Road Traffic Accidents & Obstructive Sleep Apnea Paper

Do a research design for my topic “Road Traffic Accidents in Patients Diagnosed with Obstructive Sleep Apnea”

PART I WHAT IS RESEARCH DESIGN? 1 THE CONTEXT OF DESIGN Before examining types of research designs it is important to be clear about the role and purpose of research design. We need to understand what research design is and what it is not. We need to know where design fts into the whole research process from framing a question to fnally analysing and reporting data. This is the purpose of this chapter. Description and explanation Social researchers ask two fundamental types of research questions: 1 2 What is going on (descriptive research)? Why is it going on (explanatory research)? Descriptive research Although some people dismiss descriptive research as ‘mere descrip­ tion’, good description is fundamental to the research enterprise and it has added immeasurably to our knowledge of the shape and nature of our society. Descriptive research encompasses much government spon­ sored research including the population census, the collection of a wide range of social indicators and economic information such as household expenditure patterns, time use studies, employment and crime statistics and the like. Descriptions can be concrete or abstract. A relatively concrete descrip­ tion might describe the ethnic mix of a community, the changing age profle of a population or the gender mix of a workplace.

Alternatively 2 WHAT IS RESEARCH DESIGN? the description might ask more abstract questions such as ‘Is the level of social inequality increasing or declining?’, ‘How secular is society?’ or ‘How much poverty is there in this community?’ Accurate descriptions of the level of unemployment or poverty have historically played a key role in social policy reforms (Marsh, 1982). By demonstrating the existence of social problems, competent description can challenge accepted assumptions about the way things are and can provoke action. Good description provokes the ‘why’ questions of explanatory research. If we detect greater social polarization over the last 20 years (i.e. the rich are getting richer and the poor are getting poorer) we are forced to ask ‘Why is this happening?’ But before asking ‘why?’ we must be sure about the fact and dimensions of the phenomenon of increasing polarization. It is all very well to develop elaborate theories as to why society might be more polarized now than in the recent past, but if the basic premise is wrong (i.e. society is not becoming more polarized) then attempts to explain a non­existent phenomenon are silly. Of course description can degenerate to mindless fact gathering or what C.W. Mills (1959) called ‘abstracted empiricism’. There are plenty of examples of unfocused surveys and case studies that report trivial information and fail to provoke any ‘why’ questions or provide any basis for generalization. However, this is a function of inconsequential descriptions rather than an indictment of descriptive research itself. Explanatory research Explanatory research focuses on why questions.

For example, it is one thing to describe the crime rate in a country, to examine trends over time or to compare the rates in different countries. It is quite a different thing to develop explanations about why the crime rate is as high as it is, why some types of crime are increasing or why the rate is higher in some countries than in others. The way in which researchers develop research designs is funda­ mentally affected by whether the research question is descriptive or explanatory. It affects what information is collected. For example, if we want to explain why some people are more likely to be apprehended and convicted of crimes we need to have hunches about why this is so. We may have many possibly incompatible hunches and will need to collect information that enables us to see which hunches work best empirically. Answering the ‘why’ questions involves developing causal explana­ tions. Causal explanations argue that phenomenon Y (e.g. income level) is affected by factor X (e.g. gender). Some causal explanations will be simple while others will be more complex. For example, we might argue that there is a direct effect of gender on income (i.e. simple gender discrimination) (Figure 1.1a). We might argue for a causal chain, such as that gender affects choice of feld of training which in turn affects THE CONTEXT OF DESIGN 3 a) Direct causal relationship Income level Gender b) Indirect causal relationship: a causal chain Gender Field of training Promotion opportunities Occupation Income level c) A more complex causal model of direct and indirect causal links Field of training Occupation Income level Gender Child-care responsibility Figure 1.1 Part time or full time work Three types of causal relationships occupational options, which are linked to opportunities for promotion, which in turn affect income level (Figure 1.1b). Or we could posit a more complex model involving a number of interrelated causal chains (Figure 1.1c). PREDICTION, CORRELATION AND CAUSATION People often confuse correlation with causation.

