A: (Knock)

B: Come in.

A: Ah, Is this the right room for an argument?

B: I told you once.

A: No you haven’t.

B: Yes I have.

A: When?

B: Just now.

A: No you didn’t.

B: Yes I did.

A: You didn’t

B: I did!

A: You didn’t!

B: I’m telling you I did!

A: You did not!!

B: Oh, I’m sorry, just one moment. Is this a five minute argument or the full half hour?

Monty Python, "The Argument Clinic"

Argumentation in contemporary social science descends from the ancient art of rhetoric and the equally ancient science of logic. A complete argument consists of a set of key concepts, testable hypotheses (aka propositions), and perhaps a formal model or larger theoretical framework. A causal argument should also contain an explication of causal mechanisms (Chapter 8). An argument is what we speculate might be true about the world; it engages the realm of theorizing.

Sometimes, it is important to distinguish among arguments lying at different levels of abstraction. The most abstract may be referred to as macro-level theories, theoretical frameworks, or paradigms. Examples would include structural functionalism, modernization theory, exchange theory, symbolic interactionism, or conflict theory. At a slightly less abstract level one finds meso-level theories or models. And at the most specific level one speaks of hypotheses, inferences, micro-level theories, or propositions, which are assumed to be directly testable. (Explanations may apply to any level.) So, for example, work on the topic of school vouchers might include a general theory about why consumer choice enhances the educational process, a formal model incorporating various elements of that theory, and a specific hypothesis or set of hypotheses regarding the impact of a voucher-based intervention on educational attainment.

We have now reviewed randomized and nonrandomized approaches to the assignment problem, and to causal analysis more generally. Our discussion has focused on the ways in which these strategies attempt to isolate the covariational pattern between a causal factor, X, and an outcome, Y. This pattern may be of many sorts: positive or negative, proximal or distal, and so forth. There are many types of causal relationships (as summarized in Table 9.2), and each presupposes a somewhat different covariational pattern. The key point is that X/Y covariation is a necessary condition of causality. If Y does not vary with X, at least some of the time and in some real or imagined sample, then X cannot be a cause of Y. Accordingly, the discovery of X/Y covariation may provide strong evidence of causality, if various assumptions hold.

However, the problem of confounders is ubiquitous when one is dealing with nonexperimental data. (Additionally, background factors lying orthogonal to the factor of theoretical interest may present an obstacle to causal inference if the signal is overwhelmed by stochastic noise.) Consequently, one is often obliged to move beyond an exclusive focus on X and Y. Other factors will need to be measured, and conditioned, if the covariation between X and Y is to be interpreted as evidence of a causal relationship.

Broadly stated, the goal of science is to discover new things about the world and to appraise the truth-value of extant propositions about the world. Consider our exemplars, democracy and vouchers, introduced in Chapter 1. We want to uncover new things about the process of democratization and the impact of vouchers on school performance. At the same time, we want to test extant theories about these two subjects. Social science may, therefore, be understood as a twin quest for discovery and for appraisal, as summarized in Table 2.1.

The chapter begins by introducing these goals, followed by a review of their implications for more specific methodological tasks. The next section approaches the goal of discovery through the concrete task of finding a research question. Since the remaining chapters of the book assume that a research question – perhaps even a specific hypothesis – has been identified, this chapter functions as a prologue to the rest of the book.

Description will be understood in this book as any empirical argument (hypothesis, theory, etc.) about the world that claims to answer a what question (e.g., how, when, whom, or in what manner). By contrast, wherever there is an implicit or explicit claim that a factor generates variation in an outcome the argument will be regarded as causal. The distinction between these two key concepts thus hinges on the nature of the truth-claim – not on the quality of the evidence at hand, which may be strong or weak. Description is the topic of Part II, while causation is the topic of Part III. Description rightly comes first; one must describe in order to explain (causally). However, the reader will find many comparisons and contrasts across the two topics interwoven throughout the book.

Because this book is focused on generalizing statements about the world (Chapter 1), I am not concerned with descriptions that reflect only on individual cases or events (without any attempt to exemplify larger patterns). Consequently, in this book description is always an inferential act. To generalize is to infer from what we know (or think we know) to what we do not know. One sort of inferential leap is from observations within a sample that are deemed secure to those that are uncertain or missing (problems of “measurement error” or “missing data”) and to dimensions that are inherently unobservable (“latent characteristics”). Another sort of inferential leap is from a studied case or sample to a larger (unstudied) population. In both respects, descriptive models offer a “theory” about the world, “a ‘formula’ through which the data can be reproduced.”

All the tasks, strategies, and criteria noted in the previous chapters are considered valid, ceteris paribus. And yet ceteris is not always paribus. A key theme of this book is that methodological choices frequently involve tradeoffs. Satisfying one dimension may involve sacrificing another. Tasks, strategies, and criteria often conflict. Accordingly, for every dimension listed in Table 1.1 and in subsequent tables throughout the book one can locate conflicting imperatives.

The call for discovery is at odds with the call for accurate appraisal. Indeed, exploratory research is typically carried out in a different fashion than research whose primary goal is confirmatory (falsificationist), as emphasized in Chapter 2.

How do social scientists describe a social reality? What arguments do we employ in our attempts to bring order to the great blooming, buzzing confusion of the world? One might suppose that the shape of a descriptive inference is limited only by the social phenomenon that we seek to describe, the models (cognitive, linguistic, mathematical, and visual) that we have at our disposal, and our imagination. In practice, however, descriptive inferences draw from a standard itinerary of tropes.

