not, contemporary teachers are expected to play a significant role in this form of regulation. After all, teachers are now not simply responsible for transmitting a given curriculum and keeping children in line, they are de-facto psychologists, responsible for the mental health, regulation and development of their pupils.

    Myth #1 The sole purpose of psychology is to understand the nature of the human mind. ‘All societies have mentally ill people. Psychology began by explaining the workings of the normal human mind, and why some people were different. From there, the discipline eventually came to be used in schools’. ‘Mental illness’ is a relatively new way of understanding forms of conduct associated with ‘unreason’, and regardless of its function today, psychology originated not as an attempt to know the normal human mind, but rather as a system for finding and managing those unlikely to fit into the new, mass school.

    Myth #2 My conduct may be governed, but my mind remains free. ‘Disciplinary societies like the school certainly regulate our conduct, but they can't control our minds. That is, the power of the school may cover our bodies, but it doesn't extend inside our heads’. Just as contemporary forms of governance have rendered our objective conduct amenable to regulation and normalisation, so too has the discipline of psychology made our subjective lives – our desires, motivations, intellectual capacities, perceptions, systems of thought – open to rigorously structured intervention and regulation.

    Myth #3 Psychology simply discovers new behaviour disorders; it plays no role in creating them. ‘Psychology reveals new facts about the human mind. It has recently found, existing in nature, a wide range of behaviour disorders affecting children's performance at school. It has also developed a range of treatments for these conditions’. Just as other forms of governance operate by subdividing the population into manageable, bite-sized categories, each of which can be intervened upon differently, so too does psychology, and it is unrealistic to think that psychology had no part to play in the creation of these new pathologies.

  • 2 - Arguments

  • from PART I - BUILDING BLOCKS
  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 14-30

  • Summary

    Social science is organized around arguments. This is what we hope to prove, or disprove, by an empirical analysis. Science proceeds as arguments are advanced, revised, and proven or disproven.

    A theory is the reasoning behind an argument. It explains a relationship, its mechanisms (if the relationship is causal), scope-conditions, background conditions, and any additional information needed to interpret the argument. One might theorize that democratization enhances economic growth because it holds leaders accountable, and accountable leaders are more likely to adopt policies that serve the public good.

    If a theory is presented in a formal manner, perhaps with a set of mathematical expressions, it may be referred to as a model. At present, however, we do not distinguish between an argument, theory, or model; these terms will be used more or less synonymously.

    A hypothesis is the specific, testable element(s) of any argument. One might hypothesize that as a country becomes more democratic its GDP (gross domestic product) growth increases. Our use of the term “argument” is thus meant to encompass both theory and hypothesis.

    This chapter begins by distinguishing various types of social science argumentation. Arguments central to social science are generally classifiable as either descriptive or causal, so we spend considerable time on these genres. Next, we contrast these genres with other sorts of arguments such as those that are predictive, normative, or prescriptive. Finally, we lay out the characteristics of a good descriptive or causal argument. (Additional criteria specific to causal arguments are postponed until Part II of the book.)

    Descriptive Arguments

    A descriptive argument describes some aspect of the world. In doing so, it aims to answer what questions (e.g., when, whom, out of what, in what manner) about a phenomenon. Descriptive arguments are about what is or what was.

    Note that many features of the world are intrinsically important. We want to know which countries are democratic and which are not, and in what ways they are democratic or undemocratic. We want to know how many Jews, gypsies, gays, and socialists were killed in the European Holocaust. We want to know the intellectual origins of individualism, and how it evolved through time and across cultures. We want to know whether the media in a country presents a biased view of news events, and in what direction that bias runs.

  • 16 - Ethics

  • from PART III - PROCESS AND PRESENTATION
  • John Gerring, University of Texas, Austin, Dino Christenson, Boston University
  • Book:
    Applied Social Science Methodology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 262-268

  • Summary

    Ethics refers to formal and informal codes of behavior, i.e., the rightness or wrongness of an action as judged by a community. In the present context, we are of course concerned with ethical norms that apply to the activity of social science.

    Ethical concerns are often raised in conjunction with experiments, where some subjects are assigned to a treatment condition and others to a control condition. If the treatment condition is potentially dangerous – or potentially beneficial – there are obvious ethical concerns. Why should one group of subjects be singled out in a wholly arbitrary fashion? What could justify such an intervention?

    While they may be less apparent, ethical concerns also impinge upon other aspects of social science, e.g., to the collection of data from human subjects by observational methods, data analysis, and the dissemination of data and results. Arguably, everything that one might do, or refrain from doing, has ethical implications. Consequently, we take a broad view of the subject.

    The chapter begins by discussing the juridical conception of ethics – centered on statutes, legal precedents, and institutional rules. Next, we discuss the central importance of protecting human subjects. Finally, we explore the activity of social science as an ethical sphere of action. What is right (moral) conduct for a social scientist?