Simply because one event follows another, or two factors co­vary, does not mean that one causes the other. The link between two events may be coincidental rather than causal. There is a correlation between the number of fre engines at a fre and the amount of damage caused by the fre (the more fre engines the more damage). Is it therefore reasonable to conclude that the number of fre engines causes the amount of damage? Clearly the number of fre engines and the amount of damage will both be due to some third factor – such as the seriousness of the fre. Similarly, as the divorce rate changed over the twentieth century the crime rate increased a few years later. But this does not mean that divorce causes crime. Rather than divorce causing crime, divorce and crime rates might both be due to other social processes such as secular­ ization, greater individualism or poverty. 4 WHAT IS RESEARCH DESIGN? Students at fee paying private schools typically perform better in their fnal year of schooling than those at government funded schools. But this need not be because private schools produce better performance. It may be that attending a private school and better fnal­year performance are both the outcome of some other cause (see later discussion). Confusing causation with correlation also confuses prediction with causation and prediction with explanation. Where two events or charac­ teristics are correlated we can predict one from the other. Knowing the type of school attended improves our capacity to predict academic achievement. But this does not mean that the school type affects aca­ demic achievement. Predicting performance on the basis of school type does not tell us why private school students do better. Good prediction does not depend on causal relationships. Nor does the ability to predict accurately demonstrate anything about causality. Recognizing that causation is more than correlation highlights a problem. While we can observe correlation we cannot observe cause. We have to infer cause.

These inferences however are ‘necessarily fallible . . . [they] are only indirectly linked to observables’ (Cook and Campbell, 1979: 10). Because our inferences are fallible we must minimize the chances of incorrectly saying that a relationship is causal when in fact it is not. One of the fundamental purposes of research design in explanatory research is to avoid invalid inferences. DETERMINISTIC AND PROBABILISTIC CONCEPTS OF CAUSATION There are two ways of thinking about causes: deterministically and probabilistically. The smoker who denies that tobacco causes cancer because he smokes heavily but has not contracted cancer illustrates deterministic causation. Probabilistic causation is illustrated by health authorities who point to the increased chances of cancer among smokers. Deterministic causation is where variable X is said to cause Y if, and only if, X invariably produces Y. That is, when X is present then Y will ‘necessarily, inevitably and infallibly’ occur (Cook and Campbell, 1979: 14). This approach seeks to establish causal laws such as: whenever water is heated to 100 °C it always boils. In reality laws are never this simple. They will always specify par­ ticular conditions under which that law operates. Indeed a great deal of scientifc investigation involves specifying the conditions under which particular laws operate.

Thus, we might say that at sea level heating pure water to 100 °C will always cause water to boil. Alternatively, the law might be stated in the form of ‘other things being equal’ then X will always produce Y. A deterministic version of the relationship between race and income level would say that other things being equal (age, education, personality, experience etc.) then a white person will [always] earn a higher income than a black person. That is, race (X) causes income level (Y). THE CONTEXT OF DESIGN 5 Stated like this the notion of deterministic causation in the social sciences sounds odd. It is hard to conceive of a characteristic or event that will invariably result in a given outcome even if a fairly tight set of conditions is specifed. The complexity of human social behaviour and the subjective, meaningful and voluntaristic components of human behaviour mean that it will never be possible to arrive at causal statements of the type ‘If X, and A and B, then Y will always follow.’ Most causal thinking in the social sciences is probabilistic rather than deterministic (Suppes, 1970). That is, we work at the level that a given factor increases (or decreases) the probability of a particular outcome, for example: being female increases the probability of working part time; race affects the probability of having a high status job.

We can improve probabilistic explanations by specifying conditions under which X is less likely and more likely to affect Y. But we will never achieve complete or deterministic explanations. Human behaviour is both willed and caused: there is a double­sided character to human social behaviour. People construct their social world and there are creative aspects to human action but this freedom and agency will always be constrained by the structures within which people live. Because behav­ iour is not simply determined we cannot achieve deterministic explana­ tions. However, because behaviour is constrained we can achieve probabilistic explanations. We can say that a given factor will increase the likelihood of a given outcome but there will never be certainty about outcomes. Despite the probabilistic nature of causal statements in the social sciences, much popular, ideological and political discourse translates these into deterministic statements. Findings about the causal effects of class, gender or ethnicity, for example, are often read as if these factors invariably and completely produce particular outcomes. One could be forgiven for thinking that social science has demonstrated that gender completely and invariably determines position in society, roles in families, values and ways of relating to other people. Theory testing and theory construction Attempts to answer the ‘why’ questions in social science are theories. These theories vary in their complexity (how many variables and links), abstraction and scope. To understand the role of theory in empirical research it is useful to distinguish between two different styles of research: theory testing and theory building (Figure 1.2). Theory building Theory building is a process in which research begins with observations and uses inductive reasoning to derive a theory from these observations. 6 WHAT IS RESEARCH DESIGN? Theory building approach Empirical level Start here Obs 1 Obs 2 Obs 3 Obs 4 Inductive reasoning Conceptual-abstract level Theory Theory testing approach Conceptual-abstract level Empirical level Figure 1.2 Theory Start here Deductive reasoning Obs 1 Obs 2 Obs 3 Obs 4 Theory building and theory testing approaches to research These theories attempt to make sense of observations. Because the theory is produced after observations are made it is often called post factum theory (Merton, 1968) or ex post facto theorizing.

