MULTI-METHODOLOGY DALAM KAJIAN LINGKUNGAN DAN PEMBANGUNAN Bahan kajian

1. MULTI-METHODOLOGY DALAM KAJIAN LINGKUNGAN DAN PEMBANGUNAN Bahan kajian MK Interdisciplinary Environmental Studies Disarikan oleh Soemarno, pm-pslp ppsub 2010

2. Multi-methodology Multimethodology, mixed methods research, compatibility thesis or pragmatist paradigm IS an approach to research that combines the collection and analysis of quantitative and qualitative data. The term 'multimethodology' appears to be more widely used in operations research than in other branches of social science. This approach has been gaining in popularity since the 1980s

3. METODE RISET Approaches to studying human behavior using the scientific method (systematic, empirical observation)

4. The five research methods we’ll be looking at are…. • Naturalistic observation • Case studies • Surveys & Interviews • Quasi-experiment • Controlled experiments

5. Defining mixed methods Combining quantitative and qualitative methods sounds like a good idea. Using multiple approaches can capitalise on the strengths of each approach and offset their different weaknesses. It could also provide more comprehensive answers to research questions, going beyond the limitations of a single approach.

6. Classification of combinations of research methods There are many ways in which different research methods can be combined in social research and we’ll look at these in later units in this module. But first, let’s try to classify the types of combination, if we can. In the research literature, a distinction is often made between multi-method and mixed method studies.

7. Multi-method studies Multi-method studies use different methods of data collection and analysis within a single research paradigm. For example, you might conduct a qualitative study in which you observe as a participant and also interview people. Or in a quantitative study you might carry out an attitude survey of students and also collect information from computer records about the frequency of ‘hits’ in the use of web-based course materials. In other words, you make use of methods that are broadly compatible within a paradigm or a set of beliefs and values.

8. Mixed method studies Mixed method studies attempt to bring together methods from different paradigms. In a mixed method study you might conduct a series of semi-structured interviews with a small number of students and also carry out a large-scale survey. This kind of integration, of qualitative with quantitative methods, is also referred to sometimes as multi-strategy research. In this module, we shall use the terms multi-method and mixed method to maintain the distinction described above. However, you will find that there is little consistency in the use of the terms multiple and mixed methods in the research literature.

9. Mixed method studies At the next level of complexity, the meaning given to ‘mixed methods’ is influenced by how methods are combined. For instance, you might collect information by using each method concurrently (at the same time), or sequentially if your aim is to use one method to inform another (say, interviewing before surveying).

10. Mixed method studies These two approaches are different. The first (MULTI) is more like two parallel studies that only come together once the data are being analysed, whereas, in the second (MIXED) , the aim is to use the methods in a more integrated way. The actual methods used may be the same, but the ways in which they are sequenced and combined can make a big difference in the process of conducting the study and in the results.

12. Multi-method designs Multi-method designs are generally intended to supplement one information source with another, or ‘triangulate’ on an issue by using different data sources to approach a research problem from different points of view. There are two types: A1. Multi-method quantitative studies stay within a quantitative paradigm but use more than one method of data collection. One example might be the use of a survey mailed to distance students used in conjunction with other data collected from the same students from other sources – perhaps student record data. This kind of research design might allow you to crosscheck between (for example) students’ opinions of the assessment process and their actual assessments, or the dates they returned assignments.

13. Multi-method designs Multi-method designs are generally intended to supplement one information source with another, or ‘triangulate’ on an issue by using different data sources to approach a research problem from different points of view. There are two types: A2. Multi-method qualitative methods might combine student interviews, observations made of email discussions and staff interviews. Again the key design idea is to cross-check between sources and to supplement one kind of data with another.

14. Mixed methods designs Mixed methods designs are conceptually more complex.They may provide a basis for triangulation but, more often, they become the source of different ways of conceptualising the problem. They might set out to look at the same things from different points of view, but it often turns out that the viewpoint implies such different ways of seeing that the lines of sight do not converge. B1. Mixed method studies might include a survey followed up by detailed individual interviews, or observations used as the basis for constructing a questionnaire.

15. Mixed methods designs B2 The final category ‘mixed model studies’ requires some explanation. In an earlier book Tashakkori and Teddlie (1998) extend the issue of mixing methods to a set of broader considerations than the use of different methods per se. They argue that the issues are not narrowly about method, but also involve mixes of methodology (i.e. the ‘logic of methods’). This might sound abstract but it has significant implications.

