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38647 results in Cambridge Textbooks

Page 1324 of 1933

  • 22 - Regression

  • 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 331-352

  • Summary

    In this chapter we build on the concept of correlation with ordinary least squares (OLS) regression, even though the latter was invented first. If we think about our scatter plot, we could draw one line through the data that best fits all of our data points. Correlation is a statement about how close the points are to the line. The objective of regression is to determine the best fitting line for the data. Using regression, we can determine the average effect of our independent variable and make predictions about cases outside our sample. We will begin with the simplest regression model, bivariate, where the dependent variable is a function of a single independent variable, before expanding to consider multivariate models with multiple independent variables.

    Bivariate Regression

    As social scientists we primarily want to explain why variables of interest vary and vary together. Regression allows us the ability to measure the effect of one variable on another. It tells us the effect of an independent variable on a dependent variable. Furthermore, it provides us with the degree of the effect, thereby providing more explanatory leverage than in any other technique we have discussed thus far. Not unlike correlation we can find the strength and direction of association between two variables. Here, however, we can also get at the specific nature of the relationship; i.e., how much variance in the dependent variable is “explained” by the independent variable.

    In discussing correlation we implied without much specificity that one could draw a straight line that passed through the set of points in a manner that represented the overall pattern, positive or negative, steep or shallow. In addition to moving to thinking about causal relationships between independent and dependent variables (which was not required for correlation), with regression we also ask: Which linear relationship? In other words, of all the lines in Figure 22.1 that pass through the graph centroid – the point where X and Y intersect and marked in the figure by the intersection of the dotted lines – which fits the data the best?

    Before delving into the math, it is useful to graphically illustrate the characteristics of the best fitting regression line, as in Figure 22.2. For any line, we can measure the vertical distance between the line and each observation, which is called a residual.

  • 10 - Diverse Tools of Causal Inference

  • 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 156-166

  • Summary

    This section of the book has covered a lot of ground. We have presented a variety of causal frameworks (Chapter 5), defined the problem of causal inference (Chapter 6), and introduced an itinerary of research designs, which we classified as experimental (Chapter 7), large-N observational (Chapter 8), or case study (Chapter 9).

    This short chapter summarizes some of the themes discussed in this long, and complex, part of the book. We begin by reviewing the problem of confounding, and the various types of confounding that impede causal inference. Next, we review the panoply of research designs discussed in previous chapters, illustrating their interrelationship in a tree diagram, and discussing whether there is a “best” research design. We conclude by discussing the multi-method approach to research.

    Identifying and Avoiding Confounders

    Causal inference is possible only if confounders can be avoided or controlled in the analysis. Recall that we adopted a very broad definition of confounding, including any factor that produces a spurious (or biased) association between X and Y.

    This idea may be rendered in a more concrete fashion using the language of causal graphs. A causal model will generally render a valid estimate if the “frontdoor” path from X to Y is unblocked and any existing “backdoor” paths from X to Y are blocked. Simply stated, a causal model should allow no covariation between X and Y except that which is a product of X's causal impact on Y. This is another way of describing causal comparability, i.e., the expected value of Y for a given value of X is the same for all units throughout the period of analysis. (You will see that there are many languages by which to express the same general idea.)

    Five types of confounders, introduced in previous chapters, are recapitulated in Figure 10.1. In each case, Z stands for the confounder, generating a backdoor path from X to Y. Recall that in interpreting these graphs one must distinguish settings in which Z is conditioned from settings in which it is not (indicated by square brackets). In some settings, a confounder is created because a factor is not conditioned, in other settings because it is conditioned.

  • 12 - Brainstorming

  • 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 182-192

  • Summary

    How does one go about identifying a fruitful research question and, ultimately, a specific research hypothesis? This is the very early exploratory phase, when one quite literally does not know what one is looking for, or at. Arguably, it is the most crucial stage of all. Nothing of interest is likely to emanate from research on topics that are trivial, redundant, or theoretically bland – no matter how strong its research design components are.

    Methodologists generally leave this task to the realm of metaphor – bells, brainstorms, dreams, flashes, impregnations, light bulbs, showers, sparks, and whatnot. The reason for this lack of attention is perhaps because beginnings are inherently unformulaic. There are few rules or criteria for uncovering new questions or new hypotheses. Methodologists may feel that there is nothing – nothing scientific, at any rate – that they can say about this process. Karl Popper states the matter forthrightly. “There is no such thing as a logical method of having new ideas,” he writes. “Discovery contains ‘an irrational element,’ or a ‘creative intuition.’”