This form of theory building entails asking whether the observation is a particular case of a more general factor, or how the observation fts into a pattern or a story. For example, Durkheim observed that the suicide rate was higher among Protestants than Catholics. But is religious affliation a particular case of something more general? Of what more general phenomenon might it be an indicator? Are there other observations that shed light on this? He also observed that men were more suicidal than women, urban dwellers more than rural dwellers and the socially mobile more than the socially stable. He argued that the common factor behind all these observations was that those groups who were most suicidal were also less well socially integrated and experienced greater ambiguity about how to behave and what is right and wrong. He theorized that one of the explanations for suicidal behaviour was a sense of normlessness a disconnectedness of individuals from their social world. Of course, there may have been other ways of accounting for these observations but at least Durkheim’s explanation was consistent with the facts. Theory testing In contrast, a theory testing approach begins with a theory and uses theory to guide which observations to make: it moves from the general to the particular. The observations should provide a test of the worth of the theory. Using deductive reasoning to derive a set of propositions from the theory does this. We need to develop these propositions so that THE CONTEXT OF DESIGN 7 Parents divorced? No Yes Low (a) (b) High (c) (d) Parental conflict Figure 1.3 The relationship between divorce and parental conflict if the theory is true then certain things should follow in the real world. We then assess whether these predictions are correct. If they are correct the theory is supported. If they do not hold up then the theory needs to be either rejected or modifed. For example, we may wish to test the theory that it is not divorce itself that affects the wellbeing of children but the level of confict between parents. To test this idea we can make predictions about the wellbeing of children under different family conditions. For the simple theory that it is parental confict rather than divorce that affects a child’s wellbeing there are four basic ‘conditions’ (see Figure 1.3). For each ‘condition’ the theory would make different predictions about the level of children’s wellbeing that we can examine. If the theory that it is parental confict rather than parental divorce is correct the following propositions should be supported: • Proposition 1: children in situations (a) and (b) would be equally well • • • • • off That is, where parental confict is low, children with divorced parents will do just as well as those whose parents are married. Proposition 2: children in situations (c) and (d ) should be equally poorly off That is, children in confictual couple families will do just as badly as children in post­divorce families where parents sustain high confict. Proposition 3: children in situation (c) will do worse than those in situation (a) That is, those with married parents in high confict will do worse than those who have married parents who are not in confict. Proposition 4: children in situation (d ) will do worse than those in situation (b) That is, those with divorced parents in high confict will do worse than those who have divorced parents who are not in confict. Proposition 5: children in situation (b) will do better than those in situation (c) That is, children with divorced parents who are not in confict will do better than those with married parents who are in confict. Proposition 6: children in situation (a) will do better than those in situation (d ) That is, children with married parents who are not in confict will do better than those with divorced parents who are in confict. 8 WHAT IS RESEARCH DESIGN? Starting point of theory testing Theory Inference Implications for propositions Deduction New theory Propositions Develop measures, sample etc. Analyse data Collect data Starting point of theory building Figure 1.4 The logic of the research process No single proposition would provide a compelling test of the original theory. Indeed, taken on its own proposition 3, for example, would reveal nothing about the impact of divorce. However, taken as a pack­ age, the set of propositions provides a stronger test of the theory than any single proposition. Although theory testing and theory building are often presented as alternative modes of research they should be part of one ongoing process (Figure 1.4). Typically, theory building will produce a plausible account or explanation of a set of observations. However, such explanations are frequently just one of a number of possible explanations that ft the data. While plausible they are not necessarily compelling. They require systematic testing where data are collected to specifcally evaluate how well the explanation holds when subjected to a range of crucial tests. What is research design? How is the term ‘research design’ to be used in this book? An analogy might help. When constructing a building there is no point ordering materials or setting critical dates for completion of project stages until we know what sort of building is being constructed. The frst decision is whether we need a high rise offce building, a factory for manufacturing machinery, a school, a residential home or an apartment block. Until this is done we cannot sketch a plan, obtain permits, work out a work schedule or order materials. THE CONTEXT OF DESIGN 9 Similarly, social research needs a design or a structure before data collection or analysis can commence. A research design is not just a work plan. A work plan details what has to be done to complete the project but the work plan will fow from the project’s research design. The function of a research design is to ensure that the evidence obtained enables us to answer the initial question as unambiguously as possible. Obtaining relevant evidence entails specifying the type of evidence needed to answer the research question, to test a theory, to evaluate a programme or to accurately describe some phenomenon. In other words, when designing research we need to ask: given this research question (or theory), what type of evidence is needed to answer the question (or test the theory) in a convincing way? Research design ‘deals with a logical problem and not a logistical problem’ (Yin, 1989: 29). Before a builder or architect can develop a work plan or order materials they must frst establish the type of building required, its uses and the needs of the occupants. The work plan fows from this. Similarly, in social research the issues of sampling, method of data collection (e.g. questionnaire, observation, document analysis), design of questions are all subsidiary to the matter of ‘What evidence do I need to collect?’ Too often researchers design questionnaires or begin interviewing far too early – before thinking through what information they require to answer their research questions. Without attending to these research design matters at the beginning, the conclusions drawn will normally be weak and unconvincing and fail to answer the research question. Design versus method Research design is different from the method by which data are collected. Many research methods texts confuse research designs with methods. It is not uncommon to see research design treated as a mode of data collection rather than as a logical structure of the inquiry. But there is nothing intrinsic about any research design that requires a particular method of data collection. Although cross­sectional surveys are fre­ quently equated with questionnaires and case studies are often equated with participant observation (e.g. Whyte’s Street Corner Society, 1943), data for any design can be collected with any data collection method (Figure 1.5). How the data are collected is irrelevant to the logic of the design. Failing to distinguish between design and method leads to poor evaluation of designs. Equating cross­sectional designs with question­ naires, or case studies with participant observation, means that the designs are often evaluated against the strengths and weaknesses of the method rather than their ability to draw relatively unambiguous conclu­ sions or to select between rival plausible hypotheses. 10 Design type Method of data collection Figure 1.5 methods WHAT IS RESEARCH DESIGN? Experiment Case study Longitudinal design Cross-sectional design Questionnaire Questionnaire Questionnaire Questionnaire Interview (structured or loosely structured) Interview (structured or loosely structured) Interview (structured or loosely structured) Interview (structured or loosely structured) Observation Observation Observation Observation Analysis of documents Analysis of documents Analysis of documents Analysis of documents Unobtrusive methods Unobtrusive methods Unobtrusive methods Unobtrusive methods Relationship between research design and particular data collection QUANTITATIVE AND QUALITATIVE RESEARCH Similarly, designs are often equated with qualitative and quantitative research methods. Social surveys and experiments are frequently viewed as prime examples of quantitative research and are evaluated against the strengths and weaknesses of statistical, quantitative research methods and analysis. Case studies, on the other hand, are often seen as prime examples of qualitative research – which adopts an interpretive approach to data, studies ‘things’ within their context and considers the subjective meanings that people bring to their situation. It is erroneous to equate a particular research design with either quantitative or qualitative methods. Yin (1993), a respected authority on case study design, has stressed the irrelevance of the quantitative/ qualitative distinction for case studies. He points out that: THE CONTEXT OF DESIGN 11 a point of confusion . . . has been the unfortunate linking between the case study method and certain types of data collection – for example those focusing on qualitative methods, ethnography, or participant observation. People have thought that the case study method required them to embrace these data collection methods . . . On the contrary, the method does not imply any particular form of data collection – which can be qualitative or quantitative. (1993: 32) Similarly, Marsh (1982) argues that quantitative surveys can provide information and explanations that are ‘adequate at the level of meaning’. While recognizing that survey research has not always been good at tapping the subjective dimension of behaviour, she argues that: Making sense of social action . . . is . . . hard and surveys have not traditionally been very good at it. The earliest survey researchers started a tradition . . . of bringing the meaning from outside, either by making use of the researcher’s stock of plausible explanations . . . or by bringing it from subsidiary in­depth interviews sprinkling quotes . . . liberally on the raw correlations derived from the survey. Survey research became much more exciting . . . when it began including meaningful dimensions in the study design. [This has been done in] two ways, frstly [by] asking the actor either for her reasons directly, or to supply information about the central values in her life around which we may assume she is orienting her life. [This] involves collecting a suffciently complete picture of the context in which an actor fnds herself that a team of outsiders may read off the meaningful dimensions. (1982: 123-4) Adopting a sceptical approach to explanations The need for research design stems from a sceptical approach to research and a view that scientifc knowledge must always be provisional. The purpose of research design is to reduce the ambiguity of much research evidence. We can always fnd some evidence consistent with almost any theory. However, we should be sceptical of the evidence, and rather than seeking evidence that is consistent with our theory we should seek evidence that provides a compelling test of the theory. There are two related strategies for doing this: eliminating rival explanations of the evidence and deliberately seeking evidence that could disprove the theory. PLAUSIBLE RIVAL HYPOTHESES A fundamental strategy of social research involves evaluating ‘plausible rival hypotheses’. We need to examine and evaluate alternative ways of explaining a particular phenomenon. This applies regardless of whether the data are quantitative or qualitative; regardless of the particular research design (experimental, cross­sectional, longitudinal or case 12 WHAT IS RESEARCH DESIGN? Causal relationship Academic achievement School type Alternative explanation: selectivity on child’s initial ability School type Child’s ability Academic achievement Alternative explanation: family resources Parental resources