I shall argue that most descriptive claims can be classified as indicators, syntheses, typologies, or associations, along with their various subtypes as illustrated in Table 6.1. This is how social scientists carve up nature at the descriptive level. These are the patterns that we look for when attempting to describe classes of events in the social world.

Since Hume, writers have been aware that the assessment of causal relationships is rather ethereal. One can never know with absolute certainty whether some factor caused an outcome to occur, because one cannot go back in time to re-play events exactly as they happened, changing only the factor of interest and observing the outcome under this altered condition. The causal counterfactual can never be directly observed for there are no time-machines. This is sometimes referred to as the fundamental problem of causal inference.

In recent years, social scientists have become acutely conscious of the insubstantial nature of the evidence that typically undergirds causal propositions in anthropology, economics, political science, sociology, and various offshoots of these disciplines. Methodologists have little confidence in inferences drawn from observational data, and no statistical machinery seems likely to provide secure foundations. There is, some have insinuated, a “crisis of causality.”

So we have now come to the end of this volume. As I have said in the Preface, what I write in this book may not make everyone happy because there is no way that I can cover every angle of performing cross-cultural research in one volume. I have also deliberately excluded specific qualitative methods because I believe that they do not do justice to sensitive cross-cultural research. You can blame me if you like.

Throughout the book, however, I have advocated the performance of cross-cultural research more ethically, sensibly and responsibly. I have pointed to many challenges that cross-cultural researchers may have to deal with in their research. I have suggested culturally sensitive and appropriate research methods that would work well with cultural groups. I have offered many thought-provoking angles which I am sure will be of value to many new and experienced researchers who wish to perform cross-cultural research. If you have stayed with me throughout the book, I am sure that you will be able to perform your cross-cultural research responsibly and successfully. This is your reward.

In this chapter, I will discuss the way we write to represent the voices of our research participants in cross-cultural research. There are several salient issues that I believe deserve great attention from researchers. For example: How do we write our research findings in a way that what we write will not further marginalise our participants? In what language should we write the findings? Who owns the research findings? I shall also suggest ways that we can write up the findings sensitively and make use of innovative writing strategies. When we have finished our research project, it is important for us to disseminate the research findings. How do we do this in cross-cultural research so that the findings can be fed back to our participants and reach wider audiences? This will be addressed in this chapter.

Writing cross-cultural research

How do we write in a way that our writing will not further marginalise our research participants? This is a contentious issue in any sensitive research, but even more so in cross-cultural research, when we often work with very marginalised people.

This chapter continues people's testimonies in cross-cultural research. In the previous chapter, I focused on personal and collective testimonies, but this chapter will emphasise personal and collective testimonies within a collaborative effort. That is, through the combination of personal and collective testimonies, people work together with others (the researchers) to resist colonisation and find ways to improve their lives and situations. Hence, this chapter will be dedicated to the methodology of community based participatory research (CBPR), which will include discussions on CBPR, participatory action research (PAR) and the photovoice method.

In the past few decades, Nina Wallerstein and Bonnie Duran (2008: 27) contend that we have seen an emergence of a new paradigm of participatory research. This has raised many challenges to the practice of positivist science. It has also asked crucial questions about the construction and use of knowledge and the importance of the power relations which permeate the research process.

A generation ago, cross-cultural research was dominated by white researchers. Most researchers would either belong to white, middle-class backgrounds and travelled a long distance to ‘study’ native, or they would be local people or those who come from privileged positions such as university researchers doing a project with ethnic minority groups in their own countries. Recently, however, we have seen a growing number of scholars who carry out research among people who share their own cultural identity (see Bulmer & Solomos 2004; Ramji 2008).

Based on theoretical frameworks that I discussed in Chapter 1, I now introduce some qualitative methods in this chapter and the next. I make no claim that all qualitative methods will be appropriate in cross-cultural research. This is precisely what Esther Madriz (2000: 840) has suggested: ‘Some methodologies are more suitable than others for shattering a colonizing discourse in which images of research subjects as the Other are constantly reproduced’ (see also Fine 1994; Denzin et al. 2008a; Liamputtong 2008). Thus, I will focus only on those methods that will allow cross-cultural researchers to work more sensitively with the research participants. I shall, however, provide some useful reading materials for readers to consult further at the end of the chapter.

Oral/life history, according to Valerie Yow (2005: 3), ‘is the recording of personal testimony delivered in oral form’ (see also Benmayor 1991; Banks-Wallace 2002).

In this chapter, I shall point to the importance of language and communication in cross-cultural research. Often, researchers and the participants are from different linguistic backgrounds. But language, as Monique Hennink (2008: 22) contends, ‘represents data in qualitative research and communication’. It is ‘the process through which data are generated between a researcher and study participant’. Often, in carrying out cross-cultural research, the researchers are linguistically and culturally distant from their research participants (Hennink 2008). Differences in the language spoken and the meanings that are conveyed can create problems, and this has implications for the research findings. It is also an ethical issue, as misunderstanding may occur and this may result in the misinterpretation of the research findings (Irvine et al. 2008).

In this chapter, I shall first discuss issues pertaining to language. I then look at bicultural researchers and the need to provide training and support for them in cross-cultural research.