    Accordingly, what we have to offer in this chapter is more in the character of a homily than a framework. It reads like an advice column. We urge you to Study the Tradition, Begin Where You Are, Get Off Your Home Turf, Play With Ideas, Practice Disbelief, Observe Empathically, Theorize Wildly, and Think Ahead.

    This advice is largely commonsensical and by no means comprehensive. It cannot help but reflect our own views and experiences, though we have drawn extensively on the writings of other scholars. Nonetheless, it may help to orient those who are setting out on their first journey, or who wish to begin again.

    Study the Tradition

    Consider the state of the field on a topic. What are the frontiers of knowledge? What do we – collectively, as a discipline – know, and what don't we know? We doubt if anyone has happened upon a really interesting research topic simply by reading a review of the extant literature. However, this is an efficient method of determining where the state of a field lies and where it might be headed.

  • 1 - A Unified Framework

  • 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 3-13

  • Summary

    The purpose of social science is to make a difference in the world by applying reason and evidence to problems of general concern. Every question of social science relates (or ought to relate) to normative concepts such as justice, democracy, human rights, prosperity, happiness, or equality.

    What distinguishes social science from casual conversation, journalism, or political rhetoric may be summarized as follows. First, social science involves the systematic application of reason and evidence to problems with explicit attention to method and to possible sources of error. Second, social science is accompanied by realistic estimates of uncertainty with respect to whatever conclusions are drawn from the evidence. Third, social science attempts to provide a comprehensive treatment of a subject within whatever scope-conditions are defined by the study. All relevant information should be included; none should be arbitrarily excluded. Finally, social science adopts a disinterested posture with respect to all goals except the truth. Its purpose is to get as close to the truth as possible, in all its complexity, rather than to provoke, entertain, elucidate moral truths, or advance polemical claims.

    These features render social science less stimulating than other media, where there is generally a premium on brevity, accessibility, provocation, righteousness, or humor. Social science is a sober profession. However, for those excited by the prospect of getting it right, and willing to expend some energy to get there, the practice of social science may be highly rewarding.

    Consider the problem of crime, a topic that often evokes hot rhetoric and strong opinions. Most media reports and political speeches offer little useful information about the prevalence of crime, its sources, and its potential solutions. Instead, they exploit the public's fascination with gruesome events and, in the process, provoke fear. From this perspective, the cold gaze of social science offers some relief.

    Researchers have spent a good deal of time studying the rise and fall of violent crime in the United States and elsewhere. In the early 1960s, the United States enjoyed a low homicide rate of 5 murders per 100,000 inhabitants. Over the next two decades this rate doubled – to 10 per 100,000 inhabitants – peaking in the late 1970s or early 1980s, at which point the United States could claim the highest rate of violent crime of any advanced industrial country.

  • 9 - Case Study 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 139-155

  • Summary

    There are two ways to learn about a subject. One might study many examples at once, focusing on a few selected dimensions of the phenomena. We shall refer to this as an extensive approach, as laid out in Chapters 7–8.

    Alternatively, one might study a particular example, or several examples, in greater depth. We shall refer to this as an intensive, or case study, approach – the topic of this chapter.

    A case connotes a spatially delimited phenomenon (a unit) observed at a single point in time or over some period of time. It may be a political unit with a defined area of semi-sovereignty (e.g., empire, nation-state, region, municipality), organization (e.g., firm, non-governmental organization, political party, school), social group (as defined, e.g., by ethnicity, race, age, class, gender, or sexuality), event (e.g., foreign policy crisis, revolution, democratic transition, decision-point), or individual (e.g., a biography, case history).

    However defined, a case must comprise the type of phenomenon that an argument attempts to describe or explain. In a study about nation-states cases are comprised of nation-states (observed over time). In a study that attempts to explain the behavior of individuals, cases are comprised of individuals. And so forth.

    A case study research design is an intensive study of a single case or a small number of cases that promises to shed light on a larger population of cases. The individual case(s) is viewed as a case of something broader, just as large-sample analysis is also generally viewed as exemplary of a broader phenomenon. Thus, both intensive and extensive analyses generally make inferences from a sample to a population, even though the sample sizes are very different.

    Case study research may incorporate one or several cases. The latter is a defining characteristic of comparative historical analysis, associated with the work of Barrington Moore, Theda Skocpol, David Collier, and James Mahoney. However, as the sample of cases expands it becomes less and less feasible to investigate each case intensively. The case study format is thus implicitly a small-sample format.