Facilities in home for study Academic achievement School type Alternative explanation: educational values Parental valuation of education Child’s valuation of education Academic achievement School type Figure 1.6 Causal and non-causal explanations of the relationship between school type and academic achievement study); and regardless of the method of data collection (e.g. observation, questionnaire). Our mindset needs to anticipate alternative ways of interpreting fndings and to regard any interpretation of these fndings as provisional – subject to further testing. The idea of evaluating plausible rival hypotheses can be illustrated using the example of the correlation between type of school attended and academic achievement. Many parents accept the causal proposition that attendance at fee paying private schools improves a child’s academic performance (Figure 1.6). Schools themselves promote the same notion by prominently advertising their pass rates and comparing them with those of other schools or with national averages. By implication they propose a causal connection: ‘Send your child to our school and they will pass (or get grades to gain entry into prestigious institutions, courses).’ The data they provide are consistent with their proposition that these schools produce better results. THE CONTEXT OF DESIGN 13 But these data are not compelling. There are at least three other ways of accounting for this correlation without accepting the causal link between school type and achievement (Figure 1.6). There is the selectivity explanation: the more able students may be sent to fee paying private schools in the frst place. There is the family resources explanation: parents who can afford to send their children to fee paying private schools can also afford other help (e.g. books, private tutoring, quiet study space, computers). It is this help rather than the type of school that produces the better performance of private school students. Finally, there is the family values explanation: parents who value education most are prepared to send their children to fee paying private schools and it is this family emphasis on education, not the schools themselves, that produces the better academic performance. All these explanations are equally con­ sistent with the observation that private school students do better than government school students. Without collecting further evidence we cannot choose between these explanations and therefore must remain open minded about which one makes most empirical sense. There might also be methodological explanations for the fnding that private school students perform better academically. These methodolo­ gical issues might undermine any argument that a causal connection exists. Are the results due to questionable ways of measuring achieve­ ment? From what range and number of schools were the data obtained? On how many cases are the conclusions based? Could the pattern simply be a function of chance? These are all possible alternative explanations for the fnding that private school students perform better. Good research design will anticipate competing explanations before collecting data so that relevant information for evaluating the relative merits of these competing explanations is obtained. In this example of schools and academic achievement, thinking about alternative plausible hypotheses beforehand would lead us to fnd out about the parents’ fnancial resources, the study resources available in the home, the parents’ and child’s attitudes about education and the child’s academic abilities before entering the school. The fallacy of affrming the consequent Although evidence may be con­ sistent with an initial proposition it might be equally consistent with a range of alternative propositions. Too often people do not even think of the alternative hypotheses and simply conclude that since the evidence is consistent with their theory then the theory is true. This form of reasoning commits the logical fallacy of affrming the consequent. This form of reasoning has the following logical structure: • If A is true then B should follow. • We observe B. • Therefore A is true. 14 WHAT IS RESEARCH DESIGN? If we apply this logic to the type of school and achievement proposition, the logical structure of the school type and achievement argument becomes clearer. Initial proposition: • Private schools produce better students than do government schools. The test: • If A then B If private schools produce better students (A) then their • • students should get better fnal marks than those from government funded schools (B). B is true Private school students do achieve better fnal marks than government school students (observe B). Therefore A is true Therefore private schools do produce better students (A is true). But as I have already argued, the better performance of private school students might also refect the effect of other factors. The problem here is that any number of explanations may be correct and the evidence does not help rule out many of these. For the social scientist this level of indeterminacy is quite unsatisfactory. In effect we are only in a position to say: • If A [or C, or D, or E, or F, or . . .] then B. • We observe B. • Therefore A [or C, or D, or E, or F, or . . .] is true. Although explanation (A) is still in the running because it is consistent with the observations, we cannot say that it is the most plausible explanation. We need to test our proposition more thoroughly by evaluating the worth of the alternative propositions. FALSIFCATION: LOOKING FOR EVIDENCE TO DISPROVE THE THEORY As well as evaluating and eliminating alternative explanations we should rigorously evaluate our own theories. Rather than asking ‘What evidence would constitute support for the theory?’, ask ‘What evidence would convince me that the theory is wrong?’ It is not diffcult to fnd evidence consistent with a theory. It is much tougher for a theory to survive the test of people trying to disprove it. Unfortunately some theories are closed systems in which any evidence can be interpreted as support for the theory. Such theories are said to be non­falsifable. Many religions or belief systems can become closed systems whereby all evidence can be accommodated by the theory and THE CONTEXT OF DESIGN 15 nothing will change the mind of the true believer. Exchange theory (Homans, 1961; Blau, 1964) is largely non­falsifable. It assumes that we always maximize our gains and avoid costs. But we can see almost anything as a gain. Great sacrifces to care for a disabled relative can be interpreted as a gain (satisfaction of helping) rather than a loss (income, time for self etc.). We need to frame our propositions and defne our terms in such a way that they are capable of being disproven. THE PROVISIONAL NATURE OF SUPPORT FOR THEORIES Even where the theory is corroborated and has survived attempts to disprove it, the theory remains provisional: falsifcationism stresses the ambiguity of confrmation . . . corroboration gives only the comfort that the theory has been tested and survived the test, that even after the most impressive corroborations of predictions it has only achieved the status of ‘not yet disconfrmed’. This . . . is far from the status of ‘being true’. (Cook and Campbell, 1979: 20) There always may be an unthought­of explanation. We cannot anticipate or evaluate every possible explanation. The more alternative explana­ tions that have been eliminated and the more we have tried to disprove our theory, the more confdence we will have in it, but we should avoid thinking that it is proven. However we can disprove a theory. The logic of this is: • If theory A is true then B should follow. • B does not follow. • Therefore A is not true. So long as B is a valid test of A the absence of B should make us reject or revise the theory. In reality, we would not reject a theory simply because a single fact or observation does not ft. Before rejecting a plausible theory we would require multiple disconfrmations using different measures, different samples and different methods of data collection and analysis. In summary, we should adopt a sceptical approach to explanations. We should anticipate rival interpretations and collect data to enable the winnowing out of the weaker explanations and the identifcation of which alternative theories make most empirical sense. We also need to ask what data would challenge the explanation and collect data to evaluate the theory from this more demanding perspective. 16 WHAT IS RESEARCH DESIGN? Summary This chapter has outlined the purpose of research design in both descrip­ tive and explanatory research. In explanatory research the purpose is to develop and evaluate causal theories. The probabilistic nature of causation in social sciences, as opposed to deterministic causation, was discussed. Research design is not related to any particular method of collecting data or any particular type of data. Any research design can, in principle, use any type of data collection method and can use either quantitative or qualitative data. Research design refers to the structure of an enquiry: it is a logical matter rather than a logistical one. It has been argued that the central role of research design is to minimize the chance of drawing incorrect causal inferences from data. Design is a logical task undertaken to ensure that the evidence collected enables us to answer questions or to test theories as unambiguously as possible. When designing research it is essential that we identify the type of evidence required to answer the research question in a convincing way. This means that we must not simply collect evidence that is con­ sistent with a particular theory or explanation. Research needs to be structured in such a way that the evidence also bears on alternative rival explanations and enables us to identify which of the competing explana­ tions is most compelling empirically. It also means that we must not simply look for evidence that supports our favourite theory: we should also look for evidence that has the potential to disprove our preferred explanations. • Definition Research Designs The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense. Importance of a Good Methodology Section You must explain how you obtained and analyzed your results for the following reasons: • • • • • • • Readers need to know how the data was obtained because the method you choose affects the results and, by extension, how you likely interpreted those results. Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and it misappropriates interpretations of findings. In most cases, there are a variety of different methods you can choose to investigate a research problem. Your methodology section of your paper should make clear the reasons why you chose a particular method or procedure. The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from. The research method must be appropriate to the objectives of the study. For example, be sure you have a large enough sample size to be able to generalize and make recommendations based upon the findings. The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that did arise, you must describe the ways in which their impact was minimized or why these problems do not affect the findings in any way that impacts your interpretation of the data. Often in social science research, it is useful for other researchers to adapt or replicate your methodology. Therefore, it is important to always provide sufficient information to allow others to use or replicate the study. This information is particularly important when a new method had been developed or an innovative use of an exisiting method has been utlized. Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences. Thousand Oaks, CA: Corwin Press, 2008. Structure and Writing Style I. Groups of Research Methods There are two main groups of research methods in the social sciences: • • The empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences. This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for hypotheses that need to be tested. This approach is focused on explanation. The interpretative group is focused on understanding phenomenon in a comprehensive, holistic way. This research method allows you to recognize your connection to the subject under study. Because the interpretative group focuses more on subjective knowledge, it requires careful interpretation of variables. II. Content An effectively written methodology section should: • • • • • • • Introduce the overall methodological approach for investigating your research problem. Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance? Indicate how the approach fits the overall research design. Your methods should have a clear connection with your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is unsuited to achieving the stated objective of your paper. Describe the specific methods of data collection you are going to use, such as, surveys, interviews, questionnaires, observation, archival research. If you are analzying existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Explain how you intend to analyze your results. Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Provide background and rationale for methodologies that are unfamiliar for your readers. Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and consice in your explanation. Provide a rationale for subject selection and sampling procedure. For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of statisics being used? If other data sources exist, explain why the data you chose is most appropriate. Address potential limitations. Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up. NOTE: Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. III. Problems to Avoid Irrelevant Detail The methodology section of your paper should be thorough but to the point. Don’t provide any background information that doesn’t directly help the reader to understand why a particular method was chosen, how the data was gathered or obtained, and how it was analyzed. Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method, not on the mechanics of doing a method. NOTE: An exception to this rule is if you select an unconventional approach to doing the method; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall research process. Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose. Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey]. It’s More than Sources of Information! A description of a research study’s method should not be confused with a description of the sources of information. Such a list of sources is useful in itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project’s methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources. Azevedo, L.F. et al. How to Write a Scientific Paper: Writing the Methods Section. Revista Portuguesa de Pneumologia 17 (2011): 232-238; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars. Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis. New York: Palgrave Macmillan, 2012; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences. Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College. Writing Tip Statistical Designs and Tests? Do Not Fear Them! Don’t avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed. If you questions about locating and using statistical information, GO HERE. Another Writing Tip Knowing the Relationship Between Theories and Methods There can be multiple meaning associated with the term “theories” and the term “methods” in social sciences research. A helpful way to delineate between them is to understand “theories” as representing different ways of characterizing the social world when you research it and “methods” as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce. Introspectively engage in an ongoing dialectic between theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory. Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics. Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom. Powered by Springshare; All rights reserved. Report a tech support issue. View this page in a format suitable for printers and screen-readers or mobile devices. * Asterisk in guide title indicates core subject guide © University of Southern California COMMUNITY WATER FLUORIDATION Method This quantitative research/correlational study will demonstrate the continued effectiveness of community water fluoridation as an oral health intervention to communities without access to fluoridated water. Despite decades of evidence supporting the effectiveness of water fluoridation, efforts to begin a community water fluoridation program in non-fluoridated areas are repeatedly abandoned. Between October 1, 2019 and October 31, 2019, oral health data was obtained on children eight years of age participating in their prospective counties schoolbased sealant program. Water fluoridation status, rural/urban status and socioeconomic status (SES) was obtained from a parental questionnaire and each child’s zip code. This study compared eight-year-old children’s decay experience and prevalence between areas with and without water fluoridation in Ohio. Population The participants for this study were obtained from The Ohio Department of Health’s (ODH) School-based sealant program (SBSPs) which targets children from low-income families who many have difficulty accessing dental care. Oral health data was obtained by convenience sampling of children participating in their local school-based sealant program. Enrollment is voluntary and parents were sent a letter requesting their child’s participation in a study investigating the associations between oral health and systemic fluoride consumption. Guidelines for this study included only children that were eight years of age by the examination date and had either exposure to systemic fluoride or no exposure to systemic fluoride. Any children over or under the age of eight or who were given supplemental fluoride were excluded. Eight-yearold children were selected as permanent first molars and incisors have erupted and overexposure to fluoride can be exhibited by dental fluorosis indicating previous fluoride contact. Data Collection Methods The children that had parental consent to participate in the study and had parental questionnaire completed, were examined by two dentists, three dental hygienists and four second-year dental students using standard epidemiologic criteria as outlined by the World Health Organization. Disease prevalence was defined as the presence of one or more teeth with decay that extended into the dentin, that were filled as a result of decay or that was extracted because of decay. Disease was recorded as a count of the total number of decayed, missing or filled teeth. The deciduous (baby teeth) were identified with “dmft” and permanent dentition were identified with “DMFT.” Despite the examiners working or trained to operate under similar conditions, examiner calibration was conducted prior to the study. The level of fluoride in the water was obtained from a database maintained at the facility which supplies water to Cuyahoga County, the Cleveland Water Department, and kept updated by regular communication with the Cuyahoga County Board of Health and the Ohio Environmental Protection Agency. The optimum concentration of fluoride in the water is considered