    Juridical Ethics

    The juridical conception of ethics refers to rules one is obliged to follow as a member of a university, a profession, or a country. Universities in the United States are obliged, as a condition of receiving federal funding, to establish a set of procedures to govern research with human subjects. This involves the formation of an Institutional Review Board (IRB) to apply the protocol and to approve specific research proposals submitted by students or university employees. (IRBs may also be referred to as ethics review boards.) All research on human subjects is subject to approval by the IRB. Even research that does not involve human subjects – such as historical research – may require IRB exemption. Thus, IRBs have become an integral part of scientific research at all American universities – at least, all universities that receive federal funding.

  • PART III - CULTURAL CONTEXTS OF CONTEMPORARY EDUCATION

  • Gordon Tait, Queensland University of Technology
  • Book:
    Schooling and Society
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 181-184

  • Summary

    Having addressed some of the theoretical foundations of education, both from within the familiar modernist paradigm – though with some ground given to the logic of postmodernity – and also from within a Foucault-inspired governmental approach, the next part of this book examines some of the cultural issues, concerns and contexts impacting upon contemporary education. While there is no direct, correlative link between the culture of a society, and the kind of schools it contains – where the latter is simply a function of the former – it would be a mistake to think that there are not important connections between the two. Indeed, a better approach might be to not consider them as discrete entities at all.

    Chapter 8 – The Media – addresses the relationship between education and the news media. It questions the assertions that ‘media studies’ is not a serious subject, and doesn't belong in the school curriculum; that media messages need an education to decode properly; and that the media is not responsible for whipping up moral panics, whether to further specific political ends, or to simply sell papers and gain viewers.

    Chapter 9 – Popular Culture – investigates recent concerns over the increasing presence of popular culture in the classroom. It questions the common view that all popular culture is rubbish, unlike ‘high culture’; that teachers need to be popular culture experts if they are going to connect with their students; and that the classroom is no place for popular culture, as it has absolutely no educational value.

    Chapter 10 – Technology – examines the rise of digital technologies and their implications for mass schooling. It questions the belief that these technologies represent the solution to many of education's enduring problems, such as limited access to information, particular kinds of social-class disadvantage, and inconsistent standards of education; and that digital technology spells the end for traditional teaching, since learning resources can be accessed instantly from almost anywhere, and students now have access to virtually limitless information.

    Chapter 11 – Globalisation – addresses the emergence of a global economy, as well as global cultures and global forms of politics, and makes connections between these phenomena and our education system.

  • 11 - Social psychology and immigration: relations between immigrants and host societies

  • Edited by Linda Steg, Rijksuniversiteit Groningen, The Netherlands, Kees Keizer, Rijksuniversiteit Groningen, The Netherlands, Abraham P. Buunk, Rijksuniversiteit Groningen, The Netherlands, Talib Rothengatter, Rijksuniversiteit Groningen, The Netherlands
  • Book:
    Applied Social Psychology
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 235-254

  • Summary

    Introduction

    Globalization, migration, and increasing cultural diversity within nations have resulted in a growing need to understand and enhance intercultural relations in plural societies. The purpose of this chapter on relations between immigrants and host societies is to highlight current trends and new advances in the study of acculturation and intergroup relations. To accomplish this, the chapter provides an overview of migration and cultural diversity across four major geographical regions; describes the evolution of acculturation theory, models, and research; briefly reviews the developments in the application of social-psychological theory to the study of immigration and intergroup relations; discusses the convergence of these two approaches; offers suggestions on how applied social psychology may help to solve intercultural problems; and makes recommendations for the course of future research.

    Migration and cultural diversity in the twenty-first century

    It is estimated that in 2010 there were 214 million international migrants on a worldwide basis. Western Europe currently hosts the largest number (over 40 million), which is about 8 per cent of its population. However, relative to the total population, Oceania (15 per cent) and North America (13 per cent) are world leaders. Brief synopses of their immigration trends and issues are presented in the following sections.

  • • The United States (313 million inhabitants) has been culturally dominated by European Americans for centuries, while African-Americans formed the largest minority. Remarkably, African-Americans have recently been surpassed by Americans with a Hispanic background who – numbering more than 40 million – now form over 13 per cent of the population. Hispanics continue to migrate to the US and have a higher birth rate than Americans of European descent. After Mexico and Spain, the US has the world's largest Spanish-speaking population. Within 50 years, Hispanics will form a quarter of the US population. We may safely predict that the US is becoming a country with two powerful cultures, the Anglo-Western and the Hispanic culture, and two important languages, English and Spanish.

  • • In Western Europe (342 million inhabitants) the situation is more diverse, because each country has its own language and its particular immigration history. In the 1960s, when economic growth in Western Europe led to a scarcity of labour, many ‘guest workers’ from the Mediterranean countries moved to Germany, Belgium, France, and the Netherlands.

Page 1325 of 1933