  • Chapter 14 - Truth and Postcolonialism

  • from PART IV - PHILOSOPHY AND MASS EDUCATION
  • Gordon Tait, Queensland University of Technology
  • Book:
    Schooling and Society
    Published online:
    28 May 2018
    Print publication:
    24 April 2017, pp 335-362

  • Summary

    This chapter argues that the issue of ‘truth’ has played a foundational role, not only within the discipline of philosophy, but also within many different aspects of our culture. However, there seems to be little agreement on what it really is, and while some philosophers contend that truth is a meaningless concept – a linguistic mirage – most would argue there's something of importance there, but what is it?

    Even if we struggle to determine the real nature of truth – as we did with the real nature of right and wrong, in Chapter 13 – at least we structure our culture, our knowledges, and our school curricula around stuff we know to be unequivocally true… or do we? Arguably, many of the assumptions we make do not stand up to close scrutiny, even within a domain as supposedly ‘objective’ as the school curriculum. Worldviews are often presented as ‘truth’ that actually remain open to dispute and contestation, worldviews that suit particular groups and that subtly act to reinforce given social, political and national interests.

    Myth #1 ‘Truth’ is the most straightforward thing there is. ‘Truth is simple. Certain things about the world are “facts”. If we report them accurately, we are telling the truth; if we don't, we aren't. That's all there is to it’. Actually, no it isn't. Philosophers have been debating the notion of truth for over 2500 years, debates that have resulted in very different approaches to this issue, most frequently divided into realist and anti-realist models. These models in turn generate different kinds of truth tests, which can be applied to given problems and statements. All in all, ascertaining what is true, and what is not – in the curriculum, or elsewhere – remains a complex and contested process.

    Myth #2 The knowledge in our school curriculum is both true and culturally neutral. ‘There is nothing untrue or biased about our school curriculum. You could teach it anywhere in the world and it would be appropriate’.

  • 14 - The social psychology of organizations

  • 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 298-318

  • Summary

    Introduction

    Organizations pervade our lives in several ways. Most people work for 40 to 50 years as an employee or leader in different organizations, as an entrepreneur, or perhaps as a volunteer. Many students are already working in an organization, to pay for their studies and to cover living expenses. Even if you are not, being a student still means you spend time interacting with representatives from organizations, for instance at your university. Students depend on employees at the information desk and the security department; on people working at the IT department for access to the electronic learning environment and university library. They benefit from the work performed by those who clean the lecture theatre and definitely from those working at the coffee stand or canteen who can provide what students desperately need. And when university employees do not perform well, students are hindered too.

    Obviously, there are many types of organizations. Textbook definitions clarify what they have in common: “An organization is a social arrangement for achieving controlled performance in pursuit of collective goals” (Huczynski and Buchanan, 2007; p. 6; see Figure 14.1). Organizations place people together in a building, provide them with tools, computers, or other resources, define their tasks, and prescribe the rules and procedures they have to follow. All this is intended to make individuals coordinate their efforts and achieve the organization's goals, be it building a car together, selling flowers, or offering health care. Thus, one of the primary challenges for organizations is to motivate people to work together as a team to benefit the organization. We will show how the application of insights from social psychology can help understand when and why people are motivated to work in teams and organizations. This approach reveals that – despite their individual characteristics – workers may behave quite differently depending on the relationship with their colleagues, the encouragement they receive from their leaders, or the policy communicated by the organization.

    We will illustrate the added value of this approach by focusing on three recent contributions from social psychology that can help meet the challenge organizations face: to facilitate the ability of different individuals to work together towards joint goals.

  • 13 - Data Gathering

  • 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 193-229

  • Summary

    Insofar as social science research rests on an empirical base it involves an encounter with data. Data may be qualitative or quantitative. It may be numerical, textual, visual, olfactory, auditory, or artifactual. It may be drawn from primary or secondary sources. It may be collected by the researcher or by someone else. “Data” is understood here in the broadest sense, including any sort of evidence that serves as the empirical basis for understanding and explanation.

    In this chapter we lay out various methods of gathering data, a topic that is important for both producers and consumers of social science. Note that in order to judge the quality of a study you need to be familiar with the data-collection method that underlies the analysis.