16. It means looking beyond stitching together methods from different paradigms and instead considering other aspects of research design, specifically: • Overall inquiry purpose – whether the aim is to confirm or refute hypotheses or whether it is more exploratory • Instrument design data collection – whether qualitative or quantitative • Data analysis and inference – whether statistical or qualitative.

17. Case study and mixed methods Case studies are research and evaluation studies that focus on specifics andgive an account of the instance in action. A case study can describe a project, a course, an institution or a particular innovation. Its value lies in its capacity to provide vicarious experience to the reader – to give you the feeling of ‘being there’ and perhaps to set you thinking about how you might respond to dilemmas and conflicts as events unfold.

18. Case study and mixed methods Generally, case studies are not very good as sources of theory or explanation that goes beyond the conditions in which they are located. They are more effective as a source of interpretations and ideas than as a way of verifying them or providing generalisations that can be confidently applied system-wide.

19. Case study, measurement and mixed methods The term case study is often taken to be synonymous with qualitative methods, for to study ‘cases’ seems to imply looking up close and being drawn into the world of alternative perceptions and different views about common and shared tasks and workplace contexts. But there is no reason why cases cannot be measurement-based. Accountants might look at a school or a course, a hospital or a project primarily through a balance sheet and a social statistician or demographer could approach the study of a neighbourhood or a local service through an analysis of census data. These methods can be used alone or combined with qualitative methods to investigate cases by mixed method approaches.

20. Case study, measurement and mixed methods In fact many quantitative research approaches are easier to use in a mixed method context now than they used to be, since many databases are accessible, and available for interrogation on-line. Indeed such approaches have become much more common as many education systems have accumulated achievement test data on total populations of students (where before they would have had mostly small patchy samples). So the scene is set for mixing methods. Databases can be searched for anomalous figures or gaps and contradictions in the numerical data that can be used as leads to be followed to identify specific case studies.

21. Case study, measurement and mixed methods Notice the methodological shift involved here. If we use numerical data bases in this way – to identify particular cases for investigation – then, in effect, we are treating the quantitative material in an exploratory manner (inductively) and using qualitative methods to identify ‘hard’ data that offers explanations and identifies causes (deductively).

22. Strengths of Mixed Research • Words, pictures, and narrative can be used to add meaning to numbers. • Numbers can be used to add precision to words, pictures, and narrative. • Can provide quantitative and qualitative research strengths . • Researcher can generate and test a grounded theory. • Can answer a broader and more complete range of research questions because the researcher is not confined to a single method or approach. • The specific mixed research designs discussed in this article have specific strengths and weaknesses that should be considered. A researcher can use the strengths of an additional method to overcome the weaknesses in another method by using both in a research study. • Can provide stronger evidence for a conclusion through convergence and corroboration of findings. • Can add insights and understanding that might be missed when only a single method is used. • Can be used to increase the generalizability of the results. • Qualitative and quantitative research used together produce more complete knowledge necessary to inform theory and practice.

23. Weaknesses of Mixed Research • Can be difficult for a single researcher to carry out both qualitative and quantitative research, especially if two or more approaches are expected to be used concurrently; it may require a research team. • Researcher has to learn about multiple methods and approaches and understand how to mix them appropriately. • Methodological purists contend that one should always work within either a qualitative or a quantitative paradigm. • More expensive. • More time consuming. • Some of the details of mixed research remain to be worked out fully by research methodologists (e.g., problems of paradigm mixing, how to qualitatively analyze quantitative data, how to interpret conflicting results).

24. Using a multi-method qualitative approach to examine collaborative relationships

25. Mixed methods research There are two broad classes of research studies that are currently being labeled “mixed methods research”: single approach designs (SADs) in which additional qualitative and/or quantitative strategies are employed to enhance research quality; and mixed approach designs (MADs). These definitions require that a distinction be made between research strategies and research approaches.

26. Mixed methods research A research strategy is a procedure for achieving a particular intermediary research objective—such as sampling, data collection, or data analysis. We may therefore speak of sampling strategies or data analysis strategies. The use of multiple strategies to enhance construct validity (a form of methodological triangulation) is now routinely advocated by most methodologists. In short, mixing or integrating research strategies (qualitative and/or quantitative) in any and all research undertaking is now considered a common feature of all good research.