to be approximately one part per million (ppm), although this varies slightly according to mean daily temperatures within a county. Fluoride concentrations for this study were categorized as negligible (0.0–0.29 ppm), suboptimal (0.3–0.69 ppm), or optimal (≥0.7 ppm). In Cuyahoga County, Lake Erie naturally has 0.1 to 0.3 mg/L of fluoride. Between 0.7 and 0.9 mg/L is added to the water supply totaling approximately 1.0 mg/L which is designated by Ohio Revised Code 6109.20. A small percentage of children in rural Ohio do not have access to fluoridated water. Location status was assessed by using Rural-Urban Continuum Codes (RUCC) from the United States Department of Agriculture Economic Research Service. The RUCC classifications were used to determine if the location was rural or metropolitan. Socioeconomic status was determined by a parental questionnaire collecting information on household income, education attained, occupation and if they had dental insurance. Details included in the consent comprised of where the data would be used, identities of participants and families would not be revealed for safety and privacy and compensation would not be provided other than the usual services provided by the sealant program. Other data collected were race, ethnicity and enrollment in the Free and Reduced Meal Program (as an estimate of family income.) If a parent did not give informed consent for the study, it did not exclude children from the sealant program. All indices were matched for the zip codes of the child or the zip code of the clinic if the resident code was not available. Running Head: TRAFFIC ACCIDENTS IN PATIENTS WITH OSA 1 Hypothesis: Patients diagnosed with severe Obstructive Sleep Apnea (OSA) are more likely to be involved in a motor vehicle accident (MVA) than patients without OSA. Null Hypothesis: There is no difference in the likelihood of patients diagnosed with severe Obstructive Sleep Apnea (OSA) and patients without OSA being involved in a motor vehicle accident (MVA). The revised hypothesis is that patients diagnosed with severe Obstructive Sleep Apnea (OSA) will have an increased risk of involvement in a motor vehicle accident (MVA) compared to patients without OSA. This hypothesis is based on the literature which suggests that OSA reduces sleep quality, leading to drowsiness and decreased alertness while driving and increasing the risk of MVA. The null hypothesis is that patients with severe OSA will not have an increased risk of involvement in a motor vehicle accident compared to patients without OSA. This null hypothesis is based on the idea that OSA does not necessarily have a negative impact on sleep quality, alertness, and driving behavior, and therefore would not lead to an increased risk of MVA. 1 Problem Statement Road Traffic Accidents (RTAs) in patients diagnosed with severe Obstructive Sleep Apnea (OSA) is a major public health concern that has been increasingly gaining attention due to its associated serious injuries and fatalities (Udholm et al., 2022). OSA is a sleep-related breathing disorder that is characterized by pauses in breathing, or shallow breaths during sleep due to partial or complete blockage of the upper airway. AASM reports that OSA is a common sleep disease that affects about 25 million adult in the United States (Purtle et al., 2020). The problem is even worse in other developing countries where the diagnosis of this condition is low. The signs and symptoms of this condition include gasping, chocking and snoring during sleep and silent breathing pauses during sleep which may affect quality sleep during the night. OSA can lead to excessive sleepiness during the day and cognitive impairment, which can damage an individual’s ability to safely operate a vehicle, leading to an increased risk of RTAs. Severe Obstructive Sleep Apnea (OSA) can significantly impact a person’s life and can even lead to road traffic accidents. As a sleep disorder, OSA interrupts a person’s breathing during sleep due to a blockage in the airway. This blockage can lead to decreased oxygen flow to the brain, leading to daytime sleepiness and fatigue. This can cause a person to be less alert and have slower reaction times, increasing the chance of a road traffic accident (Myllylä et al., 2020). In addition, people with OSA may experience difficulty concentrating, impaired judgment, and even memory problems, all of which can contribute to an increased risk of a road traffic accident. OSA can also cause cardiovascular issues, such as high blood pressure, which can make a person more prone to accidents (Udholm et al., 2022). All of these factors can combine to create a serious risk of road traffic accidents for those who suffer from OSA. 2 Therefore, the purpose of this research is to investigate the relationship between severe OSA and RTAs. Specifically, the study will examine the prevalence of OSA among drivers involved in RTAs and assess the impact of OSA on driving performance, accident risk, and outcomes. Furthermore, the study will also explore potential protective and risk factors, such as age, gender, and driving experience that may influence the relationship between OSA and RTAs. This research aims to improve the understanding of the association between severe OSA and RTAs, and to identify the most effective preventive strategies to reduce the risk of RTAs among those with OSA. The findings of this research will inform the development of targeted interventions and public health initiatives to reduce the burden of RTAs in patients diagnosed with severe OSA. 3 References Myllylä, M., Anttalainen, U., Saaresranta, T., & Laitinen, T. (2020). Motor vehicle accidents in CPAP-compliant obstructive sleep apnea patients-a long-term observational study. Sleep And Breathing, 24(3), 1089–1095. https://doi.org/10.1007/s11325-020-02023-2 Purtle, M. W., Renner, C. H., McCann, D. A., Mallen, J. C., Spilman, S. K., & Sahr, S. M. (2020). Driving with undiagnosed obstructive sleep apnea (OSA): High prevalence of OSA risk in drivers who experienced a motor vehicle crash. Traffic Injury Prevention, 21(1), 38–41. https://doi.org/10.1080/15389588.2019.1709175 Udholm, N., Rex, C. E., Fuglsang, M., Lundbye-Christensen, S., Bille, J., & Udholm, S. (2022). Obstructive sleep apnea and road traffic accidents: a Danish nationwide cohort study. Sleep Medicine, 96, 64–69. https://doi.org/10.1016/j.sleep.2022.04.003

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