    Data-gathering methods may be classified broadly as obtrusive or unobtrusive. Obtrusive methods comprise standardized surveys as well as less structured interpersonal settings such as interviews, focus groups, and ethnography. Unobtrusive measures include those that are surreptitious and ex post. After outlining these techniques of data collection we discuss the problem of data assessment: how do we know that our data (or someone else's) is true?

    Before launching into the chapter, a few caveats and clarifications are in order.

    First, each section of the chapter is cumulative. For example, many of the ingredients of a good survey are also ingredients of a good interview, focus group, or ethnography. This explains why the section on surveys is somewhat longer than the later sections of the chapter. It also means that this chapter should probably be read from front to back rather than browsed in a selective fashion.

    Second, each method will be treated as a stand-alone technique of data collection. However, readers should be aware that in the course of conducting a study it is common to combine several data-collection strategies. Where a multi-method approach is adopted, the strengths and weaknesses of each strategy should be understood within that context.

    Third, experiments and case studies are regarded in this book as research designs rather than data-gathering techniques and are therefore discussed in Part II rather than in this chapter.

  • 21 - Bivariate Statistics

  • 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 313-330

  • Summary

    Bivariate statistics allow us to test the relationship between two variables. While simple, they provide great empirical leverage for hypotheses of association and, with the appropriate research design, causality. We next hone our ability to make controlled comparisons and introduce inference making about sample means with the difference of means test. We then proceed to correlation, which moves us beyond making a simple claim of a relationship or no relationship between two variables to a measure of both the strength and direction of the relationship.

    Revisiting Levels of Measurement

    In the previous chapter, we found out some important substantive information about our population means. Indeed we learned how to estimate a population mean from the information that we gain in a single sample. However, we are still not in a position to assess a hypothesis. We do not generally have expectations as simple as “the mean number of parties in a democracy is x.” Our hypotheses typically suggest a relationship between an independent and dependent variable, and we have not made reference to bivariate analyses yet. We will explore a number of statistical tests for assessing hypotheses. We can divide hypothesis tests into three types based on the information they provide about the relationship between the independent and dependent variable:

  • 1. Those that simply analyze whether or not there is a relationship between variables (e.g., Difference of Means).

  • 2. Measures of association, which tell us the direction and strength of the relationship between two variables. (e.g., Correlation).

  • 3. Measures of average effect, which tell us the amount of change in the dependent variable given a unit change in the independent variable (e.g., Regression).

    • It is important to note that knowing which test to use requires us to think not just about the question we would like to answer but also our variables’ levels of measurement, their population distributions, and the sample size. In addition, all the tests discussed here demand that the data come from a random sample. In Chapter 3 we learned about levels of measurement. In quantitative analyses understanding the level of measurement is essential because it helps us decide which hypothesis test to use.

  • Chapter 8 - The Media

  • from 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 185-211

  • Summary

    It is important, for students and adults alike, to understand the pervasive, corporate, global and continually developing nature of our news media. The news media does not occupy a realm somehow removed from our schooling system; indeed it impacts upon education in a wide range of ways, both directly and indirectly. However, for much of the time, most of us barely pay attention to the thousands of media messages we are exposed to every day, and even when we do, we generally assume that those messages are both objective and neutral. This is a mistake.

    Myth #1 We do not need pointless subjects like ‘media studies’ in our already crowded curriculum. ‘Children need to learn to read and write, and until they can do that properly, we can do without media studies; it doesn't add anything’. On the contrary, understanding the media represents a new and important form of literacy for the twenty-first century. In addition to contributing to critical thinking skills, it contributes to the production of active, global citizens, and it helps them understand the workings of one of our most important contemporary institutions.

    Myth #2 With the news media, you don't need to think, you just need to read and listen. ‘You don't need to “decode” the news media; there's nothing mysterious or ambiguous about it. It tells us what's going on, nothing more, nothing less’. The news media is not simply the objective and transparent communication of current events to a unitary audience. News gathering and selection is a subjective process, and those items selected generally have specific elements – negativity and conflict ranking high among them – and these stories can often support a given agenda, the conservative media campaign against ‘progressive education’ providing a good example.

    Myth #3 The media doesn't create moral panics, but even if it does, this doesn't affect education. ‘Even though the news media is under pressure to be entertaining, they aren't responsible for whipping up public hysteria when it happens’. The evidence suggests that from time to time, the news media can create highly visible ‘folk devils’, not only to sell newspapers and garner viewers, but also to reinforce the boundaries between ‘us’ and ‘them’.

Page 1324 of 1933