27. Mixed methods research A research approach refers to an integrated set of research principles and general procedural guidelines. Approaches are broad, holistic (but general) methodological guides or roadmaps that are associated with particular research motives or analytic interests. Two examples of analytic interests are population frequency distributions and prediction. Examples of research approaches include experiments, surveys, correlational studies, ethnographic research, and phenomenological inquiry.

28. Mixed methods research Each approach is ideally suited to addressing a particular analytic interest. For instance, experiments are ideally suited to addressing nomothetic explanations or probably cause; surveys—population frequency descriptions, correlations studies—predictions; ethnography—descriptions and interpretations of cultural processes; and phenomenology—descriptions of the essence of phenomena or lived experiences.

29. Mixed methods research In a single approach design (SAD) only one analytic interest is pursued. In a mixed approach design (MAD) two or more analytic interests are pursued. NOTE: a mixed approach design may include entirely “quantitative” approaches such as combining a survey and an experiment; or entirely “qualitative” approaches such as combining an ethnographic and a phenomenological inquiry.

30. Mixed methods research A word of caution about the term “multimethodology”. It has become quite common place to use the terms "method" and "methodology" as synonyms (as is the case with the above entry). However, there are convincing philosophical reasons for distinguishing the two. "Method" connotes a way of doing something — a procedure. "Methodology" connotes a discourse about methods—i.e., a discourse about the adequacy and appropriateness of particular combination of research principles and procedures.

31. Mixed methods research The terms methodology and biology share a common suffix "logy." Just as bio-logy is a discourse about life—all kinds of life; so too, methodo-logy is a discourse about methods—all kinds of methods. It seems unproductive, therefore, to speak of multi-biologies or of multi-methodologies. It is very productive, however, to speak of multiple biological perspectives or of multiple methodological perspectives.

32. Mixed methods research Desirability The case for multimethodology as a strategy for intervention and/or research is based on four observations: Narrow views of the world are often misleading, so approaching a subject from different perspectives or paradigms may help to gain a holistic perspective There are different levels of social research (ie: biological, cognitive, social, etc), and different methodologies may have particular strengths with respect to one of these levels. Using more than one should help to get a clearer picture of the social world and make for more adequate explanations Many existing practices already combine methodologies to solve particular problems, yet they have not been theorised sufficiently Multimethodology fits well with postmodernism.

33. Mixed methods research Feasibility There are also some hazards to multimethodological approaches. Some of these problems include: Many paradigms are at odds with each other. However, once the understanding of the difference is present, it can be an advantage to see many sides, and possible solutions may present themselves. Cultural issues affect world views and analyzability. Knowledge of a new paradigm is not enough to overcome potential biases; it must be learned through practice and experience. People have cognitive abilities that predispose them to particular paradigms. The logical thinker can more easily understand and use quantitative methodologies. It is easier to move from quantitative to qualitative, and not the reverse.

34. Mixed methods research Conclusion Multimethodology is desirable and feasible because it gives a more complete view, and because the requirement during the different phases of the intervention (or research project) make very specific demands on a general methodology. While it is demanding, it is more effective to choose the right tool for the job at hand.

35. Mixed methods research Criticism Multimethodology is criticized by the adherents of incompatibility thesis - particularly post-structuralist and post-modernists. Its critics argue that multimethodology is inherently wrong because quantitative and qualitative research paradigms should not be mixed.

36. Operations research Operational research, also known as operations research, is an interdisciplinary mathematical science that focuses on the effective use of technology by organizations. In contrast, many other science & engineering disciplines focus on technology giving secondary considerations to its use. Employing techniques from other mathematical sciences --- such as mathematical modeling, statistical analysis, and mathematical optimization --- operations research arrives at optimal or near-optimal solutions to complex decision-making problems.

37. Operations research Because of its emphasis on human-technology interaction and because of its focus on practical applications, operations research has overlap with other disciplines, notably industrial engineering and management science, and draws on psychology and organization science. Operations Research is often concerned with determining the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost) of some real-world objective. Originating in military efforts before World War II, its techniques have grown to concern problems in a variety of industries

38. Operational research encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency. Some of the tools used by operational researchers are statistics, optimization, probability theory, queuing theory, game theory, graph theory, decision analysis, mathematical modeling and simulation. Because of the computational nature of these fields, OR also has strong ties to computer science. Operational researchers faced with a new problem must determine which of these techniques are most appropriate given the nature of the system, the goals for improvement, and constraints on time and computing power.

39. Work in operational research and management science may be characterized as one of three categories: • Fundamental or foundational work takes place in three mathematical disciplines: probability, optimization, and dynamical systems theory. • Modeling work is concerned with the construction of models, analyzing them mathematically, implementing them on computers, solving them using software tools, and assessing their effectiveness with data. This level is mainly instrumental, and driven mainly by statistics and econometrics. • Application work in operational research, like other engineering and economics' disciplines, attempts to use models to make a practical impact on real-world problems.

40. The major subdisciplines in modern operational research, as identified by the journal Operations Research, are: • Computing and information technologies • Decision analysis • Environment, energy, and natural resources • Financial engineering • Manufacturing, service sciences, and supply chain management • Policy modeling and public sector work • Revenue management • Simulation • Stochastic models • Transportation.

41. What position does the mixed researcher take on the compatibility thesis and pragmatist philosophy? • According to the mixed research paradigm,  researchers should : • Use the pragmatist philosophy (especially in terms of mixing methods is a way that works) and • Follow the compatibility thesis (i.e., quantitative and qualitative are compatible and they can be fruitfully mixed in many ways that can work quite well).

42. Why is the fundamental principle of mixed research important? • According to the fundamental principle of mixed research, the researcher should use a mixture or combination of methods that has complementary strengths and nonoverlapping weaknesses:  • This principle is important because provides researcher with a logic for mixing quantitative and qualitative research approaches. • Mixing quantitative and qualitative approaches in a haphazard way will produce undesirable results. • Mixing should be systematic and well though out by the researcher when planning and designing a research study.

43. Give an example of a within-stage mixed model research study. In within-stage mixed model research, quantitative and qualitative approaches are mixed within one or more stages of research. A simple example would be a study where you constructed a questionnaire that is composed of closed-ended items (quantitative approach) and open-ended items (qualitative approach).

44. Give an example of an across-stage mixed model research study. • In across-stage mixed model research, quantitative and qualitative approaches are mixed across at least two of the stages of research. • A simple example would be a study where the researcher wishes to explore why people willingly handle snakes in certain churches (qualitative purpose); the researcher goes to the churches and observes the services and informally interviews some church members (qualitative data collection); during data analysis, the researcher enters all of the verbal data into a computer program and then obtains word counts and calculates the percentages for different responses (quantitative data analysis).

45. Give an example of an across-stage mixed model research study. In across-stage mixed model research, quantitative and qualitative approaches are mixed across at least two of the stages of research. 2. In the above example, the across-stage model mixing took place from the qualitative data collection to the quantitative data analysis. 3. For additional across-stage mixed model designs, take a look at follow figure. Here it is for your convenience.

46. In across-stage mixed model research, quantitative and qualitative approaches are mixed across at least two of the stages of research.

47. What is the difference between mixed model research and mixed method research? Here are the definitions: Mixed model research = The method where quantitative and qualitative approaches are mixed within or across the stages of the research process. This is where you use the within-stage mixing approach or the across-stage mixing approach. Mixed method research = The method where a quantitative phase and a qualitative phase are included in the overall research study. This is like having a quantitative and a qualitative mini-study in the overall research study.

48. What is the difference between a sequential and a concurrent design feature? One major dimension on which mixed method designs are differentiated is the time dimension. The time dimension is either sequential or concurrent. A sequential time order means that the qualitative and quantitative phases are conducted one after the other. A concurrent time order means that the quantitative and qualitative phases occur at approximately the same time—this is like running parallel mini-studies. Note that a sequential design is important when the results of one phase will be needed to inform the next phase and when the nature of the questions require that a phase occurs after or before another phase. A concurrent design can be done when both kinds of information are needed, but they can be collected at roughly the same time without causing any problems (logistically or informational/theoretical).

49. What are the eight stages of the mixed research process? I’m going to provide Figure 14.4 here, which lists the eight stages.

50. What is the difference between quantizing and qualitizing, and are these used in mixed method or mixed model designs? Quantitizing means that you convert qualitative data into quantitative data. Qualitizing means that you convert quantitative data into qualitative data.